esa-sceva/satcom-chunk-collection huggingface dataset:
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 3 ETC Data-Based Traffic Sensing and Feature Extraction
### Traffic State Feature Extraction Methods
#### 3.2.1 Microscopic Feature Extraction
The multi-perspective travel time prediction model based on ETC data developed by [PERSON] et al. demonstrates the technological frontier in this field [18]. The precision of traffic flow statistics technology forms an important foundation for traffic management decisions. By counting vehicles passing through ETC gantries within unit time periods, precise vehicle-type-specific flow data can be obtained. Compared with traditional detectors, ETC data can not only provide accurate vehicle counts but also distinguish different vehicle types and identify specific vehicles, enabling refined traffic analysis. More importantly, the vehicle identity information in ETC data makes flow statistics traceable, which holds significant value for traffic behavior analysis and policy effect evaluation. From technological development trends, microscopic feature extraction is evolving toward higher precision, more dimensions, and greater intelligence. The applications of new technologies such as artificial intelligence-based feature extraction algorithms, edge computing-based real-time processing technologies, and multi-source fusion-based precision improvement methods are continuously advancing microscopic feature extraction capabilities.
## 4 Results and Discussion
### Traffic CO Prediction Results  
Two MLP models were trained and tested. The first model was trained based on MLP algorithm using nine parameters (those listed in Table 1 plus LULC) to predict the traffic CO concentration. The second model was trained based on the combination with the correlation-based feature selection (CFS-MLP) and six parameters that resulted from the CFS model. These parameters were: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed, and DSM. The CFS algorithm was implemented to select the highly correlated parameters and best parameters to predict the traffic CO that led to increased accuracy of the prediction process.  
Table 5 shows the proposed model's results when the input parameters were filtered and reduced from nine parameters to six based on the CFS algorithm which finds features that have higher correlation with the class but are uncorrelated with each other. Therefore, the highest correlated parameters were used for the prediction analysis, which resulted in improving the prediction accuracy. The relative absolute error decreased from 30.94% to 21.99% and the root relative square error also decreased from 23.48% to 19.40%. On the other hand, the correlation coefficient increased from 0.866 ppm to 0.98 ppm. The mean absolute error (MAE) was reduced from 0.99 ppm to 0.89 ppm. The root mean square error (RMSE) also decreased from 1.29 ppm to 1.27 ppm. The prediction results showed that the prediction improvement occurred after the implementation of the CFS algorithm which is able to reduce the high dimensionality, remove the low correlated data and improve the learning accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Road Traffic CO Predictors** & **R-Squared** & **F-Statistic** \\ \hline Number of heavy vehicles & 0.7546 & 32.784 \\ Number of vehicles & 0.5322 & 18.277 \\ Number of motorbikes & 0.0472 & 1.951 \\ DSM (m) & 0.0168 & 1.231 \\ Wind speed (mph) & 0.0016 & 0.124 \\ Temperature (\({}^{\circ}\)C) & 0.0014 & 0.1178 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of assessing the contribution of traffic CO predictors using the Chi-square method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**MLP Model** & \multicolumn{2}{c}{**CFS-MLP Model**} \\ \hline Best structure & 9-4-1 & Best structure & 6-3-1 \\ Correlation coefficient & 0.8657 & Correlation coefficient & 0.980 \\ Mean absolute error (ppm) & 0.991 & Mean absolute error (ppm) & 0.8925 \\ Root mean squared error (ppm) & 1.2862 & Root mean squared error (ppm) & 1.2736 \\ Relative absolute error \% & 30.94\% & Relative absolute error \% & 21.99\% \\ Root relative squared error \% & 23.48\% & Root relative squared error \% & 19.40\% \\ Total number of instances & 247 & Total number of instances & 247 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of predictions with MLP model and the proposed model (CFS-MLP) model.
