esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Temporal and Spatial Variation of Vegetation in the Dongting Lake Basin  
The average NDVI in the Dongting Lake Basin was 0.60 (Figure 2), which showed a decreasing trend from west to east. The high-value zone of NDVI (>0.60) is mainly located in the hilly and woodland-covered areas with high terrain, while the low-value zone (<0.40) is mainly located near the Dongting Lake area and in the central part of the basin. From the interannual variation in the NDVI (Figure 3), the NDVI of the basin showed a smooth upward trend in general (\(y\) = 0.0048\(x\) - 9.069, \(R^{2}\) = 0.89, \(p\) < 0.01). The greening trend was greater in 2011-2020 (0.06) than in 2001-2010 (0.03), which is consistent with the previous studies [19; 38]. With a series of ecological restoration projects (i.e., the ReturningFarmland to Forest), the vegetation has shown a continuous greening trend in the past two decades [38].  
Figure 4 shows the spatial characteristics of changes in NDVI from 2001 to 2020. The vegetation index (NDVI) significantly increased over a wide range, mainly distributed in the forestland, arable land, and grassland areas in the northwest and central east of the basin, accounting for 88.11% of the total area of the basin. The main reasons may be related to the construction of shelterbells in the middle and upper reaches of the Yangtze River; the growth of vegetation on the banks was caused by the use of the Three Gorges project, af-forestation in mountain and lake areas, returning farmland to lakes, and closing mountains for forest cultivation and other national policy-oriented factors [38; 39]. The zones where NDVI decreased significantly were mainly distributed around urban construction land such as Changzhutan and local cultivated areas in the Dongting Lake plain, accounting for 1.66% of the total basin area, which is closely related to the anthropogenic disturbances and economic development [40]. Overall, the area with an increasing trend of NDVI (90.10%) was much larger than the area with a decreasing trend (2.08%), indicating a significant greening trend in the Dongting Lake Basin.  
Figure 3: Temporal trends of the average NDVI in the Dongting Lake basin from 2001 to 2020.  
Figure 2: Spatial distribution in the average NDVI in the Dongting Lake basin from 2001 to 2020.
## 5 Sensing Experiment and Discussions  
The experimental setup for sensing temperature was composed of a high and low temperature chamber and the developed SAW sensor system, as shown in Figure 3. The antenna distance was set to 0.5 m during the experiment. In the experiment, 14 temperature detection points were selected from \(-\)30 \({}^{\circ}\)C to 100 \({}^{\circ}\)C with equal intervals of 10 \({}^{\circ}\)C. To determine the number of data available, 150 tests were carried out at each temperature point in the experiments. The dynamic response of the sensor in the temperature range was recorded in Figure 6. Obviously, as the temperature increases, the phase of the signal gradually increases.  
The calibration curve of the sensor response of the proposed temperature sensor is shown in Figure 7a. The results were calculated from the 1500 sets of temperature measurement data, and each set of data includes 800 original data points. Higher sensitivity of 36.5\({}^{\circ}\)/\({}^{\circ}\)C was achieved. In addition, the temperature value displayed in the temperature chamber was taken as the true value of the temperature, and then the experimental test value was compared with it to evaluate the temperature measurement accuracy of the system. As shown in Figure 7b, within the range of \(-\)30 \({}^{\circ}\)C \(\sim\) 100 \({}^{\circ}\)C, the temperature measurement error of the system is less than 0.2 \({}^{\circ}\)C, which means higher accuracy of \(\pm\)0.2 \({}^{\circ}\)C in temperature measurement was achieved successfully. The achieved accuracy from the proposed SAW sensor was better than that of the similar works listed in Table 2. It means the adaptive LMS filtering algorithm and the combination method proposed in this work contributes greatly towards improving the accuracy in temperature measurement. In addition, low-temperature (below 0 \({}^{\circ}\)C) detection was carried out in this work, which was ignored in other work. Theoretically, it is possible to detect up to 300 \({}^{\circ}\)C because the thermostat at high temperature cannot display precisely the true internal temperature. Therefore, the range is \(-\)30\(\sim\)100 \({}^{\circ}\)C in this work.  
Figure 5: The phase characteristics processed by three algorithms.
