esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Flexural Performance of Fiber-Reinforced Geopolymer Concrete
#### 4.3.2 Load-Deflection Relationship  
Based on the results obtained from experiments, flexural performance, including flexural strength, load carrying capacity, deflection at peak load and toughness are shown in Table 10. Figure 19a-c describes the load-deflection curve of the specimens using fiber with three volume fractions in a  
Figure 18: Flexural strength.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Volume Fraction of Fiber (\%)**} & \multicolumn{4}{c}{**Flexural Strength (MPa)**} \\ \cline{2-5}  & **P1** & **P2** & **H1** & **H2** \\ \hline
0.0 & 5.89 & 5.89 & 5.89 & 5.89 \\
0.5 & 6.45 & 6.30 & 6.82 & 7.02 \\
1.0 & 7.45 & 7.17 & 8.48 & 9.78 \\
1.5 & 7.98 & 7.32 & 9.65 & 10.22 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Flexural strength.  
flexural test. All specimens perform the similar load-deflection relationship with the deflection at peak load, between the net 1/300 and 1/600 of span length, are 1-2 mm and the peak load from 21 to 34 kN.  
The series of P1-15, P2-15, H1-15 and H2-15 in Figure 19c had the highest behavior of the flexural test, which means that the loading capacity of these series were the best. The flexural performance of hooked end steel FRGC was better than polypropylene FRGC, with the highest load carrying capacity 34,070.4 N, compared with 26,611.2 N; the difference was 21.89%. The L/d ratio of the fibers also influenced the flexural behavior, with 70 outperforming 60 to hook  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen** & **Flexural Strength (MPa)** & **Deflection at Peak (mm)** & **Peak Load (N)** & **Toughness (Nm)** \\ \hline GC & 5.89 & 1.206 & 19,638.26 & 7.091 \\ P1-05 & 6.45 & 1.252 & 21,553.72 & 9.843 \\ P1-10 & 7.45 & 1.839 & 24,833.59 & 13.685 \\ P1-15 & 7.98 & 1.100 & 26,611.20 & 14.444 \\ P2-05 & 6.30 & 1.253 & 21,096.30 & 8.354 \\ P2-10 & 7.17 & 1.691 & 23,901.60 & 12.740 \\ P2-15 & 7.32 & 1.392 & 24,409.17 & 13.461 \\ H1-05 & 6.82 & 0.949 & 22,769.98 & 16.794 \\ H1-10 & 8.48 & 1.892 & 28,412.16 & 19.138 \\ H1-15 & 9.65 & 1.488 & 32,188.80 & 23.330 \\ H2-05 & 7.02 & 1.170 & 23,442.05 & 17.578 \\ H2-10 & 9.78 & 1.765 & 32,592.00 & 21.126 \\ H2-15 & 10.22 & 1.628 & 34,070.40 & 24.191 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Flexural performance.  
Figure 19: Load–deflection relationship. (**a**) At a volume fraction of 0.5%; (**b**) at a volume fraction of 1.0%; (**c**) at a volume fraction of 1.5%.  
outperforming 200 to polypropylene fiber. Because hooked end steel fiber had better properties than polypropylene fiber, such as elastic modulus, tensile strength and surface properties. Moreover, hooked end steel fiber had a special shape, different from the smooth shape of polypropylene fiber. Thus, this shape leads to the increment of the adhesion between steel fiber and materials and the improvement of strength under the impact of the load.
# Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry
## 5 Results
### Inter-Comparisons of SA-Based ASL Trends
#### 5.4.2 Sea-Level Trends of Different SA Orbit Inclination Categories
However, for some stations, both SA groups have large discrepancies, including Ventspils (#3) and Ustka (#4), which could be due to the TG estimation problems, as is also shown in Figure 7b,c.
