esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Tracking
#### 4.2.2 Tracking Results Using a Deep Learning Detector  
To ensure a proper comparison, the evaluation of the deep learning-based tracking system is done analogously to the evaluation of the classical approach. Again, IDs are assigned randomly to the tracks, and the accuracy and robustness metrics are determined per track. Table 3 summarizes the performance results of the tracking.  
The tracking system exhibits a satisfactory performance on average over all sequences. Especially on well-illuminated scenes, a high accuracy is provided as indicated by the AOS and CER of Scene 1. The system's performance on this scene is very robust since all workers were tracked almost over the entire length of this video sequence. However, Track 1, which is located in a heavily overexposed area, shows that overexposure affects the quality of the tracking. As the TL shows, the track remains robust, but the accuracy measured by the AOS decreases by about 10% This is also confirmed by a slight increase in the CER. Nevertheless, the system performs very well if the scene is only slightly overexposed as is the case in Scene 2. Here, the AOS barely indicates any negative effects despite the overexposure. Only the CER shows a minor increase. Similar to the highly overexposed case, the TL measurements again emphasize the tracking system's robustness, although slight overexposure is present on the entire scene.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **ID** & **AOS** & **CER** & **TL** \\ \hline \multirow{4}{*}{Scene 1} & 0 & 0.94 & 0.002 & 0.98 \\  & 1 & 0.87 & 0.003 & 0.98 \\  & 2 & 0.96 & 0.002 & 1 \\  & 5 & 0.98 & 0.002 & 0.95 \\ \hline \multirow{4}{*}{Scene 2} & 0 & 0.96 & 0.001 & 0.99 \\  & 1 & 0.95 & 0.005 & 1 \\  & 2 & 0.98 & 0.002 & 0.99 \\  & 3 & 0.90 & 0.004 & 1 \\ \hline Scene 3 & 0 & 0.98 & 0.002 & 0.98 \\ \hline Average & & 0.95 & 0.003 & 0.98 \\ \hline \end{tabular}
\end{table}
Table 3: Results of the deep learning-based tracking system applied to all three evaluation scenes. For each scene, the resulting measures according to Equations (2)-(4) are given. Tracking IDs are assigned randomly to each particular track. The last row highlights the performance of this system averaged over all scenes.  
Figure 9: Examples of the classical tracking system applied to different scenes. (**a**) An example of the tracking system applied to Scene 2 shows that the tracking is sufficient even on slightly overexposed scenes. (**b**) On Scene 3, the tracking results show that the worker is tracked successfully while the moving vehicle remains untracked as intended.  
Besides this, the results of this experiment also show that the system does not confuse construction workers with any other object in the scenes. As Table 3 shows, the correct number of tracks was set for each scene. Alongside the four construction workers, Scenes 1 and 2 include static construction related objects, such as traffic cones and barriers. These stay correctly undetected and untracked during the course of the video sequences. Moving objects such as the car in Scene 3 also remain correctly unnoticed by the tracker.
# Review of Autonomous Intelligent Vehicles for Urban Driving and Parking
## 4 Research Gaps in Autonomous Driving  
Although the research carried out in the autonomous vehicle area is extensive, several areas have not been discussed. Firstly, the literature has not yet included a sudden obstacle while the autonomous vehicle executes the parking trajectory. A child may dash into the parking lot to retrieve some stuff, or an adult may unknowingly walk into that area. There is a rear camera for driver reference, and a sensor is installed, which sounds off if it detects any obstacles behind. However, the driver may not look at the rear camera's view, or the sensors may fail to sound off. An autonomous vehicle should stop accordingly if such a sudden obstacle appears when executing the parking trajectory. However, it should be smart enough to identify if the obstacle is negligible. For example, if an identified obstacle is a balloon, the autonomous vehicle should proceed with the parking instead of stopping.  
The current literature [118; 119] discusses systems catered toward obstacle avoidance. [PERSON] et al. [119] provided an additional consideration that caters toward pop-up obstacles such as deer crossing the road. Still, none of the research has discussed how autonomous vehicles should react to objects falling off a vehicle. Using the example of a heavy vehicle transporting a pile of iron rods for construction purposes, if such rods fall off the vehicle,there is a possibility that the rods pierce the car window and cause fatal injury. Although the priority of emergency vehicles in intersections has been considered [120], the literature has not considered giving way to an emergency vehicle.  
