esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds
## 4 Experiments and Analysis  
### Study Area and Experimental Data  
The study area of the HV transmission corridor is located in Guangdong Province, China. It is a mountainous area with a moderate elevation fluctuation. Two corridors were scanned, Qingyuan 220 kV line (Dataset 1) and Zhaoqing 220 kV line (Dataset 2), as shown in Figure 13a,b.  
The two datasets were captured by an unmanned helicopter Lidar system [72] from Guangdong Power Research Institute, covering areas of \(0.14\times 19.3\) km\({}^{2}\) and \(0.14\times 14.7\) km\({}^{2}\) at flying heights of about 220 m and 180 m for Dataset 1 and Dataset 2, respectively. The scanning angles were set to 22.5\({}^{\circ}\), covering a 140 meter width. The laser pulse rate of the scanner was set to 300 kHz. The acquisition procedures concerning the autonomous navigations of the UAV, etc., have been elaborated by [PERSON] et al. [73]. Due to topographic fluctuations and changes in flight pitch, the flight speed was not constant, so the gaps between adjacent points in the two datasets were between 0.2 and 3.6 m. Further details of the two datasets are shown in Table 2. The unmanned aerial helicopter inspection system is shown in Figure 13c, and was equipped with a laser scanner of RIEGL VUX-1 LR from _REIGL_ and a position and orientation system (POS) (Figure 13d). The sensor specifications and accuracy specifications are listed in Table 3.  
Dataset 1 and Dataset 2 were collected on calm weather days. The datasets expressed no wind-bias phenomenon. Primary objects in both datasets included transmission lines, high forest trees, and a few low-rise buildings. The forest trees were tall, dense, and widely distributed, and were close to power lines, hence causing difficulty for classification and extraction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Dataset 1** & **Dataset 2** \\ \hline Voltage type (kV) & 220 & 220 \\ Length (km) & 19.3 & 14.7 \\ Point density (pts/m\({}^{2}\)) & 30.7 & 42.2 \\ Number of points (million) & 97.11 & 124.92 \\ Number of conductors & 320 & 200 \\ Number of pylons & 41 & 26 \\ Feature of landform & Hills & Mountains \\ Laser scanner & RIEGL VUX-1 LR scanner \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental datasets.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Position Accuracy (m)** & **Velocity (m/s)** & **Description** \\ \hline
**Laser scanner** & 0.015 & 200 line/s & RIEGL VUX-1 LR \\
**POS** & 0.05 (Horizontal), 0.08 (Vertical) & 0.005 m/s & Custom-built model \\ IMU\({}^{1}\) & 0.1 (Horizontal), 0.2 (Vertical) & 100 Hz & Custom-built model \\ GPS\({}^{2}\) & 0.04 (Horizontal), 0.06 (Vertical) & & NovAtel Propak6 \\ \hline \hline \end{tabular} \({}^{1}\) Inertial Measurement Unit, \({}^{2}\) Global Positioning System.  
\end{table}
Table 3: Equipment specifications and some parameters of the platform.
## 3 Results
### Inter-Group (Healthy vs. PD Groups) Postural Differences in Upright Standing

Table 2: Comparison of sagittal parameters assessed in upright posture between healthy and PD groups via parametric _t_-test for independent samples or non-parametric Wilcoxon Mann–Whitney test (level of significance \(\alpha\) = 0.05). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **T0** & **T1** & \multicolumn{3}{c}{**Multiple Comparisons Across**} \\  & **Without K1** & **with K1** & **without K1** & \multicolumn{3}{c}{**Repeated Four Measures**} \\ \cline{2-5}
**Sagittal** & **A** & **B** & **C** & **D** & **A vs.** & **A vs.** & **B vs.** & **B vs.** & **C vs.** \\ \cline{2-5}
**Parameters** & **A** & **B** & **C** & **B** & **C** & **D** & **C** & **D** \\ \hline \multicolumn{5}{c}{**Mean \(\pm\) SD (min, max) or Median (Percentiles 25\%; 75\%)**} \\ \hline CNA [Deg] & \(61.2\pm 5.8\) (\({}^{52.64,68.4}\)) & \(58.5\pm 5.18\)(\({}^{53.68,60.9}\)) & \(90.3\pm 4.7\)(\({}^{52.78,4.23}\)) & \(57.0\pm 4.64\)(\({}^{52.84,67.7}\)) & 0.295 & 0.317 & **0.033 \({}^{*}\)** & 0.998 & 0.306 & 0.330 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Multiple comparisons of sagittal parameters assessed in upright posture across four conditions (T0 without K1, T0 with K1, T1 without K1 and T1 with K1) via