esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Elimination Method of Wind Field Influence in Retrieving a Sea Surface Current Field
###### Abstract
For two SAR acquisitions of the same area on the ground, at two times separated by a few milliseconds, the pixel-by-pixel phase difference between the two complex focused SAR images is related to the line-of-sight (LOS) velocity of the imaged surface block, also known as the radial velocity. ATI-SAR can be realized using two antennas installed on the same platform. Additionally, experiments and studies on the applicability of the satellite formation system are presented in [14]. The results indicated that the satellite formation has a long baseline, close to the theoretical optimization analysis, and it has advantages in current measurement. Besides the experimental systems described above, there are no operational civilian satellites with ATI capability. To verify ATI technology and provide support for future satellites, domestic ATI sea surface current measurement experiments were conducted for the first time based on China's Gaofen-3 satellite (GF\(-\)3) [15] from 2018 to 2019. [PERSON] et al. [16] proposed using ATI technology to retrieve the ocean surface current field. The Doppler velocity obtained using ATI technology includes the contributions of the current field and the images of various wind-generated currents, the Bragg wave phase velocity, and the orbital velocity. Only by removing these effects can the current field be accurately obtained. The process of removing the effect of the wind field is similar to that of removing the effect of the wind field using Doppler anomaly. Considering the scattering contribution of large-scale waves and Bragg waves in the ocean, these ocean surface wind fields play a decisive role. Compared with the wind field retrieved from SAR images, the ECMWF wind field has errors, due to its large spatial grid, and the root SAR images do not match well. The above research ignored the influence of different ocean wind fields on ocean surface current field retrieval. In this paper, an SAR image of the Gaofen-3 satellite interference mode was used. First, the SAR image was preprocessed using radial velocity retrieval. Then, different wind fields were input into the CDOP model, to obtain the retrieved radial velocity, which was quantitatively compared with the radial velocity obtained by a shore-based radar. To investigate the influence of different wind fields, this paper compared the parameters of two wind fields input into the CDOP model (one was based on SAR image retrieval and the other was reanalyzed from ECMWF), and the ocean surface current field was finally retrieved. The results show that the wind field correction result based on the SAR-retrieved wind field was closer to the current field measured by the shore-based HF radar than the wind field correction result based on ECMWF.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
###### Abstract
This work aims to apply remote sensing techniques to enable efficient detection of weed in irrigation channels. The challenges particular to this application include the narrow width of the channels (compared with previous works which focussed on larger bodies of water such as lakes and rivers). Irrigation networks are also sparse and spread over extensive areas, rather than concentrated in a tight area. In addition, irrigation channel water is often highly turbid. The study takes multispectral imagery from UAVs and satellites, processes them in Google Earth Engine (GEE) [18], using unsupervised and supervised classification algorithms to automatically map areas of weed infestation. This information is of great use to irrigation water delivery companies as they seek to optimise their networks to enable timely and high capacity delivery of water to farms.
###### Abstract  
This paper aims to use the Lamb wave local wavenumber approach to characterize flat bottom defects (including circular flat bottom holes and a rectangular groove) in an isotropic thin plate. An air-coupled transducer (ACT) with a special incidence angle is used to actuate the fundamental anti-symmetric mode (A0). A laser Doppler vibrometer (LDV) is employed to measure the out-of-plane velocity over a target area. These signals are processed by the wavenumber domain filtering technique in order to remove any modes other than the A0 mode. The filtered signals are transformed back into the time-space domain. The space-frequency-wavenumber spectrum is then obtained by using three-dimensional fast Fourier transform (3D FFT) and a short space transform, which can retain the spatial information and reduce the magnitude of side lobes in the wavenumber domain. The average wavenumber is calculated, as a real signal usually contains a certain bandwidth instead of the singular frequency component. Both simulation results and experimental results demonstrate that the average wavenumber can be used not only to identify shape, location, and size of the damage, but also quantify the depth of the damage. In addition, the direction of an inclined rectangular groove is obtained by calculating the image moments under grayscale. This hybrid and non-contact system based on the local wavenumber approach can be provided with a high resolution.  
