esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Method
### Numerical Simulation
Nowadays, shadow analysis methods mainly include the ray-tracing algorithm and the hemi-cube method. The ray-tracing algorithm originated from computer graphics, which calculates the incident path of sunlight based on the solar altitude angle and azimuth angle in order to judge the intersecting part between the sunlight and the surface of the steel structure, while permanent shadow due to closed spaces, structural self-shadow due to geometry, and environmental shadow caused by surrounding terrain can be identified by the algorithm [106; 107; 108]. The hemi-cube method was originally used to calculate the angle coefficient, which is the percentage of radiation energy emitted form one surface to another [109]. In the finite element software, the calculated angle coefficients are used to judge the irradiation elements and shaded elements, so the dynamic boundary between the irradiation and the shadow parts of the structural surface can be obtained in real time [110; 111]. Convective heat transfer, which follows [PERSON]'s law of cooling, refers to the heat transfer between a solid surface and a fluid when the fluid flows through the solid, and its formula is shown in Equation (11). \[F_{avg}=(1-cos\alpha)/2 \tag{2}\]3where \(q_{c}\) is the convective heat flux (W/m\({}^{2}\)), \(h_{c}\) is the convective heat transfer coefficient (W/(m\({}^{2.\circ}\)C)), and \(T_{a}\) and \(T\) are the atmospheric temperature (\({}^{\circ}\)C) and the structure surface temperature (\({}^{\circ}\)C), respectively. At present, there is no unified formula for calculating the convective heat transfer coefficient. Generally, the expressions of the convective heat transfer coefficient include two parts: free convection and forced convection. The air movement caused by the temperature difference between structure surface and atmosphere results in free convection, while forced convection is mainly caused by wind; free convection can be ignored when the wind speed is great enough. However, according to the principle of heat transfer, the coefficient of convective heat transfer should have an exponential relationship with wind speed. Nevertheless, many experiments have shown that the linear relationship can also be consistent with the test results; thus, the linear relation is feasible to use in many cases. Some representative expressions of the convective heat transfer coefficient in the study of the temperature fields of building structures are listed in \[F_{avg}=(1-cos\alpha)/2 \tag{2}\]4
# Soil Moisture Retrieval from Multi-GNSS Reflectometry on FY-3E GNOS-II by Land Cover Classification
## 4 Results and Discussion
### Comparison with SMAP  
The GNOS-II soil moisture estimations were compared with SMAP reference data on a global scale with the independent test set. The test set is from 7 July 2022 to 5 August 2022, and from 5 October 2022 to 31 October 2022. A gap was found in the comparison because the SMAP data acquisition was interrupted for almost two months. The density scatter plots of GNOS-II soil moisture results from GPS/BDS/GAL reflections versus SMAP soil moisture reference data are shown in Figure 10. For data with a vegetation water content below 5 kg/m\({}^{2}\), the overall RMSE of the estimated soil moisture was 0.0503 cm\({}^{3}\)/cm\({}^{3}\) for GPS, 0.0497 cm\({}^{3}\)/cm\({}^{3}\) for BDS, and 0.0482 cm\({}^{3}\)/cm\({}^{3}\) for GAL at volumetric water content, with a correlation coefficient of 0.83, 0.85 and 0.86, respectively, as shown in Table 4. The three systems have similar measurement accuracy. The overall RMSE is 0.0490 cm\({}^{3}\)/cm\({}^{3}\). The results indicate that soil moisture retrieval with reflected signals from different GNSS systems is feasible, which enables GNSS-R observations to have improved spatial and frequency coverage.