## 5 Conclusions  
The presence of the vast ocean of sand sheets and dunes in the Great Sahara of Africa limits the ability to map, in detail, the region and understand its past landscape. The use of optical and microwave satellite data has led to the identification of a sandy buried complex crater-like structure in Northwestern Sudan. The long wavelength of radar data, in particular, enabled the penetration of desert sands and revealed the hidden Rimaal structure. The structure has a diameter of \(\sim\)10.5 km with a low topographic expression. It exhibits a clear outer rim with traces of concentric faults, an annular basin, and an inner ring surrounding remnants of an extensively-eroded central peak. The structure is most likely of an old origin (\(\leq\)65 Ma) considering the situation in Nubian Sandstone formation of Cretaceous age and the strongly eroded nature of its morphological elements. The discovery of this buried structure in the desert of Sudan demonstrates that the database of possible impact craters can be greatly improved in sandy desert regions worldwide by combining optical and radar satellite  
Figure 9: Oblique satellite image view of the (**a**) Upheaval Dome impact complex crater in Utah, USA and (**b**) the Aorounga crater in Chad (images acquired from Google Earth). The morphology of the Rimaal structure bears a distinct morphological resemblance to these impact craters, particularly with the presence of an inner ring structure.  
data through image fusion and transformation. This work shows that fusing multispectral data with microwave data of different frequencies (C and L-bands) and polarizations (HH, HV, and VV), can be of fundamental help for further in situ studies of the impact structures.  
Field visits and rock samples are still required from in and around the structure for confirming or discarding Rimaal as an impact crater. Yet, visiting the region is currently quite challenging due to the environmental inaccessibility of the area and for security reasons. If proven to be of an impact origin, the Rimaal structure could be the first complex crater ever found in Sudan and one of the few structures with a developed inner ring known so far in Africa. More importantly, if confirmed, this structure could hold promise for hosting economically valuable ore deposits, such as gold and uranium, as well as hydrocarbon resources, including oil and natural gas, for the benefit of the nation of Sudan.  
The author conducted all the research work solely, including the detection of the previously-unknown buried circular structure, image processing, analyzing the data, obtaining the results, and writing the manuscript.  
The author would like to express her appreciation for the anonymous reviewers and editors, whose comments have helped to improve the overall quality of this paper.  
The authors declare no conflict of interest.
# Carbonyl Composition and Electrophilicity in Vaping Emissions of Flavored and Unflavored E-Liquids
## 3 Results and Discussions
### Potential Limitations  
Some potential limitations in this study should be noted. First, emissions of carbonyls during vaping are influenced by multiple factors, including types of e-cigarette device used, battery voltage, PG/VG ratios, levels of flavoring chemicals in e-liquids, and vaping topography [51; 61; 62]. These factors make it difficult to directly compare our results of carbonyl emissions with previously published work. For instance, studies have shown that flavored e-liquids are linked to increased levels of carbonyl emissions, such as formaldehyde, acetaldehyde, and propionaldehyde [11; 12]. Results from this study (Figure 2) do not show increased levels of formaldehyde and acetaldehyde in vaping emissions of flavored e-liquids, except for acetone/propionaldehyde. Furthermore, our house-made e-liquids do not reassemble commercially available e-cigarette products, which frequently contain a complex mixture of base solvents (e.g., PG/VG at different ratios), nicotine, flavoring chemicals, and other ingredients [10; 33; 63]. Our e-liquid formulations contained only PG-VG and flavoring chemicals for direct comparison of carbonyl emissions between PG-VG and flavored e-liquids. Additional research is needed to better understand emissions of carbonyls and other toxic compounds during vaping using commercially available e-liquids. Finally, the carbonyls listed in Table 1 analyzed by GC/EI-MS were identified using their signature ions. To confirm the presence of these compounds in emission samples, authentic standards are required.
