esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### ERA5 Relative Humidity Assessment  
Figure 8a,b display the absolute deviations in relative humidity at various heights. In Figure 8a, for winter, absolute humidity deviations from 500 to 2000 m are mostly above 0%, indicating that ERA5 reanalysis data overestimate local relative humidity. Green dashed boxes in Figure 8c highlight outliers between 1000 and 2000 m, indicating poor predictions of relative humidity at these heights by ERA5. Time series in Figure S4(f1-h1) show periods of the overestimation of actual relative humidity by ERA5 reanalysis data during winter.  
Figure 7: (**a–i**) Scatter plots of temperature comparisons for January, February, and March.  
At the ground level, the majority of winter relative humidity deviation points are above 0%, indicating a slight overestimation by ERA5. Between 50 and 200 m, ERA5 slightly underestimates relative humidity. In the time series of Figure S4, the bottom layer below 100 m (Figure S4(a1-c1)) shows a distinct underestimation during dust storms from January 7 to 17, indicated by purple coloring. Above 1000 m, ERA5 overestimates relative humidity in winter, shown in Figure S4(f1-h1), while at 3000 m, ERA5 underestimates it, as shown by purple outliers in Figure S4(j1).  
In Figure 8c, ERA5 reanalysis data underestimate actual relative humidity at 3000 m in winter. Similarly, in summer, Figure 8d shows that ERA5 underestimates relative humidity at 3000 m.  
In Figure 8b, for summer, absolute humidity deviations from 500 to 2000 m are mostly above 0%, indicating overestimation by ERA5. Between 50 and 200 m, ERA5 slightly underestimates relative humidity. In the time series of Figure S4(o1-q1), many red points indicate the significant overestimation of relative humidity by ERA5 reanalysis data between 1000 and 2000 m in summer. At 3000 m, ERA5 underestimates relative humidity, as shown by the purple coloring in Figure S4(r1).  
Overall, ERA5 reanalysis data overestimate relative humidity between 1000 and 2000 m, while underestimating it at 3000 m.  
Figure 8: Absolute and relative deviations of relative humidity at various heights. (**a**) Absolute deviations of relative humidity (ERA5 reanalysis data minus MWR observations) for January, February, and March; (**b**) absolute deviations for July and August; (**c**) relative deviations (ERA5 reanalysis data minus MWR observations, divided by MWR observations) for January, February, and March; (**d**) relative deviations for July and August. Red circles and blue dots indicate the distribution of deviation data, with deeper colors representing denser areas. Blue dots represent outliers, and the light green box plot shows the interquartile range (75 th percentile, median, and 25 th percentile).
## 3 Results
### Verification  
To verify the retrieval results of our experiments, SFM algorithm inversion was used in this study. Figure 5 shows the results of the SFM algorithm and the corresponding SIF  
Figure 4: The frequency distribution of SIF values in different spectral regions: (**a**) NIR and (**b**) R-NIR.  
Figure 3: PCA retrieval results with different spectral regions: (**a**) R-NIR and (**b**) NIR.  
frequency distribution diagram in NIR, whereas Figure 6 shows the SIF correlation analysis of both the SFM and PCA algorithms.  
Figure 5: SIF retrieval based on the SFM algorithm: (**a**) SFM retrieval results and (**b**) the frequency distribution of SIF values.  
In terms of numerical verification (as shown in Figure 5), the SIF values of the SFM algorithm fell around 4 mW\({}^{-2}\)sr\({}^{-1}\)nm\({}^{-1}\), which was close to the NIR retrieval values in Figure 4. It is worth pointing out that the SFM approach combines both the characteristics of the physical model and the data-driven algorithm, leading to more reliable results. However, the calculation process used the same irradiance data, and thus errors might still accrete to some extent that cannot be ruled out.  
Besides that, this study also randomly selected 3000 pixels in the image and performed a least squares fitting analysis of the corresponding SFM algorithm and PCA algorithm values. The diagram is shown in Figure 6. It is clear that the results had a certain correlation (\(R^{2}\) = 0.3426), which proves that the SIF trends of the two algorithms were consistent, although the actual values in the corresponding pixels were different.  
