esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
## 4 Methods
### Robust Density Change Detection
 \tag{5}\]  
where \(|\cdot|\) denotes the absolute value. We chose the threshold after a preliminary analysis, in which we observed that pixels with densities >0.4 consistently lie within coconut plantations.
###### Abstract  
A novel approach to underwater optical wireless coherent communications using liquid crystal spatial light modulators (LC-SLMs) and an aperture averaging lens, in combination with optical phased-array (OPA) antennas, is presented. A comprehensive channel model that includes a wide range of underwater properties, including absorption, scattering, and turbulence effects, is employed to simulate the underwater optical wireless communication (UOWC) system in a realistic manner. The proposed system concept utilizes aperture averaging and adaptive optics techniques to mitigate the degrading effects of turbulence. Additionally, OPA antennas are integrated into the system to provide electronic beam steering capabilities, facilitating precise pointing, acquisition, and tracking (PAT) between mobile underwater vehicles. This integration enables high-speed and reliable communication links by maintaining optimal alignment. The numerical results show that under strong turbulence, our combined turbulence-compensation approach (LC-SLM plus aperture averaging) can extend the communication range by approximately threefold compared to a baseline system without compensation. For instance, at a soft-decision FEC threshold of \(1.25\times 10^{-2}\), the maximum achievable link distance increases from around \(10\,\mathrm{m}\) to over \(30\,\mathrm{m}\). Moreover, the scintillation index is reduced by more than \(90\%\), and the bit error rate (BER) improves.  
underwater optical wireless communication; oceanic turbulence; turbulence compensation; adaptive optics; aperture averaging; liquid crystal spatial light modulator; optical phased array; beam steering; laser applications +
Footnote †: journal: \(a\)  
_Article_
# Wind Speed Measurement via Visual Recognition of Wind-Induced Waving Light Stick Target
## 4 Experimental Validation
### Camera Calibration  
The intrinsic and extrinsic camera matrices were determined through [PERSON]'s calibration algorithm in MATLAB. According to this method, the intrinsic and extrinsic parameters of a camera can be estimated from several checkerboards taken at different orientations by the camera in question. In this way, the camera projection matrix can be estimated. While the intrinsic parameters do not change, the extrinsic parameters change with the change in camera position and orientation for side, front, and bottom views but are constant for any given validation since the camera is fixed. The camera used for image capturing was the HIKVISION wide-angle camera, the resolution of which is configured as 3024 \(\times\) 4032.  
An asymmetric checkerboard of high precision was adopted, where a total of 18 images shot at different orientations were collected and applied for calibration. Among them, four orientations are given, as in Figure 15 below. The intrinsic parameter matrix is estimated as in Equation (33).  
\[\begin{bmatrix}\frac{f}{\rho_{\mathrm{w}}}&0&u_{0}\\ 0&\frac{f}{\rho_{\mathrm{h}}}&v_{0}\\ 0&0&1\end{bmatrix}=\begin{bmatrix}3271.91&0&1474.78\\ 0&3273.81&1992.86\\ 0&0&1\end{bmatrix} \tag{33}\]  
where \(f\) is the focal length, \(\rho_{\mathrm{w}}\) and \(\rho_{\mathrm{h}}\) are the width and height of each pixel, \((u_{0},v_{0})\) is the principal point in pixels, i.e., the coordinate of the point where the optical axis intersects the image plane.
###### Abstract  
Risk assessments of people who are trapped are an important basis for scientific and effective emergency rescue after an earthquake. Currently, most models are based on the kilometer grid scale or community scale that gauge the population and extent of the earthquake burial under distinct intensities. The estimation results of the methods are on coarse scales; therefore, the methods cannot meet the requirements of rapid rescue after an earthquake. In response to the above statements, this study uses multi-source data to propose a way to estimate the number and distribution of people trapped under the scale of single buildings. Firstly, we use pre-earthquake optical high spatial resolution remote sensing images for building detection, and then we combine them with multi-source data for population distribution simulation. Secondly, indoor ratio assessment models are constructed by analyzing human behavior. Then, aerial remote sensing images are used for building seismic damage level detection. Finally, based on these three factors, a single building crush burial estimation model is constructed to obtain the number and distribution of personnel trapped. In this paper, the reliability of the proposed workflow is demonstrated by the casualty results in experiments conducted in the nearby Moxi town after the Luding 6.8 magnitude earthquake on 5 September 2022. For future natural disaster events, this method can provide reliable information support and decision references for earthquake emergency rescue.  