# On the Modeling and Analysis of Brittle Failure in Existing R/C Structures Due to Seismic Loads
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Analysis** \\ **Case** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **PlanStory** \\ **No.** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Story** \\ **of Peak** \\ **FD** \\ \end{tabular} } & \multicolumn{3}{c}{**Test Results**} & \multicolumn{3}{c}{**FE Model–All Mechanisms**} & \multicolumn{3}{c}{**FE Model–Flexure Only**} \\  & & & **Time (s)** & **Time (s)** & **Time (s)** & **Time (s)** & **Time (s)** & **Time (s)** & **Time (s)** & **Time (s)** \\  & & & **of Peak** & \(\times\) **10\({}^{3}\) rad** & **at Peak** & **at Peak** & \(\times\) **10\({}^{3}\) rad** & **at Peak** & **at Peak** & **at Peak** & **at Peak** \\ \hline \multirow{6}{*}{s10} & & 1 & 5.17 & 0.591 & 5.17 & 5.10 & 0.385 & 5.10 & 12.03 & 1.049 & 12.03 \\  & & 2 & 5.17 & 1.127 & 5.16 & 5.10 & 0.544 & 5.11 & 12.05 & 1.335 & 12.06 \\  & & 3 & 5.15 & 0.943 & 5.14 & 5.11 & 0.487 & 5.11 & 12.07 & 0.937 & 12.13 \\  & & 1 & 4.86 & 0.674 & 4.86 & 3.07 & 0.279 & 3.07 & 11.01 & 0.899 & 11.01 \\  & & 2 & 4.49 & 1.028 & 4.49 & 3.07 & 0.358 & 4.47 & 11.01 & 1.324 & 11.01 \\  & & 3 & 4.82 & 0.952 & 6.63 & 3.07 & 0.267 & 5.87 & 11.01 & 1.054 & 4.61 \\ \hline \multirow{6}{*}{s11} & & 1 & 11.41 & 5.026 & 11.41 & 11.52 & 5.904 & 11.52 & 4.81 & 3.812 & 4.81 \\  & & 2 & 12.12 & 12.052 & 12.13 & 12.28 & 11.487 & 12.27 & 4.82 & 3.731 & 5.19 \\  & & 3 & 12.16 & 8.054 & 13.02 & 12.27 & 8.762 & 12.25 & 4.83 & 2.511 & 5.19 \\  & & 1 & 13.11 & 3.877 & 13.11 & 11.79 & 5.294 & 11.79 & 3.13 & 3.3252 & 3.13 \\  & & 2 & 13.09 & 6.648 & 13.04 & 4.59 & 8.319 & 4.63 & 3.14 & 3.975 & 3.15 \\  & & 3 & 13.00 & 6.076 & 6.02 & 4.65 & 8.716 & 4.69 & 3.14 & 2.849 & 3.16 \\ \hline \multirow{6}{*}{s12} & & 1 & 10.13 & 8.197 & 10.13 & 10.84 & 6.723 & 10.84 & 4.83 & 5.995 & 4.83 \\  & & 2 & 10.92 & 19.022 & 10.95 & 10.87 & 10.308 & 10.91 & 4.84 & 5.140 & 7.82 \\ \cline{1-1}  & & 3 & 10.97 & 11.931 & 11.04 & 12.39 & 15.164 & 12.46 & 4.85 & 3.031 & 7.81 \\ \cline{1-1}  & & 1 & 11.80 & 10.185 & 11.80 & 3.17 & 5.760 & 3.17 & 5.91 & 4.985 & 5.91 \\ \cline{1-1}  & & 2 & 11.88 & 15.737 & 11.92 & 4.63 & 10.750 & 4.66 & 5.92 & 5.832 & 5.92 \\ \cline{1-1}  & & 3 & 11.90 & 10.857 & 11.96 & 4.75 & 14.613 & 4.78 & 5.92 & 3.895 & 5.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Peak floor displacements and inter-story drifts in the two plan directions of the SPEAR building were obtained from the test and the analyses (ID = Inter-story drift; FD = Floor Displacement).