This research also raises three questions worthy of further discussion. First, the band regions can be further refined. In this study, through controlled band regions, NIR band inversion performed better than the results of R-NIR inversion by the comparison of SIF value distribution. However, the spatial resolution of the hyperspectral data was 1 nm, whereas the near-infrared band selected in the study covered 30 bands. Neighboring bands may still cause information redundancy. Therefore, more in-depth research on band selection needs to be further explored. Another question is how to improve the verification method, which is necessary. The verification method in this study can only be determined from a comparison perspective and not an exact measurement of SIF values. This also gives further difficulties for determining the band regions. Finally, after uncovering the PCA parameters' influence toward the SIF retrieval results in this study, it is indispensable to confirm its transferability to a broader spatial and dataset extent.
# Augmented Reality in Surgery: A Scoping Review
## 4 Results
### Spinal Surgeries  
AR is often used in spinal surgeries [68; 69]. In this application, the accuracy is fundamental since an imprecision in the placement of an instrument can lead to spinal cord, nerve root or vascular injuries [70]. Open methods and direct visualization supporting the placement of the instrumentation, such as pedicle screw, have historically characterized this type of surgery [70]. The use of AR in spine surgery dates back to 1997 when [PERSON] and his team developed a system for visualizing a verteb during surgery [71]. For the past 20 years, Minimally Invasive Surgery (MIS) has been under investigation. Many articles have targeted study of Minimally Invasive Surgery (MIS) over the past 20 years. This has led to the introduction of new approaches such as the inoperative navigation that introduces several advantages to visualizing anatomy and precisely guiding surgeries. Furthermore, MIS ensures a higher level of accuracy, while minimizing possible damage to contiguous structures, providing access to deeper ones and improving dynamics and logistics in the operating room. The union of AR and MIS allows the surgeon to see more accurately inside the patient, possibly visualizing the preoperative planned drilling trajectory over the display, ensuring advantages in terms of accuracy, reduction of radiation exposure, blood loss and hospital stay. The drawbacks are mainly related to high costs and to the steepness of the learning curve, still too high [72].
## 6 Conclusions  
In this paper, the calculation formulas of the stability factor under the four arc slip surface of a filled-slope reinforced by a frame with prestressed anchor-plates are derived by using the improved Bishop method, the corresponding search method of the most dangerous slip surface is given, and the calculation formulas of the pullout force of anchor-plates is improved. Based on two examples, the stability results calculated by the proposed algorithm are compared with those calculated by PLAXIS 3D and GeoStudio 2012 finite element software, and the following conclusions are drawn:  
1. Compared with the original calculation method of the pullout force of the anchor-plate, the pullout force of the improved anchor-plate takes into account the friction of the front and rear surface of the anchor-plate and the effect of fill cohesion in the passive earth pressure on the front end of the anchor-plate, which makes the force of the anchor-plate more complete. At the same time, it also makes the calculation theory of pullout force of anchor-plates more applicable.
2. The stability factor of example 1 calculated by this method differs from the results simulated by PLAXIS 3D and GeoStudio 2012 finite element software by 4.6% and 7.1%, respectively, the stability factor of example 2 calculated by this method differs from the results simulated by PLAXIS3D and GeoStudio 2012 finite element software by 3.2% and 4.5%, respectively, which can meet the engineering requirements.
3. The stability analysis method of a filled-slope reinforced by a frame with prestressed anchor-plates proposed in this paper is reasonable and suitable for the case of arbitrary arc slip surface in the filled-slope reinforced by a frame with prestressed anchor-plates, and it provides some guiding values for the design of practical engineering.  
Conceptualization: S.Y.; data curation: J.Z.; formal analysis: J.Z., W.L. and S.Y.; investigation: J.Z., W.L. and S.Y.; methodology: J.Z., W.L. and S.Y.; software: J.Z.; writing--original draft: J.Z. and W.L.; writing--review and editing: [PERSON], W.L. and S.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (No. 52168050).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** The authors listed in this paper all agree to publish this paper.  
**Data Availability Statement:** The data of simulation and theoretical calculation in the article are not freely available due to legal concerns and commercial confidentiality. Nevertheless, all the concepts and procedures are explained in the presented research and parts of the research may be available upon request.  
**Acknowledgments:** The authors of the present work feel grateful and would like to thank Lanzhou University of Technology, GanSu CSCEC Municipal Engineering Investigation And Design Institute Co., Ltd., National Natural Science Foundation of China, for providing financial and technical support in research.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.
