esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Introduction to the Study Area and the Dataset
### Dataset  
The SAR dataset is composed of 75 Sentinel-1A ascending orbital images from January 2020 to June 2022, with a 12-day image sampling interval. The image specific parameter table is shown in Table 1. The SRTM 90 m digital elevation model provided by the National Aeronautics and Space Administration (NASA) was used in the study to eliminate topographic phases, and the orbital errors were eliminated by using the precise orbit ephemerides (POD) provided by the European Space Agency (ESA).  
The temperature data were obtained from the Wuhan National Basic Meteorological Observatory (No. 57494) climate element statistics. The national meteorological station is located at Cihui Farm (30.36\({}^{\circ}\) N, 114.03\({}^{\circ}\) E), East-West Lake District, Wuhan, about 15.5 km  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Satellite Model** & **Sentinel-1A** \\ \hline Orbit direction & Ascending orbit \\ Angle of incidence & 39.7\({}^{\circ}\) \\ Band & C \\ Polarization mode & VV \\ Amount & 75 \\ Time span & 3 January 2020–21 June 2022 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sentinel-1A data basic parameters.  
Figure 1: (**a**) Picture of the Wuhan Yangtze River Bridge (**b**) satellite image of the Wuhan Yangtze River Bridge (**c**) location of the Wuhan Yangtze River Bridge (**d**) location of the Wuhan Yangtze River Bridge in the the Wuhan district.  
Figure 2: The Wuhan Yangtze River Bridge structural characteristics.  
away from the study area. The monthly average temperature for the 30 months during the study period is calculated based on the daily average temperature.
# Grouting Process Simulation Based on 3D Fracture Network Considering Fluid-Structure Interaction
## 1 Introduction
In this study, a grouting simulation approach considering fluid-structure interaction is developed based on the 3D fractured network model. Firstly, fracture parameters are randomly simulated by the Latin hypercube sampling (LHS) method based on the statistical information from fracture survey and borehole imaging of the exposed surface, the relationship between fracture apertures and trace lengths is used to obtain the value of fracture aperture, then a more reliable 3D fracture network model for dam foundation rock mass is established with VisualGeo software [29]. Next, the CFD simulation model of grout (fluid) and the finite element model of fractured rock mass (structure) are established respectively, and their governing equations are solved in different ways, with the results exchanged through the fluid-structure interface to realize the two-way fluid-structure interaction simulation of the grouting process. Finally, the approach is used in a case study to analyze the dam foundation grouting to investigate the effects of fluid-structure interaction on grouting processes; the results show that the grouting simulation considering fluid-structure interaction are more realistic and can simultaneously reveal the grout diffusion and fracture deformation under the interaction of grout and rock mass, which can provide more valuable information for optimizing the grouting process. The remaining parts of this paper are organized as follows: the methodology of 3D fracture network modeling and fluid-structure interaction simulation are introduced in Section 2. In Section 3, the approach is applied to analysis of the grouting performance of hydropower station X and the studies on the grouting characteristics are given in this section. Finally, the conclusions are provided in Section 4.
## 3 Results and Discussion
### Simulation of Batch Mode MFC
#### 3.2.1 Effect of Initial Substrate Concentration
During the first few days of anode inoculation, the growing voltage was caused by the growing attached biomass and eventually reached a plateau value of about 0.39 V. Since the substrate lasted longer in the 100 mg L\({}^{-1}\) MFC than in the 2400 mg L\({}^{-1}\) MFC, the electricity generation also lasted longer by 4 more days. Energy generated was 25.1 and 40 J for 2400 mg L\({}^{-1}\) MFC and for 100 mg L\({}^{-1}\) MFC, with overall energy efficiencies extracted from acetate (\(\eta_{overall}\)) of 0.46% and 17.7%, respectively. Therefore, based on electricity energy generation and overall energy efficiency, the MFC with 100 mg L\({}^{-1}\) of substrate substantially outperformed the one with 2400 mg L\({}^{-1}\). Controlling the initial substrate concentration was thus demonstrated as a way of regulating the suspended biomass concentration, and was helpful in harvesting more energy from the substrate for electricity. Figure 3: Simulated time-course profiles of process variables at different initial substrate concentration. (**A**) the substrate concentration; (**B**) suspended biomass concentration; (**C**) attached biomass concentration, and (**D**) the external voltage \(U_{ext}\). The growing amount of the attached biomass not only increased the external voltage (\(U_{ext}\)), but also improved the external power, evidenced by the gradual expansion of the polarization curves against the time in Figure 4. These simulations were based on the high initial substrate concentration of 2400 mg L\({}^{-1}\) in order to avoid a noticeable substrate decrease in 2 d. The pattern of the curve expansion was similar to that which was observed in the literature [4; 6], illustrating the importance of the attached biomass on power generation.