## 3 Incremental Nonlinear Dynamic Inversion Control Design  
### Incremental Nonlinear Dynamic Inversion  
The goal of INDI is to obtain an incremental form of the control input by inversing the nonlinear system at the present moment, after it has been linearized. The state space expression of a conventional nonlinear system can be written as:  
\[\left\{\begin{array}{l}\dot{x}(t)=f(x(t))+g(x(t),u(t)),\\ y(t)=h(x(t)),\end{array}\right. \tag{10}\]  
where \(x(t)\) is the state vector, \(u(t)\) is the control input vector, and \(y(t)\) is the output vector. We rewrite (10) in the form of a Taylor expansion, ignoring higher-order terms as follows:  
\[\left\{\begin{array}{l}\dot{x}=\dot{x}_{0}+\frac{\partial f(x)}{\partial x} |_{x_{0},u_{0}}(x-x_{0})+\frac{\partial g(x,u)}{\partial x}|_{x_{0},u_{0}}(x -x_{0})+\frac{\partial g(x,u)}{\partial u}|_{x_{0},u_{0}}(u-u_{0})+\Delta,\\ \dot{y}=\frac{dh}{dx}\dot{x},\end{array}\right. \tag{11}\]  
where \(\Delta\) denotes the higher-order infinitesimal. Let \(\Delta u\) and \(\Delta x\) represent the increment of \(x\) and \(u\), respectively. We assume that the flight control computer has a high sampling rate such that \(\Delta u\) changes significantly faster than \(\Delta x\). Therefore, we can ignore the contribution of \(\Delta x\) to the output derivative and only keep \(\Delta u\). It follows that  
\[\dot{y}\approx\frac{dh}{dx}\{\dot{x}_{0}+\frac{\partial g(x,u)}{\partial u}|_ {x_{0},u_{0}}(u-u_{0})]=\dot{y}_{0}+H(x_{0},u_{0})\Delta u\enspace, \tag{12}\]  
where \(H(x_{0},u_{0})\) denotes the control matrix of the system. We can derive INDI control law as  
\[\left\{\begin{array}{l}\Delta u=H^{-1}(x_{0},u_{0})(\dot{y}_{c}-\dot{y}_{0} ),\\ u=u_{0}+\Delta u,\end{array}\right. \tag{13}\]where \(\dot{y}_{c}\) is the desired output derivative, \(\dot{y}_{0}\) is the output derivative at the current time, and \(H^{-1}\) is the inverse matrix of the control matrix \(H\). The input to the output dynamic of the system can be linearized as \(\dot{y}=\dot{y}_{c}\).
# ADAL-NN: Anomaly Detection and Localization Using Deep Relational Learning in Distributed Systems
## 3 Methodology
### Anomaly Localization  
Identified instance IDs are labeled as anomalous during detection. Using an instantiated directed acyclic \(R\) graph (DAG) representation of the system, we now employ a heuristic technique for localizing anomalies in a distributed system. Beginning with a set of anomalous instances, the ADAL-NN heuristic technique follows the edges of the DAG \(R\) graph to determine the root cause of the anomalies.  
The procedure begins with the collection of anomalous instances, denoted by the letter \"A\". The algorithm then examines each of these IDs to determine if they are leaf nodes (i.e., nodes with zero out-degree). If an ID is a leaf node, the search is terminated and the ID is returned as the anomaly's cause. If an ID is not a leaf node, the algorithm examines the ID's child nodes to determine if any are anomalous. If any of the child nodes are atypical, the corresponding ID is removed from the set \"A\". If none of the child nodes are anomalous, the search proceeds to the children of the children. This procedure is repeated until the anomaly's root cause is identified. This method yields a collection of anomalous instances, completing the instance-level localization of anomalies as discussed in Algorithm 3.  
```
Data:\(graph=[],anomalusInstances=[]\) Result:\(rootCauses=[]\) \(rootCauses\leftarrow\emptyset\) for\(instance\in anomalousInstances\)do if\(degree_{out}(instance)=0\)then \(rootCauses\gets rootCauses\cup\{instance\}\) else \(children\gets getChildren(graph,instance)\) \(anomalousChildren\gets anomalyLocalization(graph,children)\) if\(anomalousChildren=\emptyset\)then \(rootCauses\gets rootCauses\cup\{instance\}\) return\(rootCauses\)
```  
**Algorithm 3**Anomaly localization through the use of relation graphs  
The anomaly localization algorithm has a time complexity of \(O(n)\), where n is the number of nodes in the relation graph. This is due to the fact that the algorithm loops through all of the anomalous instances and checks their graph children but does not traverse the entire graph. The algorithm's time complexity could potentially be reduced to \(O(k)\), where k is the number of root causes, by adding a break statement after the root cause is identified; however, this would increase the algorithm's complexity.
