esa-sceva/satcom-chunk-collection huggingface dataset:
# Making Automotive Radar Sensor Validation Measurements Comparable
## 1 Introduction  
Automated driving is one of the biggest challenges facing the automotive industry. Advantages are expected in the areas of connection of village regions, better utilization of existing resources with accompanying minimization of vehicle downtimes, and additional technological development. However, any benefits depend on the safety of such ADFs being proven. Until it is reliable that automation is safer than humans, such systems will not be deployed in large numbers and in all areas of road transport [1]. For a variety of reasons, it is currently not possible to prove safety for such an ADF. One approach that addresses this problem is scenario-based testing [1]. These tests will be performed alongside real test drives in different simulation environments, such as software-in-the-loop, hardware-in-the-loop, or vehicle-in-the-loop. In this way, safety-critical scenarios can be performed for the vehicle and involved agents.  
Along with path planning and control, the perception of the environment is an elementary component of ADFs. Therefore, in addition to vehicle dynamics and environment models, sensor models are required to represent the entire ADF pipeline in simulation. However, it is fundamental for the simulation validation process to objectively validate the models used. Both the simulation models and real-world validation measurements play a crucial role in this process. Although the first approaches to validation measurements exist, the comparability, reproducibility, and repeatability of these measurements are often problematic, especially for Radio Detection and Ranging (radar) sensors [2] (p. 68), [3] (p. 84).  
In addition to radar, cameras, Light Detection and Ranging (lidar), and ultrasonic sensors are established perception sensors in production vehicles [4]. Radar sensors determineposition information, intensities, and speeds by means of frequency modulation, which is determined by the Doppler effect. There are different processing levels in the sensor that can be used for radar sensor model validation measurements. Radar cuboid as a level after a fast Fourier transform (FFT) algorithm in the radar processing chain and detections as a result of the application of dynamic thresholding algorithms [5], which are of interest for this publication. The object list will not be explored further in the course of the paper, as the identification of effects in further processed interfaces increases in complexity.  
Therefore, this paper presents a method to compare radar measurements at the radar cuboid and detection levels. The application of this method is intended to provide objective, comparable, and quantifiable radar sensor validation measurements for radar sensor models as part of the safety argumentation of ADFs in the context of scenario-based testing.
# Analysis of the Propulsion Performance and Internal Flow Field of an Underwater Launcher
## 4 Conclusions  
The influence of jet structure on the propulsion characteristics of an underwater gas-curtain launcher was discussed experimentally and numerically in this research. The following are the conclusions that have been reached.  
1. The experimental results show that under the same charge condition, the maximum pressure and muzzle velocity of projectile A are 53.4 MPa and 159.8 m\(\cdot\)s\({}^{-1}\), respectively. For projectiles B and C, the maximum pressure and muzzle velocity are 41.8 MPa/177.5 m\(\cdot\)s\({}^{-1}\) and 70.9 MPa/150.1 m\(\cdot\)s\({}^{-1}\), respectively. This demonstrates that the interior ballistics characteristics of a gas-curtain launcher are heavily influenced by jet structures.
2. The calculation results reveal that the three jet structures have good drag reduction effects and interior ballistics performance, but there are still some variances in the propulsion performance and flow field. In general, the central nozzle is more conductive to drag reduction than the sidewall grooves, but it also loses the effective cross-sectional area of the projectile. However, the effective cross-sectional area has a higher impact on the propulsion performance of an underwater gas-curtain launcher.
3. The nozzle construction of projectiles A and B is identical, as are the gas-curtain evolution and pressure distribution characteristics. While projectile C has a more complicated sidewall groove jet structure than the first two, its gas-curtain evolution and flow field pressure distribution characteristics are more complicated.  
[PERSON] is responsible for experimental study and numerical simulations, as well as writing the thesis. [PERSON] is responsible for modelling and thesis revision. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Key Laboratory Fund (6142604200309) and the China Postdoctoral Science Foundation (2020M681596). Not applicable. Not applicable. Not applicable.  