## 5 Discussion
### Method Applicability  
It should be noted that the ASRB parameter design method proposed in this paper is based on deeply buried hard rock TBM-constructed tunnels, and a rock deterioration model was used in the numerical simulation of the engineering application process. The conclusions drawn apply only to the conditions defined in this paper, and the relationship between the parameters of ASRB and the parameters of rock mass was not discussed. The design of borehole parameters for ASRBs may vary under different engineering backgrounds, different calculation methods, and calculation conditions. The conclusions and parameter rules drawn in this paper are for reference only. In addition, this study investigated the correlation between ASRB parameters and the effect of stress release and energy dissipation from two levels: the reduction level of maximum principal stress and the reduction degree of strain energy. However, the level of reduction in the degree of kinetic damage in real geologic environments after the implementation of ASRBs and then rockbursts was not investigated in this study, which is a direction for future improvement.
## 5 Algorithm
### RSU Clustering
12: Compute \(F[i,:]=\frac{U_{L}[i,:]}{\|U_{L}[i,:]\|}\) to construct the normalized feature matrix \(F\)
13: Use K-means clustering with a minimum capacity constraint \(S_{K}=\eta\sum\limits_{k=1}^{K}s_{k},\eta\geq 1\) to cluster each row of the feature matrix \(F\) as an input sample, obtaining clustering results \(\{C_{l}\}\). 14:\(CLU\leftarrow\{C_{l}\}\)
```  
**Algorithm 1** RSU Access Preference-based Spectral Clustering Algorithm (RAPSCA)  
For the complexity analysis, first, constructing the similarity matrix requires computing the weight relationship between every pair of RSU nodes, resulting in a complexity of \(O(n^{2})\). Next, computing the Laplacian matrix involves row-wise normalization, which also has a complexity of \(O(n^{2})\). The spectral decomposition has a complexity of \(O(n^{3})\), while feature normalization has a complexity of \(O(nL)\). The K-means clustering with minimum capacity constraints has a complexity of \(O(nLT)\), where \(T\) represents the number of iterations. Thus, the total time complexity of RAPSCA is \(O(n^{3})\).
## 4 Applications
### Non-Contact Imaging  
The use of a non-contact imaging scheme opens up a wide series of applications both in the medical field as well as on the neuropsychology side. For the former, there are plenty of situations where the physical contact with the subject has to be avoided such as in case of burn wounds or in all those situations where the contact of the fibers can alter the hemodynamics [117; 118]. On the other hand, the use of non-contact techniques to monitor the brain activation could dramatically push the neuroeconomy field, allowing one to follow the customers in their decisions.  