Future autonomous vehicles must depend not only on internal sensors but also on the sensors of other vehicles. Measurement data will be shared by means of V2V to enable better environmental awareness. The fusion of ADAS and smart-lighting infrastructures can be deployed using radar-camera-based traffic-monitoring devices [121] and low-cost global navigation satellite system (GNSS) receivers [122], together with microscale traffic information and other networks [123; 124; 125; 126].
## 1 Introduction  
Fruit aromas consist of many aroma volatiles, and their formation involves several biosynthetic pathways. Fatty acid metabolism is an important metabolic pathway involved in the biosynthesis of aroma volatiles in fruits. Fatty acids liberated by lipase activity and those further metabolized by \(\beta\)-oxidative enzymes and/or lipoxygenase (LOX) are generally regarded as the initial precursors of straight-chain esters, alcohols, and aldehydes produced in fruits during development and maturation [1; 2; 3].  
Transgenic modification of fatty acid metabolism in plant tissues resulted in significant changes in aroma compound profiles [4]. For fruits, higher enzymatic activities and smaller amounts of linoleic acid and linoleic acid were measured in ripe fruits compared to green fruits. Fatty acid deficiency in fruits was the primary cause of the poor aroma of fruits [1; 5; 6; 7; 8].  
Studies examining aroma production in fruits feeding on short or long-chain fatty acids (FA) (up to 16 C atoms) [2; 6; 7] have shown that aroma production increases, even under conditions in which natural aroma volatiles production is limited by storage. Such results have demonstrated the importance of fatty acids as precursors for aroma volatile biosynthesis and the feasibility of _in vitro_ aroma volatile biosynthesis modifications.  
_Pyrus bretschneideri_ Dangshansuli is a pear cultivar widely planted in China for its great size, rich juice and sweet, crisp flesh. The ripe fruits contain many straight-chain aldehydes and alcohols, giving them a \"green\" flavor note. _Pyrus ussuriensis_ Nanguoli is another cultivar widely planted in the northern China for its attractive color, exquisite flesh and pleasant flavor. Esters are the important volatiles of the ripe fruits, endowing them a \"fruity\" flavor note [9]. The two cultivars grow well in the Liaoning Province of China and have similar bloom and maturity dates, but their aroma volatile composition and concentration are distinctly different. The majority of plant aroma volatiles originate on a quantitative and qualitative basis from saturated and unsaturated fatty acids [10]. Most aroma volatiles of fruit are biosynthetically derived from fatty acids, however the fatty acid metabolism of aroma volatiles in different cultivars have not been thoroughly examined. In this study, the aroma volatile metabolisms of \"Dangshansuli\" and \"Nanguoli\" were examined by incubating fruits with linoleic acid, linolenic acid, hexanol and hexanal to further understand aroma volatile biosynthesis and regulation.
# A Performance Prediction Method Utilizing Time-Dependent Subsystem Transfers between Family Systems
## 6 Examples of Verification
### Discussion and Prospects  
The absolute error of the hydraulic actuator is selected as the key performance parameter in this section and the family system is categorized based on its proximity to this parameter. By continuously updating the inter-family transfer matrix and current family system label of each subsystem in real time, it becomes possible to determine both the transfer probability of any subsystem and the total number of subsystems that are likely to transition into a failure-state family system. This information can be utilized for the further determination of remaining life and has been experimentally validated for prediction accuracy, thus establishing the credibility of this method.  