The authors declare no conflict of interest.
# Frequency-Domain Lifting-Line Aerodynamic Modelling for Wing Aeroelasticity
## 1 Introduction
Among them, [PERSON] developed a method capable of analysing complex wing geometries with the inclusion of winglets, based on an expanded vector form of the Kutta-Joukowski theorem proposed by [PERSON] [21]. Furthermore, [PERSON] and [PERSON] [22] developed a ULLT defined in the time domain through the combination of the Prandtl LLT with the [PERSON] unsteady airfoil theory applied at each wing section, coupled with [PERSON]'s version of the Kutta-Joukowski theorem. Almost in the same period, [PERSON] et al. [23] presented a similar method consisting of a state-space adaptation of the ULLT, where both the [PERSON] indicial response function and the circulation are expressed through a small number of additional states. Recently, [PERSON] and [PERSON] [24] presented a development of the ULLT introduced by [PERSON] that provides a less complex model, maintaining a good level of accuracy even at high frequencies. Following the research line of these last works, the aim of this paper is to present frequency-domain LLT-like formulations based on distributed loads given by (steady or unsteady) sectional theories, combined with the normalwash generated by the wake vorticity derived either from the Kutta-Joukowski theorem or its exact extension to linear unsteady aerodynamics [25]. They are applied to evaluate the aerodynamic matrix collecting the transfer functions that relate the Lagrangian coordinates of a bending-torsion wing to the corresponding generalised aerodynamic forces. Furthermore, it is shown that, sampling this aerodynamic matrix in a given range of frequency, it is possible to determine its ra tional matrix approximation and hence identify a state-space aerodynamic reduced-order model [26] that, coupled with the wing structural dynamics equations, yields a state-space formulation for wing aeroelasticity that may conveniently be applied for a stability analysis and control design purposes. In the following, the detailed derivation of the proposed frequency-domain LLT aerodynamic modelling, the definition of the corresponding aerodynamic matrix and its rational matrix approximation procedure are described. Then, the numerical investigation examines the influence on the evaluated aerodynamic transfer functions of the different sectional load theories considered, of the wake vorticity modelling and of different approximations of the Kutta-Joukowski theorem for unsteady flows. The results, shown in terms of the magnitude and phase of the components of the aerodynamic matrix, are compared with the predictions determined by a computational tool based on a boundary element method (BEM) for three-dimensional potential flows extensively validated in the past [27; 28; 29; 30; 31; 32; 33; 34].
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 5 Future Trends
### Model-Driven or Data-Driven Control  
One of the most significant advantages of a DT is its use for single management platform data collection, processing, sharing, and future state prediction for control. However, this is quite challenging at the moment due to the numerous platforms (owned by different companies) applicable to different components within the system. Data collection and sharing across these platforms is extremely limited. Even when a single platform is considered, granular information required for decision-making on other platforms may not be accessible. This becomes a huge challenge to the implementation of any form of DT (model-driven/data-driven).  
While there may not be a straightforward recommendation for a DT to either be model-driven or data-driven, it is considered that a full PV-DT would benefit from hybrid processing and control. This is because several functional aspects of the PV installation strongly rely on physics-based models to accurately model their expected performance. In addition, during the initial stages of the PV installation completion, there is very limited data from the specific site. However, over time, data-driven approaches become more suitable for tracking or benchmarking the site performance. The possibility of hybrid control implies a need to develop data fusion techniques that would suit various geographical locations in the presence of climate change.