people trapped; multi-source data; distribution evaluation; earthquake emergency rescue 2023
## 3 Results and Discussion
### Characterization of Effluents Generated in Electro-Coagulation and Electro-Oxidation Experiments
#### 3.2.2 Eh-pH Diagram for the Electro-Coagulation Experiments  
To monitor the EC process, an Eh-pH diagram was developed from t = 0 min to t = 120 min (Figure 6). According to the trajectories, Eh-pH can establish the presence of stable forms of Fe during the process [85]. Ferrous ions (Fe\({}^{2+}\)) are highly soluble and poorly coagulant and do not adsorb contaminants [23]. The efficiency of the process is enhanced when ferrous ions are oxidized to ferric ions (Fe\({}^{3+}\)), which occurs when the pH is higher  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**\% R COD**} \\ \hline
**Experiment** & **Electro-Coagulation** & **Electro-Oxidation** \\ \hline E1 & \(36\pm 1\) & \(32\pm 1\) \\ E2 & \(45\pm 2\) & \(43\pm 2\) \\ E3 & \(38\pm 1\) & \(33\pm 1\) \\ E4 & \(42\pm 1\) & \(36\pm 1\) \\ E5 & \(62\pm 2\) & \(52\pm 2\) \\ E6 & \(57\pm 2\) & \(49\pm 2\) \\ E7 & \(42\pm 2\) & \(38\pm 1\) \\ E8 & \(52\pm 1\) & \(46\pm 2\) \\ E9 & \(46\pm 2\) & \(44\pm 2\) \\ \hline \hline \end{tabular} \(R\) COD—ermotion COD; COD—chemical oxygen demand; E—experiments.  
\end{table}
Table 4: Chemical oxygen demand removal results in electro-coagulation and electro-oxidation experiments.  
than 5 (that, in fact, was what happened in the experiments), although this oxidation was conducted completely at a pH of 8 to 9 [(23; 86)]. Fe (OH)\({}^{+}\) was observed in all experiments.  
In the EC process, positive values of Eh (200 mV \(<\) Eh \(<\) 50 mV) were accompanied by lower pH values (6 \(<\) pH \(<\) 9), whereas low values of Eh (0 mV \(<\) Eh \(<\) \(-\)280 mV) were associated with higher pH values (9 \(<\) pH \(<\) 11). A statistically significant negative correlation between Eh and pH was found for all data grouped in the seven experiments (R = \(-\)0.85, \(p\)\(<\) 0.05, n = 63) and for the individual experiments (R = \(-\)0.98, experiment 9 to R: \(-\)0.86, experiment 2; \(p\)\(<\) 0.05, n = 7). Eh is the physicochemical property of the solutes that can exchange electrons with inert electrodes. If Eh is negative, there is a reduction, that is, the reducing power is stronger than that of the oxidant. If Eh is positive, it means that there has been oxidation, that is, that the oxidizing agent is stronger than the reducer [(87)]. A possible explanation for this negative correlation between Eh and pH is that the positive values of Eh are indicative of the presence of hydronium ions, which explains the relationship between the high values of Eh and lower pH values in the experiments (more acidic and less alkaline). As the EC process develops, the values of Eh decrease, unequivocal signal generation of hydroxyl ions (which is the beginning of the EC process) occurs, the Fe anode is oxidized (loses electrons), and electrons that are captured in the cathode favor the reduction process and increase the pH.
## 2 Materials and Methods
### Profiling of Bioactive Compounds by UHPLC-QTOF Mass Spectrometry  
The profiling of bioactive compounds of _R. angustifolius_ was performed using UHPLC-QTOF mass spectrometry acquired in MS full scan mode through (Agilent Technologies, Santa Clara, CA, USA), as previously optimized [9]. The dry matter of _R. angustifolius_ extracts, obtained through three techniques of extraction--i.e., HAE, MAC, and SOX, and four solvents--i.e., EA, MetOH, EtOH, and water, were resuspended in 80% methanol acidified with 0.1% formic acid and finally filtered with 0.2 \(\upmu\)m cellulose membrane into vials for UHPLC. The injection volume was set to 6 \(\upmu\)L, and each sample was analyzed in triplicate using a randomized pattern of injection.  
