esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Properties of Sandwich Composites Reinforced by Nanoclas: An Overview
## 4 Synthetic Summary for an Overview of the Benefits Obtained with Nanoclay Reinforcements  
A review of all published works is presented in the previous section, where the benefits obtained with nanoclays are reported in detail. It is possible to conclude that the content of nanoclays varies significantly to maximize a specific property of sandwich composites. In fact, the mechanical properties depend not only on the clay content, but also on the type of resin and manufacturing process [40]. [PERSON] et al. [30], for example, report that epoxy matrices have a saturation limit between 5 wt% and 10 wt% of nanoclays. Therefore, a high content of nanoclays leads to immiscible nanostructures, promoting, in this case, \"hot spots\" for cracks nucleation. On the other hand, montmorillonite (MMT) clays generally contain impurities and a wide particles size distribution, which causes high dispersion in the mechanical and physical properties of polymeric nanocomposites [40]. However, regardless of this remark, this section intends to summarize the main benefits, in terms of mechanical performance, obtained with the addition of nanoclays into sandwich composites, both to the level of their cores (Table 1) and their skins (Table 2). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Author**} & **Sandwich** & **Type of** & **Wt\% of** & **Improvements** & **Ref. ** \\  & & **(Skin/Core)** & **Nanoclay** & **Nanoclay** & & & \\ \hline \multirow{3}{*}{2005} & \multirow{3}{*}{[PERSON] et al.} & \multirow{3}{*}{Syntactic foam} & \multirow{3}{*}{MMT} & \multirow{3}{*}{2 and 5} & \multirow{3}{*}{Toughness around 80\% and 125\% for low density foams, 150 and 200\% for medium density and 125\% for high density foams for 2 wt\% and 5 wt\% of NC *, respectively.} & \multirow{3}{*}{[13]} \\  & & & & & & \\ \hline \multirow{3}{*}{2008} & \multirow{3}{*}{[PERSON] et al.} & Glass & \multirow{3}{*}{NF **} & The fracture toughness is 69\% higher & \multirow{3}{*}{[14]} \\  & & fiber/polyurethane foam core & & & & \\ \cline{1-1} \cline{5-5}  & & Carbon fiber/PU & \multirow{3}{*}{MMT} & \multirow{3}{*}{0.5 to 2} & Nanophased foams pre-sent higher & \multirow{3}{*}{[15]} \\  & & foam & & & & \\ \hline \multirow{3}{*}{2008} & \multirow{3}{*}{[PERSON] et al.} & \multirow{3}{*}{PU foam} & MMT, Nanocor & \multirow{3}{*}{0.5 to 2} & \multirow{3}{*}{0.5 to 2} & \multirow{3}{*}{[16]} \\  & & & & & & \\ \cline
# Decentralized Consensus Protocols on \(SO(4)^{N}\) and \(Tso(4)^{N}\) with Reshaping
## 2 Background  
### Communication Graph  
Assume that there are \(N\) agents in a network represented by an undirected graph \(\mathcal{G}=(\mathcal{V},\mathcal{E})\), where \(\mathcal{G}\) is the graph, \(\mathcal{V}=\{1,\ldots,N\}\) is the node set, and \(\mathcal{E}\subseteq\mathcal{V}\times\mathcal{V}\) is the edge set with \(n_{e}\) edges. For agents \(i,j\in\mathcal{V}\), \(\mathcal{A}=[a_{ij}]\) is the \(N\times N\) non-negative adjacency matrix of \(\mathcal{G}\), where \(a_{ij}=1\) if agents \(i\) and \(j\) have a bidirectional communication path and \(a_{ij}=0\) if not. In addition, it is assumed that there are no self-loops, so \(a_{ii}=0\). The degree matrix of the graph is a diagonal matrix \(\mathcal{D}=diag(d_{ii})\) where  
\[d_{ii}=\sum_{k=1,k\
eq i}^{N}a_{ik}. \tag{1}\]  
The graph Laplacian is then defined as \(\mathcal{L}=\mathcal{D}-\mathcal{A}\). Given an arbitrary edge orientation of the undirected graph, an oriented incidence matrix \(\mathcal{B}\) can be constructed of size \(N\times n_{e}\), where \(\mathcal{L}=\mathcal{B}\mathcal{B}^{T}\). If the undirected graph is connected, then there is exactly one zero eigenvalue of the \(\mathcal{L}\) matrix. The corresponding eigenvector, which is a vector of ones (possibly scaled by some real number), represents the consensus subspace. The proof involves recognition that the rows of \(\mathcal{L}\) sum to zero. For an undirected and connected graph, the linear Laplacian flow \(\dot{x}=-\mathcal{L}x\) solves the average consensus problem.  
