esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 HJ-1B instrument description
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|} \hline \multirow{2}{*}{**Sensor**} & \multicolumn{3}{c|}{**CCD Camera**} & \multicolumn{3}{c|}{**Infrared Scanner**} \\ \cline{2-9}  & \multicolumn{3}{c|}{**Band**} & \multicolumn{3}{c|}{Four bands} & \multicolumn{3}{c|}{Four bands} \\ \cline{2-9}  & \multicolumn{3}{c|}{B1} & B2 & B3 & B4 & B1 & B2 & B3 & B4 \\ \cline{2-9}  & 0.43\(\sim\)0.52 & 0.52–0.60 & 0.63–0.69 & 0.76–0.90 & 0.75–1.1 & \multirow{2}{*}{1.55–1.75} & \multirow{2}{*}{3.50–3.90} & \multirow{2}{*}{10.5–12.5} \\  & \(\upmu\)m & & & & & & 5 \\ \hline
**MTF** & \(\geq\)0.20 & \(\geq\)0.20 & \(\geq\)0.20 & \(\geq\)0.14 & 0.28 & 0.27 & 0.26 & 0.25 \\ \hline
**Radiant** & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} & \(\leq\)1K & \(\leq\)1K \\
**Resolution** & \multicolumn{3}{c|}{\(\backslash\)} & \multicolumn{3}{c|}{0.5\%} & 0.5\% & \(\leq\)1K & \(\leq\)1K \\
**(NEdp or** & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{(500K)} & \multicolumn{3}{c|}{(340K)} \\ \cline{2-9}  & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} \\ \hline
**Ground** & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} \\
**Resolution** & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{30m} & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} \\
**at nadir** & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} \\ \hline
**Swath width** & \multicolumn{3}{c|}{360 km*2 swath} & \multicolumn{3}{c}{} & \multicolumn{3}{c|}{} & \multicolumn{3}{c|}{} \\ \hline
**Field of view** & \multicolumn{3}{c|}{31\({}^{\circ}\)} & \multicolumn{3}{c|}{\(\pm\)29\({}^{\circ}\)} \\ \hline S/N & \multicolumn{3}{c|}{average\(\geq\)48 dB, min\(>\)6 dB} & \multicolumn{3}{c|}{\(\backslash\)} \\ \hline
**Quantization** & \multicolumn{3}{c|}{8 bit} & \multicolumn{3}{c|}{10 bit} \\ \hline
**Detector** & \multicolumn{3}{c|}{CCD} & \multicolumn{3}{c|}{infrared scanner} \\
**type** & \multicolumn{3}{c|}{48 hours} & \multicolumn{3}{c|}{48 hours} \\ \hline
**Orbit** & \multicolumn{3}{c|}{sun-synchronous} & \multicolumn{3}{c|}{sun-synchronous} \\ \hline \end{tabular}
\end{table} 
Table 1: Specifications of the HJ-1B main payloads.
# Low Delay Inter-Packet Coding in Vehicular Networks
## 3 Packet Recovering Codes
### Binary Wyner-Ash codes
#### Decoding
However, there are many combinations of weight 3 which are not codewords but are stopping sets. For example, consider the column block  
\[ \begin{pmatrix}H_{0}\\ H_{1}\\ H_{2}\end{pmatrix} = \begin{pmatrix}1&1&1\\ 1&0&1\\ 1&1&0\\ 0&0&0\\...&...&...\end{pmatrix} \]  
The corresponding positions of the codeword form a stopping set. Notice, that spectrum of stopping sets is determined not by code, but by its parity-check matrix. We can modify a parity-check matrix to eliminate some stopping sets, and thereby make performance of BP decoding close to that of ML decoding (see [27; 29]). To do this, an additional (redundant) parity-check should be added to the code parity-check matrix. For each window, we suggest to use the extended parity-check matrix  
\[H_{\text{EZT}}= \begin{pmatrix}1100&1010&1111\\ &1100&1010&1111\\ &&1100&1010&1111\\ 1100&0110&1001&0101&1111\end{pmatrix}  \tag{11}\]  
instead of Equation (10). The additional row is obtained as modulo 2 sum of all previous rows. It is easy to see that the former stopping set consisting of positions 9-11 (as well as many others) is not a stopping set for this matrix. The efficiency of the decoding technique based on using the redundant parity-check (RPC) matrix will be demonstrated in Section 4.