According to the definition and method framework of cluster systems, the approach proposed in this article is applicable in diverse engineering practices, facilitating the real-time life prediction of complex systems that adhere to the characteristics of cluster systems. Table 4 presents a performance comparison between this approach and traditional analysis methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Additional Samples** & **Fault Data Required** & **Content of Subjective Analysis** & **Others** \\ \hline The proposed method & No & No & K value and state interval & Secondary analysis if the environment changes \\ \hline Weibull distribution & Yes & Yes & Location, shape, and scale parameters & With high sample numbers \\ \hline Degradation model & Yes & No & Multiple stress levels & Fitting parameter estimation \\ \hline Duane model & Yes & Yes & \begin{tabular}{c} Scale parameters and \\ growth rate \\ \end{tabular} & \begin{tabular}{c} Fault maintenance and \\ accumulation test \\ \end{tabular} \\ \hline Bayesian network & No & No & Fault tree or node relationship & Component failure rate \\ \hline Minimum cut set & No & Yes & \
\begin{tabular}{c} Fault events and \\ logical relations \\ \end{tabular} &
\begin{tabular}{c} Rely on expert experience \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with traditional methods.  
Figure 10: Inter-family transfer and prediction of the number of normal subsystems.  
This approach primarily addresses the limitations of traditional methods, which heavily rely on exhaustive life testing, fault data accumulation, and expert knowledge. It facilitates precise life prediction for complex systems that are either newly developed or deployed in challenging environments where conducting comprehensive life testing and accumulating fault data are arduous.
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 3 Material and Methods
### Dataset  
The lung CT-scan dataset from [PERSON] et al. [45] served as the foundation for training and testing the CT-scan segmentation models in this study. This dataset comprises 20 CT-scans acquired from radiopaedia [46] and the Corona Cases Initiative (RAIOSS) [47], featuring COVID-19 patients. Beyond supplying the CT-scan data, the reference [45] also offers three segmentation masks, denoted as the \"lung mask\", \"infection mask\", and \"lung and infection mask\". Within our investigation, we specifically leveraged the \"lung and infection mask\" to perform segmentation of both the lung area and the infected regions in the CT-scans. Notably, this dataset boasts comprehensive annotations manually crafted by two radiologists and validated by a more experienced radiologist, in accordance with the work by [PERSON] et al. [48]. Furthermore, the utilization of this dataset is particularly advantageous due to its ample size, inclusion of various levels of disease severity, and the availability of ground truth masks. A comprehensive overview of the CT-scan dataset's attributes is provided in Table 1[14].  
Every CT-scan in the [45] dataset has a unique width, height, depth (slice), and infection severity level. The more thorough CT-scan profile for each patient is displayed in Table 2.
## 5 Conclusions  
In this work, we present transitional RANS models for the SA equation. Flow regimes relevant for wind energy applications are considered. In particular, starting from a previous implementation of the authors, we carefully assess the impact of a low-Re number correction for the \(c_{w2}\) term. Furthermore, a new implementation for a one-equation (\(\gamma\)) transition model for incompressible flows is presented.  
The obtained results put in evidence that the \(\gamma\)-\(\widetilde{\text{Re}_{\theta,t}}\)-SA model is rather mature for predicting laminar separation bubbles. In fact, good results are highlighted from low-Re numbers up to high-Re numbers. In this framework, the approach introduced by [PERSON] and [PERSON] [19] is a key ingredient for improving the performance of the flow model at low-Re numbers. On the other hand, log \(\gamma\)-SA shows fairly encouraging results. For this specific model, we proposed to switch to the log \(\gamma\) formulation, since unstable computations were evidenced in the standard configuration. It is worth noting that for the SD7003 airfoil, we obtain a satisfactory feedback. However, the \(c_{p}\) distribution is still far from the reference data. Only for the E387 airfoil, we obtain results prior to the stall region. On the contrary, for S809 at \(\text{Re}=2\times 10^{6}\), this approach is not able to detect the laminar-to-turbulent transition. Finally, the one-equation LCTM approach is very attractive because it allows predicting complex transitional flows with only a further transport equations. However, in our experience, it can be considered to be only as a premature stage for the SA equation. For this reason, future work will be devoted to its further development.  
V.D., S.M., and R.R.: conceptualization, methodology, software, validation, formal analysis, data curation, writing--original draft preparation, project administration. All authors read and agreed to the published version of the manuscript. This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors are grateful to all the persons involved in the GARTEUR AG59 IMoLa activities. Particular acknowledgment is given to [PERSON] and [PERSON], Centro  
Figure 13: E387, \(\text{Re}=2\times 10^{5}\). Force coefficients.  