Ring graphs are a unique communication topology, known as difficult networks in which to achieve consensus or synchronization, as described in [21]. Ring graphs have equal numbers of edges to nodes, \(n_{e}=N\). Ring lattices consist of nodes arranged in a ring,and each node is connected symmetrically to \(m\) of its nearest neighbors on each side. These graphs are illustrated in Figure 1.  
The connectivity of a graph is defined as [21]  
\[\mu=\frac{\delta}{N-1} \tag{2}\]  
where \(\delta=\min(d_{ii})\) and \(N-1\) is the maximum possible degree for any node in the \(N\)-agent graph. The connectivity ratio \(\mu\) varies between 0 and 1, where a fully connected (complete) graph results in \(\mu=1\), an \(N\)-node ring graph has \(\mu=2/(N-1)\), and an \(N\)-node ring lattice with \(m=2\) has \(\mu=4/(N-1)\). Specifically, the graphs in Figure 1 have connectivities of (a) \(\mu=0.5\), (b) \(\mu=0.25\), and (c) \(\mu=0.5\). The connectivity and symmetry properties of the network, along with the reshaping strategy to be introduced, influence the existence of any stable non-consensus equilibria in certain non-linear multi-agent systems on undirected graphs [21].
## 5 Results and Discussions  
This section outlines and discusses the simulation results to study the performance of RL enabled UAV path planning for remote sensing applications by measuring four route planning metrics including (1) average End-to-End delay (ETE), (2) average number of captured data, (3) average battery consumption and (4) average traversed distance. This compares the results of RL-enabled UAV path planning with two well-known benchmarks  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Range** & **Parameter** & **Range** \\ \hline Simulation time & 3600 s & network initialization & 50 s \\ repetition & 300 & ambient noise and obstacles & disabled \\ Remote sensing range & 200 m & SP distribution model & random \\ Base station location & Farm’s center & Area size & 3 \(\times\) 3 km \\ MAC & CSMA/CA & Communication protocol & IEEE 802.11 \\ UAV battery threshold & 10\% & UAV Max flight (one battery) & 30 min \\ Mobile SP speed & 1 m/s & Mobile SP ratio & 25\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
(IEMF and TBID) to highlight the benefits of RL-based route planning against heuristic (IEMF) and tree-based (TBID) routing techniques.
# A New DGNSS Positioning Infrastructure for Android Smartphones
## 2 Background  
One of the basic observables available in the GNSS receiver is the pseudorange (\(\rho\)), which contains the true range \(r\) contaminated by various errors:  
\[\rho\ =\ r\ +\ b\ -\ B\ +\ I\ +\ T\ +\ M\ +\ e, \tag{1}\]  
where \(b\) and \(B\) denote the receiver and satellite clock error; \(I\) and \(T\) are the ionospheric delay and the tropospheric delay, respectively; \(M\) represents the multipath error; and \(e\) represents the error caused by receiver noise and interferences. Note that the pseudorange in Equation (1) contains other errors, such as the relativistic error and the differential code bias. For simplicity consideration, these terms are not included.  
Most of the errors in Equation (1) can be reduced either using navigation messages broadcast from satellites, or applying pseudorange corrections from a nearby reference station. These two approaches are known as the standalone GNSS and the range domain DGNSS. This section presents the fundamentals, and then introduces the NMEA protocol, which is widely employed to deliver positioning information to users in most of the GNSS chipsets.
## 6 Milan Project: Technical Specifications and Procedures to Ensure High-Quality Work
### Confidentiality of Data  
A crucial aspect, increasingly required in many fields, is the establishment of data confidentiality protocols. Leaving aside issues of quality and regarding privacy of the company's data management infrastructure, with respect to survey activities and instrumentation characteristics, a few key points should be highlighted.  