## 3 Numerical Modelling  
### Model Set-Up  
An overview of the modelling scope is provided in Figure 3. The numerical simulation includes both a frequency and a time domain model. The coefficients of hydrodynamic forces were obtained by the frequency-domain boundary element method solver OrcaWave [28]. The diffraction analysis was considered as 31 wave periods ranging from 4 s through to 200 s and 9 wave headings between 0 and 180 degrees, including 0, 45, 90, and 120 degrees. First, the OC4 floating platform model (Figure 4), with the mesh sensitivity checked by [26], generated the meshes using ANASYS AQWA and imported them to OrcaWave for diffraction analysis. Then, the OrcaWave-generated data, including first-order wave load data and second-order drift loads plus stiffness, added mass and damping data were brought into the Orcaflex model. Then, the other components including the mooring system and wind turbine were built and connected to the platform. Finally, the physical properties of the wind turbine, mooring system and floating platform were implemented in a coupled nonlinear time-domain model to estimate the hydrodynamic response.  
As shown in Figure 5, the investigated FOWT houses an OC4 DeepCwind semi-submersible platform and a 5-MW baseline wind turbine. This selected turbine has been publicly used as a reference in several previous studies. The general parameters of the 5-MW turbine and the tower are listed in Tables 2 and 3, respectively.  
Figure 2: Discretized mooring line model.  
Figure 4: The DeepCWind OC4 semi-submersible panel mesh.  
Figure 5: OC4 DeepCwind semi-submersible system: (**A**) Side view; (**B**) Front view.  
Figure 3: The flowchart of the simulation.  
The OC4 DeepCwind platform includes a 6.5 m-diameter main column and three offset columns that are connected to the main column through a series of smaller diameter pontoons and cross members, and the tower is cantilevered at an elevation of 10 m above the still water level (SWL) to the top of the main column (MC). The centre of mass (CM) is located at 13.6 m along the platform centrelineF below the SWL. The draft of the platform is 20 m. Details of the floating platform are presented in Table 4[29].  
The configuration and parameters of the three-leg catenary mooring system used in the FOWT are shown in Figure 6[29]. Three mooring lines were applied, each with an unstretched length of 835.5 m\({}_{i}\); the other properties are presented in Table 5[25]. The code comparison of the OC4 DeepCWind model is presented in Appendix A. The hydrodynamic results displayed good agreement with the other numerical tools[30].
# A Tale of Many Networks: Splitting and Merging of Chord-like Overlays in Partitioned Networks
## 4 Evaluation
### Evaluation Goals--Metrics  
We specify criteria which explain the performance and correctness of a single merger algorithm. First of all, a merger should be able to merge multiple overlay constructs in  
Figure 9: Example of two nodes merging the same ring, using the Ring Reunion Algorithm.  
a certain time, meaning that multiple constructs should be combined in such a way that one global ring is created as a result. Second, a merger should be capable of arranging all successor pointers in a proper way, since this is the most important criterion that routing within a ring is possible. Finally, the costs in terms of traffic are to be evaluated to decide whether the overhead of the approach is acceptable. Next, we discuss the metrics in detail.  
**Number of Constructs:**  
With this metric, the current number of constructs is observed in order to determine the number of isolated communication islands in the overlay. To ascertain the number and types of constructs, we maintain in our evaluation environment information about peers and their successor pointers. Each connection between peers is then traversed in order to detect present constructs.  
**Correct Pointers:**  
For better evaluation of any merger algorithm, we determine the number of correct successor pointers at a specific time. Therefore, we extended the analyzer in our evaluation environment in the way that each successor pointer is periodically compared with the value it should contain in a single global ring. Consequently, this metric describes perfectly the fraction of current correct pointers in comparison to overall correct pointers.  
**Traffic Overhead:**  
In order to judge the quality of the presented algorithms, it is also necessary to quantify the overhead in terms of message and bandwidth consumption. Ideally, the traffic overhead of an merging approach should be in relation to the number of constructs in the underlying network. With one large ring, the overhead should be minimal; meanwhile, with more rings and constructs, the contact searching and merging overheads are acceptable, and can rise.