For what concerns the TD technique, only few works [34; 119] have been published with non-contact imaging but using a CCD camera which, as explained in Section 3, is not suitable for fast gating technique. Indeed, due to the small \(\rho\) (i.e., confocal scheme of the optics) the fast-gating technique is needed to reject the huge peak of early photons, thus allowing to probe deeper structure. This first proof-of-principle was developed in the paper by [PERSON] et al. [120], where the authors showed the suitability of the system working almost at null \(\rho\) (1 mm) based on fast-gated SPAD. They were able to detect an absorption perturbation yielding and equivalent a \(\Delta\mu_{a}\) = 0.1 cm\({}^{-1}\) in a 1 cm\({}^{3}\) volume buried at a depth of 15 mm inside a homogeneous medium (\(\mu_{a}\) = 0.1 cm\({}^{-1}\), \(\mu_{s^{\prime}}\) = 10 cm\({}^{-1}\)) with an improvement in the detectability of the perturbation due to the use of larger delays. Later on, a galvo-scanner was added to the previous setup thus enabling a scanning of the region with a densegrid of points. The final setup was characterized following internationally agreed protocols (e.g., BIP and nEUROPt protocols [60,121]) for performance assessment of TD diffuse imagers and compared to a state-of-the-art brain imager. It turned out that, despite the much lower responsivity of the non-contact system (due to the smaller detector size as well as to the much lower coupling efficiency of the system), there was a large improvement in the achievable dynamic range (seven decades) and CNR as well as in the spatial resolution of the system. All those advantages derived from the use of the quasi-null \(\rho\) approach coupled to the fast-gated detection.  
The same system was employed to demonstrate the possibility to achieve non-contact scanning functional measurements covering an area of about 4 \(\times\) 4 cm\({}^{2}\) with a framerate of 1 Hz [122]. Measurements of both arterial and venous occlusions of the arm as well as brain monitoring during a Valsalva maneuver and different types of activation task were published [122,123]. In all cases, the hemodynamic changes were clearly found thus showing the suitability of the proposed system for imaging applications.
# Estimation of Saprolite Thickness Needed to Remove _E. coli_ from Wastewater
## 3 Results and Discussion
### Effective Thickness of Saprolite
Figure 2: Relationships between (**A**) silt percentage, (**B**) sand percentage, and (**C**) saturated hydraulic conductivity (Ksat) and saprolite thickness needed to filter _E. coli_. saturated hydraulic conductivity to vary with particle size distribution, with finer textured materials producing smaller Ksat values, but root channels and other macropores may also be present, which could increase Ksat by by-passing the pores created by the packing of sand or silt grains. While Equation (3) was developed for saprolites, it may be applicable to some soils that do not possess a strong soil structure. For example, using the water-retention data for the sandy loam soil material reported by [PERSON] et al. [13], we estimated the material would require approximately 212 cm of soil thickness to filter _E. coli_. Nevertheless, experimental data showed that 60 cm would more than enough. However, the sandy loam of [PERSON] et al. [13] had a silt content of 30%, and when using the regression line in Figure 2A, a saprolite thickness of 59 cm would be predicted to be sufficient to remove _E. coli_ for this percentage of silt. This is in line with the experimental results of [PERSON] et al. [13]. The 212 cm thickness value is approximately three times the thickness we computed for the coarse sand texture. The sandy loam material used by [PERSON] et al. [13] was dried and passed through a 2-mm-mesh sieve before packing into cylinders for the experiments. Because water retention was determined using repacked cores, it is likely that the water retention results did not take into account all pores within the soil's aggregates formed by sieving. On the other hand, the water retention data for the saprolites used in our assessment were obtained using intact cores of non-aggregated saprolite. Non-aggregated materials consist mainly of interparticle pores (i.e., pores between mineral grains), and their volumes are more directly assessed in the water retention measurement procedure.
## 4 Dynamic Leader Multi-Verge Optimizer (DLMVO)
### Dynamic Leader Search Strategy  
As seen from Equation (15), the optimal solution plays a vital role in the search process, as it guides and draws other individuals to its region. This search strategy performs well on unimodal optimization problems especially. However, it easily makes complex multimodal optimization functions converge prematurely. The reasons are mainly because the step of each individual's perturbation around the best individual through wormholes is random, making it difficult to further improve the quality of the offspring. Additionally, overuse of optimal solutions may reduce diversity of the population and cause the algorithm process to fall into a local optimum [42]. Taking into consideration these shortcomings, a new search strategy based on dynamic elite leaders is suggested in this paper, and a detailed description is shown next.  