# A Brief Review of Multipath TCP for Vehicular Networks
## 4 Cross Layer MPTCP, MPTCP with SDN, and Energy Efficient MPTCP  
With the improvement of congestion control and the scheduler, MPTCP was improved significantly. It is believed that the performance can be enhanced by increasing the granularity of control. In order to achieve a fine-grained control, MPTCP conducts cross-layer management by considering the information of other layers. For example, MPTCP takes how the network interfaces are used into consideration. Similarly, the characteristics of the centralized control of SDN is also considered to be a promising solution to refine MPTCP control. Meanwhile, there is also one concern for using MPTCP, specifically, the high energy consumption for maintaining multiple active interfaces. However, the congested flow can be shifted to a less congested one without breaking the connection in MPTCP. By doing this, the completion time can be reduced, and thus energy can be saved. In the following section, we introduce several studies that are related to cross layer MPTCP, MPTCP combined with SDN, and MPTCP energy efficiency.
## 4 Technological Challenges and Integration Issues
### ICT Infrastructure for Large-Scale Real-Time Operations
The EU's investment in exascale computing through initiatives like EuroHPC and Destination Earth provides a foundation for executing complex multi-scenario analyses at the continental scale [40]. Designing simulation platforms that can exploit parallelism, modularity, and containerized services is critical to leveraging these resources [5]. At the same time, the digital twin must remain tightly synchronized with the physical grid. This means continuously updating inputs with real-time data and recalculating system states as conditions change [20]. Achieving this requires precise timestamping, advanced state estimation algorithms, and possibly increased deployment of PMUs to enable time-aligned, wide-area measurements [39]. Integration with legacy systems is another significant challenge. Many TSOs and DSOs operate EMS and DMS platforms based on older standards and protocols, which cannot be replaced overnight. The digital twin must interoperate with these systems via adapters and protocol converters, accommodating a heterogeneous landscape of technologies [25]. Moreover, the platform must be highly available, scalable, and resilient, operating with the same level of reliability as the physical grid it mirrors. This demands fault-tolerant architectures, elastic computing environments, and rigorous cybersecurity protections [20]. The scale of investment required is considerable; the European Commission estimates that at least EUR 170 billion will be needed by 2030 to modernize ICT systems across the EU's electricity networks [41]. These investments cover not only the digital twin but the broader digitalization of grid operations, automation, and intelligence [1]. Despite these challenges, Europe is well positioned for success. Several utilities and technology providers are already piloting grid digital twin platforms, and initiatives such as ETIP SNET are promoting shared research, best practices, and architecture development. The convergence of advanced sensing, high-speed communication, scalable computing, and real-time analytics is increasingly within reach. If implemented with strong technical foundations and effective institutional coordination, the resulting ICT infrastructure will establish the basis for a fully integrated digital representation of the European power system. This will enable faster, smarter, and more resilient grid management across both national borders and interconnected sectors.
# Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change +
## 3 Experimental Verification
### Simulation with Specified Traffic
#### 3.2.3 Scenario III, Unexpected Acceleration in Target Lane  
In this scenario, the behind vehicle in target lane \(V_{IR}\) take an emergency acceleration at the start of lane change. The acceleration of vehicle is selected as \(+2\) m/s\({}^{2}\), \(+3\) m/s\({}^{2}\) and \(+4\) m/s\({}^{2}\) respectively, and the duration of brake is 3 s. Here, \(V_{H}\) can choose to increase the speed to finish the lane change, or return back to the original lane. From the Table 4, simulation A achieved safer results than simulation B and C.  
From the detailed results with \(+4\) m/s\({}^{2}\) acceleration in Figure 9, both the simulation B and C lead to a collision with \(V_{tR}\) at \(t=4.65\) s. In the simulation A, due to stricter constraints used during trajectory planning, \(V_{H}\) abandons the lane change maneuver and return to \(L_{1}\) firstly, and then succeeds in doing so before potentially being rear-ended by \(V_{tR}\). During the whole progress, the \(V_{H}\) is controlled with minimal speed change.  