Lamb wave; local wavenumber; air-coupled transducer; wavenumber domain filtering; hybrid and non-contact system +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Experimental Design  
Thirty tilapia were randomly stocked into glass aquaria with a water volume of 200 L. The concentrations of methomyl tested were 0 (control), 0.2, 2, 20 and 200 ugL\({}^{-1}\), and each concentration had three replicates. The concentration range was chosen according to information obtained from our previous research to determine the acute toxicity of methomyl to tilapia (i.e., 96-h LC\({}_{50}\) was 430 ug L\({}^{-1}\)), the methomyl residue levels found in environmental water (0-55.3 ug L\({}^{-1}\)) [2], and the maximum permissible methomyl concentration in drinking water (200 ug L\({}^{-1}\)) established in 2012 by the United States Environmental Protection Agency. The actual concentrations of methomyl in the experimental water were measured using ultra-performance liquid chromatography-tandem mass spectrometry, following a method established by our research team [12].  
Half the aquarium water was renewed daily, and methomyl was added as needed to maintain the given concentrations. The experiment lasted for 48 days, with 30 days for methomyl exposure and 18 days for recovery in methomyl-free water. Testis (\(n\) = 6 per group) were sampled at 10 min (day 0), and at days 6, 12, 18, 24 and 30 in the exposure period (note: only the samples taken at day 30 were used here), and at 18 days after the fish were moved to methomyl-free water. Fish were euthanized using 250 mg L\({}^{-1}\) MS-222, and then measured and weighed. Thereafter, the testis was immediately sampled, snap-frozen using liquid nitrogen and stored at \(-80\)\({}^{\circ}\)C until analysis. An electric homogenizer (PRO200; PRO Scientific Bio-Gen, USA), refrigerated centrifuge (Sigma 2-16K, Germany), and ultraviolet-visible spectrophotometer (UV-Vis 759S, China) were used to homogenize, centrifuge, and quantify the samples.
## 2 Vehicle State Estimation
### EKF-Based State Estimation  
Since (1) includes typical nonlinear formulations, the extended Kalman Filter (EKF) is used for state estimation. The standard formulations of EKF are shown as:  
\[\left\{\begin{array}{l}\mathbf{x}(k|k-1)=f_{d}(\mathbf{x}(k-1|k-1),\mathbf{u}(k-1))\\ \mathbf{z}(k|k-1)=h(\mathbf{x}(k|k-1),\mathbf{u}(k-1))\\ \mathbf{P}(k|k-1)=\mathbf{A}(k)\mathbf{P}(k-1|k-1)\mathbf{A}(k)^{\mathrm{T}}+T_{s}^{2}\mathbf{Q} \\ K_{g}(k)=\mathbf{P}(k|k-1)\mathbf{C}^{\mathrm{T}}(k)\Big{(}\mathbf{C}(k)\mathbf{P}(k|k-1)\mathbf{C} ^{\mathrm{T}}(k)+\mathbf{R}\Big{)}^{-1}\\ \mathbf{x}(k|k)=\mathbf{x}(k|k-1)+\mathbf{K}_{g}(k)(z(k)-\mathbf{z}(k|k-1))\\ \mathbf{P}(k|k)=\big{(}\mathbf{I}-\mathbf{K}_{g}(k)\mathbf{C}(k)\big{)}\mathbf{P}(k|k-1)\end{array}\right. \tag{6}\]  
where \(f_{d}\) is the discrete form of the state function; \(\mathbf{x}(k-1|k-1)\) and \(\mathbf{P}(k-1|k-1)\) denote the last optimal estimation and error covariance matrix, respectively; \(\mathbf{K}_{g}\) is the coefficient of Kalman gain; \(\mathbf{I}\) is the identity matrix; \(\mathbf{T}_{s}\) is the sampling period \(R=diag(0.04,0.04,0.0012,0.0012)\) and \(Q=diag(10^{-2},10^{-2},10^{-6},10^{-4},10^{-4},10^{-4})\) represent the covariance matrices of the measure and model systems, respectively.  