## 3 Results and Discussion
### Daily Energy Loss
#### 3.4.3 Diffuse Component  
Figure 11 shows the diffuse component in the current scenario (the red line), in the scenario \(\beta_{\max}=\pm 55\) (\({}^{\circ}\)) (the blue line), and in the scenario \(\beta_{\max}=\pm 50\) (\({}^{\circ}\)) (the green line). For this, Equation (2) was used.  
The following conclusions can be drawn from Figure 11:  
1. If the diffuse component is predominant, the longer the duration of operation in static mode, the worse the performance of the current scenario. Since the current scenario starts the normal tracking mode first, the rest of the scenarios follow in the horizontal position that favours the diffuse component.
2. During the static operation mode, on day \(n=80\), the Miraflores \(PV\) power plant has the smallest diffuse component. This favours the current scenario. In contrast, the Canredondo \(PV\) power plant has the highest diffuse component. This fact favours a scenario that is different from the current one.
3. During the static operation mode, on day \(n=172\), the Miraflores \(PV\) power plant has the smallest diffuse component. This fact favours the current scenario. In contrast, the Canredondo \(PV\) power plant has the highest diffuse component. This fact favours a scenario that is different from the current one.  
Figure 11: Diffuse component in static operation mode.  
It can be concluded that the high values of the beam component favour the current scenario. In contrast, high values of the diffuse component favour scenarios that are different from the current scenario.
## 4 Results  
In the course of SBAS-InSAR data processing, 64 Sentinel-1A SAR images covering the study area were used to monitor mining-induced surface subsidence in the Wanli mining area. This analysis provided detailed insights into the deformation along the satellite's line of sight (LOS), including the locations of subsidence areas, the annual average deformation velocity, and the time series of subsidence events.
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 2 Materials and Methods
### Methods
#### 2.2.4 Loss Function  
Since the proposed UniInterNet simultaneously predicts the speech and noise signals during the training process, the total loss function \(\mathcal{L}\) comprises two components as follows:  
\[\mathcal{L}=\mathcal{L}_{s}+\mathcal{L}_{z} \tag{12}\]  
where \(\mathcal{L}_{s}\) and \(\mathcal{L}_{z}\) represent the speech loss and noise loss, respectively. Furthermore, both \(\mathcal{L}_{s}\) and \(\mathcal{L}_{z}\) are hybrid losses. Their definitions are follows:  
\[\mathcal{L}_{s}=\left\|s,s\right\|_{1}+\left\|M_{s},\hat{M}_{s}\right\|_{F}^{2} \tag{13}\]  
\[\mathcal{L}_{z}=\left\|z,\hat{z}\right\|_{1}+\left\|M_{z},\hat{M}_{z}\right\|_ {F}^{2} \tag{14}\]  
where \(\left\|\cdot,\cdot\right\|_{1}\) denotes the L1 loss between the target and predicted time domain waveforms, and \(\left\|\cdot,\cdot\right\|_{F}^{2}\) represents the mean square error (MSE) loss between the target and predicted TF domain masks.