## 4 Numerical Simulation  
In this section, the effectiveness of the designed parametric learning method and adaptive impedance controller are verified by a 2-DOF lower limb rehabilitation exoskeleton given as follows:  
\[\zeta=\left[\begin{array}{c}\zeta_{1}\\ \zeta_{2}\end{array}\right] \tag{56}\]  
The system matrices \(M(\zeta)\), \(C(\zeta,\zeta)\), and \(G(\zeta)\) are given as  
\[M(\zeta) =\left[\begin{array}{cc}M_{11}&M_{12}\\ M_{21}&M_{22}\end{array}\right]\] \[M_{11} =m_{1}l_{c1}^{2}+m_{2}\left(l_{1}^{2}+l_{c2}^{2}+2l_{1}l_{c2}\cos \zeta_{2}\right)+l_{1}+l_{2}\] \[M_{12} =m_{2}\left(l_{c2}^{2}+l_{1}l_{c2}\cos\zeta_{2}\right)+l_{2}\] \[M_{21} =m_{2}\left(l_{c2}^{2}+l_{1}l_{c2}\cos\zeta_{2}\right)+l_{2}\] \[M_{22} =m_{2}l_{c2}^{2}+l_{2}\]\[C(\zeta,\zeta) = \left[\begin{array}{cc}C_{11}&C_{12}\\ C_{21}&C_{22}\end{array}\right]\] \[C_{11} = -m_{2}l_{1}l_{2}\zeta_{2}\sin\zeta_{2}\] \[C_{12} = -m_{2}l_{1}l_{2}(\dot{\zeta}_{1}+\dot{\xi}_{2})\sin\zeta_{2}\] \[C_{21} = m_{2}l_{1}l_{2}\zeta_{1}\sin\zeta_{2}\] \[C_{22} = 0\] \[G(\zeta) = \left[\begin{array}{cc}G_{11}&G_{21}\end{array}\right]^{T}\] \[G_{11} = (m_{1}l_{2}+m_{2}l_{1})g\cos\zeta_{1}+m_{2}l_{2}g\cos(\zeta_{1}+ \zeta_{2})\] \[G_{21} = m_{2}l_{2}g\cos(\zeta_{1}+\zeta_{2})\]  
The exoskeleton system parameters chosen for simulation are selected as in Table 1.  
We define the desired target trajectory of the exoskeleton as  
\[\zeta_{d}(t)=\left[\begin{array}{c}\zeta_{d1}\\ \zeta_{d2}\end{array}\right]=\left[\begin{array}{c}a_{1}\sin(c_{1}t)+\cos( t)\\ a_{2}\sin(c_{2}t)+\cos(t)\end{array}\right] \tag{57}\]  
where \(a_{1}=a_{2}=0.1\) and \(c_{1}=c_{2}=1\). The initial values of the exoskeleton are set as  
\[\zeta(0) = [1\;\mathrm{rad},1\;\mathrm{rad}]\] \[\dot{\zeta}(0) = [0\;\mathrm{rad}/\mathrm{s},0\;\mathrm{rad}/\mathrm{s}] \tag{58}\]  
The simulation is divided into two parts. In the first part, we test whether the controller we designed for the lower limb rehabilitation exoskeleton system can track the desired trajectory and achieve the impedance control objective. In the second part, the feasibility and effectiveness of human motion intention estimation and impedance learning are verified.
## 6 Discussion and Future Research Directions
### Optimized NLoS Propagation  
The possibility of wireless communication through a clear line-of-sight communication channel is often impractical, especially in urban cities with skyscrapers and tall building infrastructure. The IRS technique is expected to enable the 6G networks to navigate NLoS scenarios by providing alternative paths for effective communication [38]. ML-assisted IRS systems have yielded improvements in signal detection, estimation, spectral efficiency, and phase angle selection in wireless communication applications. ML improves the performance of IRS-based network propagation applications by effecting low bit-error rates, low signal to noise ratio (SNR), and high spectral efficiency.
## 3 Odometry Model
Additionally, the estimated receiver clock bias \(\delta t^{+}\) and drift \(\hat{\delta}t^{+}\) are fed back to GNSS preprocessing, the estimated IMU biases \(\hat{b}_{\omega}^{b,+}\) and \(\hat{b}_{a}^{b,+}\) are fed back to the strapdown algorithm and the estimated wheel radii \(\hat{r}_{dyn}^{+}\) are fed back to odometry preprocessing.
