esa-sceva/satcom-chunk-collection huggingface dataset:
# A Combined Dynamic-Kinematic Extended Kalman Filter for Estimating Vehicle Sideslip Angle
## 5 Conclusions  
This paper presented a novel combined dynamic-kinematic extended Kalman filter (DK-EKF) for vehicle sideslip angle estimation, demonstrating improvements over a dynamic-only extended Kalman filter (D-EKF) and a kinematic-only Kalman filter (K-KF).  
Figure 9: Slalom at 60 km/h: longitudinal velocity (**top-left**), yaw rate (**top-right**), sideslip angle (**bottom-left**) and cumulative distribution function of \(\beta\) estimation error (**bottom-right**).  
By integrating both dynamic and kinematic models, the DK-EKF addresses the limitations inherent in each approach, resulting in enhanced estimation accuracy and robustness.  
The experimental validation highlights the superior performance of the DK-EKF. During a double lane change (DLC) maneuver at 50 km/h, the DK-EKF accurately predicts lateral velocity, closely aligning with reference values and outperforming both the D-EKF and the K-KF. The root mean square error comparison underscores this improvement, confirming the DK-EKF's effectiveness in dynamic conditions without diverging results from the kinematic component. In a slalom maneuver, while the improvement is less pronounced, the DK-EKF still demonstrates a corrective influence on the D-EKF's and K-KF's predictions. The tendency of the DK-EKF to deviate the dynamic estimation of the lateral velocity when not necessary suggests areas for further refinement.  
Overall, the DK-EKF offers a cost-effective accurate solution for real-time sideslip angle estimation, enhancing vehicle stability control systems. Future work will focus on optimizing the DK-EKF's performance in various driving conditions and further reducing estimation errors, by implementing variable covariance matrices, particularly at higher lateral velocities.  
Conceptualization, G.R. and B.L.; methodology, G.R. and B.L.; software, G.R. and B.L.; validation, G.R. and B.L.; formal analysis, G.R. and B.L.; investigation, G.R. and B.L.; writing--original draft preparation, G.R.; writing--review and editing, G.R. and B.L.; supervision, B.L.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the BIRD 2022 program of the University of Padova, project BIRD222197.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Problem Definition and Formulation
### Constraints
#### 3.2.6 Connectivity Constraints  
To ensure reliable data transmission and successful task completion, the nodes in the satellite network must maintain proper connectivity. For each task \(j\), it must be guaranteed that a reachable path exists between its source node and destination node. This constraint can be expressed as  
\[\text{Path}(s_{j},t_{j})\in\mathcal{G},\quad\forall j=1,2,\ldots,m \tag{8}\]  
where \(s_{j}\) and \(t_{j}\) represent the source and destination nodes of task \(j\), and \(\mathcal{G}\) represents the network graph, ensuring that a valid path exists between the source and destination nodes for each task.
## 4 Discussion
### Assessing T Responses to Different Hydrological Years  
Our result confirmed that T is jointly affected by various environmental factors, and we quantified the contributions and changes of each factor in the drought years. We used a modeling method to quantify the influence of the main drivers on T in different hydrological years. The developed T model can be used to assess the independent effects of varying meteorological conditions and soil water conditions (i.e., REW) on T. For example, compared to the reference T under the long-term means of the daily Rn, VPD, and REW, the maximum contribution factor for the mean daily T change during the growing season was REW (with a relative contribution rate of 31.7%) in the 'dry' year of 2018 and Rn (18.0%) in the 'wetter' year of 2021. The results showed that REW was the main driving factor affecting T in the 'dry' year. This is consistent with the results of [PERSON] et al. [28] for a semi-arid region. This is due to the fact that Tt is reduced during periods of insufficient soil moisture when the soil is dry. In the 'normal' year, Rn and REW were the main drivers affecting T. The main driving factor affecting T in the 'wet' and 'wetter' years was Rn. The effect of VPD on T was not obvious in any hydrological year. The previous research results on the influence of Rn and VPD on T are different. Some studies suggest that the influence of VPD is greater than that of Rn [63; 64], while other studies show that the contribution rate of Rn to T is greater than that of VPD [65]. This difference may be due to the climatic conditions of the study area and the physiological characteristics of trees. In addition, compared with those in the 'normal' year in 2017, Rn and REW limited the T variation in the 'wet' year in 2015 by 2.5% and 2.1%, respectively; and REW limited the T variation in the 'dry' year in 2018 by 22.8%; Rn and REW limited the T variation in the 'wetter' year in 2021 by 6.9% and 3.9%. This indicates that soil moisture has an important or the largest effect on T, especially when drought occurs, and low soil moisture will suppress T. When there is sufficient precipitation, Rn will become the main environmental factor limiting T. At present, extreme weather occurs frequently, which will seriously threaten forest water resources in semi-arid areas. Increased evapotranspiration demand and reduced soil moisture can promote water stress and weaken tree health [66]. Therefore, when drought occurs, special attention should be paid to the dynamics of the available soil water content.