## 5 Case Studies  
In this study, the DN of a demonstration county in China, as shown in Figure 2, is analyzed for verifying the effectiveness of the method proposed in this paper. The DN includes one gas turbine unit located at bus node 12, with the unit parameters provided in Table 1. The DES clusters are located at bus nodes 2, 3, 7, 10, 13, 17, 20, 22, 25, 29, and 31. Distributed PV systems are installed at all nodes, with PV power stations specifically located at node 19 and node 32. An ESS on the grid side is located at bus node 1. The day-ahead, intra-day, and real-time forecast curves for PV and load are shown in Figure 3. Monte Carlo sampling is employed to generate 200 initial PV output and load scenarios for both day-ahead and intra-day stages. The forecast errors for load are set at 3% and 1% for the day-ahead and intra-day stages, respectively, while the forecast errors for PV are set at 5% and 3%, respectively. Next, the initial scenario sets are reduced using a fast non-dominated sorting technique based on probability distance, resulting in 5 day-ahead scenarios and 2 intra-day scenarios. The typical scenario generation results for PV and load are shown in Figure 4. From Figure 4, it can be seen that PV output power is peaked between 11:00 AM and 1:00 PM, reflecting the typical pattern driven by maximum sunlight exposure. In contrast, the load is varied throughout the day and is peaked between 9:00 PM and 10:00 PM, due to increased residential energy consumption for lighting and appliances. Between 3:00 AM and 6:00 AM, the load is reduced to a low point as most activities are paused. At midday, PV output power is exceeded over the load, which could result in power backflow and challenges in accommodating the PV output efficiently. The model was constructed as a mixed-integer second-order cone optimization problem using the YALMIP toolbox on the MATLAB platform, and it was solved using the GUROBI solver. A flexible and intuitive interface for modeling optimization problems was provided by YALMIP, while GUROBI's efficiency and reliability aided in successfully addressing the large-scale optimization task.
## 3 Applications of IL-Based Gels
### Fuel Cell  
For decades, enormous efforts have been made in the development of a high-performance fuel cell (FC) for new clean energy generation and storage devices. Fuel cells directly generate electricity by the electrochemical oxidation of H\({}_{2}\) at the anode and the reduction of O\({}_{2}\) into water at the cathode. Although the application of IL-based gel electrolytes in FC has been limited until now, several research groups attempted to design stable FC with ionic liquid-based electrolytes [106]. In 2018, by using the sol-gel approach, [PERSON] et al. developed a TEOS/trimethylammonium methanesulfonate (ES) IL-based gel electrolyte for proton exchange membrane fuel cell (PEMFC) application [132]. The as-prepared IL-based gel showed a superior ionic conductivity of 38.5 mS cm\({}^{-1}\) at 120 \({}^{\circ}\)C under a nonhumidified condition. The electrochemical results showed that the PEMFC constructed by sandwiching TEOS/ES IL-based gel electrolyte between two gas diffusion layer (GDL) electrodes possessed a maximum power density of 43.3 mW cm\({}^{-2}\) at a current density of 92 mA cm\({}^{-2}\) at 30\({}^{\circ}\)C under a nonhumidified condition. Later, by using the solution casting method, [PERSON] and his team prepared a novel proton conducting IL-based gel consisting of N,N-diethylmethylammonium triflate (DEMATf) and poly(diallydimethylammonium) bis(trifluormethanesulfonyl) imide (PDADMATFSI) for FC applications [133]. The synthesized DEMATf/PDADMATFSI IL-based gel with 60% DEMATf exhibited an enhanced ionic conductivity of 5.8 mS cm\({}^{-1}\) at 100\({}^{\circ}\)C, which was higher than that of commercial Nafion (0.2 mS cm\({}^{-1}\)) at 120 \({}^{\circ}\)C.  
Recently, in 2021, [PERSON]'s research group used the UV-polymerization technique to fabricate a protic IL-based gel from acrylamide (AM), 2-acrylamide-2- methyl-propanesulfonic acid (AMPS) monomers with a divinylbenzene (DVB) cross-linker and 1-hydroxycyclohexylphenylketone (HCHPK) initiator in dimethylethylammonium hydrogen sulfate (DMAHMSO\({}_{4}\)) or 1-methylimidazoloylum hydrogen sulfate (MIMHSO\({}_{4}\)) IL (Figure 8a,b) [134]. The obtained IL-based gels in DMAHMSO\({}_{4}\) and MIMHSO\({}_{4}\) were denoted as PG-DHS and PG-MHS, respectively. The PG-DHS and PG-MHS possessed a high ionic conductivity of 17.1 and 10.8 mS cm\({}^{-1}\), respectively, at 120 \({}^{\circ}\)C. To construct FC electrodes, a suspension of Pt/C particles and Nafion in isopropanol and water was sprayed onto the GDL electrode. Finally, the H\({}_{2}\)/O\({}_{2}\) FC was assembled at 120 \({}^{\circ}\)C by sandwiching the protic PG-MHS IL-based gel sheet accommodated in a silicone rubber gasket between the GDL electrodes. From the polarization curve, as shown in Figure 8c, a peak power density of 3.9 mW cm\({}^{-2}\) can be achieved in PG-MHS FC at 120 \({}^{\circ}\)C.
# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
###### Abstract  
The coastline environment and urban areas of Peru overlooking the Pacific Ocean are among the most affected by El Nino-Southern Oscillation (ENSO) events, and its cascading hazards such as floods, landslides and avalanches. In this work, the complete archives of the European Space Agency (ESA)'s European Remote-Sensing (ERS-1/2) missions and European Commission's Copernicus Sentinel-1 constellation were screened to select synthetic aperture radar (SAR) images covering the most severe and recent ENSO-related flooding events that affected Lima, the capital and largest city of Peru, in 1997-1998 and 2017-2018. Based on SAR backscatter color composites and ratio maps retrieved from a series of pre-, cross- and post-event SAR pairs, flooded areas were delineated within the Rimac River watershed. These are mostly concentrated along the riverbanks and plain, where low-lying topography and gentle slopes (\(\leq\)5\({}^{\circ}\)), together with the presence of alluvial deposits, also indicate greater susceptibility to flooding. A total of 409 areas (58.50 km\({}^{2}\)) revealing change were mapped, including 197 changes (32.10 km\({}^{2}\)) due to flooding-related backscatter variations (flooded areas, increased water flow in the riverbed, and riverbank collapses and damage), and 212 (26.40 km\({}^{2}\)) due to other processes (e.g., new urban developments, construction of river embankments, other engineering works, vegetation changes). Urban and landscape changes potentially contributing, either detrimentally or beneficially, to flooding susceptibility were identified and considered in the overall assessment of risk. The extent of built-up areas within the basin was mapped by combining information from the 2011 Global Urban Footprint (GUF) produced by the German Aerospace Center (DLR), the Open Street Map (OSM) accessed from the Quantum GIS (QGIS) service, and 2011-2019 very high-resolution optical imagery from Google Earth. The resulting flooding risk map highlights the sectors of potential concern along the Rimac River, should flooding events of equal severity as those captured by SAR images occur in the future.  
flooding; synthetic aperture radar; change detection; radar backscatter; urban remote sensing; GIS-based analysis; risk classification
## 3 Methods and Materials
### Feature Selection
#### 3.4.3 Grey Wolf Optimizer (GWO)-Based Algorithm  
GWO is a bio-inspired meta-heuristic algorithm based on the social behavior of grey wolves, known for their hierarchical pack structure [50]. Figure 7 illustrates this hierarchical structure.  
Here, we utilized the GWO object from the Niapy library [51]. The GWO algorithm utilizes alpha (\(\alpha\)), beta (\(\beta\)), delta (\(\delta\)), and omega (\(\omega\)) wolves to find the optimal solution. Each wolf represents a potential solution, with the alpha wolf making major decisions. The beta (\(\beta\)) wolf aids the \(\alpha\) wolf in decision making and enforces the alpha wolf's leadership  
Figure 6: The flowchart of the Boruta algorithm.  
Figure 7: Grey wolf leadership hierarchy.  
among the lower-ranked wolves. Delta (\(\delta\)) are senior wolves or sentinels who protect the \(\alpha\) and \(\beta\) and control the omega \(\omega\) wolves. Wolves' positions are updated based on prey location and a fitness function. The best solutions are ranked as \(\alpha\), \(\beta\), and \(\delta\) according to fitness. Omega wolves' positions are adjusted based on the top three wolves positions, denoted as \(\vec{X}_{\alpha}\), \(\vec{X}_{\beta}\), and \(\vec{X}_{\delta}\). Prey encircling, hunting, attacking, and searching are key steps in the algorithm and are mathematically represented as follows:  
1. Initialize the positions of the wolves randomly within the search space.
2. Prey Encircling: update the positions of the wolves based on the alpha wolf's position, aiming to encircle the prey. The position of each wolf is updated using the following equation: \(\vec{X}_{i}\ =\ \vec{X}_{\alpha}-\vec{A}\times\vec{C}_{i}\), where \(\vec{X}_{i}\) is the position of the ith wolf, \(\vec{X}_{\alpha}\) is the position of the alpha wolf, \(\vec{A}\) is a random vector, and \(\vec{C}_{i}\) is a coefficient vector.
