esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 2 Materials and Methods
### Data
#### 2.2.2 Sentinel-1  
Our methodology relies on synthetic aperture radar (SAR) images from the Sentinel-1 constellation, operated by the European Space Agency (ESA). This constellation is composed of two satellites, Sentinel-1A and Sentinel-1B. Each satellite is equipped with an imaging radar operating in C band. Sentinel-1B ceased operating in July 2022 [36]. The native spatial resolution of Sentinel-1 imagery depends on the satellite's acquisition mode, of which there are four: Stripmap (SM), Interferometric Wide (IW), Extra Wide (EW), and Wave (WV). The respective spatial resolutions for these acquisition modes are 5 \(\times\) 5 m, 5 \(\times\) 20 m, 20 \(\times\) 40 m, and 5 \(\times\) 5 m.  
Sentinel-1 imagery is acquired each 5 to 12 days depending on where the territory being imaged is and on ESA's satellite tasking plans. Unfortunately, these plans do not support emergency retasking [39]. For Madagascar, past and planned acquisition scenarios show that images are acquired by Sentinel-1A in IW mode during the descending phase of its orbit, except for southwest Madagascar, where scenes are regularly acquired on two ascending passes. Data are available in both VV and VH polarisations and at least every 12 days. It takes 4 to 10 days to acquire images covering all of Madagascar.  
The Sentinel-1 data we used are already stored in GEE. Only GRD (ground range detected) products are available in GEE. In this format, Sentinel-1 image pixels are square--5 \(\times\) 5 m, 10 \(\times\) 10 m or 25 \(\times\) 25 m maximum, depending on acquisition mode--and noise is attenuated at the price of coarser resolution and loss of phase information. GEE performs preprocessing on the Sentinel-1 imagery after refreshing the orbit metadata before posting online to suppress edge noise and thermal noise, and to perform radiometric calibration and terrain correction. In the latter case, SRTM 30 m digital elevation model data are used [40].  
Images available in GEE are stored at several levels of resolution in pyramid mode. The pyramid is built from the original image at its native resolution (base of the pyramid). The next level is formed by combining pixels in groups of four and averaging them. This operation is repeated until an image no larger than 256 \(\times\) 256 pixels (apex of the pyramid) is obtained. The pyramid level used by GEE for processing is determined automatically by the specified output spatial resolution (display or export) [41]. This enables processing of a vast zone at optimal resolution and avoids overwhelming GEE's computing capacity. However, it is also possible to specify the desired spatial resolution, and while exporting from the JS and Python libraries is possible, automating multiple exports is easier with Python, as each export operation in Javascript in GEE's IDE requires interaction with the user. To streamline the computation without sacrificing too much information, 20-m resolution images were used.
## 5 Analysis of Deformation Performance of CFRP-Strengthened Corroded RC Beams under Fatigue Loading
### Calculation of Flexural Stiffness of CFRP-Strengthened Beam under Static Load  
The current research on the calculation of flexural stiffness of CFRP-strengthened RC beams under static load is mostly based on the formulae for calculating the flexural stiffness of ordinary RC beams, which are proposed considering the influence of CFRP, and the obtained formulae for calculating the flexural stiffness are either cumbersome in form or debatable in accuracy [26]. Through analysis and comparison, the formula for calculating the flexural stiffness of CFRP-strengthened RC beams proposed by [PERSON] [27] is shown in Equation (18).  