parametric repeated measure-ANOVA or non-parametric repeated measure–ANOVA (Friedman test). Results of post hoc tests with Tukey correction for each comparison (level of significance \(\alpha\) = 0.05). Intragroup (PD Patients) Kinematic Differences During Walking Before and After Treatment with K1 Posture Keeper  
The multiple comparison across four conditions during walking showed a significant effect of K1 Posture Keeper on the following sagittal kinematic parameters of PD patients: CNA (\(\chi^{2}\) = 8.83; \(p\) = 0.04 for average and \(\chi^{2}\) = 12.8; \(p\) = 0.005 for range), DA (\(\chi^{2}\) = 9.56; \(p\) = 0.023 for range), STA (\(\chi^{2}\) = 8.93; \(p\) = 0.030 for range), SCO (\(\chi^{2}\) = 9.0; \(p\) = 0.029 for average) and HSN (\(\chi^{2}\) = 9.38; \(p\) = 0.025 for average). In particular, the results of the post-hoc tests corrected for multiple comparisons with the Tukey method (Table 4 and Figure 4b) showed a significant decrease in CNA average in both T1 without (\(p\) = 0.048) and with K1 (\(p\) = 0.004) conditions compared to T0 without K1 one (Figure 6a); a significant increase in SCO average in T0 with K1 (\(p\) = 0.049) and  
Figure 5: Boxplot of significant differences (\(p\)-value \(\leq\) 0.05) for static parameters in multiple comparisons implemented via repeated measures-ANOVA. Figure Notes: Significant differences are highlighted in bold (\({}^{***}\)\(p\) < 0.05). T0 without K1 (A); T0 with K1 (B); T1 without K1 (C); T1 with K1 (D); (a) C7-nasion (CNA) and (b) S2-C7-occipt (SCO) angles; degree (Deg).
# A Parameter-Driven Methodology of Wheel Flat Modeling for Wheel-Rail Impact Dynamics
## 2 Three-Dimensional Wheel Flat Model  
According to the mechanism of wheel flat formation, a localized region of the wheel tread is worn away during sliding. Compared with a complete wheel, a wheel with a flat loses a three-dimensional patch. Empirically, the projection of a wheel flat on the tread surface is approximated as an ellipse, with the long axis of the ellipse parallel to the rolling direction and the short axis parallel to the lateral direction. Therefore, the three-dimensional patch corresponding to the newly formed wheel flat should resemble a semi-ellipsoidal crown, which changes shape with increasing abrasion.  
Based on [8], this paper divides the patch patterns corresponding to wheel flats into three stages.  
Stage I. New flat. Because of the initial formation of the wheel flat from abrasion, the patch morphology in this stage is a complete semi-ellipsoidal crown.  
Stage II. Edge worn flat. The leading and trailing edges of the wheel flat begin to wear away, extending the length of the flat without changing its width or depth. In this stage, the patch edges start to show wear rounding, but the primary morphology remains a semi-ellipsoidal crown.  
Stage III. Completely worn flat. The wheel flat is worn to the center, further extending in length compared with Stage II, with no change in width or depth. The patch morphology changes significantly to a near-saddle shape.  
According to these three stages of wheel flat wear, its geometric modeling is divided into two categories as follows: new flat (Stage I) modeling and worn flat (Stages II and III) modeling.
## 2 Related Works
### Deep Learning-Based Remote Sensing Imagery Methods
#### 2.2.3 [PERSON] et al. [2] proposed the spatial and channel attention-based network (SCAttNet) for semantic segmentation of high-resolution remote sensing images. The SCAttNet highlights an aspect of its function by visualizing the learned representations from the spatial and channel attention modules. This visualization provides insights into why the proposed method improves segmentation accuracy, which contributes to the interpretability of the model's performance.  
The experiments were conducted on two benchmark datasets: the ISPRS Vaihingen and Potsdam datasets. The results of these experiments demonstrated competitive performance, especially in handling small objects, which is a significant challenge in remote sensing imagery due to the high level of detail. The authors claim that SCAttNet is a comprehensive approach that could have valuable applications in various remote sensing tasks.