Figure 8: Simulation results for Scenario II. From top to bottom, the graphs of host vehicle \(V_{H}\) are: 2-D trajectory, longitudinal velocity, longitudinal acceleration, lateral position, lateral velocity, lateral acceleration and host vehicle’s distance to the nearest front/rear vehicle.
# Kiwifruit Allergy--Molecular Basis, Diagnostics and Treatment
## 1 Introduction
(2023) [3] reports findings from the literature that showed that the prevalence of kiwifruit allergy among the 0-18 age group ranges from 0.1 to 0.2% in an Isle of Wight birth cohort study in the UK [10], and up to 60% among children with an allergy to fruit and vegetables in Portugal [11]. Similarly, for adult populations (18-97 years), allergy to kiwifruit was reported among 0.35% of birch pollen-sensitized patients in Korea [12] and among 38.4% of university students in Finland who may or may not have clinical symptoms of an allergy [13]. It is also worth mentioning that from the time the first allergic reaction to kiwifruit was reported, the world's production of this fruit has increased by approximately two orders of magnitude, with countries like China, New Zealand, Italy, Greece, Iran, Chile, Turkey, Portugal, France, and the United States being the major providers [14]. Symptoms associated with kiwifruit allergy include urtic, abdominal pain, dyspnea, rhinitis, cyanosis, and systemic responses such as anaphylaxis [15; 16]. Similarly to hypersensitivity to other sources, the allergy to this fruit cannot be treated as a homogenous disorder, as different clinical subgroups can be established [17]. Allergy to kiwifruit is developed either by direct sensitization to the allergens of this fruit or by sensitization to homologous molecules of other organisms, which can cause cross-reactivity. An example of indirect sensitization to a food is pollen food allergy syndrome (PFAS), which occurs when people with pollen allergies develop an allergic reaction upon the ingestion of various foods, including kiwifruit [18; 19]. Therefore, clinical associations of kiwifruit allergy with allergies to pollens, latex, and other organisms should be taken into consideration [20]. PFAS develops due to IgE-mediated cross-reactivity between allergenic proteins from pollens and food from botanically related plant species, due to their structural and sequence similarity [21]. For example, individuals who are allergic to birch pollen often develop PFAS to kiwifruit, apples, hazelnuts, and peaches [19; 22]. It is reported that 65-72% of cases of kiwifruit allergy are associated with PFAS. In summary, kiwifruit allergy is not a homogenous disorder, and, due to the increased popularity of kiwifruits as a nutritious food, this allergy will most likely become more frequent. At the same time, there are different challenges related to the diagnosis and treatment of kiwifruit allergy. Therefore, this review focuses on the molecules responsible for kiwifruit allergy and provides the necessary molecular basis needed to understand the pathogenesis, diagnostics, and potential treatment of this disease.