Italiano Ricerche Aerospaziali (CIRA), for kindly providing us with the reference data included in the paper and the SD7003 airfoil grid.
# Data-Driven Robust Control Using Reinforcement Learning
## 2 Materials and Methods
### Robust Control Using Reinforcement Learning
#### 2.1.3 Stability Analysis
By using Equation (27) in Lemma 1 and the representation \(P_{i}=\hat{P}_{i}+\Delta P_{i}\), we can calculate the left side of Equation (18) as follows:  
\[\dot{V}_{i}(x(t))+\alpha V_{i}(x(t))\] \[= \dot{x}_{t}^{T}P_{i}x_{t}+x_{t}P_{i}\dot{x}_{t}^{T}+\alpha x_{t}^ {T}P_{i}x_{t}\] \[= (Ax_{t}+Bu_{t})^{T}P_{i}x_{t}+x_{t}P_{i}(Ax_{t}+Bu_{t})^{T}+\alpha x _{t}^{T}P_{i}x_{t}\] \[= x_{t}^{T}\big{[}P_{i}(A-BK_{i+1})+(A-BK_{i+1})^{T}P_{i}+\alpha P_ {i}\big{]}x_{t}\] \[= x_{t}^{T}\big{[}P_{i}(A-BK_{i})+(A-BK_{i})^{T}P_{i}+\alpha P_{i} \big{]}x_{t}+x_{t}^{T}[P_{i}B(K_{i}-K_{i+1})+(K_{i}-K_{i+1})^{T}B^{T}P_{i}+ \alpha P_{i}]x_{t}\] \[= -x_{t}^{T}[Q+K_{i}^{T}RK_{i}]x_{t}+x_{t}^{T}[(\bar{P}_{i}+\Delta P _{i})B(K_{i}-K_{i+1})+(K_{i}-K_{i+1})^{T}B^{T}(\bar{P}_{i}+\Delta P_{i})+ \alpha\bar{P}_{i}+\alpha P_{i}]x_{t}\] \[= x_{t}^{T}\big{[}-Q-K_{t}^{T}RK_{i}+\bar{P}_{i}BK_{i}+K_{i}^{T}B^ {T}\bar{P}_{i}+\alpha\bar{P}_{i}BK_{i}+K_{i}^{T}B^{T}\Delta P_{i}-\Delta P_{i} BK_{i+1}\] \[- K_{i+1}^{T}B^{T}\Delta P_{i}-\bar{P}_{i}BK_{i+1}-K_{i+1}^{T}B^{T} \bar{P}_{i}+\alpha\Delta P_{i}]x_{t}.\]  
By using Lemma 3, we have the following inequality:  
\[\Delta P_{i}BK_{i}+K_{i}^{T}B^{T}\Delta P_{i}\leq H_{i}, \tag{37}\]  
and the following inequality can be obtained by Lemma 2:  
\[-\Delta P_{i}BK_{i+1}-K_{i+1}^{T}B^{T}\Delta P_{i} \leq\gamma\Delta P_{i}\Delta P_{i}^{T}+\frac{1}{\gamma}(BK_{i+1} )^{T}(BK_{i+1}) \tag{38}\] \[= \dot{x}_{t}^{T}P_{i}x_{t}+x_{t}P_{i}\dot{x}_{t}^{T}+\alpha x_{t}^ {T}P_{i}x_{t}\]0  
Additionally,  
\[= \dot{x}_{t}^{T}P_{i}x_{t}+x_{t}P_{i}\dot{x}_{t}^{T}+\alpha x_{t}^ {T}P_{i}x_{t}\]1  
where \(H_{i}=\text{maximize}(\Delta P_{i}BK_{i}+K_{i}^{T}B^{T}\Delta P_{i},x)\), and \(\Delta P_{i,\max}=\text{maximize}(\Delta P_{i},x)\), utilizing the maximize operator defined in Definition 1. Hence, \(\dot{V}_{i}(x(t))+\alpha V_{i}(x(t))\) can be bounded by  
\[= \dot{x}_{t}^{T}P_{i}x_{t}+x_{t}P_{i}\dot{x}_{t}^{T}+\alpha x_{t}^ {T}P_{i}x_{t}\]2  
Using the Lyapunov theory, the system will be quadratic stable with the convergent rate \(\alpha\) if \(\dot{V}_{i}(x(t))\leq-\alpha V_{i}(x(t))\). This condition is satisfied if \[= \dot{x}_{t}^{T}P_{i}x_{t}+x_{t}P_{i}\dot{x}_{t}^{T}+\alpha x_{t}^ {T}P_{i}x_{t}\]3 \[= \dot{x}_{t}^{T}P_{i}x_{t}+x_{t}P_{i}\dot{x}_{t}^{T}+\alpha x_{t}^ {T}P_{i}x_{t}\]4  
The above condition can be written in the matrix form, as shown in Theorem 1. By using Theorem 1, it can be seen that with the proposed improved policy, the closed-loop system will be asymptotically stable. It is also noted that Theorem 1 is also applicable for unknown nonlinear systems if they can be approximated by a linear state-space equation (Equation (1)) and if their nonlinearity is within the uncertainty bound \(\Delta P\) calculated from \(\Delta w\) in Equation (16).