## 3 Results and Discussion
### Sensitivity to Lcing Environment Parameters
#### 3.1.3 Environment Temperature  
IPS heating power density of 5 kW/m\({}^{2}\) was determined under an environment temperature of \(-7\)\({}^{\circ}\)C. When the temperature dropped down to \(-10\)\({}^{\circ}\)C and \(-15\)\({}^{\circ}\)C, the power density 7.5 kW/m\({}^{2}\) and 12 kW/m\({}^{2}\), respectively, were required to protect the surface from icing, as test results suggest in Table 5 and as shown in Figure 12. In comparison to LWC and MVD, IPS was more sensitive to environment temperature. Therefore, when  
Figure 11: Comparison of surface temperature with different LWCs.  
Figure 10: Test results of IPS with and without a coating in case 1 test condition. (**a**) The 5 kW/m\({}^{2}\) with a coating. (**b**) The 5 kW/m\({}^{2}\) without a coating. (**c**) The 10 kW/m\({}^{2}\) without a coating.  
environment temperature drops, IPS must immediately provide more heating power for ice protection.  
In this test, when the temperature dropped from \(-7\,\mathrm{\SIUnitSymbolCelsius}\) down to \(-10\,\mathrm{\SIUnitSymbolCelsius}\) and \(-15\,\mathrm{\SIUnitSymbolCelsius}\), the needed IPS heating power density went from \(5\,\mathrm{kW}/\mathrm{m}^{2}\) to \(7.5\,\mathrm{kW}/\mathrm{m}^{2}\) and \(12\,\mathrm{kW}/\mathrm{m}^{2}\). Based on the results, the required heating power density was fitted linearly with the environment temperatures:  
\[\mathrm{q}=-0.877(\mathrm{T}-273.15)-1.193 \tag{2}\]  
where T is environment temperature, with units in Kelvin (K), and q is heating power density, with units in \(\mathrm{kW}/\mathrm{m}^{2}\).  
It is noteworthy that the thermocouples used for surface temperature measurement were positioned inside the model, rather than on the superhydrophobic coating surface. As a result, the surface temperature of the coating, which is crucial in IPS evaluation, would be lower than the measured temperature due to the heat transfer properties of superhydrophobic coatings. In the test, as shown in Figure 13, a heating power density of \(10\,\mathrm{kW}/\mathrm{m}^{2}\) at environment temperature \(-15\,\mathrm{\SIUnitSymbolCelsius}\) (case 7) maintained an internal temperature virtually identical to that provided by a heating power density of \(7.5\,\mathrm{kW}/\mathrm{m}^{2}\) at environment temperature of \(-10\,\mathrm{\SIUnitSymbolCelsius}\) (case 6) but could not prevent ice formation. It could be inferred that  
Figure 12: Test results of various environment temperatures. (a) Case 6 (\(5\,\mathrm{kW}/\mathrm{m}^{2}\)). (b) Case 6 (\(7.5\,\mathrm{kW}/\mathrm{m}^{2}\)). (c) Case 7 (\(10\,\mathrm{kW}/\mathrm{m}^{2}\)). (d) Case 7 (\(12\,\mathrm{kW}/\mathrm{m}^{2}\)).  
the coating surface temperature dropped below the freezing point. Therefore, it is necessary to study the heat transfer qualities between the airfoil skin and superhydrophobic coating in the future.