## 3 Problem Representation
It is represented by \(BP(a_{0},b_{0},\tilde{\mathbf{h}}_{0})\) with \(a_{0}>0,b_{0}>0,\text{ and }\tilde{\mathbf{h}}_{0}\) as the base measure. The drawing of atoms in the dictionary from the base measure can be expressed as below:  
\[\tilde{\mathbf{h}} =\sum_{k}\pi_{k}\delta_{\mathbf{\phi}_{k}}(\mathbf{\phi}),\,k\in K=\{1 \dots,K\}, \tag{5}\] \[\pi_{k} \sim\text{Beta}(\pi_{k}|a_{0}/K,b_{0}(K-1)/K),\] \[\mathbf{\phi} \sim\tilde{\mathbf{h}}_{0},\]  
where \(\delta_{\mathbf{\phi}_{k}}(\mathbf{\phi})=1\) for \(\mathbf{\phi}=\mathbf{\phi}_{k}\) and \(0\) otherwise. \(\tilde{\mathbf{h}}\) is a vector of probabilities for the selection of atoms and its \(k\)th component represents the selection probability of the atom \(\mathbf{\phi}_{k^{\prime}}\) drawn from the base measure \(\tilde{\mathbf{h}}_{0}\). To induce sparsity, a vector of Bernoulli probabilities, \(\tilde{\mathbf{B}}_{r}=\{\text{Bernoulli}(\pi_{k})\,:\,k\in K\}\), corresponding to the atoms selection probabilities \(\tilde{\mathbf{h}}\) is introduced. Similarly, a matrix \(\mathbf{Z}\in\{0,1\}^{K\times N}\) of \(N\) binary vectors can be drawn for the selection of atoms for all data examples. Training data can sparsely be approximated as \(\mathbf{A}\approx\mathbf{\phi}\mathbf{Z}\), where the number of nonzero elements in a column of \(\mathbf{Z}\) depends upon the value of \(K\). However, in case of \(K\to\infty\), the number of nonzero elements is a draw from a Poisson \(\left(\frac{a_{0}}{b_{0}}\right)\) distribution [51]. This approach was used by [25] and learned a linear classifier independently. However, discrimination in the dictionary was induced by drawing different sets of \(\mathbf{B}_{r}\)s for each class of data. Joint learning was further induced by [24]. To keep data labels intact with the data samples, they used the same Bernoulli distributions for both dictionary atoms and classifier atoms selection during joint dictionary and classifier learning. However, they trained different weights for the representation of data samples and the corresponding labels at the dictionary learning and the classifier learning stages, respectively. While predicting a test sample, weights computed over the learned dictionary are used directly as input to the classifier. The different weights learned for the representation of labels during classifier learning get ignored at the prediction stage. We introduced an approach in which this issue is mitigated by learning the same weights at both the stages, i.e., at the dictionary learning and the classifier learning stages for the representation of both the data examples and the corresponding labels.
# Real-Time Precise Point Positioning during Outages of the PPP-B2b Service
## 5 Conclusions  
In this study, the PPP-B2b correction error characteristics and PPP-B2b service interruption real-time precise point positioning were comprehensively investigated. The findings indicate that there were frequent jumps in the PPP-B2b corrections, and these jumps led to a lack of sufficiently long continuous data arcs for correction predictions. For this reason, it becomes almost impossible to make a reliable prediction of the PPP-B2b corrections. An alternative method is to continue using the PPP-B2b information received at the last epoch before losing track of the PPP-B2b signal. However, the performance of this method depends highly on the degradation of the PPP-B2b corrections with time. The results demonstrated that the quality of extended corrections remains satisfactory in the short term, and when the extension time is 10 min, the impact on the accuracy of the BDS-3 satellite orbit and clock is at the centimeter level, the impact on the accuracy of the GPS satellite orbit in the along-track direction is at the decimeter level, and the impacts on the remaining orbit directions and clock are at the centimeter level. Additionally, the results revealed that the performance of the extended corrections varies depending on the variations in positioning accuracy across different regions. In areas with high-quality PPP-B2b positioning services, i.e., BJ01, JFNG, and HKSC, the RMSE of real-time positioning results was approximately 13 cm, the available time for extended correction was also longer, and the RMSE was approximately 20 cm with outages of 20-30 min. Nevertheless, even in regions with suboptimal PPP-B2b positioning services, such as KSKT and MSSA, the use of extended corrections can preserve the accuracy level of real-time positioning within 10 min. Therefore, it is advisable to use extended corrections for positioning within a time frame of 10 min or less.  
Conceptualization, X.W. and H.L.; methodology, Y.C. and J.Z; software, Y.C., J.Z. and K.Z.; investigation, Y.C., C.Q. and S.X.; data curation, Y.C. and J.Z.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C. and X.W.; supervision, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the funding program from the Aerospace Information Research Institute.  
Data Availability Statement: The data sets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors would like to thank the GFZ for providing high-accuracy orbit and clock products and the IGS for providing the GNSS observations.  
The authors declare no conflict of interest.
# A Novel Dataset for Multi-View Multi-Player Tracking in Soccer Scenarios
## 2 Related Works  
### Datasets  
The most commonly used multi-view tracking datasets are listed in Table 1.  
