esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Consistency of Precise Products from Different Analysis Centers  
In this section, the consistency (characterized by satellite systems) among the ESA, GFZ and WHU post-processed precise products as well as the CNES real-time precise products in terms of the satellite orbits in three dimensions and the satellite clocks is analyzed. For completeness, the Signal-in-Space Ranging Error (SISRE), which can comprehensively reflect the effects of satellite orbits and clocks, is also introduced. The calculation of SISRE was well described in [PERSON] et al. [39]. The analysis period here covers a time span of about a month (31 days) from DOY 12 to 152 in 2020. The 31-day data are first divided into many sub-sets (one day from GPS time 00:00:00 to 23:59:30 or 23:59:55 for each sub-set). The epoch-wise orbit and clock difference among different analysis centers are then obtained using the single-day data. Finally, all the single-day sub-sets of epoch-wise orbit and clock difference are used together to compute the statistics of product inconsistency. In addition, the epoch-wise consistency results of precise products between GFZ and WHU over a day are also detailed as an illustration, since they are the most comprehensive products in support of all the available satellite constellations.
# Characterizing Micro-Displacements on Active Faults in the Gobi Desert with Time-Series InSAR
## 4 Results and Discussion
### The Structural Geology of the Jiujing Region
#### 4.2.6 F\({}_{\rm m}\), F\({}_{\rm n}\), and F\({}_{\rm o}\) as Reverse Faults  
Within the west side of F\({}_{\rm m}\), Figure 7 shows that JJ30 and JJ29 are displacing towards the east by 0.21 and 0.20 mm a\({}^{-1}\) and JJ22 and JJ20 are displacing towards the west by 0.01 and 0.20 mm a\({}^{-1}\), respectively. Meanwhile, as mentioned above, JJ16, JJ17, JJ18, and JJ19 on the east side of F\({}_{\rm m}\) are displacing westward by 0.10 to 0.54 mm a\({}^{-1}\). Therefore, F\({}_{\rm m}\) has been interpreted as a reverse fault due to the crustal convergence along it. Figure 7 also shows that both JJ20 and JJ19 are moving westward but JJ19 is moving at an average rate of 0.54 mm a\({}^{-1}\), which is higher than that of JJ20, moving at an average rate of 0.20 mm a\({}^{-1}\). This generates a crustal shortening along F\({}_{\rm n}\) and may be interpreted as a reverse fault.  
Vertically, JJ16, JJ17, and JJ19 on the eastern side of F\({}_{\rm m}\) and F\({}_{\rm n}\) are uplifting by 0.16 to 0.35 mm a\({}^{-1}\), whilst JJ30, JJ29, and JJ22 on the western side of F\({}_{\rm m}\) is subsiding by 0.30, 0.29, and 0.41 mm a\({}^{-1}\), respectively. JJ20 is uplifting but the rate of uplift (0.16 mm a\({}^{-1}\)) is lower than its eastern counterpart JJ19, which is uplifting at 0.35 mm a\({}^{-1}\). These all indicate that the western side blocks are generally moving downwards relative to the eastern side fault blocks along F\({}_{\rm m}\) and F\({}_{\rm n}\), suggesting F\({}_{\rm m}\) and F\({}_{\rm n}\) are east-dipping reverse faults, where blocks JJ16, JJ17, and JJ19 are part of the hanging wall and JJ30, JJ29, and JJ22 are the footwall.  
F\({}_{\rm o}\) is another east-dipping reverse fault generated by the convergence of JJ20 and JJ21 shown in Figures 6 and 7, where the east side (JJ20) is displacing towards the west by 0.20 mm a\({}^{-1}\) and is uplifting by 0.16 mm a\({}^{-1}\), and the western side of the fault (JJ21) is displacing towards the east by 0.05 mm a\({}^{-1}\) and subsiding by 0.03 mm a\({}^{-1}\). This kind of horizontal crustal shortening indicates F\({}_{\rm o}\) is a reverse fault, and the uplift of JJ20 relative to JJ21 indicates that JJ20 is part of the hanging wall and F\({}_{\rm o}\) is dipping towards the east.  
In general, due to the impacts of F\({}_{\rm m}\), F\({}_{\rm n}\), and F\({}_{\rm o}\), JJ21 is subsiding at 0.03 mm a\({}^{-1}\) whilst JJ20 is uplifting at 0.16 mm a\({}^{-1}\) and JJ19 has the greatest rate of uplift at 0.35 mm a\({}^{-1}\). This shows that the average time-series analyses at JJ19, JJ20, and JJ21 (cross-section CC', Figure 6) have a stepped vertical displacement pattern with a gradual uplift from west to east. These has been illustrated in Figure 9e,f and Figure 12, respectively.