## 4 Discussion
Combined, these reports are in agreement with the range in uncertainty in benthic cover detection as a function of depth and \(SNR\) indicated in the \(S1\), \(S2\), and \(S3\) scenarios. The engineering constraints on \(SNR\) for a sensor deployed on an aircraft are very different compared with an identical sensor placed in low Earth orbit (LEO). The number of photons collected per pixel is a product of the amount of time that the pixel is viewed (integration time), the solid angle subtended by the pixel (instantaneous field of view), and the spectral sampling. The most advanced aircraft sensors to date include the Airborne Visible/Infrared Imaging Spectrometer--AVIRIS [50], and the Portable Remote Imaging Spectrometer--PRISM [51]. These sensors collect data with high spatial resolution (\(GSD\) < 10 m), fine spectral sampling and resolution (generally < 5 nm), and peak \(SNR\geq 500\) within the mid-visible portion of the spectrum. This is possible because the sensors can be flown at low altitude resulting in a wider field of view for a specified GSD and with slow speed resulting in longer integration time. Together, these attributes result in high photon flux per pixel. A LEO sensor orbits at an altitude of approximately 2000 km and travels with a ground speed about 28,000 km h\({}^{-1}\). Therefore, for a specific GSD, an identical LEO sensor will collect only a fraction of the photons that would be received by an aircraft sensor for a specified GSD resulting in a lower \(SNR\). The \(SNR\) of a LEO sensor could be increased by incorporating a larger aperture in order to collect more light, but this would also increase the manufacturing and launch costs. The design \(GSD\), i.e., the instantaneous field of view per pixel, could be increased with minimal impact on size and weight, but this will result in coarser spatial resolution and potentially significant loss of fine scale information due to spectral mixing. Finally, the sensor could operate with lower spectral resolution, thus increasing the number of photons per spectral band, but this would lead to loss of information necessary to discriminate between spectrally dissimilar properties. Thus, there exists a well-defined \(SNR\) trade space between \(GSD\), spectral sampling, and the cost of manufacturing and launching a LEO sensor, all of which impact the amount of retrievable environmental information. The next generation of Earth imaging systems, motivated largely by reasoning articulated in the latest NASA Decadal Survey of Earth remote sensing needs [52], will attempt to optimize the sensor design tradeoffs in order to most effectively address concerns about coastal ecosystem responses to population and climate change [53]. Coral reefs are a key environment globally due to their high biodiversity and societal services. This study indicates that \(SNR\) is a key sensor design attribute for remote sensing in support of coral reef ecological research and management and, more generally, investigations of shallow coastal environments and that data value is diminished considerably when \(SNR<500\).
