esa-sceva/satcom-chunk-collection huggingface dataset:
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
## 3 Range Determination for a Specular Target
### Simplified Algorithm  
Our simplified approach begins with the recognition that significant echo power is limited to an optimum duration. For a narrow water body (<100 m), the duration is half the main lobe. For a wide water body (>100 m), the duration is the width of the water body. The 100 m breakpoint was explained in Section 2.2. There is little value in including data beyond the recommended duration, because the echo has likely fallen into the noise. In addition, going further from the intended water center, the chance of encountering interference from another scatterer or another water body increases.  
It will be shown in Section 3.2.3 that, in our proposed approach, the water level is estimated by a least-squares fitting to the echo power in two or three range bins. However, before this fitting a burst of echoes are coherently summed. Coherent summing increases the SNR with respect to background noise and possible interference. Interference could be from a nearby water body, a natural land feature or even some artificial structure. Interference rejection was introduced in our earlier paper [8] as the \"zero Doppler algorithm\" (zero Doppler means that the echoes were de-spun for the expected Doppler and summed).  
The coherent summing is performed on a moving window of 25 echoes, which is approximately 100 m track length over the Earth surface. This is because all water bodies, narrow and wide, have significant echoes strength for an equivalent length of at least 100 m, as discussed in Section 2.2. This alleviates the need to know exactly the water body size and location.  
The coherent sum is defined as:  
\[P_{n,r}=\left|\sum_{k}z_{n+k}e^{-i\phi_{k}}\right|^{2} \tag{4}\]  
where \(\phi_{k}\) is the phase shift on the \(k\)-th sample required to align it in phase with the other echoes. The sum is over a burst centered on echo \(n\), \(k=n-12\),..., \(n+12\). Over the relatively short time of a burst, we can assume a constant Doppler frequency \(\omega\), and use the following equation:  
\[P_{n,r}=\left|\sum_{k}z_{n+k,r}e^{-i\omega k}\right|^{2} \tag{5}\]  
The optimum coherent burst is centered on the midpoint in the lake or river. The midpoint may be established from overlaying the nadir track on a map or a satellite image. In addition to, or as an alternative, the midpoint can be identified by the zero crossing of the Doppler velocity.  
For comparison, the incoherent sum, which is defined as follows:  
\[P_{n,r}=\sum_{k}\lvert z_{n+k}\rvert^{2} \tag{6}\]  
is also useful for analysis. The standard processing approach (that is generally called \"Low Resolution Mode\" or LRM) for inland waters uses incoherent summing, as mentioned in Section 1.
# Fractional Flow Reserve (FFR) Estimation from OCT-Based CFD Simulations: Role of Side Branches
###### Abstract  
The computational fluid dynamic method has been widely used to quantify the hemodynamic alterations in a diseased artery and investigate surgery outcomes. The artery model reconstructed based on optical coherence tomography (OCT) images generally does not include the side branches. However, the side branches may significantly affect the hemodynamic assessment in a clinical setting, i.e., the fractional flow reserve (FFR), defined as the ratio of mean distal coronary pressure to mean aortic pressure. In this work, the effect of the side branches on FFR estimation was inspected with both idealized and optical coherence tomography (OCT)-reconstructed coronary artery models. The electrical analogy of blood flow was further used to understand the impact of the side branches (diameter and location) on FFR estimation. Results have shown that the side branches decrease the total resistance of the vessel tree, resulting in a higher inlet flowrate. The side branches located at the downstream of the stenosis led to a lower FFR value, while the ones at the upstream had a minimal impact on the FFR estimation. Side branches with a diameter larger than one third of the main vessel diameter are suggested to be considered for a proper FFR estimation. The findings in this study could be extended to other coronary artery imaging modalities and facilitate treatment planning.  
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Technical Flow Reserve (FFR) Estimation from OCT-Based CFD Simulations: Role of Side Branches
## 6 Simulations Results
### Effect of SNR on Success Rate and RMSEs  
The simulation conditions were the same as in Section 6.1. Among them, only the environment SNR changed from \(-10\) dB to \(10\) dB, and the step size was \(1\) dB. The number of Monte Carlo cycles was \(1000\). The relationship curve between success rate and environment SNR is shown in Figure 6, and the relationship curve between the root-mean-square errors (RMSEs) and environment SNR is shown in Figure 7. With the increased environment SNR, the success rate increased and the RMSEs decreased. A8 and A9 always had better success rates than others, especially at low SNR. The RMSEs of A7 and A9 were consistently lower than those of others, especially at high SNR. A9 was overall optimal, with a high success rate and small RMSEs.  