# A BDGIM-Based Phase-Smoothed Pseudorange Algorithm for BDS-3 High-Precision Time Transfer
## 3 Dataset and Processing Strategies for SF SPP Time Transfer  
In this work, we collect observations from six MGEX stations, all of which can receive the signals of BDS-3. The dataset covers a five-day period from 27 March to 31 March 2022 (DOY 86-90, 2022). The broadcast ephemeris is provided by the China Satellite Navigation Office (CSNO) ([[ftp://ftp2.csno-tarc.cn/cnav/](ftp://ftp2.csno-tarc.cn/cnav/)]([ftp://ftp2.csno-tarc.cn/cnav/](ftp://ftp2.csno-tarc.cn/cnav/)), accessed on 10 September 2022), and the GIM ionosphere products are downloaded from CODE. Figure 2 displays the distribution of the six stations. The information of the six stations is listed in Table 1. Note that the USUD is taken as the central node for time transfer, and five time links, namely, BRUX-USUD, LCK3-USUD, CUSV-USUD, STR1-USUD, and HOB2-USUD, are designed. Table 2 summarizes the processing strategies of SF SPP. The BDS-3 B1I signal is adopted to implement SF PPP time transfer with five consecutive days of ephemeris data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Station** & **Receiver** & **Antenna** & **Clock** \\ \hline BRUX & SEPT POLARX5 TR & JAVRINGANT\_DM & IMASER 3000 \\ HOB2 & SEPT POLARX5 & LEIAR25.R4 & H-MASER \\ USUD & SEPT POLARX5 & AOAD/M\_T & H-MASER \\ STR1 & SEPT POLARX5 & ASH701945\_C\_M & CESIUM \\ LCK3 & TRIMBLE ALLOY & LEIAR25.R3 & H-MASER \\ CUSV & JAVAD TRE\_3 DELTA & JAVRINGANT\_DM & INTERNAL \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the six stations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Strategy** \\ \hline Observations & BDS-3 B1I \\ Cut-off elevation & 10\({}^{\circ}\) \\ Sampling rate & 30 s \\ Relativistic effect & Model corrected [26] \\ Sagnac effect & Model corrected [26] \\ Tropospheric delay & \begin{tabular}{c} Hydrostatic delay: saas model [27] \\ Wet delay: estimated+GMF [28] \\ \end{tabular} \\ PCO & \
\begin{tabular}{c} Igs14.arc \\ Model corrected [26] \\ \end{tabular} \\ Observation weight &
\begin{tabular}{c} \(\sin^{2}(E)/k\) \\ \end{tabular} \\ Filter & Weighted least squares \\ Receiver clock & Estimated \\ \hline \hline \end{tabular}
\end{table}
Table 2: Strategies for SF SPP.  
Figure 2: Location of the six stations.
# Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images
## 4 Experiment
### Data Sets  
There are two types of widely used fundus images: posterior and wide-angle fundus images. A posterior fundus image can clearly show the fundus because it is generated by a white light source [51]. However, it can only display the fundus in a narrow FOV [52]. Wide-angle fundus images can demonstrate the fundus in a wide FOV, but their quality is lower than that of posterior fundus images because wide-angle fundus images are only generated by red and green light sources. Since posterior fundus images have a history of more than 150 years [53], there are many public posterior-fundus-image datasets, such as MESSIDOR [54], ORIGA-light [11], DRIONS-DB [55], and DRISHTI-GS [13]. Because of the high resolution and reliable ground truth (GT), DRISHTI-GS was applied in this paper.  
**DRISHTI-GS**[13]: a widely used public dataset in OD segmentation that contains 101 posterior fundus images (Figure 19). Each fundus image is centered on the OD, and the FOV is 30\({}^{\circ}\). The resolution ranges from 2047 \(\times\) 1745 to 2468 \(\times\) 1762. The GT for OD and OC boundaries was marked by four clinicians with 3, 5, 9, and 20 years of clinical experience.  