# A Comprehensive Review of Computer Vision in Sports:
## 1 Introduction
#### Features of the Proposed Review
The features of this review are:  
* In contrast to recently published review papers in the sports field, this article comprehensively reviews statistics of studies in various sports and various AI algorithms that have been used to cover various aspects observed and verified in sports. * It provides a roadmap of various AI algorithms' selection and evaluation criteria and also provide some of the publicly available datasets of different sports. * It discusses various GPU-based embedded platforms for real-time object detection and tracking framework to improve the performance and accuracy of edge devices. * Moreover, it demonstrates various applications in sports vision and possible research directions. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Articles**} & \multirow{3}{*}{\begin{tabular}{c} **Hand Crafted** \\ **Algorithms** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Machine** \\ **Learning** \\ **Algorithms** \\ \end{tabular} } & \multicolumn{4}{c}{**Sport and Application**} & \multirow{3}{*}{\
\begin{tabular}{c} **Classification** \\ **and Movement** \\ **Recognition** \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} **Discussion** \\ **about Dataset** \\ \end{tabular} } & \multirow{3}{*}{**Aim of Review**} \\  & & & & & & & & & \\ \hline
[1] & ✗ & & Badminton & ✗ & ✗ & ✗ & Motion analysis \\
[2] & ✗ & ✔ & - & ✔ & ✗ & ✗ & Sport data mining \\
[3] & ✗ & ✔ & - & ✗ & ✗ & ✔ & - \\
[7] & ✗ & ✔ & - & ✗ & ✗ & ✗ & - \\
[4] & ✗ & - & ✗ & ✗ & ✗ & & Motion Capture \\
[8] & ✔ & ✔ & Soccer & ✔ & ✗ & & Ball Tracking \\
[5] & ✗ & ✔ & Soccer & ✔ & ✔ & & & Player \\
[6] & ✗ & ✓ & - & ✔ & ✗ & & & detection/tracking \\
[9] & ✗ & ✗ & - & ✔ & ✗ & & ✔ & Availability of \\
[10] & ✗ & ✗ & - & ✗ & & & ✗ & Content-Aware \\
[10] & ✗ & ✓ & - & ✗ & ✗ & ✗ & - \\
[11] & ✗ & ✓ & - & ✗ & ✗ & ✗ & & Video \\
[12] & ✗ & - & - & - & - & & & Summarization \\
[13] & ✗ & - & - & - & - & & ✗ & in sports \\
[13] & ✗ & - & - & ✔ & ✔ & ✗ & Wearable technology \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of previous surveys and reviews in different sports. The rest of this paper is organized as follows. Section 2 provides statistical details of research in sports. Section 3 presents extraction data vis-a-vis various sports playfields, followed by a broader dimension that covers a wide range of sports and is reviewed in Section 4. Some of the available datasets for various sports along and embedded platforms have been reviewed in Sections 5 and 6. Section 7 provides various application-specific tasks in the field of sports vision. Section 8 covers potential research directions, as well as different challenges to be overcome in sports studies. Last but not least, Section 9 concludes by describing the final considerations.
# Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
## 4 Scheduling for Adaptive Time-Triggered Systems
### Context Model and Event Calendar  
The context model within the ATMA defines the set of external and internal conditions, or \"contexts\" that influence the system's behavior. These contexts include system faults, varying levels of computational task demand (dynamic slack), resource alerts like overheating or low battery, and changes in environmental conditions. The model categorizes these events based on their characteristics, such as predictability, urgency, and impact on system safety and functionality. It provides the metascheduler with the information necessary to prepare for and respond to these events, ensuring the system can adapt its operation to maintain performance and safety standards.  
An event calendar, linked with the context model, schedules the monitoring and management of these events over time. It ensures that the system recognizes and responds to context changes on time. For instance, synchronous events, which are predictable in their occurrence, are checked at specific times, allowing the system to prepare and adjust schedules predictably. Asynchronous events occur at unpredictable times and are sampled periodically to detect and react to changes as quickly as possible. The calendar's role is crucial in managing the system's adaptation to context changes, whether shifting to more energy-efficient operation modes in response to a detected slack or reconfiguring tasks and message paths due to a hardware fault.
## 3 Materials and Methods
### CT Scan Workflow  
Core segments were first scanned with a Nikon line scan camera, and high-resolution digital photos were acquired. These photos were used for a first evaluation of the laminated sequence, with reference (top/bottom depth) of each laminated section. Since the Lake Vouliagmeni core presents distinct organic (black) and calcite (white) laminations, RGB colors and a grayscale profile were also extracted through ImageJ software, providing an even clearer distinction of laminated sections' boundaries.  