DukeMTMC [38] was not included in Table 1, because the dataset's camera field of views do not strictly overlap, with only two cameras having a slight overlap. The Federal Institute of Technology in Lausanne [21] pioneered the creation of the multi-view datasets for multi-person detection and tracking, including Laboratory Terrace and Passageway. Campus [32] incorporated four sequences into its dataset, including Garden, Auditorium, and Parking Lot. CMC [31] focuses on walking people, with five different sequences featuring varying levels of person density and occlusion. SALSA [33] was gathered during a social event involving 18 individuals in a limited indoor environment. The participants wore sociometric badges equipped with microphones, accelerometers, Bluetooth, and infrared sensors, and the event was split into two sections of roughly equal length. However, the annotation provided by these six datasets has a low frame rate, making it hard to train and evaluate algorithms. Furthermore, the range of activities and the number of characters are limited, and the activity trajectories are pre-arranged, which is not representative of the actual motion of players on the sports field. EPFL-RLC [35] is yet another multi-view dataset, captured using only three cameras. It offers a balanced mix of positive and negative multi-camera annotations, albeit within a limited field of view. The primary objective of the dataset is to determine if a particular location is occupied by a pedestrian or not. Originally, only a tiny portion of the 8000 total frames, specifically the last 300, were labeled for testing purposes. The PETS-2009 [36] challenge aims to detect three types of crowd surveillance characteristics/events in a public outdoor scene. However, due to the broad scope of the challenge, only a portion of the dataset is devoted to people tracking. Additionally, the presence of a slope in the scene creates a calibration issue that can significantly degrade homography mapping, resulting in substantial inaccuracies in the projection of 3D points between views. WildTrack [34] is the most widely used dataset with an overlapping camera setup. Similar to sports scenes, the multi-view camera focuses on a 36 \(\times\) 12 m\({}^{2}\) rectangular area. The dataset annotates pure passengers without any scripted movements, with a larger number of annotated people and annotations compared to previous datasets. However, the dataset only includes one scene from a single time period, which limits its richness of data.
## 3 Results and Analysis of the Ground Test
### Transmission Capability Test of the Airborne Communication System  
#### 3.2.1 Analysis of Key Indicators  
The airborne communication system is used to achieve real-time communication between the flight carrier and the ground data processing center in the flight environment. The working link of the airborne communication system is shown in Figure 4, which operates in a bidirectional transmission mode of \"aircraft-satellite-ground\" and \"ground-satellite-aircraft\". In order to meet the data volume and speed requirements of real-time monitoring of natural disasters, airborne communication systems should have the following functions. First, the real-time transmission system has a bidirectional real-time transmission function. Second, the reliability of the servo system connecting to the satellite reaches over 98%, and the success rate of image transmission reaches over 80%. Third, the data transmission rate reaches 2 Mbps. Fourth, the data compression rate of the real-time transmission system can be selected between 2 and 128 times; fifth, the opening time of the real-time transmission system equipment shall not exceed 5 min (excluding aircraft takeoff time), and the data transmission delay time shall not exceed 5 s. Based on the above functions, the key indicators are transmission speed and transmission success rate. Only with high speed and success rate can real-time and data integrity requirements be met.  
Figure 4: Schematic diagram of the composition of the “aircraft–satellite–ground” real-time transmission system.
## 2 Related Work
### NAS for Object Detection  
DetNAS [31] refers to DARTS and propose to search neural architectures for object detection by SGD. However, it requires vast GPU memory and search time to discover an architecture. Recently, researchers [27; 28; 29] proposed the adoption of a single-path searching strategy to reduce the memory cost of searching for image classification. However, such a single-path searching strategy increases the difficulty of training supernets. Since the object detection neural architectures are more complex and hard to train, the single-path strategy is not quite suitable for searching object detection architectures. Moreover, object detection networks have feature pyramid networks (FPN) to fuse features at different scales, which differs from those for classification. Since FPN plays an important role in object detection networks, many works are dedicated to searching architectures for optimal FPN. NAS-FPN [33] aims to search FPN architecture for RetinaNet [59], a popular one-stage detection framework. Specifically, FPN architectures are generated by an RNN controller, which is trained by reinforcement learning (RL). However, NAS-FPN requires vast GPU memory and search time. EAutoDet [43] proposed an efficient architecture search method for YOLO framework and achieves great performance on COCO dataset. Inspired by the above related works, we propose to search on YOLO framework and propose to directly apply DARTS [22] to joint search for backbone and FPN architectures.
