esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Current Research Status  
#### 1.2.1 Research Status of Urban Stormwater Simulation Model  
Accurately simulating urban flooding has become an indispensable practice for mitigating its impacts, especially as the frequency of such events increases. Countries in Europe and the United States began researching the simulation of stormwater runoff in modern cities relatively early, and their research content is more comprehensive. The results of numerical model simulations are intuitive and easy to analyze quantitatively, and they play an important role in urban flooding analysis.  
The SWMM (Storm Water Management Model) proposed by the EPA (US Environmental Protection Agency) in the 1970s simulates the flow state of pipe networks by solving the Saint-Venant equation system and can analyze the current state of drainage systems, plan the underlying surface, simulate water quality, design pipe networks, and has additional LID functions. It has been widely used in municipal planning and scientific research [7; 8; 9; 10]. During the same period, [PERSON] and others developed the ILLUDAS (Illinois Urban Drainage Model), which has the disadvantage of being able to perform only hydraulic simulations [11; 12]. In 1983, the Danish Hydraulic Institute developed MIKE URBAN, which integrates various modules such as rainfall data processing and surface runoff infiltration modules, enabling more accurate rainfall and flood simulations [13; 14]. MIKE FLOOD can dynamically couple MIKE 11 and MIKE URBAN to obtain a more accurate simulation of the urban drainage network by setting boundary conditions and parameter checks [15]. In addition, InfoWorks was developed by Innovyze, and STORM models were developed by the US Army Corps of Engineers (HEC) and are also widely used [16; 17; 18; 19].
###### Abstract  
Accurate mapping of recent lava flows can provide significant insight into the development of flow fields that may aid in predicting future flow behavior. The task is challenging, due to both intrinsic properties of the phenomenon (e.g., lava flow resurfacing processes) and technical issues (e.g., the difficulty to survey a spatially extended lava flow with either aerial or ground instruments while avoiding hazardous locations). The huge amount of moderate to high resolution multispectral satellite data currently provides new opportunities for monitoring of extreme thermal events, such as eruptive phenomena. While retrieving boundaries of an active lava flow is relatively straightforward, problems arise when discriminating a recently cooled lava flow from older lava flow fields. Here, we present a new supervised classifier based on machine learning techniques to discriminate recent lava imaged in the MultiSpectral Imager (MSI) onboard Sentinel-2 satellite. Automated classification evaluates each pixel in a scene and then groups the pixels with similar values (e.g., digital number, reflectance, radiance) into a specified number of classes. Bands at the spatial resolution of 10 m (bands 2, 3, 4, 8) are used as input to the classifier. The training phase is performed on a small number of pixels manually labeled as covered by fresh lava, while the testing characterizes the entire lava flow field. Compared with ground-based measurements and actual lava flows of Mount Etna emplaced in 2017 and 2018, our automatic procedure provides excellent results in terms of accuracy, precision, and sensitivity.  
volcano hazard monitoring; satellite remote sensing; artificial networks; automatic detection; Sentinel Mission; Pleiades Mission +
Footnote †: journal: Accepted: 14 August 2019; Published: 16 August 2019
# Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks
## 3 System Design
### Edge-Aided Data Forwarding  
In this subsection, we detail the forwarding of edge servers. When a packet is overheard by the edge server, it estimates the potential gain of forwarding the current packet. We define the gain as the  
Figure 2: The framework: The edge servers can empower data transmission in sensor networks via _Forwarding_ and _retransmissions_.  
expected number of saved transmissions over the number of interfered nodes. The gain, denoted as \(g\), is then calculated as  
\[g=n_{s}-n_{i} \tag{1}\]  
where \(n_{s}\) denotes the number of saved transmissions, and \(n_{i}\) denotes the number of interfered nodes. \(n_{s}\) is calculated as  
\[n_{s}=\sum_{k=1}^{|N_{c}|}q_{k} \tag{2}\]  
where \(N_{c}\) denotes the set of destination nodes covered by the edge server, and \(q_{k}\) denotes the link quality from the edge server to node \(k\). For the measurement of \(q_{k}\), we can follow the existing works to reduce the measurement delay such as in [31].  