**Tianjin Medical University Eye Hospital (TMUEH): currently, there is no public wide-angle fundus image dataset, so some fundus images were required from TMUEH. It is a non-public dataset. It contains 37 wide-angle fundus images (Figure 20). Each fundus image is centered on the fovea, and the FOV is 200\({}^{\circ}\). The resolution is 3900 \(\times\) 3072. The OD GT was marked by a clinician with 17 years of clinical experience.**  
Figure 19: An example of the DRSHTI-GS dataset. (left to right) Original fundus image, GT soft map, and the GT used in this paper (In a soft map, the pixel value of each annotation is 0.25. The part with a pixel value greater than or equal to 0.75 was used as the GT).
## 3 Experiment Results and Analysis
### Performance Analysis
However, this approach fails to exploit the large uniform blocks in the MSB bit planes, resulting in an augmented amount of the auxiliary information. The obtained ERs by our method for the five images are better than those achieved by previous methods. For the Lena image the ER is 3.606 bpp, which is approximately 26% better than the best result obtained with the [10] method. By mapping the prediction errors,  
Figure 19: The comparison of ER (in bpp) for the five test images includes [PERSON] et al. [12], [PERSON] et al. [16], [PERSON] et al. [19], [PERSON] el al. [8], [PERSON] et al. [10], and Proposed method. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Image** & **Total EC (bits)** & **Auxiliary Information (bits)** & **Net Payload (bpp)** \\ \hline Lena & 1,075,378 & 129,984 & 3.606 \\ Baboon & 668,056 & 169,152 & 1.903 \\ Jetplane & 1,084,546 & 129,808 & 3.642 \\ Man & 976,450 & 174,776 & 3.058 \\ Tiffany & 876,568 & 110,872 & 2.920 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The embedding capacity and the auxiliary information of five test images by using the GAP predictor. using the quadtree decomposition in order to find as many large homogenous blocks as possible and by reordering the embeddable bit planes, we achieved a significantly higher ERs than those obtained with state-of-the-art methods. Finally, Table 4 show the ERs of our proposed method and compares them with state-of-the-art methods on the two image datasets, respectively. The average ERs for the image datasets BOSSbase and BOWS-2 are 3.813 bpp and 3.696 bpp, respectively. In addition, it can be seen from Table 4 that our proposed method has a higher ER than the other methods. In Table 4, \"Gain\" represents the percentage improvement in ER achieved by our approach when compared to other methods. In comparison to the most effective method of [PERSON] et al. 's [10], our results exhibit an improvement of up to 9% and 8.9% for BOSSbase and BOWS-2 datasets, respectively. Comparatively larger improvements are observed when our results are compared to the other methods.
# Vascular Structure Identification in Intraoperative 3D Contrast-Enhanced Ultrasound Data
###### Abstract  
In this paper, a method of vascular structure identification in intraoperative 3D Contrast-Enhanced Ultrasound (CEUS) data is presented. Ultrasound imaging is commonly used in brain tumor surgery to investigate in real time the current status of cerebral structures. The use of an ultrasound contrast agent enables to highlight tumor tissue, but also surrounding blood vessels. However, these structures can be used as landmarks to estimate and correct the brain shift. This work proposes an alternative method for extracting small vascular segments close to the tumor as landmark. The patient image dataset involved in brain tumor operations includes preoperative contrast T1 MR (cT1 MR) data and 3D intraoperative contrast enhanced ultrasound data acquired before (3D-iCEUS\({}_{start}\)) and after (3D-iCEUS\({}_{end}\)) tumor resection. Based on rigid registration techniques, a preselected vascular segment in cT1 MR is searched in 3D-iCEUS\({}_{start}\) and 3D-iCEUS\({}_{end}\) data. The method was validated by using three similarity measures (Normalized Gradient Field, Normalized Mutual Information and Normalized Cross Correlation). Tests were performed on data obtained from ten patients overcoming a brain tumor operation and it succeeded in nine cases. Despite the small size of the vascular structures, the artifacts in the ultrasound images and the brain tissue deformations, blood vessels were successfully identified.  
cT1 MR; 3D-iCEUS; neurosurgery; vascular structure identification Vol. 1, 2016
# Implementing and Testing a U-Space System: Lessons Learnt
## 3 The U-Space Services
### Airspace Management/Geo-awareness  
* **Drone Aeronautical Information Management Service.** The purpose of this service is to collect and supply the real-time information that can affect each flight. In this project, since no other sources of information were available, it is only in charge of keeping the map of existing geo-fences, whether static or dynamic. The definition of the static geo-fences was kept in a file in the server. Dynamic ones could be created/deleted using the API or the Web app, and have an associated duration, name, description and a bottom and/or top altitude (we could have floating geo-fences). The duration could be specified as a number of minutes since its creation or as a date timespan, which is useful to define temporary corridors for priority flights (Figure 8). In principle, only allowed users should be able to create dynamic geo-fences, but for research purposes, like testing the tactical deconfliction service, any user was allowed to create them.