The raw MS data were processed using an Agilent Profinder (Agilent Technologies) software (version B.06) using a 'find-by-formula' algorithm, according to the parameters previously described [9]. The profiling of bioactive compounds was carried out using the comprehensive database Phenol-Explorer3.6 (version) [10], subsequently integrated with terpenoids and alkaloids obtained from the literature [11]. The putative annotation process was based on level two confidence (i.e., putative identification exploiting the isotopic profile of each compound and a mass accuracy <5 ppm). Finally, the major classes of bioactive compounds were quantified using standard compound solutions analyzed with the same MS conditions. The pure compounds were: cyanropicrin (sesquietpene lactones), artemisinin (triterpenes), sanguinarine (alkaloids), cyanidin (anthocyanins), luteolin (flavones and other flavonoids), quercetin (flavonols), catechin (flavanols), sesamin (lignans), tyrosol (LMWPs), ferulic acid (phenolic acids), and resveratrol (stilbenes). Compounds were provided by Extrasynthese (Lyon, France), and they were characterized by purity >98%. Linear regression curves were built to quantify the main representative subclasses obtained (R\({}^{2}\) values > 0.98 using the following five concentrations: 0.1, 1, 10, 100, and 1000 mg/L), and the semi-quantitative values were then expressed as mg equivalents/g dry matter (DM).
## 3 Dynamic Performance Analysis
### Dynamic Peak Value  
The variation in the dynamic peak strain of the sandstone ring specimen with the inner diameter following the dry-wet cycle is presented in Figure 5.  
It can be seen in Figure 5 that the dynamic peak strain of the sandstone ring specimen increased with the increase in the inner diameter of the specimen following the dry-wet cycle. The dynamic peak strain of the complete specimen was \(2.30\times 10^{-3}\), and it increased to \(4.23\times 10^{-3}\) when the inner diameter was \(25\) mm, which represents an increase of \(83.91\%\). The curve of the dynamic peak strain with inner diameter was positively correlated with the quadratic function, and the correlation was significant. The fitting relationship is shown in Formula (2).  
\[\varepsilon_{\mathrm{T}}(\mathrm{d})=2.287+0.033\mathrm{d}-0.002\mathrm{d}^{2 }\ (\mathrm{R}^{2}=0.9991) \tag{2}\]  
where \(\varepsilon_{\mathrm{T}}\) is the dynamic peak strain.  
As the inner diameter of the sandstone ring specimen increased, the wall thickness decreased, and the resistance to failure decreased. Simultaneously, the radial effective force area decreased and the force per unit area increased, making this area more prone to being  
Figure 5: Dynamic relationship of sandstone ring specimens with different inner diameters following dry and wet cycling.  
unstable and destroyed. Therefore, the dynamic peak strain of the specimen increased with the addition of the aperture.
# Research on Fast Detection Technology of Dark Currents in a Ge-Si Detector Array
###### Abstract  
A shortwave infrared Ge-Si photodetector will become the core device of the LiDAR optical receiver. In order to meet the urgent demand for photodetectors in the LiDAR field, we designed and produced a 32 \(\times\) 32-pixel Ge-Si photodetector array, which was proposed and developed to meet the performance requirements of the detector array. A dark current detection system for fast scanning and detecting large-scale Ge-Si detector arrays was proposed and developed to achieve the rapid detection of dark current in each detector pixel, with a detection accuracy of less than 1 nA. The system validated the primary performance indicators of the detector array we designed, achieving rapid discrimination of the array performance and rapid localization of damaged pixels. The scanning test results showed that the average dark current of the designed detector array chip was on the nanoampere level, and the proportion of bad points was less than 1%. The consistency of the array chip was high, which meets the requirements of light detection at the receiving end of LiDAR. This work laid the foundation for our subsequent development of a LiDAR prototype system.  
shortwave infrared; LiDAR; photodetector array; dark current detection +
Footnote †: journal: Applied sciences
# Fall Recognition System to Determine the Point of No Return in Real-Time
## 5 Real-Time Implementation and Verification  
The real-time execution program was operated using Python3.x, Tensorflow 2.0, and the period of data input was 100 Hz. The main platform used was Jetson TX2, and only the CPU was utilized for execution.  
Figure 10 shows how to validate a fall prediction program in real-time. As shown in Figure 10a, the server collects sensor data and converts it into a transport packet. Sensor data collection may be performed in real-time from a data acquisition device, or data collected in advance and stored in CSV format may be loaded. The server transmits packet data to the client at 100 Hz. The client connects to the server, receives sensor data, and executes the fall prediction program. In the fall prediction program, the prediction result is output by inferring the input data using the pre-trained model. In Figure 10b, the true_label(1) value at the bottom means the correct answer to be predicted, the top target_pred(2), is the result inferred from the learned model by receiving the test fall data file from the server through TCP and executing the fall prediction program implemented in the multi-process structure in the client. (c) is the result of executing the model on LeNet5, a kind of CNN, on the target platform. (d) is the result of running the LSTM model on the target platform. The input data used in (c) and (d) are the same. The LSTM model generates fewer errors than the CNN model.  