Then, we calculate the number of interfered nodes, \(n_{i}\), as follows:  
\[n_{i}=\sum_{k=1}^{|N_{i}|}(1-q_{k}) \tag{3}\]  
where \(N_{i}\) denotes the set of interfered nodes, and \(q_{k}\) denotes the link quality of the interfered links. Following the signal-to-noise-ratio (SNR) model [32; 33], we estimate \(q_{k}\) as follows:  
\[ber_{k}=Prob[\frac{g_{k}}{ns_{k}}\geq\delta_{a}] \tag{4}\]  
where \(g_{k}\) denotes the received gain of signal, and \(ns_{k}\) denotes the overall received noise at node \(k\), which includes the received signal strength from the edge server to node \(k\).  
By combining packet length \(pl\) and bit error rate \(ber_{a}^{t}\), we can calculate the expected packet reception rate for each sample, \(q_{k}\), according to [34]:  
\[q_{k}=((1-ber_{k})^{8}+8 ber_{k}(1-ber_{k})^{7})^{pl/4}. \tag{5}\]  
By combining the above equations, the edge server is able to obtain the expected gain of packet forwarding. If the gain is larger than a given threshold, the edge server starts forwarding the packet. Otherwise, the packets is dropped and the edge server continues overhearing.  
We conduct an experiment to study the relationship between symbol-error-rate (SER) and the packet capture probability (PRR). Figure 3 shows the results. We can infer that the symbol error rate is directly impacted by the environmental noise and the interfering signals from the edge server. Similar results have been observed in [33].  
Figure 3: The symbol error rate versus the packet capture probability.
## 3 Analysis of Port Competition Mode and Equilibrium Result  
The main factor for ports to attract goods is the port service price (i.e., port charges). In this paper, both port A and port B make decisions at the same time and non-cooperatively to maximize their profits and find the Nash equilibrium solution of two types of container  
Figure 3: Hinterland division of port A and port B.  
service prices. Based on the above analysis, given the port cargo demand function, the profit function of port A and port B can be expressed as follows:  
\[\pi_{A}=(p_{A}-C_{A}^{port})\cdot Q_{A} \tag{9}\]  
\[\pi_{B}=(p_{B}-C_{B}^{port})\cdot Q_{B} \tag{10}\]  
Furthermore, we can substitute Equations (7) and (8) into Equations (9) and (10), respectively, to obtain the profits of port A and port B as follows:  
\[\pi_{A}=(p_{A}-C_{A}^{port})\frac{P_{B}-P_{A}+C_{B}^{sea}-C_{A}^{sea}+C_{bB}^{ land}+b\sum\limits_{m=1}^{M}w_{m}\Big{(}\delta^{m}+\varepsilon\frac{1}{\overline {V}^{m}}\Big{)}+H_{B}}{2\sum\limits_{m=1}^{M}w_{m}\Big{(}\delta^{m}+\varepsilon \frac{1}{\overline{V}^{m}}\Big{)}+H_{A}+H_{B}} \tag{11}\]  
\[\pi_{B}=(p_{B}-C_{B}^{port})\frac{P_{A}-P_{B}+C_{A}^{sea}-C_{B}^{sea}-C_{bB}^{ land}+(2-b)\sum\limits_{m=1}^{M}w_{m}\Big{(}\delta^{m}+\varepsilon\frac{1}{ \overline{V}^{m}}\Big{)}+H_{A}}{2\sum\limits_{m=1}^{M}w_{m}\Big{(}\delta^{m}+ \varepsilon\frac{1}{\overline{V}^{m}}\Big{)}+H_{A}+H_{B}} \tag{12}\]  
In this paper, port A and port B make decisions on the pricing of import and export container services, maritime transportation costs, cross-border land transportation costs, and port cargo handling capacity in the hinterland, and both aim to maximize their own profits in a non-cooperative manner.