Figure 5: Comparison of weak signal results estimated by various algorithms.  
Figure 6: The relationship curve between success rate and SNR.  
Figure 7: The relationship curve between RMSEs and environment SNR.
# A Single Image Enhancement Technique Using Dark Channel Prior
###### Abstract
For example, in order to improve the speed of the defogging algorithm, a bilateral filtering instead of soft matting is used to refine the transmission. For defogging of the sky area, a tolerance mechanism [12] is proposed to correct the transmittance. Although these methods make up the shortcomings of [PERSON]'s algorithm to some extent, there is still room for improvement. Specifically, traditional dark channel prior methods for defogging have problems of high time complexity, edge effect, and failure of dark channel prior. In this paper, we propose an improved image defogging method based on dark channel prior. For the first two disadvantages, we have the following improvements. First, the atmospheric light is accurately estimated by using a four-point weighting algorithm, then, guided filtering is used to repair the coarse transmittance obtained by using the dark channel prior. Finally, a fog-free image is restored through an atmospheric scattering model. Experimental results show that the improved algorithm can shorten the processing time and improve the edge effect. Furthermore, aiming at solving the problem of failure of dark channel prior in large areas with high brightness, we have the following improvements. First, the sky and non-sky areas are separated according to the method of edge detection. Then we use the enhancement algorithm via sequential decomposition to enhance the sky areas, and use the optimized dark channel prior method to handle the non-sky areas. Finally, we propose an improved image defogging method based on dark channel prior, which can effectively solve the problem of the failure of dark channel prior.
## 4 Experimental Results
### Deep Learning Approach Using Transfer Learning
Given the fact that only the last fully connected layer will be trained in this study, the trainable parameters will be different. Regardless of the difficulty of estimating arousal and valence dimensions when only using audio modality, the imbalanced issue makes it hard for training. Finally, the results show that the state-of-the-art architecture, ResNext-50, achieved an average top-1 validation accuracy of 65.45%, which outperforms other deep models. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Performance Comparison**} \\ \hline
**Model Name** & **Top-1 Acc.** & **Top-5 Acc.** & **Best Performance** & **Trainable Params.** \\ \hline VGG-11 & 64.79 \(\pm\) 1.51\% & 95.52 \(\pm\) 2.17\% & 66.56\% & 32,776 \\ VGG-13 & 65.05 \(\pm\) 1.32\% & 96.62 \(\pm\) 1.83\% & 65.61\% & 32,776 \\ VGG-16 & 64.77 \(\pm\) 1.44\% & 96.36 \(\pm\) 2.08\% & 66.93\% & 32,776 \\ VGG-19 & 64.58 \(\pm\) 1.49\% & 95.86 \(\pm\) 2.06\% & 66.77\% & 32,776 \\ AlexNet & 65.00 \(\pm\) 1.01\% & 96.07 \(\pm\) 1.74\% & 65.61\% & 32,776 \\ Inception-V3 & 64.53 \(\pm\) 1.44\% & 95.36 \(\pm\) 1.64\% & 66.40\% & 16,392 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification results using transfer learning scheme. The pre-trained deep neural networks used are VGG, AlexNet, Inception, ResNet, DenseNet and ResNext. Figure 5: (a) Sample images from each class, (b) the scatter plot for arousal vs. valence domain values.
## 6 Results and Discussion
### Performance Metrics for Three Sessions

Table 3: PCA performance metrics for machine learning models, KNN, NB, and the SVM, using data from one session. The scatter plot in Figure 27 provides a visual comparison of the data that have undergone dimensional reduction, illustrating the clustering differences as the dataset increases from one session to the complete dataset of three sessions. A confusion matrix is a tool used to assess the performance of a classification model by comparing its predictions to the actual outcomes across various classes. Rows correspond to true classes and columns to predicted classes, with diagonal elements indicating correct predictions and off-diagonal elements representing misclassifications. It offers valuable insights into the model's accuracy, precision, recall, and other performance metrics. The confusion matrix for the full GRABMyo dataset is shown in Figure 28. It is a \(5\times 5\) matrix that compares the data with those predicted by the machine learning model. Please note that only five gestures were selected to represent the five classes for prediction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation Metrics** & \multicolumn{5}{c}{**Classes (Five Hand Gestures)**} & \multicolumn{2}{c}{**Macro Average**} \\ \hline \hline  & \multicolumn{5}{c}{**LDA–Support Vector Machine**} \\ \hline True Positive & 26 & 28 & 29 & 30 & 32 & 29 \\ False Positive & 13 & 19 & 12 & 9 & 17 & 14 \\ False Negative & 17 & 15 & 14 & 13 & 11 & 14 \\ True Negative & 159 & 153 & 160 & 163 & 155 & 158 \\ Precision & 0.66667 & 0.59574 & 0.70732 & 0.76923 & 0.65306 & 0.6784 \\ Sensitivity & 0.60465 & 0.65116 & 0.67442 & 0.69767 & 0.7449 & 0.67442 \\ Accuracy & 0.86047 & 0.84186 & 0.87907 & 0.89767 & 0.86977 & 0.86977 \\ F-measure & 0.63415 & 0.62222 & 0.69048 & 0.73171 & 0.69565 & 0.67484 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 27: Comparison of scatter plots between one session and the complete dataset of three sessions of data. The outcomes presented in Table 5 demonstrate an improvement in accuracy compared to the previous single session. Specifically, the LDA-SVM model's performance increased from 86.98% accuracy to 90.69% when considering data from three sessions.