\[B=\frac{E_{\mathrm{s}}A_{\mathrm{s}}h_{0}^{2}+E_{\mathrm{cf}}A_{\mathrm{cf}}h ^{2}}{1.15\varphi+6.28(\alpha_{\mathrm{E}}\rho_{\mathrm{s}}+\alpha_{\mathrm{F} }\rho_{\mathrm{cf}})+0.27} \tag{18}\]  
where \(A_{\mathrm{s}}\) and \(A_{\mathrm{cf}}\) are the cross-sectional areas of a tensile steel bar and CFRP, respectively. \(E_{\mathrm{s}}\) and \(E_{\mathrm{cf}}\) are the elastic modulus of the tensile steel bar and CFRP, respectively. \(h_{0}\) is the effective height of the beam section and \(h\) is the height of the beam section. \(\rho_{\mathrm{s}}\) is the reinforcement ratio of the tensile steel bar, where \(\rho_{\mathrm{s}}=A_{\mathrm{s}}/(bh_{0})\). \(\rho_{\mathrm{cf}}\) is the reinforcement ratio of CFRP, where \(\rho_{\mathrm{cf}}=A_{\mathrm{cf}}/(bh_{0})\). \(\alpha_{\mathrm{E}}\) is the ratio of elastic modulus of tensile steel bars to concrete, where \(\alpha_{\mathrm{E}}=E_{\mathrm{s}}/E_{\mathrm{c}}\). \(\alpha_{\mathrm{F}}\) is the ratio of elastic modulus of CFRP to concrete, where \(\alpha_{\mathrm{F}}=E_{\mathrm{cf}}/E_{\mathrm{c}}\). \(\varphi\) is the coefficient of non-uniformity of longitudinal tensile steel bar strain between cracks.
# Differences Evaluation of Pavement Roughness Distribution Based on Light Detection and Ranging Data
## 2 Data Acquisition and Processing
### Data Processing
#### 2.2.1 LiDAR Angle Correction  
During LiDAR acquisition, vehicle vibration may cause deflections in the LiDAR device. This angular deflection can be classified into three types: yaw angle, pitch angle, and roll angle. Specifically, the yaw angle corresponds to the angular deflection resulting from rotation around the Z-axis of the coordinate system. In contrast, the pitch angle corresponds to the angular deflection resulting from rotation around the Y-axis, and the roll angle corresponds to the angular deflection resulting from rotation around the X-axis, respectively. These definitions are illustrated in Figure 2a. If there is a roll angle between the LiDAR device and the vehicle, the road cross-section at that location has an inclination angle, as shown in Figure 2b. The red dotted line represents the road cross-section in the point cloud data, while the solid blue line represents the actual road cross-section. This causes the road cross-section in the point cloud to have an inclination angle, and the farther it is from the LiDAR scanning center, the greater the height error. Figure 2c illustrates the correction diagram, and the theoretical correction is provided in Formula (1):  
\[\left\{\begin{array}{l}\Delta l=\frac{h(\sin(\theta+\alpha)-\sin\theta)}{\cos (\theta+\alpha)}\\ \Delta h=h\cdot\left(\frac{\cos\theta}{\cos(\theta+\alpha)}-1\right)\end{array}\right. \tag{1}\]  
where \(\Delta h\) and \(\Delta l\) indicate the height error and the lateral distance error, respectively, \(h\) is the height of the LiDAR laser transmitter, \(\theta\) is the angle between the incoming LiDAR laser ray and the vertical direction, and \(\alpha\) is the roll angle of the LiDAR scanner.  
When the LiDAR device is inclined with a pitch angle, the angle between the LiDAR scan lines and the ground surface increased. This inclination causes the collected point cloud data from the rear of the vehicle to be higher than the original position. As shown in Figure 2d, the red dot above represents the position of the road cross-section in the obtained data, while the blue dot below represents the actual position of the road cross-section. Since the theoretical equation cannot correct the pitch angle error, it is typically compensated by adding a pitch angle to each frame of the point cloud data. Similarly, when the LiDAR device has a yaw angle, the scan line is no longer perpendicular to the road direction, resulting in an error that the theoretical equation cannot correct. This is illustrated in Figure 2e, wherein the pavement elevation of the red dashed line is not aligned with the actual elevation of the straight line. Therefore, during field tests, the joints were strengthened when installing the LiDAR device to minimize the angular deflection.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 4 Electrical System Design
### Power Board  
The track width was estimated using Equation (16):  
\[\text{Width = Area/ Thickness $\times$ 1.378 mile/oz} \tag{16}\]  
For every 1 A, 1 mm track width is required for PCB input. Thus, a 30 mm track width is necessary to meet the desired current (30A), resulting in a massive space for the electronics in the electric enclosure [30]. To maintain a smaller track width size, we coated it with a composite mixture of silver, tin, and gold. As the PCB thickness increases, the track decreases. Figure 8 depicts the power board.  