1. First, LiDAR-based systems, even more so if equipped with a camera, are able to acquire a digital twin of the surveyed environments with extreme care and detail. Even the raw data, before its processing, contains sensitive and detailed information. Therefore, if the surveying activity is performed not by the owner of the instrument but by an external service firm, confidentiality rules may require that the data not only be handed over to the client immediately after the completion of measurement operations but also that such data not be stored in the instrument's memory so that it can be retrieved, even if physically erased from such storage devices. As such, instruments with data storage on removable physical storage media (such as memory cards or USB sticks) may be required.
2. Second, the spread of cloud-based data processing services, where the data processing software is not provided to the surveyor but rather takes the form of an online data processing service, may involve uploading the raw data to an online portal. Once data processing is completed, the result is shared with the surveyor. Again, before proceeding with the activation of such procedures, it should be verified with the site owner whether such online data sharing, even if limited to the raw data processing operation, may be permissible under the management and confidentiality protocols of the client. Particularly in industrial or sensitive sites, such procedures may not be allowed. In the case of the Milan project, the issue of sending data for cloud processing did not arise because the chosen instrumentation provides, along with the instrument, the appropriate software for on-premise data processing.
# Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager
###### Abstract  
Industrial heat sources (IHSs) are key contributors to anthropogenic heat, air pollution, and carbon emissions. Accurately and automatically detecting their production areas (IHSPAs) on a large scale is vital for environmental monitoring and decision making, yet this is challenged by the lack of high-resolution thermal data. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data with the highest resolution (30 m) in the civilian field and a three-band advantage were first introduced to detect IHSPAs. In this study, an IHSPA identification model using multi-features extracted from SDGSAT-1 TIS and Landsat OLI data and support vector machine (SVM) was proposed. First, three brightness temperatures and four thermal radiation indices using SDGSAT-1 TIS and Landsat OLI data were designed to enlarge the temperature difference between IHSPAs and the background. Then, 10 features combined with three indices from Landsat OLI images with the same spatial resolution (30 m) and stable data were extracted. Second, an IHSPA identification model based on SVM and multi-feature extraction was constructed to identify IHSPAs. Finally, the IHS objects were manually delineated and verified using the identified IHSPAs and Google Earth images. Some conclusions were obtained from different comparisons in Wuhan, China: (1) IHSPA identification based on SVM using thermal and optical features can detect IHSPAs and obtain the best results compared with different features and identification models. (2) The importance of using thermal features from the SDGSAT-1 TIS to detect IHSPAs was demonstrated by different importance analysis methods. (3) Our proposed method can detect more IHSs, with greater spatial coverage and smaller areas, compared with the methods of [PERSON] and [PERSON]. This new way to detect IHSPAs can obtain higher-spatial-resolution emissions of IHSs on a large scale and help decision makers target environmental monitoring, management, and decision making in industrial plant processing.  
Keywords:industrial heat source production areas; SDGSAT-1 TIS; multi-feature extraction; target identification +
Footnote †: journal: Applied Sciences
## Materials and Methods
### Method
#### 2.3.2 Drought Identification  
The Standardized Precipitation Evapotranspiration Index (SPEI) was proposed by [PERSON] et al. (2010), and the index considers the difference between temperature sensitivity, potential evapotranspiration, precipitation, and multi-time scale characteristics, making it a reasonable method for drought monitoring and assessment ([PERSON] et al., 2010). The principle is to analyze the statistical distribution pattern of the difference between potential evaporation and precipitation, thus reflecting the changes in drought conditions. Therefore, this study chose to use the SPEI, which is more suitable for monitoring drought under climate warming. In this study, precipitation and temperature changes were calculated for multi-drought scenarios (2 extreme boundaries \(\times\) 3 drought initiation seasons \(\times\) 4 drought intensities \(\times\) 12 drought durations \(\times\) 12 SPEI time scales) from 1993 to 2022 using the SPEI at 12 time scales. Further, four drought intensities were distinguished according to the SPEI values (when the SPEI \(>-0.5\), drought severity was defined as non-drought; when \(-0.5\) > the SPEI \(>-1\), drought severity was defined as mild drought; when \(-1\) > the SPEI \(>-1.5\), drought severity was defined as moderate drought; when \(-1.5\) > the SPEI \(>-2\), drought severity was defined as severe drought; and when the SPEI \(\leq-2\), drought severity was defined as extreme drought).