Green-note aroma compounds affect the green character of food products and the impression of freshness. They are popularly used in the food industry to reconstitute the fresh green odor of fruits and vegetables lost during processing. They are associated with the aromas of green apples, melons, cucumbers, and freshly cut grass. C\({}_{6}\)-C\({}_{9}\) carbon aldehydes and their corresponding alcohols such as hexanal, 3-_cis_-hexanal, 2-_trans_-hexanal, 3-_cis_-nonenal, 2-_trans_-nonenal, and (3-_cis_, 6-_cis_) and (2-_trans_, 6-_cis_) nonenadien are important components of green aroma notes [26]. One of the most popular green-note molecules is hexanal, characterized by a grease, grass, or leaf fresh odor. **Scheme 1. ** Scheme of the synthesis pathway of the fat-derived fragrances gamma-decalactone (**a**) and hexanal (**b**), mediated by microorganisms. The biosynthesis of green-note aroma compounds takes place in the presence of lipooxygenase (LOX, EC. 1.13.11.12) from polyunsaturated fatty acids: linolenic and linoleic acid. In the lipoxygenase pathway, polyunsaturated fatty acids are transformed into aldehydes and alcohols by the enzymes lipooxygenase, hydroperoxide lyase (HPL, EC 4.1.2.92), and alcohol dehydrogenase (EC 1.1.1.1), respectively. Hexanal is synthesizedvia the LOX and HPL degradation of linoleic acid (Scheme 1). When a plant oil is used as a reagent, then the hydrolysis of triacylglycerols by lipases into fatty acids needs to be conducted prior to the biosynthesis [15; 26; 27]. In most scientific reports concerning the biosynthesis of green aroma compounds, lipooxygenase from soy seeds has been used [28; 29; 30]. The biosynthesis of green aroma compounds is still being improved by means of genetic engineering, by incorporating genes responsible for the expression of lipoxygenase and hydroperoxide lyase into the cells of microorganisms. This is particularly important in the case of the creation of recombinants with genes for hydroperoxide lyase, which is an enzyme limiting biocatalysis [27]. The aim of this research was to analyze the influence of the selected factors on the catalytic activity of _Yarrowia lipolytica_ in the synthesis of GDL and hexanal, a representative compound of the green aroma. In the biosynthesis of both compounds with the use of whole-cell catalysts, the influence of the concentration of lipid substrate (castor oil and linoleic acid, respectively) on the obtained levels of bioaroma compounds was analyzed. Moreover, in the synthesis of GDL, in order to improve the process, the effectiveness of adding heptane (organic phase) or surfactants to the biotransformation medium and the influences of the size of the inoculum used and the emulsification of the medium were determined. In the biotransformation aimed at hexanal synthesis, attention was paid to the activity of lipoxygenase (a key enzyme in the synthesis of green fragrance compounds) and the levels of aroma synthesis inside and outside yeast cells.
# Image Dehazing Enhancement Strategy Based on Polarization Detection of Space Targets
## 5 Image Dehazing Method Based on Full Polarization Information Inversion and Image Fusion
### Basic Principle of Image Dehazing Based on Full Polarization Information Inversion
#### 5.1.3 Inversion Method for Radiation Intensity of Circularly Polarized Light  
To improve the accuracy of image restoration, the influence of circularly polarized light on image quality needs to be considered. Circularly polarized light mostly exists in nature, so there is less circularly polarized information in the reflected light of space targets; the circularly polarized state in the skylight is only to be considered instead. Polarization imaging systems with MPA cannot be directly used to acquire circular polarization information. At present, the circular MPA is still in the research and development stage; its delineation technology is not mature enough, and it still needs to be further developed. In view of the above reason, in order to better characterize the circular polarization information in the skylight in our paper, it is approximated using the circular polarization calculation method based on Stokes; the derivation method is shown in Equation (24):  
\[Docp_{air}(x,y)=\frac{\sqrt{S_{0}^{air^{2}}(x,y)\times Dolp_{air}(x,y)^{2}-S_{ 1}^{air^{2}}(x,y)-S_{2}^{air^{2}}(x,y)}}{S_{0}^{air}(x,y)} \tag{24}\]  
Since circularly polarized light exists only in skylight, its transmittance does not change with the change in detection distance. Under the condition of uniform distribution of the atmosphere, the calculation of the circular polarization degree of the skylight area can be approximated as shown in Equation (25).  
\[Docp_{air}=\frac{1}{|\Omega|}\sum_{(x,y)\in\Omega}\frac{\sqrt{S_{0}^{air\ 2}(x,y)\times Dolp_{air\ 2}(x,y)-S_{1}^{air\ 2}(x,y)-S_{2}^{ air\ 2}(x,y)}}{S_{0}^{air}(x,y)} \tag{25}\]  
In the space target image, \(\Omega\) is the total number of pixels for the selected skylight area. After the calculation of the circular polarization information of the skylight area, the average circular polarization degree is solved by using the Stokes vector corresponding to the position of pixels. Because the atmosphere is uniformly distributed, the circular polarization information of any pixel in the image can be obtained. The calculation method is shown in Equation (26).  
\[S_{0}^{cp}(x,y)=S_{0}(x,y)\cdot Docp_{air} \tag{26}\]