The power PCB receives 48 V from the tether and uses 2 Murata DC-DC converters (buck) to convert the 48 V to 12 V with an excellent nominal efficiency of 96% [31]. They were used due to their small size and low weight. Designing the power PCB that can handle the required current in a small size to fit in the electric enclosure was a challenge. After conversion, the 12 V bus was distributed into two buses; the first bus was converted  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Quantity** & **Voltage (V)** & **Power per Device (W)** & **Total Power (W)** \\ \hline T100 Thruster & 6 & 12 & 135 & 800 \\ ESC & 6 & 12 & 6 & 36 \\ Cameras & 2 & 12 & 6 & 12 \\ Arduino Mega & 1 & 12 & 6 & 6 \\ DVR & 1 & 12 & 15 & 15 \\ Total power & & & & 879 \\ Tether power loss & & & & 230 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Power distribiton.  
to 5 V to DC-DC converter, and the second was distributed using terminals to provide the ESCs and the cameras with the required operating power.
# Vibration Prediction of Space Large-Scale Membranes Using Energy Flow Analysis
## 4 Discussion  
Vibration prediction of large-scale membrane structures demands more feasible methods considering computation cost and structural uncertainty. As a widely applied method, EFA is potentially a reasonable method. Existing analytical formulations of energy flow derived from deterministic representations only apply to structures of regular shapes. FEM can be introduced in solving energy governing equations to extend the applications. However, numerical expressions raise the bar in dealing with structures with uncertainties. And extra computation is brought in since a set of equations needs to be solved to find even only one single response value. In this work, a general method was proposed to predict the energy responses for space large-scale membranes. Based on the Green kernel of vibrating membranes, a new formulation of averaged energy response for membranes was obtained with the summation of reflected waves. With the proposed method, formulations of energy response were derived for membranes with random parameters. Simulations were performed for a membrane with the length being a Gaussian random variable to validate the formulations. The developed formulation is of analytical form with which uncertainties can be dealt with directly and demands much less computation than numerical methods. Besides, the formulations can be applied to various shapes of membranes since it describes travel distances of energy waves with the mean free path derived from the statistical point of view. This work provides a systematic way to analyze energy responses of two-dimensional large-scale structures of various shapes with structural uncertainties and promotes the analysis of vibration in energy point of view. The extension of the method to large-scale structures of other types is the subject of future research.  
Conceptualization, K.W. and J.L.; methodology, Q.Z.; software, K.W. and J.L.; validation, K.W. and Q.Z.; formal analysis, K.W. and J.L.; investigation, K.W. and Q.Z.; resources, K.W.; data curation, K.W.; writing--original draft preparation, K.W.; writing--review and editing, Q.Z.; visualization, K.W.; supervision, J.L.; project administration, K.W.; funding acquisition, K.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the National Natural Science Foundation of China (Grant No. 12172385 ) and Ph.D. Funding of NCIAE (BKY-2020-12).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Architectures  
### DNN-HMM ASR  
This system is based on the Sigma ASR system [32] submitted to the Albayzin-RTVE 2018 Speech-to-Text Challenge [33], where it was in the top 2 ranking for both closed- and open-condition evaluation.  
This hybrid ASR system was built by using the Kaldi Toolkit [2]. The acoustic model is based on Deep Neural Networks and Hidden Markov Models (DNN-HMMs), following the so-called chain models [19], whose neural part is a subsampled Time-Delay Neural Network (TDNN) [34]. This implementation uses a 3-fold reduced frame rate at the output of the network.  