## 1 Introduction
Existing studies focusing on individual missions [22] or a selected airline network [23] showed the climate mitigation potential of ISO with dependence on the set boundary conditions. However, this has not yet been analysed comprehensively on a broader scale. Furthermore, a systematic variation of cruise flight levels and location of intermediate stops as characteristics of the respective ISO missions has not been performed so far. The same holds true for an analysis of the trade-off between cost-increase and climate-impact changes in this concept, referred to as eco-efficient operations [24]. On this basis, the objective of this study is to assess the climate mitigation potential of ISO. For this purpose, adjustments to the concept aiming for fuel efficiency are twofold:  
1. The intermediate stop airport is not selected according to fuel-optimal criteria, but based on minimal climate impact. 2. Cruise flight levels are reduced and step climbs are avoided if this reduces the climate impact. Consequently, we assess characteristics such as flight time, detour, fuel consumption and climate impact as well as the location of selected intermediate stop airports and preferred flight level. Our analysis contributes to the current state of research by (1) providing a methodology to characterise climate-optimised ISO, (2) assessing the climate mitigation potential of implementing ISO on long-haul flights departing from or landing in Europe, and (3) comparing this to a fuel-optimised set-up of ISO in terms of climate and non-climate metrics. The structure of this paper is as follows: After this introduction, the applied data as well as the developed methodology are explained in Section 2. Section 3.1 presents the results in terms of the climate-mitigation potential of ISO and the characteristics of climate-optimised operations, followed by a comparison of fuel-optimised and climate-optimised operating methods in Section 3.2, and a combination of both in an eco-efficient set-up in Section 3.3. In Section 4, we critically reflect on the achieved results and their impact on aviation industry's stakeholders before we summarise the study in a final conclusion.
# Focus Estimation Methods for Use in Industrial SFF Imaging Systems
## 2 Materials and Methods
### Method for Assessing the Quality of Height Map Representation  
The results obtained in the form of a 2D matrix height map do not allow a direct comparison of focus estimation methods. It is necessary to propose metrics for comparison. Limiting the range of imaged scenes to flat surfaces makes it possible to define the accuracy of the scene representation as the spread of the obtained results around the expected value [31; 32]. The expected value for the entire height map is constant due to the imaging of flat surface scenes. The root mean square error (RMSE) value was used as a determinant. The second proposed indicator of height map quality is the absolute fit error (\(AFE\)). It was defined as the absolute value from the difference between the height map mode and the expected value (Equation (4)). The expected value was taken heuristically separately for each set of images. This indicator shows the distance between the plane calculated according to a given operator from the actual value.  
\[AFE=abs\Big{(}mode(HM)-E(HM)\Big{)} \tag{4}\]  
where:  
_\(AFE\)_--absolute fit error;  
_\(mode(HM)\)_--value that appears most often in the height map values;  
_\(E(HM)\)_--expected value of height map for the surface.
# Airglow Observation and Statistical Analysis of Plasma Bubbles over China
## 5 Discussion  
Through the analysis of the main features of EPBs in Hainan, it is found that EPB exhibits plume-shaped irregularities in morphology. The spatial scale of the EPB gradually increases with time, and EPB gradually becomes wider in the east-west direction. Moreover,  
Figure 9: Seasonal statistical results of EPB occurrence over Fuke (9.9 degrees MLAT).  
the EPB scale is about hundreds of kilometers, which is similar to the results of radar observations. Optical observation results indicate that EPB exhibits the characteristics of westward tilt. Previous studies show that EPBs often tilt westward in most cases [15; 21], which may be caused by plasma drift shear [24]. With the increase in time, it is observed that a bifurcation appears at the top of the EPB, the bifurcation becomes more pronounced with time, which is similar to other events [13; 21]. Moreover, it is found that there are some bifurcations on the east wall of EPB, the EPB bifurcation is not apparent at the beginning. Afterwards, the branches of the east wall gradually spread outwards over time, the degree of bifurcation gradually increases with time, and the bifurcation phenomenon becomes more obvious. This phenomenon may be due to the instability of the east wall of EPB [14]. In terms of the studies on EPB bifurcation, [PERSON] et al. [20] investigated the generation and evolution of plasma bubbles by simulating the EPB, explained the physical mechanism for the EPB bifurcation, and conclude that the variation of the polarization electric fields inside the bubble makes an important influence on the bifurcation mechanism.  
Optical observations show that EPB has the features of eastward drift, and the drift velocity of EPB is about 40-130 m/s (+/ -20 m/s), which is close to the previous studies of the EPB drift velocity in Hainan, the drift speed of EPB is typically 50-150 m/s [21; 25]. In terms of the seasonal variation of EPB occurrence, the statistical results show that EPBs have a high occurrence rate in spring and autumn, and EPBs generally appear less in winter and summer. [PERSON] et al. reported that EPBs mainly appeared in the months of September-October and February-April [14], and the typical seasons for EPB occurrence are spring and autumn. [PERSON] et al. also revealed that EPBs tend to occur in spring and autumn [15]; our findings are consistent with their studies on the EPB occurrence.