## 3 Semantic Understanding of Visual Layer
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Spatial** & **Image Size** & **Number of** & **Number of Samples** & **Total Number** & **Year of** \\  & **Resolution (m)** & **Image Size** & **Categories** & **per Category** & **of Samples** & **Publication** \\ \hline UC-Merced [23] & 0.3 & 256 \(\times\) 256 & 21 & 100 & 2100 & 2010 \\ WHU-RS19 [24] & 0.5 & 600 \(\times\) 600 & 12 & 50 & 950 & 2010 \\ RSSCN7 [25] & - & 400 \(\times\) 400 & 7 & 400 & 2800 & 2015 \\ RSSC11 [26] & 0.2 & 512 \(\times\) 512 & 11 & About 100 & 1232 & 2016 \\ SIRI-WHU [27] & 2 & 200 \(\times\) 200 & 12 & 200 & 2400 & 2016 \\ NWPU-REISIC45 [28] & 0.2–30 & 256 \(\times\) 256 & 45 & 700 & 31,500 & 2017 \\ PatternNet [29] & 0.062–4.693 & 256 \(\times\) 256 & 38 & 800 & - & 2017 \\ AID [30] & - & - & 30 & - & 10,000 & 2017 \\ EuroSAT [31] & - & 64 \(\times\) 64 & 10 & 2000–3000 & 27,000 & 2019 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of existing public datasets. structure. After the CENTRIST transformation, the image still retains the global and local structure information. Therefore, it can simulate the human visual system and describe the shape and texture of objects accurately. Global features and local features have their own advantages and disadvantages. Different visual features are suitable for different tasks of geographic scene understanding. In HSR remote sensing image description, visual feature extraction should not only keep the invariance of features but should also fuse the spatial structure information of the features.
# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 3 Methodology  
### Study Area  
The region of interest for this scoping study is North East England, which borders Scotland to the North, the North Sea to the East, the Pennine Hills to the West, and the county of North Yorkshire to the South (see Figure 2).  
Despite being one of the lowest-performing economic regions of the UK, North East England has a burgeoning space economy: In 2021, the region achieved an income of GBP 124 m and employed around 1300 people [1]. With a diverse landscape and a population of approximately 2.65 million [37], the region also poses unique challenges for disaster management. Due to its diverse geography (urban, rural, land and sea) within a small area footprint, it serves as an appropriate testbed for assessing the application of satellite data for emergency management purposes.
# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 3 Methodology
### Model Validation  
The performances of the yield estimation models were evaluated by using three validation measurements: the mean absolute relative deviation (MARE), coefficient of determination ( \(R^{2}\)) and the Lin's concordance correlation coefficient (CCC):  
\[MARE=\frac{1}{n}\sum_{i=1}^{n}\frac{\left|r_{i}-R_{i}\right|}{r_{i}} \tag{7}\]  
\[R^{2}=\left(\frac{\sqrt{\sum_{i=1}^{n}(r_{i}-\overline{r})\big{(}R_{i}- \overline{R}\big{)}}}{\sqrt{\sum_{i=1}^{n}\big{(}r_{i}-\overline{r}\big{)}^{2} }\sqrt{\sum_{i=1}^{n}\big{(}R_{i}-\overline{R}\big{)}^{2}}}\right)^{2} \tag{8}\]  
\[\text{CCC}=\frac{2S_{Rr}}{\big{(}\overline{R}-\overline{r}\big{)}^{2}+V_{r}+V _{R}} \tag{9}\]  
where \(r_{i}\) and \(R_{i}\) refer to the reported and estimated maize yield in year \(i\); \(\overline{r}\) and \(\overline{R}\) are the mean values of the reported and estimated values of maize yield; \(V_{r}\) and \(V_{R}\) are the variance of the reported and estimated values of maize yield; \(n\) is the number of samples, \(S_{Rr}\) is the covariance between the reported and estimated values of maize yield. MARE reflects the credibility of an estimation. The CCC measures the agreement between two variables. The model results become increasingly more accurate as \(R^{2}\) and CCC approach 1 and MARE approaches 0.
## 2 Materials and Methods  
### Materials  
The materials used here were vibration-damping Al, aluminum alloy (Al5052-H32), steel alloy (GA590 DP), and a thermoset CFRP 7 cross-ply (at 0\({}^{\circ}\) and 90\({}^{\circ}\) angles) laminated structure in an epoxy matrix. As shown in Figure 1, the vibration-damping Al panel consists of two Al5052-O sheets (with a thickness of 0.7 mm) bound by a 0.04 mm thick viscoelastic adhesive polymer layer. The strengths and the thicknesses of the materials used are listed in Table 1, evaluated using ASTM E8M specified samples.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Panel Material** & **Ultimate Tensile Strength (MPa)** & **Thickness (mm)** \\ \hline Vibration-damping Al & 188 & 1.44 \\ Al5052-H32 & 233 & 1.5 \\ GA590 DP & 609 & 1.4 \\ CFRP & 1032 (0\({}^{\circ}\))/234 (45\({}^{\circ}\))/1014 (90\({}^{\circ}\)) & 1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The ultimate tensile strength and thickness of the materials tested here. The results for three different directions (0\({}^{\circ}\), 45\({}^{\circ}\), and 90\({}^{\circ}\)) are shown for carbon-reinforced plastic (CFRP).