# Temporal Continuity Expression for Network Topology of Space Information Systems
## 1 Introduction
In this paper, a temporal continuity expression method is proposed for the dynamic characteristics of the network topology of space information system. The core of this method is to construct the link relationship graph of space information system, which uses the time-related adjacency matrix as a mathematical expression. If the element in the matrix is 0, it means that there is no link between the two nodes. If the element in the matrix is non-zero, then the value of it is time-dependent, which indicates that there is a link existing between the two nodes and the link quality is also time-dependent. After the construction of the adjacency matrix of the link relationship graph, the network topology of the space information system at any time is a specific assignment of the adjacency matrix. That means the network topology of the space information system considering the link quality constraints at any time can be obtained, and the route planning can be carried out more accurately. The main contribution of this research is to propose a temporal continuity expression method suitable for space information systems. Compared with the existing network topology description methods, the method extends the traditional link establishment relationship from a binary function to a time-dependent function, which brings the following benefits: (1) the change trend of link quality in the network topology of space information system can be grasped in real time so that prerequisites for route planning can be provided more accurately; (2) the link quality of the space information system network topology can be predicted which affords conveniency for reconstruction and optimization of the network topology.
## 4 Lower-Level Dynamic Response Priority-Based Response Instruction Allocation in VPP
### Requirements for VPP Instruction Allocation  
There are two ways to implement the control instruction allocation in the VPP. The first method is that after receiving the control amount instruction, the VPP control center adopts the optimization algorithm according to the latest status and emergency regulation capacity of each DER device to obtain the best combinations of DER devices. The second method is that the VPP control center creates the response priority sequence of DERs in advance and selects the DER devices participating in EFC by querying the priority sequence after receiving the instructions. Compared with the time-consuming optimization solution of the first method, the method of querying the priority sequence is faster, so the decision-making of the VPP control center takes less time. Therefore, the second method is adopted in this work.  
The response sequence of DERs is determined by the VPP control center based on DERs dynamic priority sequence, so that the VPP can follow the upper control instructions. When users agree that DERs such as EVs and TCLs are managed by VPP, the VPP control  
Figure 7: Flowchart of EFC optimization model solving.  
center considers these devices to be equally important to users. The following factors need to be considered in constructing the DERs dynamic priority:  
1. fairness of different types of DER devices  
Different device types have different storage energy scales, leading to significant differences in the flexible time length. For example, EV discharge time can reach one hour or even several hours, while the flexible time of household AC is usually only a few minutes. If ignoring the difference in flexible time, the priority of TCLs may always be lower than the EV.
2. user comfort  
When DER participates in EFC, the status (SOC of EV, indoor temperature of TCLs) may deviate from its normal range. When the state is close to the user's tolerance limit, user comfort is poor. Therefore, DER with little impact on user comfort should have higher priority.