# Measuring Ocean Surface Current in the Kuroshio Region Using Gaofen-3 SAR Data
## 2 Measurement of the Ocean Surface Current
### Ocean Doppler Information of Single Antenna SAR Data
In some studies [4; 21; 22], the empirical model derived from actual data fitting can be used to remove the motion induced by wind and waves, including the effect of orbital speed. This processing method is in good agreement with the actual situation, but is limited by different sensor data and sea surface position, the measurement accuracy will be different. [PERSON] et al. [23] also pointed out that the influence of orbital velocity can be suppressed by space averaging. This operation is simple, but the residual error will be left. Motion errors will affect SAR imaging and sea surface current field estimation. Reference [24] states that motion errors decrease the image quality in the course of imaging. In principle, the uncertainty caused by motion errors can be corrected with the motion measurement data obtained from the global positioning system (GPS) or other ancillary instrument. In Reference [24], a deep SAR motion compensation scheme is proposed to be combined with the deep SAR imaging algorithm, which could eliminate the influence of motion errors and improve SAR imagery quality in practical applications. Reference [25] also discusses the influence of motion errors on imaging, and points out that classical motion compensation is typically not required for spaceborne systems due to their stable and homogeneous motion. The Gaofen-3 satellite adopts a dual frequency GPS system to realize precise orbit determination, which has high attitude control accuracy and stability [26]. Therefore, the motion errors have a limited impact on the imaging results. For the application of current measurements, the influence of motion and attitude errors on the predicted Doppler shift is mainly considered, as shown in Reference [8]. Due to errors in satellite position and attitude used to predict the Doppler shift, the ocean current product will deviate. After attitude control, the Gaofen-3 satellite eliminates the change of Doppler center frequency caused by earth rotation, earth ellipticity and satellite orbit oblateness [26]. Therefore, the predicted Doppler shift of SAR data is close to zero. If the azimuth angle error \(\triangle\varphi\) is considered in current measurements with actual data, the Doppler frequency caused by platform motion will have errors [27]. Therefore, Equation (8) can be obtained based on Equation (4), and the Doppler frequency caused by current becomes Equation (9):  
\[ \begin{split}\triangle f_{a}&=\frac{2v_{p}}{\lambda }[\cos(\varphi+\triangle\varphi)\sin\theta-\cos\varphi\sin\theta]\\ &\approx-\frac{2}{\lambda}v_{p}\triangle\varphi\sin\varphi\sin \theta\\ \end{split}  \tag{8}\]  
\[ \begin{split} f^{\prime}_{c}&=\frac{2\sin\theta}{ \lambda}\big{[}u_{x}\cos(\varphi+\Delta\varphi)+u_{y}\sin(\varphi+\Delta \varphi)\big{]}\\ &\approx\frac{2\sin\theta}{\lambda}\big{[}\big{(}u_{x}+\Delta \varphi u_{y}\big{)}\cos\varphi+\big{(}u_{y}-\Delta\varphi u_{x}\big{)}\sin \varphi\big{]}.\end{split}  \tag{9}\]
## 1 Introduction
The main contributions of this paper are as follows:  
* This paper presents an innovative strategy, distributed dynamic optimization approach (DDOA), to provide alternative trajectories for each UAV, by adding some essential constraints (i.e., performance, cost and distance) to make the problem treatable while the feasible trajectory generation for multi-UAV is a non-deterministic polynomial (NP) problem. * The novel causal analysis is developed to choose a preferred resolution trajectory, performing an analysis of scenario state space in order to explore the dynamic evolution and then determine all reachable states. By decomposing the complex scenario into several common clusters, the solution space is greatly reduced. * Quantitative measurement experiments are carried out to validate the feasibility and scalability of the CA system for the free flight of multi-UAV. In addition, the average computation time does not grow exponentially as the airspace density increases. To prevent mid-air collisions between UAVs, the proposed CA system has been developed to serve as the last-resort safety net, which is an on-board CDR system providing resolution advisories. This paper is organized as follows: Section 2 illustrates the CA system functional architecture; Section 3 presents the core algorithm of the CD and CR sub-modules; Section 4 achieves the simulation results using the proposed system; finally, conclusions and future work are summarized in Section 5.
# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
###### Abstract  
The comprehensive use of high-resolution remote sensing (HRS) images and deep learning (DL) methods can be used to further accurate urban green space (UGS) mapping. However, in the process of UGS segmentation, most of the current DL methods focus on the improvement of the model structure and ignore the spectral information of HRS images. In this paper, a multiscale attention feature aggregation network (MAFANet) incorporating feature engineering was proposed to achieve segmentation of UGS from HRS images (GaoFen-2, GF-2). By constructing a new decoder block, a bilateral feature extraction module, and a multiscale pooling attention module, MAFANet enhanced the edge feature extraction of UGS and improved segmentation accuracy. By incorporating feature engineering, including false color image and the Normalized Difference Vegetation Index (NDVI), MAFANet further distinguished UGS boundaries. The UGS labeled datasets, i.e., UGS-1 and UGS-2, were built using GF-2. Meanwhile, comparison experiments with other DL methods are conducted on UGS-1 and UGS-2 to test the robustness of the MAFANet network. We found the mean Intersection over Union (MIOU) of the MAFANet network on the UGS-1 and UGS-2 datasets was 72.15% and 74.64%, respectively; outperforming other existing DL methods. In addition, by incorporating false color image in UGS-1, the MIOU of MAFANet was improved from 72.15% to 74.64%; by incorporating vegetation index (NDVI) in UGS-1, the MIOU of MAFANet was improved from 72.15% to 74.09%; and by incorporating false color image and the vegetation index (NDVI) in UGS-1, the MIOU of MAFANet was improved from 72.15% to 74.73%. Our experimental results demonstrated that the proposed MAFANet incorporating feature engineering (false color image and NDVI) outperforms the state-of-the-art (SOTA) methods in UGS segmentation, and the false color image feature is better than the vegetation index (NDVI) for enhancing green space information representation. This study provided a practical solution for UGS segmentation and promoted UGS mapping.  