* **Geo-awareness Service.** When a user logs in, they receive all active geo-fences that can be seen while designing a flight plan. Users may also receive notifications of new geofences created or terminated. Dynamic geo-fences were displayed in the Web app in yellow (Figure 9). That way, operators could distinguish them from the static ones and click on them to see the expected time duration. Apart from its informative role, this service also monitors that the UAVs comply with geo-fence avoidance or that they do not leave a geo-cage (Figure 10). If this happens, pilots receive a warning (Figure 11), and tactical deconfliction is triggered if necessary. Geo-cage management was included to support the need of some final users to fly manually inside a segregated area with no predefined trajectory. Inside a geo-cage, the monitoring is addressed to verify that the drone does not get out of it unexpectedly. In geo-cages, a friendly warning is first sent if the UAV becomes close to its limits. This service could include the Geo-fence Provision Service, to directly inform the UAV of any existing or new geo-fence, but the drones used in the project were not able to process this information.  
Figure 8: Menu to create a dynamic geo-fence.  
Figure 9: Static (gray) and dynamic (yellow) geo-fences.
## 3 Model Problems and Numerical Results
### Numerical Results for Model Problem 1
#### 3.2.2 Number of Samples  
Figure 17 shows the total number of samples over the different levels for a user requested tolerance on the RMSE of \(2\times 10^{-5}\) for a moderate and a high stochastic dimension, for the different methods. The number of samples is decreasing as the level \(\ell\) increases. Samples on lower levels are computationally less expensive. It is therefore advantageous to have a high number of samples on lower levels. Also, it can be seen that the sample sizes for MLQMC are lower than for MLMC. This is because the MLQMC sample points are chosen deterministically in an optimal way. This will result in a lower total computation time for MLQMC. The number of samples for p-ML(Q)MC is lower than for its respective h-ML(Q)MC counterpart. The origin of this effect is to be found in a lower value of \(\mathbb{V}\left[\Delta P_{\ell}\right]\). We also see that for the high stochastic dimension, h-ML(Q)MC needs a total of 5 levels to satisfy its bias condition. This is to be compared with p-ML(Q)MC, which only needs a total of 4 levels, regardless of the considered stochastic dimension. Also observe that the samples for h-MC, p-MC, h-QMC and p-QMC are located on the finest level.
## 3 Observations
### Digisonde Measurements
#### 3.1.2 Auroral Conditions  
The appearance of auroral layers on mid- and low-midlatitude ionograms makes this event exceptional. The equatorward shift of the auroral oval (AO) is evident in ionograms (Figure 3), including the first-ever recorded auroral sporadic E at SO. Auroral F-layer intervals, typically seen at high latitudes, were observed from 20:30 to 04:45 UT at SO and 22:07:30-03:52:30 UT at exceptionally low latitude, at VT on the night of 10/11 May (green bars in Figure 2a-d).  
Using the four cross-loop receiver antennas of the Digisonde systems, the direction (vertical and azimuthal) and polarization of the echoes were analyzed [29; 49; 50]. At 20:30 UT, SO ionograms indicated auroral activity in the F-layer, with oblique spread reflections from north-northeast (NNE, faint blue) and north-northwest (NNW, dark blue) directions (Figure 3a,b). At this time, only a reddish aurora, with an elevation angle of \(\sim\)46\({}^{\circ}\), was visible with optical all-sky cameras from Szombathely, Hungary (Figure 3c), suggesting the southern edge of the oval may have crossed 49.45\({}^{\circ}\)N mlat, the Prague-Krakow line (the detailed calculation steps are in Appendix B section and see also [51; 52]). By 22:15 UT, a green aurora was also observed from Doba, Hungary (see Figure A9b), with an elevation angle of \(\sim\)40\({}^{\circ}\) (corresponding to the latitude of Brno, 48.27\({}^{\circ}\)N mlat, map of Figure A10a). The red aurora reached the zenith at \(\sim\)22:30 UT, extending to \(\sim\)40\({}^{\circ}\) mlat (Figure 3f).  