## 3 Materials and Methods
### Image Processing of Thermal Images  
#### 3.3.1 Principal Component Analysis (PCA)  
Because raw thermal images are often affected by noise, background radiation, and 3D thermal diffusion, image processing techniques are commonly applied to enhance contrast between defective and sound areas, thereby improving the accuracy of defect detection [41; 42]. In this study, each frame was first converted to greyscale and then reshaped into a one-dimensional vector. This resulted in a data matrix \(M\in\mathbb{R}^{T\times N}\), where \(T\) is the number of frames and \(N\) is the number of pixels per frame. Each row of \(M\) represents a thermal frame in time, while each column corresponds to the temporal evolution of a single pixel's intensity.  
PCA is a widely used statistical method for dimensionality reduction and noise suppression. It transforms the original high-dimensional data into a set of orthogonal components that capture the most significant variance in the dataset [23; 43]. Prior to PCA, the data matrix \(M\) was mean-centred along each pixel's time series. PCA was then performed by applying singular value decomposition (SVD) directly to the mean-centred matrix, or equivalently, by performing eigen-decomposition on the covariance matrix \(M^{T}M\)[23; 43]. This process yields a set of principal components, ordered by descending variance, that represent the underlying structure of the thermal data in a lower-dimensional subspace [37]. After decomposition, the selected principal components (e.g., PC1 to PC5) are reshaped back into 2D image frames for visual analysis. These component images often reveal defect-related features with improved contrast compared to the original raw thermal frames [37; 43].
# Resilient Modulus--Physical Parameters Relationship of Improved Red Clay by Dynamic Tri-Axial Test
## 4 The Dependence of Resilient Modulus on Material Properties  
### The Influence of Moisture Content and Compactness on Resilient Modulus  
The improved red clay samples, prepared at 93% compactness and 6% cement (C6%), and prepared at 93% compactness and 6% lime (L6%), were selected to study the influence of different moisture content on the dynamic resilient modulus of the improved red clay. As can be seen from Figure 4, the resilient modulus of the improved red clay is greatly affected by moisture content, and its value decreases with an increase in moisture content. For the improved red clay with 6% cement, when the confining pressure is 15 kPa and the deviatoric stress is 30 kPa, the resilient modulus for the optimum moisture content (OMC) of 21.5% (OMC minus 3%), 24.3% (OMC), 27.1% (OMC plus 3%) is  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Mass Stress**} & **Octahedral** & \multirow{3}{*}{**Measured**} & \multicolumn{6}{c}{**Resilient Modulus**} \\ \cline{4-9}  & **Shear** & & **Andrei Model** & & **NI Model** & **Uzan-Witczak Model** \\ \cline{4-9}  & **Stress/kPa** & & **Predicted** & **Value/Mpa** & **Error/\%** & **Predicted** & **Value/Mpa** & **Error/\%** \\ \hline
75 & 14.14 & 160 & 160.3371 & 0.2102 & 165.572 & 3.3653 & 162.302 & 1.4184 \\
100 & 25.93 & 150 & 152.6465 & 1.7338 & 153.1579 & 2.0618 & 150.  
171.2 MPa, 158.0 MPa, 147.7 MPa, respectively. Compared with the moisture content of 21.5% (OMC minus 3%), the resilient modulus for moisture content of 24.3%, and 27.1% (OMC plus 3%) decreases by 7.71%, and 14.1%, respectively. In addition, when the moisture content is invariable, the resilient modulus of improved red clay decreases with an increase in deviatoric stress, and increases with an increase in mass stress.  