# Design and Fabrication of a Novel Window-Type Convection Device
## 3 Discussion and Validation of Results
### Experimental Validation  
The optimized geometric dimensions of the air multiplier were experimentally validated to examine the accuracy of the ANSYS Fluent simulation and the feasibility of the convection device. Figure 13 details the assembly, physical appearance, and mass flow rate measurement for the convection device. Specifically, the device was assembled by combining a wind-deflector plate and wind-deflector cover to form the NACA 4424 airfoil with a 1.2 mm slit; the plate and cover were attached to a centrifugal fan, and the three components were mounted on the center of a device substrate (Figure 13a). Figure 13b illustrates the mass flow rate measurement method for the convection device. Specifically, in accordance with Chinese National Standard 547 regarding flow rate measurement, the convection device was positioned 438 mm ahead of the mass flow rate measurement panel. Wind speed data for 64 equally divided areas of the measurement panel were captured to estimate and sum up the mass flow rate on each of the areas, which determined the ability of the convection device to enhance air convection. Wind speed was measured using an anemometer (LM-81 AM, Lutron Electronic Enterprise Co., Ltd., Taipei, Taiwan).  
Figure 12: Mass flow rate (**a**) at the inlet of Plane S, (**b**) on Plane M1, (**c**) at the outlet of Plane M2, and (**d**) at the outlet of Plane M3 in relation to the slit sizes of the NACA4412, NACA4418, and NACA4424 airfoils.  
Figure 14 illustrates the measurement of flow fields around the convection device. As shown in Figure 14a, the flow-field direction was measured with smoke blown by the centrifugal fan powered at 12 V; the equivalent simulation results are depicted in Figure 14b. Overall, the flow-field measurement results in Figure 14a,b indicate qualitatively similar changes, confirming the feasibility of the convection device.  
To examine the quantitative differences between the experimental and simulated flow-field measurement results, wind speed data were captured, and the mass flow rates under the experimental and simulated conditions were estimated. Figure 15 shows the mass flow rate under simulated and experimental conditions at three voltages at which the centrifugal fan operated. The mass flow rate under both conditions exhibited similar changes and increased with the operating voltage of the fan. Moreover, the mean error between the simulated and experimental measurements at these varying operating voltages was 6.3%, suggesting that the ANSYS Fluent simulation conducted in this study was highly reliable. This error was attributed to the assumptions of the simulation, the surface roughness of the 3D-printed parts, and the assembly error in the convection device.  
Figure 14: (**a**) Experimental and (**b**) simulated flow-field measurement results.  
Figure 13: (**a**) Assembly of the window-type convection device, and (**b**) the mass flow rate measurement method for the convection device.
## 4 Discussion
### The Study of DeepESN Configurations on the LIWOC System  
To achieve optimal performance with DeepESN processing, the number of reservoir units must be large enough to cover the samples in one or two symbols to avoid insufficient training. However, overfitting occurs when the number of covered symbols is equal to or greater than four. The recommended size for each reservoir in a DeepESN ranges from \(\mathbf{N_{sps}}\) to \(\mathbf{3N_{sps}}\), with \(\mathbf{2N_{sps}}\) serving as a compromise between insufficient training, overfitting, and calculation time. DeepESN offers superior signal equalization capabilities compared to conventional ESNs.  
This characteristic of DeepESN also clarifies why it provides the greatest improvement in signal quality for the 167 Mbps PAM signal rather than the 100 and 125 Mbps PAM signals, as previously discussed. In the transmission experiment, the size of the reservoir was fixed at 10 units per layer. The average number of symbols that the reservoir units can cover is 3.33 for 167 Mbps, 2.5 for 125 Mbps, and 2 for 100 Mbps signals. The 167 Mbps signal hereby received the best training and compensation. This observation explains why only PAM cases experience significant signal quality improvement through DeepESN processing.  
Adding more reservoir layers slightly enhances the effectiveness of DeepESN processing but it sacrifices the efficiency of the network. In a communication system, time consumption is a crucial aspect for evaluation as well, and it requires the DSP process to operate as rapidly as possible. As a result, there is a trade-off between DeepESNs' performance and efficiency. Considering the minimal decline in BER provided by configurations with more reservoir layers after \(L=3\), the suggested number of layers for DeepESN application in UWOC systems is two or three.