The Jacobi matrices of the transform and observe formulations are regulated as:  
\[\begin{array}{l}\mathbf{A}(k)=\frac{\partial f_{d}(\mathbf{x}(k),\mathbf{u}(k))}{ \partial x(k)}\\ \\ =\left[\begin{array}{cccc}1&0&a_{1,3}&0&T_{s}cos\;x_{3}(k)&-T_{s}sin\;x_{3} (k)\\ 0&1&a_{2,3}&0&T_{s}sin\;x_{3}(k)&T_{s}cos\;x_{3}(k)\\ 0&0&1&T_{s}&0&0\\ 0&0&0&1&0&0\\ 0&0&0&T_{s}x_{6}(k)&1&T_{s}x_{4}(k)\\ 0&0&0&-T_{s}x_{5}(k)&-T_{s}x_{4}(k)&1\end{array}\right],\\ \\ \mathbf{C}(k)=\frac{\partial h(\mathbf{x}(k),\mathbf{u}(k))}{\partial x(k)}=\left[ \begin{array}{cccc}1&0&0&0&0&0\\ 0&1&0&0&0&0\\ 0&0&1&0&0&0\\ 0&0&0&1&0&0\end{array}\right]\\ \end{array} \tag{7}\]  
where \(a_{1,3}=T_{s}(-x_{5}(k)sin\;x_{3}(k)-x_{6}(k)cos\;x_{3}(k))\) and \(a_{2,3}=T_{s}(x_{5}(k)cos\;x_{3}(k)-x_{6}(k)sin\;x_{3}(k))\).  
The details of the EKF algorithm are described as shown in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{3}{c}{State estimation \(\{u,v,r\}\)} \\ \hline
1: & Input: \\
2: & current input u(k) and current observations z(\(k\)); \\
3: & calculate the optimal estimation of the last moment: \\
4: & calculate the error covariance matrix: \\
5: & set the model and measure system noise Q and R. \\
6: & State Estimation: \\
7: & estimate the states from the model: \\
8: & estimate the states from the measurement: \\
9: & estimate the error covariance matrix: \\
10: & Updates: \\
11: & update the Kalman Gain: \\
12: & calculate the current optimal estimation: \\
13: & updates the Error covariance matrix: \\
14: & Cycle 1–5 steps until the end. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The flow of EKF estimation.
## 1 Introduction  
The flying base station, also known as the aerial base station or unmanned aerial vehicle (UAV) base station, represents a new and emerging architecture for next-generation telecommunications networking. While the traditional model in mobile computing has implemented mobility on the user side, flying base stations introduce aerial mobility into the service provider's infrastructure. The flying base station uses its aerial mobility to enhance communication connectivity for the mobile user (the client of the telecommunications service) by approaching the user. This improves the communication link channel and enables a line-of-sight communication path.  
While past research and implementations of flying base stations have focused on sporadic and emergency scenarios, e.g., [1; 2; 3], future research, development, and standardization efforts are expected to broaden the use of flying base stations in upcoming network architectures, e.g., [4; 5; 6; 7]. The improved communication channel resulting from the controlled flying mobility of the flying base station enables the high rate and latency performances expected by upcoming 6G telecommunication applications. The millimeter-wave (mmWave) communications in 6G can particularly benefit from the flying base station, which provides line-of-sight communication between the base station and user equipment. The 3 rd Generation Partnership Project (3 GPP), which has standardized cellular technologies from 2G to 5G, introduces the concept, objectives, and requirements of the flying base station (termed UxNB base station) for upcoming 6G networking [7; 8]. Industries are also developing prototypes, e.g., AT&T's Flying COW, Google's SkyBender, Nokia's F-Cell, and Huawei's Digital Sky Initiative.  
Flying base stations inherently rely on stationary terrestrial base stations, which serve as the bridge gateway to the networking backhaul and cloud via wired networking. Without the stationary terrestrial base station providing the wired connection, flying base stations would not have access to the cloud or the internet. Thus, flying base stations coexist with stationary base stations and their operations are intertwined.  
Our work distinguishes itself from previous research by capturing the dependency on the stationary base station (as opposed to just focusing on the communication channel between the user and the flying base station) and by introducing a dedicated flying base station that moves and positions itself for maximum capacity (as opposed to merely controlling and analyzing performance given the relay locations and channels). Section 2 describes the previous research studies and compares them with our work in greater detail.
# Response Prediction for Linear and Nonlinear Structures Based on Data-Driven Deep Learning
## 3 Construction of Dataset and Training of Models
### Model Training  
#### 3.4.1 Model Training for the Linear Structure  
In order to eliminate the adverse effects caused by the singular sample data, the training set was normalized in the training process. Considering the training cost, training accuracy, underfitting, and overfitting, the hyper parameters of the RNN and LSTM models used to predict the linear structure are shown in Tables 6 and 7. The mean square error (_MSE_) was selected as the precision evaluation index of model training, as follows:  
\[Loss=MSE=\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2} \tag{13}\]  
where \(\hat{y}_{i}\) and \(y_{i}\) represent the predicted and true values, respectively.  