In the new search strategy, the universe is sorted based on the fitness, and the top percent of universes are regarded as a dynamic group (\(U_{top}\)). \(U_{top}\) should have different characteristics, which will ensure that the travel strategy is effective. Current individuals are taken as primary vectors that will cooperate with the random individuals in the dynamic groups to guide the population to promising areas. The details are as follows:  
\[u_{i,NFEs}=\left\{\begin{array}{ll}\begin{array}{ll}u_{topk,INEs}+TDR(u_{i, NFEs}-u_{topk,INEs})&r4<0.5\\ u_{topk,INEs}-TDR(u_{i,NFEs}-u_{topk,INEs})&r4\geq 0.5\\ u_{i,FEs}\end{array}&r3<WEP\\ \end{array}\right. \tag{22}\]  
where \(u_{topk0}\) and \(u_{topk1}\) are randomly chosen from \(U_{top}\) under the current iteration. To facilitate exploration at the beginning of the search and to exploit during the later stages, a dynamic population reduction strategy is employed. Each generation population size is formulated as follows:  
\[NP_{top}=Ceil\bigg{[}\frac{NP}{2}(1-\frac{NFEs-1}{Max\_NFEs})\bigg{]} \tag{23}\]  
where \(Ceil(y)\) is a function that returns a minimum integer greater than parameter \(y\). Of note, \(2<NP_{top}<NP\).
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} COW & Coherent One-way \\ CV-QKD & Continuous-variable QKD \\ CWDM & Coarse WDM \\ DTU & Technical University of Denmark \\ DV-QKD & Discrete-variable QKD \\ DWDM & Dense WDM \\ EDFA & Erbium-Doped Fiber Amplifier \\ FWM & Four-Wave Mixing \\ IDQ & ID Quantique \\ ITU-T & International Telecommunication Union--Telecommunication Standardization Sector \\ MDI-QKD & Measurement-device Independent QKD \\ MUX & Multiplexer \\ OSA & Optical Spectrum Analyzer \\ OSNR & Optical Signal-to-noise Ratio \\ PQC & Postquantum Cryptography \\ QBER & Quantum-bit Error Rate \\ QKD & Quantum Key Distribution \\ QMUX & Quantum Multiplexing \\ QRNG & Quantum Random Number Generation \\ SFP & Small Form-factor Pluggable \\ SpRS & Spontaneous Raman Scattering \\ TF-QKD & Twin-field QKD \\ VOA & Variable Optical Attenuator \\ WDM & Wavelength-division Multiplexing \\ \end{tabular}
## 3 Results and Discussion
### Comparison of tortuosity Studies
For carbonate samples, which are known for their greater heterogeneity and structural complexity, the effective porosity ranged from 0.03 to 0.30 with geometric tortuosity values ranging from 1.00 to 3.18, flow tortuosity from 1.00 to 3.68, and empirical tortuosity from 2.45 to 4.93 for Equation (13) and 1.59 to 3.47 for Equation (17). The wider spread in empirical estimates reflects the high sensitivity of such models to input parameters, highlighting their limitations in capturing complex microstructures without proper adjustment. Overall, these findings emphasize the relevance of using a combination of geometric, fluid streamline, and empirical methods for tortuosity characterization, providing valuable insight into transport behavior within natural porous systems of varying lithological and structural characteristics.
## 3 System Characterization Using FEM
### Current Density Analysis  
The biological cells seeded at the bottom of the culture wells, along with the growth media on top, construct a bulk conductive media. Any current applied to the bulk will construct a 3D flow through the cells and media. The primary concern of electro-biomodulation is to ensure the flow of current through the targeted biological entity. Hence, current density analysis through the cells for varying electrode sizes gives a good view of the amount of current flowing through the targeted cells.  
The current density through the cells for different sizes of electrodes is shown in Figure 4a. The density has been analyzed along a line passing through the cells from the source electrode towards the sink electrode and 1.5 mm away from both electrodes. This region is considered the region of interest where the cells are seeded. A constant current of 2 \(\mu\)A is reported to cause a proliferative effect on epidermal cells [40]. The same value was used as the injection current for simulation.  