## 3 Numerical Results and Analysis
### Influence of Different Collision Reactions on Electron Energy Distribution
It can be seen from Figure 8b that the number of \"cold\" electrons is the largest. In addition, the density of \"cold\" electrons is also highest at the BP (i.e., X = 17.5 cm, the end of the solid black line), which is largely due to the electron energy decay caused by excitation collisions. Compared with Figure 8b,c, the number of \"cold\" electrons in the Type-C group is fewer, and the density of \"cold\" electrons at BP is lower. Figure 8: The spatial distribution of “cold” electrons on the X\(-\)Z plane and the average density distribution along +X direction under four reaction types at \(T=15\) μs. The red dots represent the “cold” electrons (\(\epsilon\leq 2\) eV). (**a**) Type-A. Elastic (index:l); (**b**) Type-B. Elastic and excitations (index:l,9–18); (c) Type-C. All collisions (index:l–19); (**d**) Type-D. No collisions. It can be seen from Table 3 that \(N_{ext}\) in the Type-C group is nearly half that of the Type-B group, and \(T_{ext}\) is higher. The reason is that, although the ionization reactions (index: 2, 3, 4 in Table 2) can generate some new electrons, the new electrons share the energy remaining after subtracting the threshold from the original electrons, so the overall energy trend still decreases. However, indexes No. 5, 6, and 7 are the dominant electron destructive reactions, and No. 7 in particular is the volume production of \(H^{-}\) ions, so electrons die out further after energy decay. This indicates that the extracted electron temperature \(T_{ext}\) is less affected by ionization but more by excitation and electron destructive reactions.
# How Insoluble Inclusions and Intersecting Layers Affect the Leaching Process within Potash Seams
## 3 Results  
### Leaching Zone Growth  
Figure 7 shows the evolution of the ratio between permeated and total rock volume (halitic areas not included). In cases of low dissolution rates (\(k_{max}=5\times 10^{-6}\) cm/s), the leaching zone growth was nearly linear (Figure 7a,b). For all rock distributions, it took 13-14 years for the simulations to meet the stop criterion with 96% of the (non-halitic) rock being permeated. Only in the case of +10\({}^{\circ}\) inclination, the growth rate started to decrease after approximately six years (Figure 7a), with only 85% of the rock being permeated at the end. Halite inclusions led to a less regular growth rate compared to the homogeneous case, but large deviations from linear growth were not observed (Figure 7b).  
In cases of high dissolution rates (\(k_{max}=5\times 10^{-4}\) cm/s), the differences between rock distributions increase (Figure 7c,d). Along a homogeneous potash seam, the leaching zone expands to the right model boundary in two years, showing only a slight decrease in growth rate. However, only 50% of the rock become permeated, whereas the other half is not affected. In contrast, heterogeneous potash seams show permeation ratios of 59% to 72%, taking up to 3.6 years until the right model boundary is reached. Intersecting halite layers result in different evolutions depending on the inclination (Figure 7c). In cases of a 0\({}^{\circ}\) inclination, the permeation speed is slightly faster than for the homogeneous case, but the stop criterion is only met when 72% of the rock is permeated. Therefore, it takes  
Figure 7: Ratio between permeated and total (soluble) rock volume over time for (**a,c**) an intersecting halite layer and (**b,d**) halite inclusions (c.l., correlation length) compared to the homogeneous case.  
3.6 years instead of the 2 years in the homogenous case. In the case of a +10\({}^{\circ}\) inclination, the leaching zone growth is slower and only 60% of the rock is permeated within the same time period. An inclination of \(-10^{\circ}\) (Figure 6c) causes the highest permeation speed, with 2.6 years required to reach a 67% permeation or the right model boundary. Potash seams with halite inclusions show a very similar permeation speed compared to homogeneous ones (Figure 7d). However, because 66% to 70% of the (soluble) rock is permeated (instead of 50%), it takes the reaction front almost twice as long to arrive at the right model boundary. Accordingly, heterogeneities slow down the leaching zone growth in horizontal direction by a factor of 1.25 to 1.8 in cases of high dissolution rates.