## 3 Method  
As illustrated in Figure 2, our approach builds on a given component FE model. Whether this is the model of a predecessor or a completely new FE model is insignificant. For our use case, we are especially interested in the influences of moving the component's kinematic hard points at the connection points to neighboring components. Here, we use both a Direct Morphing-inspired (Section 3.1) and a Box Morphing (Section 3.2) approach to investigate the design changes systematically. In the practical application, one of the methods can be selected. The received simulation data are used to create metamodels that evaluate kinematic hard point location design choices and lead to fast decisions. The hard point locations can thus serve as optimization parameters for the component's FRFs. By splitting simulation and optimization, it is possible to quickly change optimization targets without recalculating the FE simulation [20]. Additionally, the used metamodels can direct design choices without numerical optimization.
## 2 Results and Discussion  
### Identification of Novel Chloroplast Calcium-Binding Proteins  
In this study we analyzed chloroplasts from Arabidopsis to identify novel calcium-binding proteins. After initial purification, chloroplasts were disrupted by treatment with hypertonic buffer and soluble and membrane proteins were separated by centrifugation. Extrinsic proteins were subsequentlyremoved from the membrane by a high-salt wash and combined with the stromal fraction. After desalting, proteins were then separated by 2D-PAGE using IEF in the first and SDS-PAGE in the second dimension (Figure 1, upper panel). Proteins were electrophoretically transferred onto a PVDF membrane and calcium-binding ability was assessed by incubation with buffer containing radioactive isotope \({}^{45}\)Ca after re-naturation of the proteins on the membrane. In order to avoid unspecific binding, the buffer contained excess of the divalent cation magnesium. Under these conditions, a single protein that clearly bound calcium was observed (Figure 1, lower panel, red arrow). This protein migrates at about 15 kDa and was focused on the acidic region of the membrane.  
Coomassieic brilliant blue staining of a SDS-PAGE duplicate from the same sample revealed a protein pattern different from what is normally observed in stromal fractions, notable easily by the near complete lack of RuBisCO. It appears that the desalting step removed larger proteins and protein complexes and thus enriched many of the smaller proteins. This might also explain, why only a single calcium-binding spot was observed. However, the stain showed a clear protein band in the same area where the radioactive signal was observed (Figure 1, upper panel, red arrow). The protein was excised  
Figure 1: Identification of novel calcium-binding proteins in chloroplasts. Combined stromal and extrinsic membrane proteins from Arabidopsis chloroplasts were separated by 2D-PAGE (IEF followed by SDS-PAGE) and assessed for calcium-binding activity using the radioactive isotope \({}^{45}\)Ca. Coomassieic brilliant blue stained gel (upper panel) and autoradiogram (lower panel) are shown. A potential calcium-binding protein of about 15 kDa indicated by arrows was excised and analyzed by MS/MS.  
from the gel and analyzed by MS/MS. Two peptides were found that matched the sequence of the known chloroplast protein CP12 as indicated by grey bars (Figure 2). The mature CP12 protein, after cleavage of the targeting peptide (Figure 2, indicated by arrow), has a predicted mass of 12 kDa, which fits well with the size observed on SDS-PAGE separation. In addition, the theoretical isoelectric point of 4.15 is in agreement with the observed position in the IEF separation [23].
# Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
## 2 Materials and methods
### Methods
#### 2.3.1 Spatial Trend Analysis Methods
\begin{array}{l}1\big{(}\,NDVI_{j} \big{)}NDVI_{i}\big{)}\\ 0\big{(}NDVI_{j}=NDVI_{i}\big{)}\\ -1\big{(}NDVI_{j}\big{(}NDVI_{i}\big{)}\end{array} \right. \tag{3}\]  
When \(n\geq 8\), \(S\) follows a normal distribution with a mean of zero and a variance of:  
\[Var(S)=\frac{n(n-1)(2n-5)}{18} \tag{4}\]  
The standardization of \(S\) is as follows:  
\[Z_{c}=\left\{ \begin{array}{l}\frac{\delta-1}{\sqrt{Var(S)}}\,(S)0\\ 0(S=0)\\ \frac{\delta+1}{\sqrt{Var(S)}}(S(0)\end{array} \right. \tag{5}\]  
where \(Z_{c}\) is the statistic normalized by the Mann-Kendall test and follows a normal distribution. When \(\beta=0\), there is no monotonic trend in the study series when the null hypothesis holds and is combined with Theil-Sen trend analysis. Conversely, when the null hypothesis does not hold, there is significant variation in the analyzed series at the alpha level if the absolute value of \(Z_{c}\) is greater than the standard normal distribution. This study tested the significance of trends in NDVI time series at a confidence level of \(\alpha=0.05\). Additionally, the NDVI values for each pixel in the study area during 2000-2019 were subjected to Theil-Sen trend analysis to obtain the spatial distribution of the trend values. If \(\beta>0\), the annual average NDVI of the pixel shows an increasing trend, whereas if \(\beta<0\), the annual average NDVI of the pixel shows a decreasing trend. Because there is essentially no region where \(\beta\) is strictly equal to 0, the interval between \(-0.001\) and \(0.001\) was classified as stable and constant in this research, based on the actual situation of \(\beta\). The results of the Mann-Kendall test, at a confidence level of 0.05, were classified as significant (\(1Z_{c}\) \(>1.96\)) or insignificant changes (\(1Z_{c}\) \(\leq 1.96\)). In this study, areas with a multi-year average NDVI of less than 0.08 were considered to be unvegetated and were excluded [62].