Figure 9: Recognition errors in transition sections.  
As shown in Figure 11, the real-time execution program executed in the client has the following multiprocessor structure: (1) a processor that manages data packets received at a cycle of 100 Hz from the server, (2) a processor that converts the input format to be used in the learned model, (3) a processor that performs inference of the learned model, and (4) a processor that outputs the result of inference.  
Moreover, the execution times of the CNN and LSTM models that were already trained on the Jetson TX2 platform were measured, as shown in Figure 12. Based on the measurement results, it was shown that the CNN model performed faster than the LSTM model and it was confirmed that both models performed within 10 ms.  
Figure 11: Real-time execution program.  
Figure 12: Real-time execution time measurement.  
Figure 10: Model verification method and verification results.  
The fall prediction program was operated at an interval of 10 ms and determined the PONR when the imbalance stage was converted to the falling down stage; three to four prediction results were derived in a row, demonstrating that real-time fall prediction can be performed using both the CNN and LSTM learning methods. Because the CNN algorithm has a shorter execution time than the LSTM algorithm, we confirmed that it has the potential to be applied to lightweight embedded systems.
# Development of Pretreatment Approaches for Authentic Representation of Tea Infusion Aroma
## 3 Results Analysis  
### Analysis of Aroma Extraction Efficiency  
#### 3.1.1 Comparison of the Total Peak Area of Aroma Extraction  
When extraction reaches equilibrium among solid, liquid, and gas phases, the adsorption capacity of the fiber for volatile compounds is directly proportional to their concentration in the liquid sample [29]. Generally, higher tea-to-water ratios yield greater aroma compound concentrations [12]. As shown in Figure 1A, the effective total peak area (after excluding silicone bleed and other interference peaks) increased significantly with a higher tea-to-water ratio, reaching its maximum at a 1 g:10 mL ratio, indicating optimal extraction efficiency. Extraction temperature determines the partition coefficient of aroma compounds between the fiber and the matrix. Higher temperatures generally increase compound volatility and reduce equilibrium time. However, excessively high temperatures may elevate water vapor content, impair fiber adsorption, or trigger undesirable chemical reactions. Figure 1B demonstrates that the tested temperature range was appropriate, with no inhibitory effects observed. The effective peak area rose significantly with temperature and peaked at 60 \({}^{\circ}\)C, indicating the optimal extraction temperature for the maximum chemical yield. Extraction time affects reproducibility and efficiency. Prolonged time initially increases enrichment but may plateau or decline due to competitive adsorption. Figure 1C shows that the effective peak area grew with time, rising rapidly from 30 to 45 min and more slowly thereafter. The maximum extraction efficiency was attained at 60 min, indicating the optimal extraction duration for the maximum chemical yield.  
In summary, both water types exhibited consistent trends: higher concentration of tea infusion, higher temperatures, and longer extraction times enhanced aroma enrichment. The optimal conditions for maximum chemical yield were a 1 g:10 mL tea-to-water ratio, extracted for 60 min under 60 \({}^{\circ}\)C.
# Study on Impact Process of a Large LNG Tank Container for Trains
## 5 Conclusions  
By performing the experiments and numerical simulations, impact process of a large LNG tank container for trains was investigated in this study. Conclusions are obtained as follows:  
1. The impact process of a large LNG tank container for trains was conducted experimentally and stresses on the container especially on the frame were measured with time. For the initial velocity of 6.1 km/h, the maximum compressive stress is \(-\)366.3 MPa occurring on the longitudinal beam near the impact side corner fittings.
2. The impact force produced by the transport vehicle is influenced by both the initial clearance and initial velocity, i.e., its maximum value increases with the clearance or velocity, which in turn directly affects the LNG impact force on the head, the tank container axial acceleration at the mass center and the frame deformation and stress distribution. This result is valuable for the design of the corner fittings and frames of the container.
3. The largest average pressure brought on by the LNG impact force is 0.053 MPa, or 8.83% of the 0.6 MPa design pressure, so the inner vessel should be designed with a thickness allowance. When the initial velocity is 8 km/h, the ratio of the maximum LNG impact force on the back head to the static inertia force at each clearance is less than 0.23, which means that the calculation method of LNG static inertia force is conservative. This result may be helpful for the design of the thickness of container.