Figure 8a,b show the loss curves of the RNN and LSTM models used to predict the linear structural response in the training process, respectively. It can be seen that the loss curves of the two models converge to the threshold range at about 300 iterations. The convergence speed of the LSTM model was slightly faster than that of the RNN model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **State** & **Variable** \\ \hline RNN/LSTM model for linear structure & Input & \(\tilde{a}_{g}=[\tilde{a}_{g}^{1},\tilde{a}_{g^{\prime}}^{2},\tilde{a}_{g}^{3}, \ldots,\tilde{a}_{g}^{n}]\) \\  & Output & \(u_{si}=[u_{si}^{1},u_{si}^{2},u_{si}^{3},\ldots,u_{si}^{n}]_{i=1,2,3,4,5,6}\) \\  & Output & \(\ddot{u}_{si}=[u_{si}^{1},\tilde{u}_{si}^{2},\tilde{u}_{si}^{3},\ldots,\tilde{ u}_{si}
## 3 Results
### Analysis of Extraction Results by D-InSAR
#### 3.1.9 Analysis of Extraction Results  
The analysis subsidence at 50 specific locations over the period from 5 March 2021 to 10 April 2021. Furthermore, the collapse magnitude is compared to the interference outcomes of three sets of interference image pairs (5 March 2021-17 March 2021, 17 March 2021-29 March 2021, 29 March 2021-10 April 2021) in the year 2021, as depicted in Table 2. When choosing leveling points, locations that are not water-logged within the influence range of the goal are selected due to the presence of water-logged areas in the mining site.  
In this paper, root mean square error (RMSE) and determination coefficient (R\({}^{2}\)) are used as accuracy verification indices to evaluate the accuracy of D-InSAR results. The calculation methods are as follows:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Elevation Point**} & \multirow{2}{*}{\begin{tabular}{c} **D-InSAR Processing** \\ **Results (cm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Elevation Point** \\ **Measurement** \\ **Value (cm)** \\ \end{tabular} } & \multirow{2}{*}{**Elevation Point**} & \multirow{2}{*}{\
\begin{tabular}{c} **D-InSAR Processing** \\ **Results (cm)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Elevation Point** \\ **Measurement** \\ **Value (cm)** \\ \end{tabular} } \\ \hline
1 & \(-6.41\) & \(-5.88\) & \(26\) & \(-7.11\) & \(-6.25\) \\
2 & \(-0.14\) & \(-0.23\) & \(27\) & \(-6.13\) & \(  
(1) Root Mean Square Error (RMSE): Quantifies the discrepancy between the observed value and the actual value, more sensitive to outliers in the data. Greater measurement precision is achieved with fewer errors. The formula is shown below.  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}{(H_{i}-h_{i})^{2}}}{N}} \tag{8}\]  
where \(H_{i}\) represents the value of the measured data at the leveling point, \(h_{i}\) represents the interference result of D-InSAR, \(N\) represents the number of level points. According to Equation (8), the data in Table 2 can be calculated as 0.76 cm, with a small error, indicating that the measurement accuracy of D-InSAR technology is high, and the D-InSAR results are consistent with reality.  
(2) Determination coefficient (R\({}^{2}\)): a statistic reflecting the goodness of fit of the model. The correlation between the observed value and the actual value can be obtained more intuitively. The closer \(R^{2}\) is to 1, the better the regression equation fits. The formula is as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}{(H_{i}-h_{i})^{2}}}{\sum_{i=1}^{n}{(H_{i}-\overline {h}_{i})^{2}}} \tag{9}\]  
where \(H_{i}\) represents the value of the measured data at the leveling point, hi represents the interference result of D-InSAR, and n represents the number of level points. Based on Equation (9), the data in Table 2 are computed, revealing that \(R^{2}\) is 0.89, a value close to 1. This indicates that D-InSAR technology has a high level of measurement accuracy, and the findings obtained align well with the actual conditions.