###### Abstract  
Ensuring long-term wellbore integrity is critical for CO2 injection and storage operations. Conventional cement degrades in CO2-rich environments, compromising zonal isolation and increasing leakage risks. This study presents a novel self-healing cement formulation incorporating Barite, Pozzolan, and Chalcedony, optimized using a Design of Experiment (DOE) approach. Geochemical simulations were conducted using PHREEQC and Python to evaluate porosity evolution, mineral stability, and self-sealing efficiency under CO2 exposure. The results demonstrate that the optimized formulations significantly reduce porosity (within 7-14 days) through the formation of calcium silicate hydrate (C-S-H) gels, enhancing crack sealing and mechanical resilience. Saturation index and phase volume analyses confirm the long-term stability of ECSH2 and Calcite, reinforcing the cement matrix. Compared to conventional cement, the self-healing formulations exhibit improved durability, lower permeability, and superior resistance to CO2-induced degradation. These findings support the use of self-healing cement in carbon capture and storage (CCS), geothermal energy, and deep-well applications, offering a cost-effective and durable solution for long-term wellbore integrity. However, further experimental validation and field-scale evaluation are needed to confirm the practical performance of these formulations under real-world reservoir conditions.  
Self Healing; cement; well integrity +
Footnote †: journal: Applied sciences
# A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising
## 2 Digital Filter as Bio-Inspired Optimization Problem  
Multi-rate systems are those that function normally but sample data at a variety of different rates at various points during processing. The sampling frequency can change dynamically during signal processing in multi-rate systems [2]. Decimation and interpolation are the two primary procedures utilized by multi-rate systems. The process of decreasing the sampling frequency to a lower sample frequency that is distinct from the initial frequency by an integer value is referred to as decimation. The process of decimation is often referred to as down-sampling. Below, in Figure 1, the p-fold decimation filter is presented. The filter is designed using a low-pass filter (such as FIR filter) for anti-aliasing followed by p-fold decimators to suppress the frequency \(f_{s}/2p\), termed the Nyquist frequency. Every \(p_{th}\) sample is passed through the filter and others are discarded. Afterward, the sampling frequency is changed to \(f_{s}/p\) and returns output as \(y_{n}\), where \(f_{s}\) is the sampling frequency of the input signal, i.e., \(x_{n}\).  
The bio-inspired optimization algorithm aims to determine a set of coefficients (\(c\)), \(c\in R^{M}\), for achieving desired characteristics by specifically designed filters. The main objective of an optimization-based filter is to find and reduce the error between the filter's ideal frequency response (\(F_{I}\)) and the desired frequency response (\(F_{D}\)) by updating the coefficient iteratively. Mathematically, it is represented as  
\[Err_{c}(e^{i\omega},\ c)=F_{I}(e^{i\omega})-F_{D}(e^{i\omega}) \tag{1}\]  
As calculated, \(Err_{c}\) is complex.  
There are several conventional approaches for designing digital decimation filters, such as windowing techniques, but the limitation of this system is that it does not provide accurate control of transition bandwidth and cutoff frequencies. Another issue in digital filters is ripple and discontinuity near band edges [7]. The weighted Chebyshev approximation technique [8] can address issues related to local minima, slow convergence, and suboptimal results in conventional filter optimization methods. Researchers are now using bio-inspired optimization algorithms to achieve faster exploration and tuning of parameters for multiple filter requirements. The design of digital FIR filters through bio-inspired optimization algorithms involves minimizing an objective or error fitness function, and the choice of the fitness function plays a crucial role in achieving an ideal filter response. Recent research has shown significant progress in digital FIR filter design using bio-inspired optimization algorithms. In this section, this paper discussed the most promising bio-inspired optimization algorithms, such as particle swarm optimization (PSO) [19], cuckoo search (CS) optimization [21], and firefly optimization (FF) [15] for designing decimation filters.