The improved red clay samples, prepared at optimum moisture content and 6% cement, and prepared at OMC and 6% lime (L6%), were selected to study the influence of different compactness on the dynamic resilient modulus. As can be seen from Figure 5, when the moisture content is invariable, the resilient modulus of the improved red clay increases with an increase in compactness. For the improved red clay with 6% cement, when the confining pressure is 60 kPa and the deviatoric stress is 30 kPa, the resilient modulus for the red clay samples, when prepared at a compactness of 91%, 93%, and 95% is 177.89 MPa, 190.5 MPa, and 204.67 kPa, respectively. Compared with 91% compactness, the resilient modulus for compactness of 93%, and 95% increases by 7.08%, and 15.05%, respectively. In addition, when the compactness is invariable, the resilient modulus of the improved red clay decreases with an increase in deviatoric stress and increases with an increase in bulk stress.
# A Digital Twin System for Adaptive Aligning of Large Cylindrical Components
## 5 DT Aligning System Development and Validation
### Software Framework and Development of the DT Process System  
For the DT process system, its software architecture consists of a data management layer, a function execution layer, and a user interaction layer, as shown in Figure 12.  
* The data management layer obtains multi-source heterogeneous data in the aligning process and uses different databases to store and manage according to data types and characteristics. In addition, the local area network and HTTP protocol and TCP/IP protocol are applied to achieve data transmission.
* The functional execution layer provides technical support for the realization of the functional modules and user interface of the DT aligning system. It mainly includes three functional software modules, i.e., data management module, offline simulation module, and process monitoring module. The data management module is mainly used for data collection, storage, and management. The offline simulation module is applied to the aligning process planning and the process performance evaluation, e.g., the aligning error prediction of the large component.
* The process monitoring module is utilized for the aligning process execution and control. The user interaction interface is implemented based on physical-virtual synchronous mapping, visualizing the simulation and monitoring results of the virtual space digital twin model, transmitting information to the user on the interaction interface, and receiving user interaction and feedback operations.  
Figure 12: Software framework of DT process System.
## 5 Results
### Inter-Channel Spatial/Temporal Correlation at Plot Scale
Over vegetated areas, \(r_{\text{s}}\) vs. \(h^{\prime}\) produces the lowest number of significant correlations as, in this case, \(\sigma^{\circ}\) is mainly ruled by canopy dielectric (plant water content) and geometric properties (\(LAI\), \(F_{\text{v}}\) and vegetation structure). However, conversely to the single regression analysis, the dual-frequencies inter-channel spatial/temporal approach pointed out some strong significant correlations. Despite \(r_{\text{t}}\) were computed over vegetated plots, some negative strong and very strong significant \(r_{\text{t}}\) were observed between \(r_{\text{s}}\) and \(m_{\text{v}}\) for pairs of \(\sigma^{\circ}\) at L and X frequencies. These results partially confirm the suitability of dual frequency approach to quantify \(m_{\text{v}}\) and biomass (\(F_{\text{v}}\) particularly), as already reported by other authors [(16; 58)]; further discussion about the improvements achievable through the multi-channel approach and the multi-frequency correlation will be provided in the next section.