# A Study on Identification of Development Status of MASS Technologies and Directions of Improvement
## 4 Design, Communication and Maintenance Studies of the MASS
### Design of MASS
Additionally, these changes of ship design require development, amendment, and interpretation of maritime rules and regulations, together with communication and cooperation of multiple stakeholders such as ship designers, equipment manufacturers, shipping companies, classification societies, and international organizations to make the autonomous ships efficient and reliable. The design approach to MASSs should be systematic and iterative and take into account life cycles, involving testing and proofing. The design of the MASS should also be performed by taking into account the involvement of human factors in a completely different way, i.e., operation from shore control center (SCC), not operation directed by an onboard crew, and risk related to the factors [42].
## 5 Conclusions  
In summary, our study showed that the land surface phenology of the Zoige alpine wetland, as described by the satellite vegetation indices, in particular the water-related vegetation indices (e.g., LSWI), corresponds well with the phenological patterns based on ecosystem physiology, as measured by the eddy covariance method. Further, long-term simulations (2000-2011) indicated increasing trends for annual mean EVI, LSWI, and annual GPP, respectively, which is in line with the observed warming trend. In addition, there were significant positive correlations between annual GPP and temperature and phenological period. Our results also show that when the climate became warmer, the SOS advanced and the EOS delayed, which resulted in prolongation of the wetland phenological period and, consequently, increases in vegetation productivity. The advanced SOS and delayed EOS phenology trends correspond with the increasing trends in seasonal and growing season temperatures. Furthermore, our results suggest that warming and extension of the growing season had positive effects on C uptake in this alpine wetland. The findings of our study have important implications for remote sensing analyses of wetland phenology and C cycling and for future simulation scenarios of vegetation growth and C budgets. Finally, further evaluation of VPM simulations for other wetlands is necessary before we can apply the model for regional, continental, and global simulations.  
This work was supported by a National Nonprofit Institute Research Grant from CAFINT (Grant No. CAFINT2014K06), the National Youth Science Foundation of China (Grant No. 31300417), the National Natural Science Foundation of China (Grant No. 31170459), Si Chuan Province Youth Scientific and technological innovation research team (Grant No. 2015 TD0026), and the NASA Earth Observing System (EOS) Data Analysis Program (Grant No. NNX09 AE93G). We thank the principal investigators of the MODIS data products as well as the Oak Ridge National Laboratory's (ORNL) Distributed Active Archive Center (DAAC) and the Earth Observing System (EOS) Data Gateway for making these MODIS products available. We thank the anonymous reviewers and editors for their valuable comments and suggestions on the earlier versions of the manuscript. All authors contributed to designing the study, developing the overall concept of data analysis, the preparation of figures and tables, and the revision of the manuscript. The authors declare no conflict of interest.
# Correlation of Optical Constants and Morphologies with Mueller Matrix for Micro-Rough Surfaces
## 3 Results and Discussion  
### The Relationships Between Optical Constants and Mueller Matrix Elements  
This section discusses the variation in the Mueller matrix elements with the optical constants of rough surfaces and compares the Mueller matrix elements of rough surfaces with those of smooth surfaces under varying optical constants.
# YOLO-OHFD: A YOLO-Based Oriented Hair Follicle Detection Method for Robotic Hair Transplantation
## 2 Materials and Methods
### Structure of YOLO-OHFD  
#### 2.2.1 Feature Extraction Network Based on Adaptive Receptive Field  
To address the inherent challenges of hair follicle detection, we introduce an enhanced feature extraction module named ECA-Res2 Block, integrated within the CSPDarknet53 framework. CSPDarknet53 [30], renowned for its application in oriented detection tasks, combines the Cross Stage Partial (CSP) strategy with the Darknet53 architecture--a 53-layer convolutional network renowned for its robust feature extraction capabilities. The CSP approach enhances training efficiency and network performance by segmenting the  
Figure 3: Illustration of the proposed YOLO-OHFD method. This diagram illustrates the architecture of a system designed for oriented hair follicle detection. The input image is first processed through a feature extraction network using ECA-Res2 Block to capture multi-scale features (C1–C5). These features are then aligned and fused in the feature fusion network via Feature Alignment Modules (FAM), producing combined feature maps (F2–F5). Finally, the feature detection network utilizes these maps to predict the location and orientation of follicles, as well as to classify them. The output includes the follicle’s position, orientation, and classification.  
network into two pathways and facilitating feature reusability and propagation through cross-stage connectivity [31].  