# Top Position Sensitive Ordinal Relation Preserving Bitwise Weight for Image Retrieval
## 4 Conclusions  
In this paper, we propose a novel method dubbed as the TORBW method, which simultaneously generates hash functions and bitwise weight functions. To guarantee the performance of an ANN search task, we demand the preservation of an original ordinal relation among data points in both a Hamming space and a weighted Hamming space. We utilize a TOG to represent a relative similarity relationship between any two data pairs, which can reduce the complexity of constructing a training dataset. Different from a two-step mechanism, which separately learns binary codes and bitwise weights, TORBW adopts an iterative mechanism to simultaneously optimize them. When the algorithm converges, binary codes and bitwise weights are effectively adapted to each other. Generally, training samples are considered to be equal. In contrast, we assign a large weight value to training samples at the top position during the learning process. As a result, it can effectively reduce the probability of tied ranking orders occurring at the top position of a ranking list. Extensive experiments on three benchmark datasets demonstrate that TORBW has a better ANN search performance than many existing state-of-the-art binary coding methods and bitwise weight methods. In this paper, we adopt linear hashing functions to map data into binary codes. However, the practical dataset may be linearly inseparable. In future work, we will employ a kernel formulation for target hashing functions to further enhance the performance of preserving ordinal relations.  
**Author Contributions:** Conceptualization, Z.W. and L.Z.; methodology, F.S.; software, L.W.; validation, Z.W., F.S., and P.L.; formal analysis, Z.W.; investigation, P.L.; resources, F.S.; data curation, L.Z.; writing of the original draft preparation, Z.W.; writing of review and editing, P.L.; visualization, L.W.; supervision, F.S.; project administration, L.Z.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of Shandong Province of China (grant number: ZR2018 PF005) and the National Natural Science Foundation of China (grant number: 61841602).  
**Acknowledgments:** The authors express their gratitude to the institutions that supported this research: Shandong University of Technology (SDUT) and Jilin University (JLU).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Nonlinear Trajectory Tracking Controller for Underwater Vehicles with Shifted Center of Mass Model
## 4 Numerical Simulations
### Results Using Indexes
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**QV**} & \multicolumn{2}{c}{**CL**} \\  & \multicolumn{2}{c}{**Controller**} & \multicolumn{2}{c}{**Controller**} & \multicolumn{2}{c}{**Controller**} \\ \hline
**Index** & \multicolumn{2}{c}{**Linear t.**} & \multicolumn{2}{c}{**Complex**} & \multicolumn{2}{c}{**Complex**} & \multicolumn{2}{c}{**Linear t.**} & \multicolumn{2}{c}{**Complex**} \\  & & & **t.(1)** & **t.(2)** & & **t.(1)** \\ \hline MIAE & \(x_{e}\) & 0.3740 & 0.2981 & 0.2973 & 0.2026 & 0.3253 \\  & \(y_{e}\) & 0.1443 & 0.2393 & 0.2517 & 0.6107 & 0.4834 \\  & \(z_{e}\) & 0.1023 & 0.0356 & 0.0388 & 0.4713 & 0.4290 \\  & \(\theta_{e}\) & 0.0436 & 0.0190 & 0.0145 & 0.0179 & 0.0134 \\  & \(\psi_{e}\) & 0.0351 & 0.0478 & 0.0518 & 0.0351 & 0.0423 \\ mean & \(x_{e}\) & \(-0.3411\) & \(-0.2666\) & \(-0.2658\) & \(-0.1616\) & \(-0.1147\) \\  & \(y_{e}\) & 0.1379 & 0.2134 & 0.1941 & 0.6108 & 0.2509 \\  & \(z_{e}\) & \(-0.0407\) & 0.0266 & 0.0276 & 0.4713 & 0.4286 \\  & \(\theta_{e}\) & 0.0097 & 0.0084 & 0.0081 & 0.0052 & 0.0076 \\  & \(\psi_{e}\) & \(-0.0297\) & \(-0.0221\) & \(-0.0221\) & \(-0.0351\) & \(-0.0206\) \\ std & \(x_{e}\) & 0.9636 & 0.9303 & 0.9285 & 0.7007 & 0.9168 \\  & \(y_{e}\) & 0.3763 & 0.7915 & 0.7991 & 0.5722 & 0.9093 \\  & \(z_{e}\) & 0.1846 & 0.0564 & 0.0535 & 0.1706 & 0.1491 \\  & \(\theta_{e}\) & 0.0854 & 0.0339 & 0.0224 & 0.0403 & 0.0205 \\  & \(\psi_{e}\) & 0.0828 & 0.0967 & 0.1002 & 0.0428 & 0.0808 \\ MIAC & \(\tau_{u}\) & 403.58 & 699.42 & 699.19 & 401.48 & 700.47 \\  & \(\gamma_{q}\) & 149.48 & 1568.8 & 1249.7 & 87.612 & 1567.5 \\  & \(\tau_{t}\) & 7.2623 & 47.137 & 50.617 & 11.758 & 48.028 \\ RMS & \(||\epsilon_{t}||\) & 1.1130 & 1.2686 & 1.2685 & 1.2109 & 1.3950 \\ RMS & \(||\epsilon_{A}||\) & 0.1229 & 0.1051 & 0.1054 & 0.0684 & 0.0861 \\ mean & \(K\) & 460.55 & 1019.6 & 1019.1 & 459.50 & 1022.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance for ROPOSgular position errors, the differences in numerical values are small (also their maximum values), and from the figures, it can be seen that the final values are not necessarily worse.