## 3 Results
### Historical Mean Precipitation
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Location** & **Min Precp: Historic (mm/year)** & **Min Precp: Future (mm/year)** & **Max Precp: Historic (mm/year)** & **Max Precp: Future (mm/year)** & **Mean Precp: Historic (mm/year)** \\ \hline Chamba & 0.139753 & 0.149073 & 0.221126 & 0.238324 & 0.192575 & 0.205318 \\ Lahul and Spiti & 0.119920 & 0.129261 & 0.138791 & 0.148975 & 0.130315 & 0.139626 \\ Anantnag & 0.146749 & 0.155421 & 0.221476 & 0.231080 & 0.189691 & 0.198665 \\ Badgam & 0.203809 & 0.211752 & 0.249637 & 0.256687 & 0.228982 & 0.235880 \\ Bandipore & 0.129565 & 0.138012 & 0.206992 & 0.213571 & 0.167014 & 0.174809 \\ Baramulla & 0.192771 & 0.200399 & 0.278808 & 0.285964 & 0.236973 & 0.244656 \\ Doda & 0.183492 & 0.195055 & 0.231000 & 0.246522 & 0.210009 & 0.222688 \\ Ganderbal & 0.144282 & 0.152274 & 0.220760 & 0.228718 & 0.179490 & 0.187322 \\ Jammu & 0.225250 & 0.241213 & 0.252056 & 0.263465 & 0.239490 & 0.253102 \\ Kargil & 0.121428 & 0.130966 & 0.159335 & 0.167298 & 0.137106 & 0.145882 \\ Kathua & 0.209757 & 0.223623 & 0.235656 & 0.255003 & 0.227792 & 0.246731 \\ Kishtwar & 0.117257 & 0.126604 & 0.211027 & 0.221873 & 0.163096 & 0.172876 \\ Kulgam & 0.202216 & 0.210235 & 0.248502 & 0.256330 & 0.228987 & 0.236885 \\ Kupwara & 0.165468 & 0.173397 & 0.274320 & 0.281537 & 0.218321 & 0.225482 \\ Poonch & 0.241594 & 0.248673 & 0.286973 & 0.297952 & 0.257829 & 0.265233 \\ Pulwama & 0.179314 & 0.187364 & 0.216565 & 0.223671 & 0.201946 & 0.209560 \\ Rajouri & 0.243920 & 0.253919 & 0.276086 & 0.287497 & 0.259228 & 0.269063 \\ Ramban & 0.213605 & 0.224464 & 0.235839 & 0.248195 & 0.224980 & 0.235639 \\ Reasi & 0.226067 & 0.236086 & 0.256687 & 0.267623 & 0.243585 & 0.253805 \\ Samba & 0.228581 & 0.246606 & 0.240320 & 0.254035 & 0.234534 & 0.252245 \\ Shupiyan & 0.202254 & 0.210270 & 0.239276 & 0.246572 & 0.222700 & 0.229776 \\ Srinagar & 0.184241 & 0.192048 & 0.220760 & 0.228718 & 0.201140 & 0.209093 \\ Udhampur & 0.221933 & 0.236613 & 0.245006 & 0.256522 & 0.232521 & 0.247574 \\ Pathankot & 0.220361 & 0.237220 & 0.233085 & 0.255353 & 0.230337 & 0.251461 \\ \hline \end{tabular}
\end{table} 
Table 6: Zonal distribution of precipitation historically and for the future (mm/year).
## 2 Results and Discussion
### Electrochemical Analysis  
The cyclic voltammograms obtained by chalcones are shown in Figure 23. For chalcone 1 (Figure 23a), a broad shoulder located around 0.70 V is noted (forward scan), and it is related to the electrochemical oxidation of chalcone 1. For chalcone 2 (Figure 23b), two broad shoulders, one around 0.4 V and the other about 0.95 V, are displayed in the forward scan, and one peak located around 0.35 V appears in the reverse scan. In Figure 23b, the process about 0.4 is related to the oxidation of the chalcone 2, and the one around 0.95 is attributed to the second oxidation process of the chalcone 2 molecule and, finally, the process observed in the reverse scan of about 0.35 is attributed to the reduction in the products formed in the forward scan. The fact that the potential related to the electrochemical oxidation of the chalcone 2 (0.40 V) is less positive than the corresponding potential for chalcone 1 (0.70 V) shows that chalcone 2 requires less energy to be oxidized, which means that the chalcone 2 has an electrophilic character in comparison to chalcone 1.  
Figure 2: Molecular structure in the ball and stick model with the numbering of atoms for chalcone 1 (**a**) and 2 (**b**).
# Development of a Pre-Evaluation and Health Monitoring System for FAST Cable-Net Structure
###### Abstract  
The Five-hundred-meter Aperture Spherical radio Telescope (FAST) is one of China's major pieces of national infrastructure. A variable cable-net structure is used as the main supporting structure of the active reflector. The displacement of the cable net works through actuators. The realization of linkage control is a multi-degree-of-freedom and complex coupling control system. Due to factors such as the temperature difference between day and night, as well as actuator failure, the reflector control accuracy and even structural safety are affected during the position-control process of the cable net, so realizing evaluation of control accuracy and fault warning of the reflector is a significant problem. This paper proposes a pre-evaluation and health monitoring system based on advanced mechanical simulation technology. Through this system, on-site staff can expeditiously analyze the model to determine whether the cable net is currently in a safe state, predict the fatigue degree of the components, and maintain the structure when appropriate. The pre-evaluation and health monitoring system adequately ensure the stable functioning of the FAST cable net, improve the efficiency of on-site maintenance work, and markedly reduce the safety risk of the structure.  
radio telescope; cable-net structure; health monitoring; mechanical simulation technology; analytical methods +
Footnote †: journal: Medical Informatics