We used the conventional feature pipeline that involves splicing 13-dimensional MFCC coefficients across 9 frames, followed by applying Linear Discriminant Analysis (LDA) to reduce the dimension to 40 and further decorrelation by means of Maximum Likelihood Linear Transform (MLLT). In addition, Feature-space Maximum Likelihood Linear Regression (fMLLR) was applied in a speaker-adaptive way. The input feature vectors were represented by 40-dimensional MFCC spliced coefficients across 7 frames and LDA+MLLT+fMLLR corresponding to 3 frames on each side of the central frame. In addition, 100-dimensional i-vectors were appended to the 40-dimensional acoustic space on each frame.  
Our main conclusion from the results of Albayzin-RTVE 2018 [33] was the need for more robust DNN training, looking for accuracy improvements, required in the most challenging scenarios (street interviews, game shows, risky sports documentaries, etc.). The environmental robustness of acoustic models has been significantly improved by using multi-condition training data. However, the data collection process is very costly compared to the artificial generation of new training data, which has become a common alternative [20].  
Thus, in our current contribution, we extended the amount of training data through data augmentation techniques. In particular, we added reverberation to the available training speech data following the approach presented in Ref. [35]. Depending on the expected scenarios and distances, different Room Impulse Responses (RIRs) can be used. They sample the room parameters and receiver position in the room and then randomly generate a number of RIRs according to different speaker positions. In short, three sets of simulated RIRs were applied: small room (1-10 m), medium room (10-30 m), and large room (30-50 m). The real computation was carried out at the feature extraction level, where the original data were mixed with their reverberated copies. The result was a 2-fold training set.  
This data augmentation technique was added to other data augmentation techniques already used in the recipe followed in our previous system such as volume and speed perturbations [20; 21].
# Reshaping the Digital Twin Construct with Levels of Digital Twinning (LoDT)
### Research Objectives and Paper Organisation  
Considering the multitude of definitions and interpretations that exist regarding the concept of DTs, this present study aims to redirect the discussion by placing greater emphasis, on the one hand, on the distinctions among DTs (reflected in their purposes, capabilities, and respective degrees of development), and, on the other hand, on the fundamental principles they share. To achieve this, this study will recover the concept of digital twinning as a common conceptual basis encompassing these characteristics and various concepts already associated with DTs. Digital twinning of physical assets is thus regarded as a process of variable complexity with evolutionary potential over time, which is aligned with the inherent life cycle approach of the physical assets to be twinned.  
The primary objective of this work is to contribute to a clarification of the various functions and capabilities associated with digital twinning of physical assets in order to provide asset managers with conceptual foundations and a common language that facilitate the identification of digital twinning opportunities and the understanding of their value.  
To this end, this study relies on the definitions proposed by leading entities in this field and the constituent elements of a DT to propose a harmonised functional architecture. With this architecture, the intention is to leave the debate over terminology and definitionsof the DT in the background and emphasise how these differences can be organised and communicated according to different levels of development. These diverse capabilities and levels of development will be organised within an assessment model for the level of digital twinning, the application of which can assist organisations in planning the digital twinning journeys for their physical assets.  
The present article is structured as follows: in Section 2, the model of functional architecture that supports the concept of digital twinning is presented; in Section 3, the evolution of the architecture and the different types of opportunities over the lifecycle of physical assets are discussed; Section 4 presents a model for assessing the level of digital twinning for various capabilities, highlighting the variability of DT types and supporting organisations in internally evaluating their current digital twinning context; finally, in Section 5, some conclusions are drawn, and potential future developments are identified.
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
## 6 Performance Analysis  
The proposed scheme (PAS) is compared with EAAP [13], TAAP [18], and LIAP [14] in computational cost and communication cost. In addition, Veins simulation framework [34] is introduced to test the performance of the schemes including average authentication delay and packet loss ratio.