###### Abstract  
Timely and continuous information about flood spatiotemporal evolution are fundamental to ensure an effective implementation of the relief and rescue operations in case of inundation events. In this framework, satellite remote sensing may provide a valuable contribution provided that robust data analysis methods are implemented and suitable data, in terms of spatial, spectral and temporal resolutions, are employed. In this paper, the Robust Satellite Techniques (RST) approach, a satellite-based differential approach, already applied at detecting flooded areas (and therefore christened RST-FLOOD) with good results on different polar orbiting optical sensors (i.e., Advanced Very High Resolution Radiometer - AVHRR - and Moderate Resolution Imaging Spectroradiometer - MODIS), has been fully implemented on time series of Suomi National Polar-orbiting Partnership (Suomi-NPP-SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) data. The flooding event affecting the Metaponto Plain in Basilicata and Puglia regions (southern Italy) in December 2013 was selected as a case study and investigated by analysing five years (only December month) of VIIRS Imagery bands at 375 m spatial resolution. The achieved results clearly indicate the potential of the proposed approach, especially when compared with a satellite-based high resolution map of flooded area, as well as with the official flood hazard map of the area and the outputs of a recent published VIIRS-based method. Both flood extent and dynamics have been recognized with good reliability during the investigated period, with only a residual 11.5% of possible false positives over an inundated area extent of about 73 km\({}^{2}\). In addition, a flooded area of about 18 km\({}^{2}\) was found outside the hazard map, suggesting it requires updating to better manage flood risk and prevent future damages. Finally, the achieved results indicate that medium-resolution optical data, if analysed with robust methodologies like RST-FLOOD, can be suitable for detecting and monitoring floods also in case of small hydrological basins.  
flood; satellite remote sensing; near real time; RST-FLOOD; SNPP; JPSS; VIIRS +
Footnote †: journal: Remote Sensing
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 3 Results  
### Dataset Acquisition  
We construct a remote sensing cage image dataset consisting of 53 images, with the training set consisting of 50 images and the test set consisting of 3 images. Through data expansion, we generate 160,000 images for the training set of \(256\times 256\) pixels and 40,000 images for the training set of \(512\times 512\) pixels. The three test set images include a dense distribution of cages, a sparse distribution of cages and a uniform distribution of cages.
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
## 4 Hybrid Engineered Water-Polymer Flooding (EWFF)
### Recovery Mechanisms
#### 4.2.4 Favorable Mobility Ratio from Polymer Flooding  
Another mechanism involved in EOR using EWPF is the improved fractional flow resulting from the use of polymer. EW alone may not be able to displace the moveable oil; however, with the addition of polymer, the formed residual oil bank can be easily displaced by the polymer solution due to stable front movement. Hence, the polymer plays a critical role and must be compatible with the EW designed for the reservoir. Fortunately, EWPF harnesses the supplementary benefits of enhanced polymer stability and decreased retention in the presence of EW, leading to higher incremental oil recovery rates and cost savings by reducing polymer consumption. As a result, the hybrid EWPF EOR method can be successfully applied to carbonate reservoirs with harsh temperature and salinity conditions. However, careful design of the EW and polymer must be applied for each field, as the composition of the EW can have both positive and negative impacts on the polymer performance. The critical factors for the polymer performance in the hybrid process include the polymer viscosity, retention, degradation, and consumption. These factors and their dependence on the EW composition are discussed in the following sections.