Despite the strengths of CSPDarknet53, utilizing it directly for hair follicle detection presents two significant challenges. Firstly, hair follicles exhibit considerable variability in terms of hair type, thickness, and length across different populations, complicating feature extraction [32]. This variability tends to diminish the representation of finer hairs following successive downsampling operations. Secondly, incorporating angle prediction into the detection of oriented hair follicles, which often possess large aspect ratios, complicates the feature extraction process further.  
To overcome these challenges, the ECA-Res2 Block module is proposed to be integrated into CSPDarknet53. This module leverages the structural benefits of Res2 Block, which features multiple parallel branches of convolution and pooling layers. These branches capture a diverse range of feature scales and resolutions, enhancing the network's ability to synthesize information across different scales effectively. Subsequently, Efficient Channel Attention (ECA) is applied to the outputs of the Res2 Block. As depicted in Figure 4c, ECA adaptively enhances the weights of crucial channels within Res2 net branches, thus boosting the network's proficiency in extracting pertinent features while preserving essential details critical for recognizing specific hair characteristics. This integration not only mitigates the issues associated with feature loss during downsampling but also enhances the network's overall ability to discern and classify complex hair follicle structures accurately.
# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 4 Performance Evaluation
### Experimental Results and Discussion
#### 4.4.1 Comparison with RCNN-Based Methods  
The performance of our technique compared with other region-based segmentation methods, i.e., RCNN [51] and Faster RCNN [56] using Figshare brain tumor dataset [38], is reported in Table 2. The problem with RCNN methods is that they require significantly too much time to train the model as these techniques randomly generate around 2000 region proposals per image for classification. Additionally, there is no learning process at the region proposal generation step because a fixed selective search algorithm is used, which leads to the generation of false candidate region proposals. Furthermore, the processing time for the test image is approximately 47 s, which is inadequate for obtaining results in real-time. Faster RCNN extracts the region proposals automatically by introducing the region proposal network and shares the CL among class and bounding box network to expedite the process and reduce the computation cost. Faster RCNN and Mask-RCNN give results in real-time, i.e., approximately 0.23 and 0.2 s, respectively, inference time per test image. The advantage of using Mask-RCNN over Faster RCNN is the automated segmentation of the brain tumor along with localization, i.e., it defines the tumor location and draws a high-quality segmentation mask of the tumor region along with classification. Mask-RCNN delineates RoIs automatically and extracts the features from the images layer by layer without providing the features previously. This gives an advantage of detecting the tumor comprehensively besides analyzing the single features. Moreover, it is easier to train and requires a negligible additional overhead as compared to Faster RCNN. Moreover, the presented work with DenseNet-41 is more robust as compared to ResNet-50 due to its dense connections, which result in calculating a more accurate set of image features. Furthermore, the DenseNet-41-based Mask-RCNN is computationally more efficient due to its small number of parameters.  
Figure 10: Example of inaccurately localized brain tumor images by the proposed method. The red and blue contour shows the predicted tumor region and respective masks.
## 4 Results
### Static Results
#### 4.1.1 Cno  
The static signal-to-noise ratio measurement was performed inside a building with large glass windows. As the testing procedure section mentioned, measures were taken at various distances from the window. As seen in Figure 5, these are the results of the measurement from the window up to 2 m into the building. As observed in the graph, the measurement made with both devices right at the window, the circularly polarized antenna, showed better results, with a difference of approximately 3 dB. This position closely resembles the clear view and, therefore, is optimum for circular polarization.  
Figure 4: The route followed by the car. First, the highway, which was 24.4 km long (red), followed by the city (Barcelona), which was 9 km long (blue), and the forest, which was 16.7 km long (yellow) to finish.  
Figure 5: Cno results were obtained in a static position inside a building with large windows. The position of this device has been moved toward the interior of the building.  
The second measurement shown in Figure 5 was performed with both devices at a distance of 1-m inward of the building. As can be seen, the circularly polarized antenna still had a better signal-to-noise ratio, but in this case, the difference dropped to approximately 1.5 dB.  
The last measurement was made with the devices at a distance of 2 m from the interior of the building. In this case, since the circularly polarized antenna did not have a direct view of the sky, it lost more signal-to-noise ratio. However, we observed that the performance of the linearly polarized antenna, although with a lower Cno than in the previous case, was better. This is because the device was less conditioned by the environment. The difference in this scenario was 0.7 dB better.