# Fast Connectivity Construction via Deep Channel Learning Cognition in Beyond 5G D2D Networks
## 4 Performance Analysis
### Proof-of-Concept and Testbed Setup  
The proof-of-concept is developed based on four devices (one smartphone and three laptops) that configure an out-band D2D network via WiFi [14]. In the D2D network system, one smartphone has its mobility with maintaining network connection to the fixed laptops through the fast connectivity construction for low latency communication. Our D2D network system and LMK are implemented with the Java programming language. A summary of the parameters used in the simulations is shown in Table 1.  
Three laptops in the network system are the node denoted by DN1, DN2, and DN3, respectively. DNs are performed as access points with their own MAC link names. The smartphone has mobility with 5 km/h moving speed in the coverage of the DNs and acquires context while moving [29]. If the smartphone moves out of coverage of the DN, it tries to make a connection to another DN because the smartphone might be able to lose L3 connection to the DN. When the smartphone moves forward passing through the DNs, latency time is measured to maintain connection to the DN, which shows the best link quality. Thus, the smartphone is always ready to send data packets to the DNs without delay for making a connection [30]. In addition, the performance related to LMK with  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline D2D Type & Outband D2D with WiFi [14] \\ Number of Nodes & 4 (3 fixed and 1 mobile) [29] \\ Number of Packets & 70\(\sim\)100 [30] \\ Moving Speed & About 5 km/h [29] \\ DNN Epoch & 100\(\sim\)1000 \\ Number of DNN Layers & 3\(\sim\)9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
changes in DNN properties is investigated in order to check through the appropriate setup for cognitive networking applications based on on-node deep learning.
## 1 Introduction
### Contributions and Paper Organization  
The definition of the optimal trajectory of an UAV to collect data from buried sensor nodes is an emerging application that, to the best of our knowledge, has not yet been addressed in the literature. The contribution of this paper is to propose an efficient algorithm, fast and easy programmable, to solve the CETSP applied to this application. The algorithm is decomposed of three steps, namely the definition of new target locations in case of intersections, the use of an evolutionary algorithm to find a sub-optimal order, and finally an iterative geometrical reduction to significantly reduce the trajectory length. This algorithm is programmed in a software developed in C++. In comparison to the previously presented work, this algorithm combines the resolution of a combinatorial problem (TSP) with geometrical heuristics, applied to a problem including the possibility of intersections.  
The paper is organized as follows. The modeling and the methodology proposed are presented in Section 2. The algorithm is applied to two user cases. The results are reported in Section 3. The conclusion, discussions and future research directions are presented in Section 4.
## 1 Introduction
Recent researchers have usually modeled it as an optimization program from the day-ahead perspective. For example, [21; 22] analyzed historical power consumption data, and formulated the energy-saving problem as a day-ahead offline optimization. Then, mixed-integer linear programming (MILP) can be derived from the original problem and computationally worked out. However, customers' satisfaction is not included in detail, and a day-ahead form is not robust to uncertainties. Additionally, machine learning techniques are adopted in some papers [23; 24]. These studies are able to identify electricity scenarios using machine learning networks, while also determining the energy-saving strategy according to the relevancy between separate appliances and scenarios. In our previous work, Hidden Markov Model (HMM) methods were also implemented in the energy-saving project, but only in a small-scale pilot project [25]. It is worth noting that these machine learning-based methods are still not practical for many residents, due to the limited computational capability of HEMS. Thanks to the prospect theory-based utility function proposed in this paper, we tackled the energy-saving problem from the perspective of a real-time evaluation. Roughly speaking, we only need to maximize the utility function at each time slot, while minimizing the energy cost. This can be formulated as a straightforward mathematics problem, which ensures that the optimal strategy can be derived efficiently. The contributions of this paper can be summarized as follows. 1. In order to define a utility function for all kinds of electric appliances for residents, this paper categorizes general appliances into two types: The time-sensitive type, and the temperature-sensitive type. Based on this clustering, we extend the utility function leveraged in [13; 14] into two types, covering more kinds of electric appliances in HEMS. 2. Considering the bounded rationality of customers in an energy-saving program, prospect theory in behavioral economics is incorporated to extend the utility functions into a novel concept; the energy-saving cost. This depicts the real behavior of customers in energy-saving better than conventional utility functions. 3. An energy-saving cost-based optimal appliance scheduling model is formulated. In our model, the energy-saving cost is used as a constraint, and HEMS only need to minimize the electric power consumption in acceptable regions. The method is computationally efficient, making it suitable for use with HEMS. The remainder of this paper is organized as follows. Section 2 introduces the time-sensitive utility function and temperature-sensitive utility function, and the energy-saving cost is also defined. Section 3 proposes the optimal energy-saving model based on energy-saving cost. A case study is shown in Section 4, and the conclusions are outlined in Section 5.