4. When the initial velocity is 8 km/h and the initial clearance is 8 mm, the maximum axial acceleration of the tank container is 63 m/s\({}^{2}\), greater than 4g inertial acceleration specified in the container design standard, meaning if assessed by the impact, the specifications of the standard are not conservative. This conclusion may provide reference for the editing or revision of the container design standard.  
Conceptualization, Z.W., C.Q. and W.L.; data curation, Z.W.; formal analysis, Z.W.; investigation, Z.W., C.Q. and W.L.; methodology, Z.W., C.Q. and W.L.; project administration, W.L.; resources, C.Q. and W.L.; supervision, W.L.; validation, Z.W., C.Q. and W.L.; visualization, Z.W.; writing--original draft, Z.W.; writing--review and editing, C.Q. and W.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 25: The axial displacement of the frame and outer vessel: (**a**) at the first peak of \(F_{1}\), (**b**) relationship with the \(F_{1}\).
## 3 Union and Intersection of Thick Ellipsoid Enclosures
### Mapping of Thick Ellipsoidal Domains via (Quasi-)Linear System Models
#### 3.1.3 Illustrating Example  
As an illustrating example, consider the system model  
\[\mathbf{\mathrm{x}}_{k+1}=\begin{bmatrix}0.5&p_{1}\\ p_{2}&0.6\end{bmatrix}\cdot\mathbf{\mathrm{x}}_{k} \tag{35}\]  
with the independent parameters \(p_{1}\) and \(p_{2}\), where  
\[\mathbf{\mathrm{x}}_{k}\in\mathcal{E}_{k}\quad\text{with}\quad\mathbf{\mathrm{Q}}_{k}= \begin{bmatrix}1&0\\ 0&2\end{bmatrix}. \tag{36}\]  
To show the influence of parameter uncertainty on the computed ellipsoidal enclosures, the following cases are distinguished in Figure 2:  
**case 1**: \(p_{1}\in[-1\;;\;2]\), \(p_{2}\in[-1\;;\;2]\);
**case 2**: \(p_{1}\in[-0.25\;;\;0.5]\), \(p_{2}\in[-0.25\;;\;0.5]\);
**case 3**: \(p_{1}\in[-0.1\;;\;0.2]\), \(p_{2}\in[-0.1\;;\;0.2]\).  
In the left column of Figure 2, the parameter matrix \(\mathbf{\mathcal{R}}_{k}=\mathbf{\mathrm{Q}}_{k}\) is chosen, while it is set to \(\mathbf{\mathcal{R}}_{k}=\tilde{\mathbf{\Lambda}}_{k}^{-T}\cdot\mathbf{\mathrm{Q}}_{k} \cdot\tilde{\mathbf{\Lambda}}_{k}^{-Obviously, for the smaller uncertainty in the **cases 2** and 3, the first setting is superior. This is not astonishing because it corresponds to that choice of matrix that would be employed for the covariance prediction in an Extended Kalman Filter (EKF). As it is well known, strong deviations from a point-valued system matrix \(\mathbf{A}_{k}\) may turn an EKF quite inaccurate. Then, other choices of the forecasted covariance are often superior, as they could be determined by the Unscented Kalman Filter technique [27]. This approach was not investigated here. However, the almost symmetric interval bounds of \(\left[\mathbf{A}_{k}\right]\) motivate the second choice of \(\mathbf{\mathcal{R}}_{k}\), which corresponds to \(\mathbf{\mathcal{Q}}_{k+1}=\mathbf{\mathcal{Q}}_{k}\).  
Moreover, it should be pointed out that the **case 1** in Figure 1(a),b corresponds to a scenario in which the inner bound is empty, the **case 2** in Figure 1(c),d to a scenario in which the inner bound just appears due to the matrix \(\left[\mathbf{A}_{k}\right]\) containing purely regular realizations; finally,Figure 2e,f shows the case of small uncertainty with a clearly visible inner solution set, where the proposed one-parameter optimization task from the previous subsections provides results that are close to the solution of the robust linear matrix inequality design, where \(\mathbf{\mathcal{R}}_{k}\) is not predefined but rather optimized with the help of the matrices \(\mathbf{\mathcal{P}}_{k+1}^{\text{O}}\) and \(\mathbf{\mathcal{P}}_{k+1}^{\text{I}}\) to obtain independent outer and inner enclosures (dashed and dotted lines, respectively).