## 1 Introduction
### Structure  
The paper is organized in the following way. In Section 2, we describe the model of the system containing a transmitter, a channel, and a receiver. In Section 3, we explain the evaluation of error probability and mutual information, while Section 4 presents numerical and simulation results. Concluding remarks are emphasized in Section 5.
# An Operational Image-Based Digital Twin for Large-Scale Structures
## 2 The Proposed Digital Twin Framework  
The process to create an operational image-based Digital Twin for large-scale offshore structures proposed in this study is shown in Figure 1. The process begins with a description of the physical structure in terms of geometry and position in a georeferenced coordinate system. Drones and/or LiDAR scans are used to obtain a large number of RGB images of the physical structure after the manufacturing process, which are then used to create a three-dimensional geometric reconstruction of the structure. This reconstruction model is produced with the use of photogrammetry. During this step, a detailed meshed CAD model of the structure containing the details of the geometry is moved to the same georeferenced coordinate system that is used during the drone flights for easier comparison with the reconstructed 3D model. Parallel to this, an artificial intelligence (AI) algorithm is applied to the RBG images to detect and classify paint defects and damages.  
A large number of images (>2000) were used to train the convolutional neural network. The state-of-the-art real-time object detection algorithm You Only Look Once (YOLO) version 5 was selected (see reference [11]), and it has proven to be an effective tool for detecting and classifying paint defects/damages. The type, position, and size of the detected paint defects/damages were subsequently extracted from the AI algorithms and mapped to the three-dimensional geometry reconstruction of the transition piece, thereby creating the first version of the Digital Twin. Information on the position, type, and size of paint defects/damages from the Digital Twin can be used to determine if maintenance is needed on the physical structure. After offshore installation of the structure, drones are used to inspect the structure at regular time intervals. The Digital Twin is then updated, based on the new images and scans. In the future, the proposed Digital Twin can be used to facilitate high-fidelity finite element analysis of the structure to evaluate its structural integrity.
## 3 Geometric Representation and Mechanical Analysis
### Mechanical Analysis  
The lifting lug used to connect the rotating arm and the mounting base plate is taken as the research object, and the loads are analyzed in combination with the working environment. Generally, the load type mainly includes two parts, i.e., the centrifugal force caused by its self-mass, and the tensile stress generated by the centrifugal force of other connected parts. The centrifugal force is represented by Equation (30).  
\[F_{C}=mr\omega^{2} \tag{30}\]  
where \(m\) is the mass of the lifting lug, \(r\) is the radius of rotation of the lifting lug, and \(\omega\) represents the rotary angular velocity.  
With the high-speed rotation of the centrifuge, the stress \(\sigma_{a}\) generated by the centrifugal load of the mounting base plate and the inspection model of centrifugal test at the joint and the centrifugal force \(F_{C}\) act simultaneously, and the combination of the two causes significant strength problems for the lifting lug. Assuming that the load caused by the centrifugal loads of the mounting base plate and the inspection model of centrifugal test are evenly distributed over the edge of the lifting lug connection, the radial stress of the load is defined by  
\[\sigma_{a}=\frac{F}{2\pi RL} \tag{31}\]  
Figure 8: Four topological configurations and related design variables.  
where \(F\) represents the centrifugal load of the mounting base plate and the inspection model of centrifugal test, \(R\) is the rotary radius of the lifting lug edge, i.e., the distance from the position where the mounting base plate is attached to the lifting lug to the center axis of the centrifuge, \(L\) represents the width of the edge of the lifting lug.  
Based on the loads and boundary conditions stated above, the weighted B-spline FCM is employed to compute the von-Mises stress distribution of the lifting lug in fixed grid. Figure 9 shows the initial von-Mises stress distribution of the four cases. A comparison of the maximum von-Mises stress and mass among the four cases is demonstrated, as described in Figure 10. From these figures, it can be noted that the maximum von-Mises stresses of the four cases appear at the junction of the lifting lug and the mounting base plate, wherein both of the maximum von-Mises stress and mass in the case \(1\) are the largest. The four cases with these topological configurations all satisfy the stress condition with a safety factor greater than or equal to 2. Thus, in order to obtain a superior solution of the lifting lug based on the four topological configurations, shape optimization is carried out for the four cases.  
Figure 9: Initial von-Mises stresses distribution of the four cases.