## 1 Introduction
Irrespective of the benefit provided by FACTS devices, they can become an economic burden (the sum of investment and maintenance cost, etc. surpassing the energy saving cost) when FACTSs are not employed properly in the power system [19]. Therefore, the deployment of optimization techniques is inevitable to obviate this challenge. Therefore, the main objective of this review paper is to discuss the roleof various FACTS devices under different power system control (voltage and frequency) and their technical benefits in restructured networks. In addition, the importance and beneficial role of FACTS controllers to eradicate congestion are also discussed in this paper. Moreover, comparison of several optimization techniques, algorithms, or methods using FACTS devices for improvement in load frequency control, voltage stability, and congestion management will be provided for reference. Therefore, it is essential to provide future researchers with a summarized comparison of various FACTS devices against three major power system challenges, i.e., congestion management and power system profiles (voltage and frequency), and up to date available optimization techniques and their comparison for reference. It is important to mention that optimization techniques irrespective of cost analysis, especially installation cost (because it is out of the scope of this paper), are discussed in this paper. A comprehensive survey on deregulated networks and their role in different areas is shown in Figure A1 (given in Appendix A). This paper is organized as follows: Section 2 presents general characteristics of electrical power systems. Section 3 discusses the significance of ancillary services in deregulated systems. Similarly, FACTS controllers, their types, and their potential role will be presented in Section 4. Section 5 shows a detailed review about FACTS controllers' role in load frequency control and voltage stability enhancement. Also, congestion management in restructured power networks by using FACTS controllers is presented in Section 6. Finally, conclusions will be drawn in Section 7.
## 5 Engineering Application
### Construction and Effect of Ecological Restoration  
To test the suitability of the spraying process, ADNB was applied by the spraying process at a content of 0.1%. According to the quantitative characterization mechanical models, the average particle diameter of the improved soil was calculated to be 0.68 mm, with a \(c\) value of 23.9 kPa, a \(\varphi\) value of 16.7\({}^{\circ}\), and a permeability coefficient of \(3.28\times 10^{-4}\) cm/s. The calculation steps are shown in Equation (9).  
Figure 14: Before construction.  
Figure 13: The hard film on the surface of a soil sample.  
Based on these calculation results, as shown in Equation (10), the improved shear strength equation of the soil mass was obtained.  
\[\left\{\begin{array}{l}d_{505}:7.93\times 0.4+118.89\tan(0.4)\log(0.1\%)= \ 0.68\ (\mathrm{mm})\\ c_{5}:2.07\times 19.1+24.77\tan(19.1)\log(0.1\%)=23.9\ (\mathrm{kPa})\\ q_{5}:1.32\times 11.9+4.79\tan(11.9)\log(0.1\%)=16.7\ (\lx@math@degree)\\ K_{TS}:1.02\times 0.95\times 10^{-4}+1.39\times 10^{5}\tan(0.95\times 10^{-4}) \times 0.1\%\\ =3.28\times 10^{-4}\ (\mathrm{cm}/s)\\ \end{array}\right., \tag{9}\]  
\[\tau=23.9\mathrm{kPa}+\sigma\tan(12.7^{\circ}), \tag{10}\]  
During construction, planting soil, super-absorbent resin, organic fertilizer, and water were placed into a hydraulic jet according to the design ratio, as shown in Table 4. Then, the mixture was sprayed onto the slope (Figure 15). After spraying, seeds were sown. Finally, portable agricultural sprayers were used to spray the ADNB solution onto the surface of the soil. After construction was completed, the soil was firmly attached to the slope (Figure 16). The soil thickness reached the design requirement, and the construction effect was good.  
Four months after construction completion, the slope experienced two heavy rains and several consecutive rains. The borrowed soil remained stable, and the amount of slump was very small. At the time of writing, plants were growing well, and the ecological restoration effect was good (Figure 17,b).  
The application of ADNB in the ecological restoration of rock slopes showed that ADNB could effectively improve the slip resistance of borrowed soil of rock slopes, and plants grew well after the material was applied. This helps to avoid repeated restoration and improve the economic and environmental benefits. At the same time, the rationality and reliability of the quantitative characterization models were verified. Moreover, this study provides theoretical support for the popularization and application of ADNB.