## 3 Incipient Fault Detection Method Based on LOPV and KL Divergence
### Detecting Incipient Faults Using LOPVs and Dynamic Thresholds  
In this section, sliding windows with the length of \(n_{1}\) are used to extract and monitor the on-line data in real time. The on-line data extracted by the \(k^{th}\) sliding window are \(Y_{k}\in R^{n_{1}\times m}\). This article intends to use the idea of hypothesis testing to detect incipient faults for each \(Y_{k}\), as shown in Figure 2. Let us assume that \(Y_{k}\) is faulty, then we test this hypothesis holds. If the hypothesis held after testing, it is considered that a fault has occurred in \(Y_{k}\). Otherwise, \(Y_{k}\) is normal. The specific hypothesis testing steps are as follows:  
1. Let \(Y=Y_{k}\), we assume that \(Y_{k}\) is faulty. The method is used as described in Section 3.1 to find the local optimum PV \(w_{k}\) for fault detection between the on-line data \(Y_{k}\) and the historical normal data \(X\).
2. Let the projection of \(X\) and \(Y_{k}\) on the local optimum PV \(w_{k}\) be \(Xw_{k}\) and \(Y_{k}w_{k}\).
3. The KL divergence \(KLD(Xw_{k}||Y_{k}w_{k})\) of \(Xw_{k}\) and \(Y_{k}w_{k}\) is calculated.
4. The threshold \(\varepsilon_{k}\) of the local optimum PV \(w_{k}\) is set according to the given significance level \(\alpha\).
5. If \(KLD(Xw_{k}||Y_{k}w_{k})>\varepsilon_{k}\), then assuming that \(Y_{k}\) is faulty is correct. Otherwise, \(Y_{k}\) is normal. Let \(k=k+1\), the next sliding window \(Y_{k+1}\) will be tested from steps 1 to 5.  
For each on-line datum \(Y_{k}\), the method proposed in this paper will find a local optimum PV \(w_{k}\) that is most sensitive to the fault. After \(w_{k}\) is obtained, we can obtain vector \(h_{X}(w_{k})\). According to reference [22], \(Kh_{X}(w_{k})\) obeys the chi-square distribution with one degree of freedom, \(Kh_{X}(w_{k})\sim\chi^{2}(1)\), Where \(K\) is a large integer. Given the significance level \(\alpha\), the method of setting the threshold \(\varepsilon_{k}\) is shown in Equation (19). In Equation (19), \(\gamma\) is a constant. We suggest \(\gamma=E(h_{X}(w_{k}))\), because there may be \(E(h_{X}(w_{k}))\
eq E(h_{X}(w_{k+1}))\), which leads to \(\varepsilon_{k}\
eq\varepsilon_{k+1}\). Generally, \(w_{k}\
eq w_{k+1}\), thus,\(\varepsilon_{k}\
eq\varepsilon_{k+1}\). Therefore, dynamic thresholds are used to detect faults.  
\[\varepsilon_{k}=\frac{1}{K}\chi_{\alpha}^{2}(1)=\gamma\chi_{\alpha}^{2}(1) \tag{19}\]