From Figure 4a, it can be seen that the current density follows a similar pattern for all sizes of electrodes, being the highest at points close to the electrodes and reaching a lower value around the center of the dish. This outcome is expected as any cross-sectional plane perpendicular to the current flow path has a smaller area closer to the electrodes and a relatively larger area around the center of the dish. However, electrodes with a smaller surface area (\(h_{elc}\) = 0.5 mm) pose a higher current density throughout the region of interest compared to electrodes with a larger surface area. Hence, placing smaller electrodes near the bottom of the culture well gives a better stimulus to the targeted cells in biomodulation.  
The current from the source electrode to the sink electrode follows a bulk conduction pathway through the cells and growth media in the well. As such, the current density through the cells (\(J_{1}\)) and the current density through the media (\(J_{2}\)) depend heavily on the size and positioning of the electrodes. By placing electrodes close to the bottom surface of the well and changing the size of the electrodes, a difference in current densities can be observed. Table 1 shows the ratio between the current densities in the cell and in the  
Figure 4: **(a)** Current density (A/m\({}^{2}\)) along the axis from the source electrode to the sink electrode through the cell; **(b)** electric field distribution (mV/mm) at different equipotential planes along the height of the culture well.  
media. It can be seen that electrodes with a height of 0.50 mm have a \(J_{1}:J_{2}\) of around 0.27174. This signifies that the current flowing through the cells is around 27% of the current flowing through the media. As the size of the electrodes increases, this ratio falls off and reaches a value of 0.25856 for electrodes with a height of 2.00 mm. Hence, it can be concluded that smaller electrodes placed near the bottom of the wells provide better electrical bio-interfacing for in vitro studies.
# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
## 2 Materials and Methods
### Optimal High-Spatial-Resolution Image Generation
#### 2.2.2 Synthesized Band Scheme  
The synthesized band scheme is a method for producing the optimal panchromatic image that has spectral characteristics similar to those of the low-spatial-resolution bands with which it will be sharpened [15]. In the selected band scheme, the existing high-spatial-resolution bands are defined as optimal panchromatic images for pansharpening, whereas multiple regression analysis is applied to the low- and high-spatial-resolution bands that will be sharpened, thereby producing new images that have similar spectral characteristics to the low-spatial-resolution band in the synthesized band scheme [15; 21].  
In the first phase of the synthesized band scheme, to produce optimal panchromatic images, the high-spatial-resolution bands are transformed to have the same number of pixels as the low-spatial-resolution bands with which they will be sharpened. Then, multiple regression analysis is applied to the low-spatial-resolution images to calculate the regression coefficient before sharpening them with the transformed bands. The regression coefficient calculation is presented in Equation (4):  
\[\mathbf{LRB}_{m}=\omega_{m_{0}}+\sum_{n=1}^{N}\omega_{m_{n}}\mathbf{HRB}_{n}^ {L} \tag{4}\]  
where \(\omega_{m_{0}}\) refers to the regression coefficient calculated through the multiple regression analysis and \(\omega_{m_{n}}\) refers to the regression coefficient that corresponds to the \(n\)th panchromatic image.  
In the second phase, optimal panchromatic images are produced that have similar spectral characteristics to those of the low-spatial-resolution bands with which they will be sharpened by applying the regression coefficient calculated through the multiple regression analysis to the original high-spatial-resolution bands. The process that produces the optimal high-spatial-resolution image is defined by Equation (5):  
\[\mathbf{P}_{m}^{syn}=\omega_{m_{0}}+\sum_{n=1}^{N}\omega_{m_{n}}\mathbf{HRB }_{n} \tag{5}\]  
where \(\mathbf{P}_{m}^{syn}\) refers to the optimal panchromatic image that has spectral characteristics similar to that of the \(m\)th low-spatial-resolution band generated through the regression coefficient. \(\mathbf{I}_{L}\) is generated from the produced optimal panchromatic image \(\mathbf{P}_{m}^{syn}\) and the low-spatial-resolution bands by applying Equation (1). The injection gain \(g_{n}\) is determined and calculated according to the pansharpening technique used, as with the selected band scheme.