urban green space; deep learning; high-resolution remote sensing images; multiscale pooling attention; feature engineering Article
## 1 Introduction  
One of the primary challenges encountered in the development of three-dimensional (3D) displays is image brightness. Even under application of current stereoscopic techniques, viewers experience a certain amount of image brightness degradation when viewing 3D images in cinemas or on stereoscopic televisions. Regardless of whether the images are recorded with half-mirror or double-parallel type filming equipment, the brightness is impaired. Both polarizing film group displays and the viewing of 3D images with 3D glasses cause brightness degradation. 3D displays must send separate images to the right and left eyes. However, image brightness loss occurs as a result of crosstalk, which manifests in the case of an overly high contrast between the images. If the brightness is increased to compensate for the 3D image brightness loss in stereoscopy only, spectators will not have the same visual experience as with two-dimensional (2D) images. Furthermore, some of the light is scattered when spectators wear 3D glasses; only half the light intensity from the screen is reflected upon the eyes, and the image brightness is reduced by 30%-50%. Clearly, the image brightness in 3D stereoscopic images is lower than that in 2D images during film playback. In addition, some spectators have strong antipathy towards 3D films, as a result of feelings of dizziness and other uncomfortable physiological phenomena [1; 2; 3; 4; 5].  
Because the inter-ocular disparities between human eyes are 5-6.5 cm, the resulting perspective is slightly different when both retinas receive the same images. This difference is also called visual disparity. Rods and cones are the retinal cells that control visual signals; they are responsible for converting the brightness of light, color, and other information into optic nerve messages in the brain. Fusing of the two different perspectives generates depth perception, so that human eyes can discern 3D objects visually [6; 7]. This study focuses on whether the 3D images in cinemas or on stereoscopic television can be adjusted for decreased 3D light degradation.
## 5 Results and Discussion
### Model Performance Validation  
To further validate the robustness and generalizability of the optimized model, four subsets corresponding to references [43; 44; 45; 46] are used to evaluate the performance of the GBDT model. Figure 11 presents the regression graphs of the GBDT model using different subsets. As shown in Figure 11a, most points are closely clustered around the diagonal, suggesting that the GBDT can effectively capture the relationship between the input features and thermal conductivity. Similarly, the points are well aligned with the diagonal line (see Figure 11b). It means that the GBDT model maintains its high accuracy and reliability. As shown in Figure 11c, there is a modest increase in the dispersion of points around the diagonal, but the GBDT model still performs well overall. This increased dispersion is primarily due to the limited data available in this subset. It can be seen from Figure 11d that the GBDT model also demonstrates a satisfactory performance (MSE: 7.8287 \(\times\) 10\({}^{-6}\), RMSE: 0.0028; R\({}^{2}\): 0.9963, MAE: 0.0017, and MAPE: 0.55%). The points are tightly clustered along the diagonal, reflecting the excellent predictive accuracy of the GBDT model.  
To further validate the model generalizability, the best model (i.e., GBDT) is employed to predict a new validation dataset, sourced from a published study [47]. The errors between the predicted and actual values are listed in Table 7. The results illustrate that GBDT exhibits a high degree of accuracy on the validation dataset, with errors predominantly minor and within permissible limits. Specifically, the absolute errors range with a minimum of 0.0001, a maximum of 0.0277, and an average of 0.0145. Overall, the GBDT model can be considered as a reliable and robust model for predicting the thermal conductivity of EG-Al\({}_{2}\)O\({}_{3}\) nanofluids in this study.  
Figure 10: Taylor diagram of all ML models in the testing phase.