Figure 3: Compilation of optical all-sky camera images (right column, (**c,f,i**)) and ionograms (first two columns (**a,b,d,e,g,h**)). Geographic and geomagnetic coordinates of the optical observation sites are shown in the subplots. Auroral reflections are marked with black frames in the ionograms. Rows 1 and 2 are for Sopron (**a–f**), and row 3 is for San Vito (**g–i**). Light/dark blue reflection points indicate NNE/NNW direction, respectively, while red points represent vertical reflections in the ionograms.  
Auroral sporadic E appeared on ionograms by this time (Figure 3d,e) as a diffuse, rising trace [53]. Reflections from auroral ionization, including spread signals and oblique echoes from NNE/NNW, confirmed that the northern lights reached SO's latitude (43.95\({}^{\circ}\)N mlat). The auroral intensity was remarkable, it was optically detected from Pesaro, Italy (22:52 UT, 39.48\({}^{\circ}\)N mlat, Figures A9c and A10) and after the horizontal distance calculation, the aurora line was above Manciano, Italy (mlat 36.14\({}^{\circ}\)N, see Appendix B section and Figure A10). San Vito's ionograms also recorded auroral ionization as spread-F and oblique reflections from NNW and NNE directions (Figure 3g,h).
## 3 Results and Discussion
### Comparisons of Different ML Models for Estimating Leaf Area of \(D\). mobile Leaves
Thus, ranking of the algorithms by average rank (AR) was chosen, whereby for each 36 ML models which includes the four ML techniques viz., MLR, SVR, GBR, and ANN ranks were assigned based on R\({}^{2}\), MAE, and RMSE. The average ranks were obtained by averaging the ranks for each model. The final rank was derived by arranging the average ranks in ascending order. Table 9 depicts a comparative statistical analysis of MLR, SVR, ANN, and GBR algorithms and assigned ranks on R\({}^{2}\), MAE, and RMSE each based on the training results and testing results to calculate the average rank. The final rank depicted that GBR7 secured the first rank among the 36 models as well as GBR algorithm has a frequency of occurrence of nearly 80% among the top 10 ML models. This provided a conclusive decision to select GBR as the best model for leaf area predictive modeling in the case of the Dendrobium orchid. Subsequent to the GBR algorithm, MLR occupied nearly 20% frequency among the top 10 ML models. Figure 7 depicts the frequency of the occurrence of the ML models studied in our experiment among the top ten ranks. It can be seen from the graph that GBR occupies 80% and MLR occupies 20% among the top ten ranks. On the other hand, SVR and ANN occupy nil percentage in the proposed rank group. Light interception by the plant is totally dependent on the two-dimensional structure viz., length (L) and width (W) [105] and thus highlighting the importance of only those two proxy variables [106; 107; 108; 109; 13; 40]. In this study, a non-destructive methodology of LA modeling in \(D\). _nobile_ through ML techniques was proposed that can be employed to develop a web portal or mobile app for providing an interactive user interface to obtain rapid, precise, and accurate leaf area estimation results from simple measurements such as leaf length (L) and leaf width (W). The proposed best model, i.e., GBR can play a crucial role in real time LA estimation of \(D\). _nobile_ avoiding the need for expensive instruments and destructive sampling procedures. Figure 7: Frequency of ML models based on top 10 ranks by AR ranking methodology.
## 5 Results and Discussion
### Structural Optimization Design
#### 5.2.4 Machine Optimization Results  
The mass of the entire machine before and after optimization was 4687 kg and 4576 kg, respectively, a reduction of 2.3%. Before optimization, the force 100 mm below the spindle nose end was 1470 N, and the reaction force of the fixture was 1470 N. After optimization, the deformation of the spindle end was 92 \(\upmu\)m, and that of the worktable was 29 \(\upmu\)m. The rigidity of the entire machine before and after optimization was 2.30 and 2.38 kg/\(\upmu\)m, respectively, increasing by 3.2%. No significant difference in natural frequency was observed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Original Design** & **Topology Optimization** & **Result Comparison** \\ \hline \hline weight 306 kg & weight 284 kg \\ \hline \hline Load 1000 N & Load 1000 N & \\ Maximum deformation 7.32 \(\upmu\)m & Maximum deformation 6.55 \(\upmu\)m \\ \hline \hline First-order frequency 510 Hz & First-order frequency 559 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 11: Head-optimization results comparison.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Original Design** & **Topology Optimization** & **Result Comparison** \\ \hline \hline Mass 545 kg & Mass 471 kg \\ \hline \hline \end{tabular}
\end{table}
Table 13: Saddle-optimization result comparison.