# Exploring Space Syntax Integration at Public Transport Hubs and Public Squares Using Drone Footage
## 2 Materials and Methods
### Calculating Space Syntax Integration  
The method for calculating Space Syntax integration (as described by [3] and applied in this paper) uses an isovist field to construct an isovist graph which is then used to calculate the integration values for different points in space. An isovist field describes the area that is visible from a specific location in space within a convex map. By creating a grid pattern of possible locations, it is possible to generate a graph network that connects each point (or node) that can \"see\" each other. Figure 1 shows an example of an isovist field with all nodes that would be connected to the point in question (larger orange dot). Notethat the procedure used in this paper considers streets, lawns and other areas which are not meant for pedestrian movements to be impassable.  
Using the isovist field for each point belonging to a given case study area, an isovist graph can be created describing the connections between these points. Once the network has been created, the Space Syntax integration can be calculated as the mean shortest path length from a specific point to all other points. This is done by first finding the shortest path within the network from each point to all other points, one by one, and then adding all the path lengths together. The Space Syntax integration value to be calculated is equal to the total path length from one node to all other nodes. However, the exact value in a specific cell is not significant; only how this value relates to the other cells in a given area is important for the analysis. Note that a good integration is indicted with a relatively low value compared to the other cells and a high integration value indicates worse integration.
## 6 Discussion and Conclusions
### Adeuacy of ML Techniques  
The study utilized various ML algorithms, such as SVM, KNN, Bagging, RF, and ET, for categorizing the RC buildings based on their vulnerability subjected to a seismic event. The input datasets are created from the building samples collected from four real-time earthquakes in Ecuador, Haiti, Nepal, and Pohang. Each dataset is approached by every algorithm utilizing the default parameter values.  
Bias and variance are essential factors to understand the performance of the models. Biased models are weak and under fitted. There is a significant disparity between the actual data and the predicted data--whereas, with high variance, the models fit the actual data but can not generalize new data well out of that basis.  
In the study, ET performed well with all four datasets compared to the rest of the ML models leaving behind Random Forest. ET and RF ensemble methods have similar tree growing procedures, and both randomly select a feature of the subset while partitioning for each node. However, RF has high variance as the algorithm sub-samples the input data with duplicates. At the same time, ET uses complete original sample data. In addition, RF finds the optimum split while splitting the nodes, but ET goes for random selection. Henceforth, ET is computationally economical and faster than RF, and for high order computational problems, ET would fit better than other supervised learning models.  
Furthermore, the ET model for each case study is configured by optimizing the hyperparameters. The results before and after the hyperparameter tuning or optimization have shown slight improvement. RF stood as the second performer among the candidate classifiers. One potential argument behind not achieving substantial growth in the accuracy score after optimizing the hyperparameters for ET might be the quality, size of the datasets, and high variance in the test data. This study's input datasets are of moderate size and quality because of the lack of proper resources. ET is an ensemble method created by multiple random trees, and its approach is to leverage the power of the crowd (trees) instead of one tree and consider the majority of the voting for the final decision. Thus, ET requires big datasets, which is essential for the model to give an optimized result. For datasets with high variance, adding bias can neutralize it. Furthermore, the study may get extended using big and good quality datasets.
# Distributed Localization of Non-Cooperative Targets in Non-Coplanar Rendezvous Processes
## 2 Distributed Localization Method  
The multi-LOS cooperative localization scheme proposed in this paper is shown in Figure 1, where the three tracking satellites are in the same orbital plane and the target is in a different orbital plane from the trackers. When the maneuver timing is determined, satellite 3 stays in its orbit and satellites 1 and 2 begin to transfer from different orbits to rendezvous with the target. At the terminal of the rendezvous, satellites 1 and 2 will pass through the target at a non-zero relative velocity and launch a capture net equipped with a Drag Sail Deorbit Device (DSDD), which can accelerate the deorbiting of space debris to achieve the purpose of clearing space debris.  
In this process, the tracking satellites get their own positional data from GNSS and obtain the measurement information from on-board cameras. The system measurement consists of the target's azimuth angles \(\alpha_{t}^{1}\), \(\alpha_{t}^{2}\), \(\alpha_{t}^{3}\) and the pitch angles \(\beta_{t}^{1}\), \(\beta_{t}^{2}\), \(\beta_{t}^{3}\). In order to achieve distributed positioning, each tracking satellite carries out filtering locally. According to the CE principle, three satellites can communicate with each other based on local filtering estimation results, then the distributed localization system established. Since the geometric configuration in the approach process directly affects the target positioning performance, satellites 1 and 2 start to transfer from different positions in different parking orbits so as to change the geometric configuration in the approach process, which lays the foundation for the subsequent configuration optimization.