3. Prey Attacking: update the positions of the wolves to attack the prey. The position of each wolf is updated using the following equation: \(\vec{X}_{i}\ =\ \vec{X}_{p}-\vec{A}\times\vec{C}_{i}\), where \(\vec{X}_{p}\) is the position of the prey.
4. Searching: update the positions of the wolves to search for the prey. The position of each wolf is updated using the following equation: \(\vec{X}_{i}\ =\ \vec{X}_{i}+\vec{A}\times\vec{C}_{i}\), where \(\vec{A}\) is a random vector, and \(\vec{C}_{i}\) is a coefficient vector.
5. Boundary Checking: ensure that the updated positions of the wolves remain within the defined search space.
6. Fitness Evaluation: evaluate the fitness of each wolf based on the problem-specific fitness function.
7. Select the three wolves with the best fitness values as the alpha, beta, and delta wolves, respectively.
8. Update the positions of the omega wolves based on the positions of the alpha, beta, and delta wolves.
9. Termination: repeat steps 2-8 until a termination criterion is met (e.g., a maximum number of iterations or a desired fitness value is reached). The GWO algorithm flowchart represented in Figure 8.
## 5 Discussion, Conclusion and Recommendations
### Conclusion and Recommendations  
The KCAP case study showed that virtual globes are helpful platforms for information integration at scales that are relevant for landscape disciplines, and they can raise interest and awareness. Not all user groups seem to benefit equally, as the bimodal ranking results indicate. For now it is recommended to include a mix of different media in face-to-face planning or design workshops, e.g., virtual globes together with posters. Virtual globes seem to be particularly powerful at regional scales with elevated oblique views, a scale that is often used in landscape planning. At that scale, orthophotos and vegetation appear most realistic as background for modified 3D data although anthropogenic influences might be under-represented. Virtual globes have improved in the representation of ground-based views but more specialized software is still advised to produce ground-based visualizations with higher realism. The strength of virtual globes is to allow seamless zoom between different scales, supporting landscape planning and design across scales and helping the viewer assessing design schemes in their spatial context. A promising workaround for the visualization of landscape architecture designs-a hand-drawn design sketch, georeferenced and overlayed in Google Earth (Figure 8)-was tested in the KCAP and generally received good feedback [15]. Further linking virtual globes with dynamic forest models may also enhance the vegetation realism at small scales.  
The process is the key to the successful use of virtual globes as decision-support tools. There are definitely differences with regard to context and types of processes, whether it is a bottom up grassroots campaign, a top-down initiative [12], or in the case of KCAP, a collaborative approach including local stakeholders, citizens, municipal staff, and external experts. The interviews identified some of the barriers why the virtual globe has not been used even more intensively, i.e., technical and usability issues, unclear data ownership and liability issues. If virtual globes include more features for the documentation of metadata and data quality, they will be more suitable for applications in government environments. In contrast, grassroots campaigns are less limited in the promotion of their virtual globe outputs, but they have less access to information and decision-making processes than collaborative approaches.  
Virtual globes such as Google Earth can be powerful and rather accessible tools for information integration and visualization in landscape related processes, particularly at regional scales. Virtual globes still have multiple limitations, some of which can be dealt with by embedding them in a well structured, transparent process that follows ethical guidelines [1,56], and addressing different user groups with a mix of complementary media [16]. Then, virtual globes can facilitate access to visual information, raise awareness and interest, and provide rather representative views of landscapes at the regional scale.
## 5 Conclusions  
The low resolution of the existing remote sensing soil moisture products render them unable to meet the needs of regional or watershed research. Therefore, soil moisture downscaling is potentially highly valuable for image applications. In this paper, a 9-km spatial resolution of the SMAP soil moisture product was conducted by a downscaling factor, and soil moisture data at 0.3-km spatial resolution were obtained. Accuracy was verified based on sampling points; the results suggested that _TVDI_ can be used as a downscaling factor to reflect the spatial differentiation of soil moisture in SMAP product pixels, possessing great clinical significance in subsequent watershed hydrology studies (especially for improving the spatial resolution of SMAP products). Likely main sources of error included SMAP soil moisture products and land-cover types; the error of downscaling treatment in wetland and cropland was greater than that of forest. In the process of building the model, it is best to avoid these errors as much as possible. No evidence indicates that the wet and dry edge fitting of the _TVDI_ influenced the downscaling results, which warrant further study.  