## 3 Methodology and the Model  
### Fluid-Structure Interaction (FSI) Model  
Based on practical engineering (Figure 1) and the spindle shape of the perforation tunnel, the FSI model is proposed, as shown in Figure 3. The fluid model was used in the fluid domain with the FSI boundary and represented the SC-CO\({}_{2}\) hydro-jet in the perforation tunnel. The model for structure was used in the structure domain, and was composed of the casing, cement, and shale rock. The FSI model was built using the ADINA (ADINA 9.2.2, ADINA R &D Inc., Watertown, MA, USA, 2016) software, which is regarded as one of the best numerical simulation programs to solve the FSI problem. In the model, the FSI boundary had the same profile, while the fluid and structure nodes on the FSI interface were coupled in ADINA. To avoid potential convergence problems, the following assumptions were made to simplify the FSI model.  
1. Before the initiation and propagation of fracture, both the fluid and the structure models have fixed boundary profiles, while the FSI boundary has limited deformation.
2. In the shale model, due to the short residence time for SC-CO\({}_{2}\) jet, the CO\({}_{2}\) seepage and phase change did not need to be considered.
3. In the SC-CO\({}_{2}\) jet model, the temperature does not significantly affect the stress field of the shale rock.  
Figure 2: Shape of the shale rock perforation.  
Based on these assumptions, it was possible to simulate the stress and deformation of shale rock before and after the fracture initiation.  
Though the FSI numerical model of SC-CO\({}_{2}\) hydro-jet fracture was established, the one-way coupling method of FSI model should also be conducted in the software. The FSI process was divided into three parts, as shown in Figure 4. Part 1 represented the computational fluid dynamics (CFD) simulation process. The perforation pressure was applied on the boundary of the shale rock model, and the results were extracted during the simulation process. Part 2 represented the finite element method (FEM) simulation, and both the perforation pressure and the in situ stress were used in the model. In addition, considering the inhomogeneous characteristics of shale rock and material failure, the model was modified through user-defined code in ADINA. Part 3 represented the restart solution step, in which the function of elemental failure was activated due to the increase in boundary load. When the simulation was started, Step 1, including the CFD and FEM simulation was performed. After the solution was obtained, Step 2 was carried out. In this step, the user-defined material failure criterion was activated, and the element turned to failure in the model, provided that the element stress reached the threshold value of the failure settings.  
Figure 4: FSI code process.  
Figure 3: Fluid-structure interaction (FSI) numerical model of SC-CO\({}_{2}\) hydro-jet fracture.
# Spitzer Resurrector Mission: Advantages for Space Weather Research and Operations
## 1 Introduction  
Space Weather (SWx) is a serious concern for all areas of our society, yet forecasting remains an extremely challenging problem. The size and complexity of the system (inner heliosphere) compared to the size of Earth--the 'target' of the forecast--is enormous (about >108:1 in area). Observing the system with only a handful of research missions results in sparse coverage and inconsistent data over time. Solving the sparse coverage problem is a complex and expensive task that requires a systems approach [1] and efficient collaboration among the government, military, and commercial sectors, as has been noted in several recent reports (e.g., SWx R2O2R Framework [2], NAS Workshop on the Future SWx Research & Operations Infrastructure [3]).  
An obvious strategy to build up the SWx space infrastructure is to leverage the increasing number of launches via rideshares (e.g., NOAA's SWFO-L1 on the NASA IMP mission). While this strategy is gaining popularity, with several rideshare missions (mostly CubeSats) on the books, it is not the only option. NASA's planetary missions have long hosted Heliophysics-funded payloads that leverage the cruise phase or orbit to make measurements with Heliophysics science priorities.  