## 3 Data  
The daily river discharge used in this study was obtained from the archives of the Global Runoff Data Centre (GRDC) ([[https://www.bafg.de/GRDC/EN/Home/homepage_node.html](https://www.bafg.de/GRDC/EN/Home/homepage_node.html)]([https://www.bafg.de/GRDC/EN/Home/homepage_node.html](https://www.bafg.de/GRDC/EN/Home/homepage_node.html)) accessed on 1 June 2023). The data for eight stations within the Niger Basin were obtained from 1950 to 1990 (Figure 1). These stations are Koulikoro, Diola, Kirango, Douna, Mopti, Dire, Ansongo, and Niamey. The period chosen has the longest continuous data for all the locations with minimal missing data points. The statistical properties of river discharge at these locations have been reported in the literature [10]. This study considered six statistical characteristics of river discharge, including minimum discharge, quantiles (0.1, 0.5, and 0.9), mean discharge, and maximum discharge values. These properties are plotted for all the locations (Figure 2).  
Figure 1: Study locations showing the gauging stations within the Niger Basin. Significant dams are marked in black triangles.
## 7 Case Study
### Simulation Results Using FCLM  
FCLM is applied to the planning area for the siting of the charging stations, which means that the travel route is the shortest path and the trip is captured only if at least one charging station on the path. With the algorithm proposed, we find that the travel success ratio at the maximum traffic flow is 80.53%. In this study, we choose one planning scheme and plan to sit the charging stations at four traffic nodes: 17, 56, 66, and 52.  
For this planning scheme, we adopt the method described in Section 3.3 to analyze the actual trip feasibility for each user choosing the shortest path, and the travel success ratio is obtained at 54.87%, that is, 25.66% of EV users will be captured in FCLM and their trips are assumed to be successful, while, in fact, the charging station planning scheme cannot satisfy their travel demands.  
Figure 19: The traffic flow of each traffic node before and after the planning of the fast charging stations; (**a**) the traffic flow before the planning of the fast charging stations; (**b**) the traffic flow after the planning of the fast charging stations  
Figure 18: The locations of EV charging stations in the power grid.  
Then we consider the impact of charging stations on travel routes. We consider the possibility of detouring and re-planning the users' travel routes, and we obtain a travel success ratio of 75.22%. The result demonstrates that 25.35% of the users can complete the trip by taking a detour.  
The success ratio under the above three scenarios is shown in Figure 20. It can be seen that the above three scenarios have a great impact on the travel success ratio. Under different scenarios, planning the fast charging stations for a maximum travel success ratio will also produce different results. Scenario 1 does not consider the trip feasibility with a simple model; Scenario 2 does analyze the trip feasibility, but ignores the impact of the siting of the charging stations on the travel routes; and Scenario 3, which considers the trip feasibility and other potential travel routes, is most consistent with the actual situation.
# On the Integration of VLBI Observations to GENESIS into Global VGOS Operations
## 3 Materials and Methods
### Scheduling
Figure 5 visualizes the ground track of the GENESIS satellite for a duration of 24 h. The position is given as small dots in intervals of 2.5 min. Every hour is marked with a big dot. The orbit shows about 6.3 revolutions per day, which is equivalent to 3.8 h per one revolution. Due to the polar orbit and frequent overflights in the polar regions, longer visibility times are expected for stations at high latitudes.
## 1 Introduction
Superpixel models have also been introduced to estimate the image noise level [20; 21]. As the superpixels can be used to segment the images effectively, using the local statistics of superpixels to recover the images may be another feasible way for speckle noise reduction from ultrasound images. In this paper, we propose a new ultrasound speckle noise reduction method, based on the strategies of superpixel reconstruction and spatial compensation. Specifically, we introduce the superpixel version of bilateral filtering to realize speckle noise reduction. Furthermore, a human visual system (HVS)-inspired model is proposed to realize the spatial compensation, in order to recover the sophisticated details effectively. The rest of the paper is organized as follows. In Section 2, we introduce our proposed despeckling model. Then, we validate the effectiveness of our approach in Section 3 and, finally, present the discussion and conclusion in Section 4.