Conceptualization, A.-X.Z.; Data curation, S.-D.F. and Z.-H.L.; Formal analysis, B.L.; Funding acquisition, Y.-M.H.; Investigation, Z.S., W.-B.W. and Y.-C.P.; Methodology, S.-D.F., Z.-H.L., G.-X.W. and A.-X.Z.; Project administration, Y.-M.H.; Resources, L.W.; Software, W.-B.W. and B.L.; Supervision, Y.-M.H., L.W., G.-X.W., A.-X.Z. and B.L.; Validation, Z.S. and Y.-C.P.; Writing original draft, S.-D.F.; Writing review & editing, S.-D.F. and G.-X.W.  
This research was funded by Guangdong Provincial Science and Technology Plan Project (2017B090907030), Qinghai Province Science and Technology Plan Project (2017-ZJ-730), Guangzhou Science and Technology Plan Project (201804020034, 201807010048), and the Science and Technology Planning Project of Guangdong Province (2014A050503060).  
This work was supported by a grant from College of Natural Resources and Environment (South China Agricultural University). The authors would like to thank [PERSON] for excellent technical support and [PERSON] for critically reviewing the manuscript.  
The authors declare no conflict of interest.
## 3 Methods  
Machine-learning data preparation for the body landmarking is not a straightforward process. The proposed mid-surface approach was compared to and designed on top of the current state-of-the-art method by [PERSON] et al. [17] utilizing orthogonal projection. No code was attached to the original article, so we directly followed the processing steps described and implemented them as precisely as possible.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Abbr.** & **Name** & **Norm** \\ \hline V & Vertex & ISO 7250-1:2017, 5.21 \\ \(\rm C_{KL}\), \(\rm C_{KR}\) & Centre Point of Kneecap (L and R) & ISO 8559-1:2017, 3.1.17 \\ \(\rm A_{FFL}\), \(\rm A_{FFR}\) & Armpit Front Fold Point (L and R) & ISO 8559-1:2017, 3.1.13 \\ \(\rm N_{SL}\), \(\rm N_{SR}\) & Side Neck Point (L and R) & ISO 8559-1:2017, 3.1.7 \\ \(\rm E_{L}\), \(\rm E_{R}\) & Elbow Point (L and R) & ISO 8559-1: 2017, 3.1.10 \\ \(\rm R_{L}\), \(\rm R_{R}\) & Lowest Rib Point (L and R) & ISO 8559-1:2017, 3.1.15 \\ \(\rm H_{L}\)\(\rm H_{R}\) & Highest Point of the Hip Bone (L and R) & ISO 8559-1:2017, 3.1.16 \\ \(\rm S_{L}\), \(\rm S_{R}\) & Shoulder Point (L and R) & ISO 8559-1:2017, 3.1.1 \\ \(\rm C_{L}\) & Lowest Point of Chin & ISO 8559-1:2017, 3.1.5 \\ \(\rm W_{L}\), \(\rm W_{R}\) & Wrist Point (L and R) & ISO 8559-1:2017, 3.1.19 \\ \(\rm B_{L}\), \(\rm B_{R}\) & Bust Point (L and R) & ISO 8559-1:2017, 3.1.11 (females), \\ \(\rm N_{B}\) & Back Neck Point & ISO 7250-1:2017, 5.17, Thelion (males) \\ \(\rm C_{R}\) & Crotch & ISO 8559-1:2017, 3.1.16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of selected standardized human body landmarks, where L—left and R—right.  
Figure 2: Sample of input with marked axis, where x—width, y—depth and z—height.  
It has to be noted, however, that the preprocessing step was extended by surface smoothing to achieve comparable results. The original approach was divided into five parts: initial processing, specific layer calculation, projection, image generation, and automatic marking process. Between the fourth and fifth steps, the machine-learning task was performed with the use of the generated images. The workflow with a step-by-step visualization is presented in Figure 3.  
In contrast, the mid-surface method was divided into eight separate subprocesses, which are described in detail in the following paragraphs. The workflow is presented in Figure 4.  
For both methods, the same 3D human whole body point clouds were used as an input. They were also outputting similar data--3D scans overlaid with the detected and marked landmarks.  
Figure 3: Workflow of orthogonal projection method.