## 2 Proposed Model: QPE Analytical Approximation Model
### Coordinate Systems for Modeling
#### 2.1.2 Orbital Plane Coordinate System  
The movement parameters of a spaceborne SAR satellite can be described in the orbital plane coordinate system. In this system, the Earth is located at one of the foci of the elliptical orbit, and the satellite can be regarded as a point. Polar coordinates are used to describe the movement parameters.  
There exist six orbital elements that uniquely identify a specific orbit of the satellite: the semi-major axis \(a\), eccentricity \(e\), and true anomaly \(\
u\) are used in the orbital plane coordinate system to determine the satellite's movement at a certain time. The inclination \(i\), the longitude of the ascending node \(\Omega\), and the argument of periapsis \(\omega\) are necessary to construct the transformation matrix between the orbital plane coordinate system and the ECI system.  
The orbital plane coordinate system and the ECI system are summarized in Figure 1.  
Figure 1: The orbital plane (OP) coordinate system and the ECI system. Elements related to the OP coordinate system are in red. \(\omega\) is the argument of periapsis, \(i\) is the orbit eccentricity, \(\
u\) is true anomaly, and \(\theta_{L}\) is the center off-nadir angle in SAR.
###### Abstract
Among these, the LDDI index based on NDVI and Gross primary productivity (GPP) can comprehensively reflect the terrestrial ecosystem's vegetation coverage, productivity, and biomass, allowing for the accurate and rapid detection of changes in the LDD process across diverse spatial and temporal scales. [PERSON] et al. [12] conducted a similar analysis of China's LDD process using an LDD index but failed to perform a quantitative analysis of the factors influencing LDD. In addition, their study focused on the entire country's LDD process at a coarse resolution (10 km) and did not carry out a detailed analysis or discussion of different regions. Monitoring and analyzing the LDD processes in coastal areas is of great importance for decision-makers and stakeholders to implement effective policy measures for the sustainable management and utilization of natural resources. Therefore, this study aims to examine the spatial and temporal variations in LDD by evaluating the vegetation coverage and primary productivity of coastal land ecosystems. Additionally, the study employs analytical methods such as residual analysis, land transfer analysis, and trend analysis to investigate the impact of human and climate factors on LDD. The study quantitatively evaluates the influence of human and climate factors on LDD in different regions and explores the possible relationship between extreme precipitation, extreme temperature, land use change, and the LDD process. Section 2 outlines the study area, data sources, and methods, while Section 3 presents the main findings. Finally, Sections 4 and 5 provide a comprehensive discussion and conclusion, respectively.
## 5 Conclusions  
In this study, we used the RF algorithm to estimate annual maize and sunflower yields in Hetao Irrigation District from 2010 to 2015 based on vegetation indexes and phenological characteristics. The main feature of this study was that the regional crop yield estimation of pixel scale was carried out for the first time in HID and this method can be easily applied to other regions, if there were auxiliary crop planting structure map and field measured crop yield data. Main conclusions of this study are as follows:  
1. The RF model could accurately estimate annual regional crop yields, with the multi-year average values of root-mean-square error and the relative error of 0.75 t/ha and 6.1% for maize, and 0.40 t/ha and 10.1% for sunflower, respectively.  
Figure 8: Maize yield maps from 2010 to 2015 (**a–f**).  
Figure 9: Sunflower yield maps from 2010 to 2015 (**a–f**).  
Among eight models, the optimal model for maize was NDVI series from the 120 th day to the 210 th day with 10 days' interval, while the optimal model for sunflower was the combination of NDVI indexes and phenological characteristics.
* The yields of maize and sunflower could be estimated fairly well with NDVI series 50 days before crop harvest, which implicated the possibility of crop yield forecast before harvest.  
[PERSON] outlined the research topic, assisted with manuscript writing, and coordinated the revision activities. B.Y. performed data collection and processing, data analysis, model building, the interpretation of results, manuscript writing, and coordinated the revision activities.  
This research was funded by National Natural Science Foundation of China, grant numbers 51479090 and 51779119.  
We are grateful to the research group of [PERSON] of China Agricultural University for providing the data of the sampling points in 2014 and 2015. Valuable comments from the editor and two anonymous reviewers are greatly appreciated.  
The authors declare no conflict of interest.