This inter-divisional collaboration has been successful in sampling heliospheric regions that would be programmatically difficult to reach otherwise. An example is the Interstellar Mapping and Acceleration Probe (IMAP) mission, which leverages collaboration amongdivisions to resolve scientific questions about the heliosphere. So, why not replicate this strategy across more government agencies including the Department of Defense (DoD) as well as the commercial sector? Dual-use instrumentation is already considered for GEO and LEO DoD missions. Can we expand this to Deep Space where our observational coverage needs are severe? Those programs, however, are expensive and thus have long development schedules preceded by cumbersome and complex acquisition procedures. Could we come up with a more agile response that lowers costs and, importantly, speeds up the observational coverage in Deep Space?  
In the following, we describe a potential pathfinder opportunity to answer these questions in the affirmative. The Spitzer-Resurrector Mission (SRM) concept leverages an unusual funding opportunity, an interdisciplinary team from academia and industry, a novel mission design, and recent research and technical advances in Heliophysics to reach a cost-effective, yet innovative, solution to both research and operations gaps in SWx.
## 4 Materials and Experimental Methodology
### Digital Image Correlation (DIC) Methodology  
Digital image correlation (DIC) was employed as a non-contact, full-field optical technique to measure surface displacements and derive strain fields on the specimens during the dynamic spalling events. Given that the inherent surface texture of concrete typically lacks the necessary unique features for high-precision correlation, an artificial speckle pattern was applied to the region of interest on each specimen. The standard preparation procedure involved first applying a thin, uniform layer of white primer paint. After complete drying of the primer, a fine mist of black paint was sprayed using an airbrush to create a random, isotropic, and high-contrast speckle pattern. The quality of this pattern is crucial for the accuracy of DIC measurements. An example of such a prepared surface is shown in Figure 16.  
Based on theoretical analyses and preliminary experiments, spall fractures predominantly occurred within the 700 to 900 mm range from the impact end for the long concrete rods under investigation. Consequently, the high-speed camera's field of view was set to cover the region from approximately 600 to 900 mm from the impact end, yielding a spatial resolution of about 0.334 mm per pixel.  
The sequences of high-speed images captured during each experiment were processed using the commercial DIC software MatchID(2022.2.3). The zero-normalized sum  
Figure 16: Example of the artificial speckle pattern applied to the concrete specimen surface for DIC analysis.  
Figure 15: Typical axial strain histories recorded by strain gauges on the specimen surface and the corresponding free-end surface velocity history measured by the laser vibrometer.  
of squared differences (ZNSSD) algorithm was utilized as the correlation criterion. Based on sensitivity analyses to balance spatial resolution and measurement noise, a subset size of \(31\times 31\) pixels and a step size of 5 pixels were adopted. Full-field displacement data (\(u_{x},u_{y}\)) were first obtained from which strain fields were calculated using a finite difference method with appropriate smoothing.  
The DIC analysis procedure involved three main steps: (1) identification of fracture locations through discontinuities in displacement fields and strain localizations; (2) definition of virtual strain gauges (VSGs) at these critical locations to extract local strain histories, each VSG was defined over a 5 \(\times\) 5 grid of adjacent calculation points; and (3) calculation of strain rates from the strain-time data using numerical differentiation with Tikhonov regularization to mitigate noise amplification. The overall methodology for applying DIC to characterize the dynamic behavior and spall strength of concrete has been previously established and validated by the authors [23].
## 3 Case Study and Simulation  
A Maltab software simulation was developed to fully explore the performance of the system. We simulated four slave clocks with different stabilities. The Allan deviation of each phase noise component at \(\tau=1\) s is shown in Table 3, and the design of the PLL control loop is shown in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline
**Delays** & **Correction Method** \\ \hline
**Ionospheric delay** & Dual-frequency correction \\
**Device delay** & Pre-calibration \\
**Relative motion delay** & Relative motion compensation \\
**Ideal propagation delay** & Dual one way ranging measurement \\ Delays only exist in GSM & **Correction method** \\
**Tropospheric delay** & EGNOS tropospheric correction model \\
**Sagnac effect** & Sagnac correction \\ \hline \end{tabular}
\end{table}
Table 2: Different delays and corresponding correction methods.  
Figure 7: Schematic of the proposed clock adjustment algorithms on a slave satellite.