Each point scanned through CT is characterized by a specific \"signal\", which is expressed in Hounsfield units (HU). Medical CT scanners, similar to the one used in this study, are set with a HU value of \(-\)1000 for air and 0 for water. Apart from sediment internal structural characteristics, the correlation between HU and the sediment density/atomic composition can provide a fast overview and distinction between sedimentological facies, thus sections where different sedimentological processes prevail in the lake. For recognition of each laminae boundary, the HU ranges of the calcite/white and organic/black laminations were detected through INOBITEC medical software and SedCT software [86]. During the examination in all core sections, the PVC tube from the core, as well as the first 1 mm of the core surface, were excluded from the 2D rendered volume, to prevent miscounting of HU values due to oxidization and sediment deformation from core splitting. By presenting each time the HU range of each lamination type while excluding the non-laminated deposits, in the 3D model, the exact lamination number was measured by the automated software Cybis Coordinate Recorder (CooRecorder; [[http://www.cybis.se/cbeewing/index.htm](http://www.cybis.se/cbeewing/index.htm)]([http://www.cybis.se/cbeewing/index.htm](http://www.cybis.se/cbeewing/index.htm))) and BMPix and Peak tools [87]. Additionally, laminations were counted manually by extracting 2D image sections, setting a scale and then measuring each lamination separately. A total of \(\sim\)3230 laminations were calculated in the core by using both proposed methods. No significant variations were observed between the different software used, except some minor miscounts of Cybis Coordinate recorder, in sections where lamination boundaries in the core surface were deformed during splitting.  
Event sedimentation into the system was examined through 4 main criteria on the non-laminated deposits, including (1) HU values, (2) magnetic susceptibility, (3) Mn content and (4) potential inverse grading. Then, 3D models were constructed by selecting regions of interest (ROI) in the Avizo Fire (Thermo Fisher) software and by projecting them with the frequency distribution and grayscale colourmap. Both HU and MS present high values at the base of the turbidite deposits [88, 89, 90].
## 4 Results and Interpretation
### Organic Geochemistry
#### 4.5.1 n-Alkanes and Isoprenoids  
The distribution of n-alkanes in the Hengtongshan Formation samples shows that low to medium molecular weight compounds (n-C\({}_{13}\)-n-C\({}_{23}\)) are dominant, and there are significant waxy alkanes (+n-C\({}_{23}\)), mainly with odd carbon numbers, thus yielding a medium carbon preference index (CPI) (Figure 6; Table 4). The distribution of alkanes in the samples is similar to that of a large number of long-chain alkanes present in algae or plants [17; 18] The same conclusion was reached based on kerogen microscopy observations of the source rock in the Hengtongshan Formation because a large amount of alginite and amorphous organic matter can be seen under the microscope (Figure 4).  
In the study of biomarkers, pristine (Pr) and phytane (Ph) are often detected, and their concentrations are very important for environmental research [19; 20]. The acyclic isoprenoids Pr and Ph were found in all samples from the Hengtongshan Formation (Figure 6a-d). The ratio of Pr to Ph has been widely used as a redox condition parameter in sedimentary environment research [21]. Previous studies have shown that Pr/Ph ratios below 0.6 indicate anoxic conditions, ratios between 0.6 and 3.0 indicate sub-oxic conditions, and ratios above 3.0 indicate oxic conditions [22]. By testing 38 samples from the source rocks of the Hengtongshan Formation, the Pr/Ph ratios are 0.54 to 0.92 (average 0.76), which indicates that the sedimentary environment of the organic matter featured sub-oxic conditions, which are conducive to the preservation of organic matter.  
The relationship between Pr/n-C\({}_{17}\) and Ph/n-C\({}_{18}\) is often used to study the source of organic matter and the sedimentary paleoenvironment of source rocks [23; 24; 25; 26]. In this study, it was found that the Pr/n-C\({}_{17}\) and Ph/n-C\({}_{18}\) values are 0.23-0.64 (average 0.50) and 0.16-0.93 (average 0.54), respectively. The overall numerical fluctuation is relatively small, and the data indicate that the source rock of the Hengtongshan Formation was deposited under reducing conditions and that the material was from a mixed source [27; 28] (Figure 7; Table 3); however, additional analysis is needed to determine the specific components.