esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Datasets  
The test vehicle collected three loops of data in each of the two experimental scenes at speeds below 20 km/h. In the industrial park's underground parking scene, the three loops of data consisted of 330 nodes, 358 nodes, and 287 nodes, respectively. In the mall's underground parking scene, the three loops of data comprised 1482 nodes, 1443 nodes, and 1658 nodes, respectively. The data from the first loop in both scenes were used for the map construction, while the data from the second and third loops were utilized for the localization experiments.  
In this paper, the collected around-view images were subjected to linear pixel value scaling to reduce the illumination, thereby simulating around-view image data acquisition under low-light conditions, as shown in Figure 10. The left image in Figure 10 represents the original image captured under sufficient lighting conditions, while the right image has had its illumination reduced to 50% of the original. With the aforementioned processing, each loop of the localization data underwent four sets of experiments: Group 1, where both the lighting conditions for the localization data and the map data were strong; Group 2, where the lighting conditions for the localization data were weak while those for the map data were strong; Group 3, where the lighting conditions for the localization data were strong while those for the map data were weak; Group 4, where both the localization data and the map data had weak lighting conditions. In summary, this paper employed the method of linear pixel value scaling to control the illumination as a variable, allowing for the assessment of the proposed localization method's robustness to varying lighting conditions.  
Figure 9: Experimental platforms. (**a**) Shows the test vehicle used in the experiments is the BYD e5 electric car; (**b**) illustrates the sensor installation locations.
###### Abstract  
Research in on-orbit processing enables edge AI deployment over satellite optical networks. However, these operations induce frequent battery discharge cycles, particularly depth-of-discharge (DoD) events, which accelerate degradation and curtail satellite longevity. To address this, we propose green task-aware routing planning (GreenRP), a task-aware routing framework that achieves sustainable edge AI through dynamic task offloading and discharge-resilient path orchestration. GreenRP employs a novel battery aging model explicitly coupling DoD effects with laser inter-satellite link dynamics under AI workloads, enhancing system sustainability. Comprehensive evaluation on a 1152-satellite constellation demonstrates that GreenRP extends network lifetime by 176% over shortest-path routing while meeting latency and completion rate targets. This work enables reliable edge AI via sustainable satellite resource utilization.  
satellite optical networks; laser inter-satellite link; edge AI; energy-efficient routing 20252025143075  
1
## 4 Numerical Solver
### Aerodynamic Solver  
The aerodynamic analysis is performed by a solver based on the BEM approach introduced in Reference [34] and further improved in Reference [35]. Considering incompressible, potential flows such that \(\mathbf{v}=\
abla\varphi\), the potential field, \(\varphi\), is assumed to be given by the superposition of an incident potential, \(\varphi_{I}\), and a scattered potential, \(\varphi_{S}\) (i.e., \(\varphi=\varphi_{I}+\varphi_{S}\)). The scattered potential is determined by sources and doublets distribution over the surfaces of the blades, \(S_{B}\), and by doublets distributed over the wake portion that is very close to the trailing edge from which emanated (near wake, \(S_{W}^{\text{N}}\)). The incident potential field is associated to doublets distributed over the complementary wake region that composes the far wake \(S_{W}^{F}\), see Reference [34]. The incident potential is discontinuous across \(S_{W}^{F}\), whereas the scattered potential is discontinuous across \(S_{W}^{N}\) and is represented by [34]  
\[\varphi_{S}(\mathbf{x},t)=\int_{S_{B}}\left[G\left(v_{n}-u_{n}\right)-\varphi_{S} \frac{\partial G}{\partial n}\right]dS(\mathbf{y})-\int_{S_{W}^{\text{N}}}\Delta \varphi_{S}\frac{\partial G}{\partial n}\,dS(\mathbf{y}), \tag{2}\]  
where \(G=-1/4\pi\,\|\mathbf{y}-\mathbf{x}\|\), while \(\Delta\varphi_{S}\) is the potential jump across the wake surface, known from the history of potential discontinuity at the blade trailing edge through the Kutta-Joukowski condition [36; 37]. In addition, \(v_{n}=\mathbf{v}_{B}\cdot\mathbf{n}\), with \(\mathbf{v}_{B}\) representing the body velocity and \(\mathbf{n}\) its outward unit normal, whereas \(u_{n}=\mathbf{u}_{I}\cdot\mathbf{n}\), with \(\mathbf{u}_{I}\) denoting the velocity induced by the far wake.  
Considering the far wake discretized into panels, assuming the potential jump constant over each panel, and recalling the equivalence between surface distribution of doublets and vortices, the incident velocity field is evaluated through the Biot-Savart law. In order to assure a regular distribution of the induced velocity within the vortex core, the Rankine finite-thickness vortex model is applied [34]. The shape of the wake surface is determined as part of the solution evaluating the motion of panel vertexes with the velocity field induced by wakes and bodies.  
Once the potential field is known, the Bernoulli theorem yields the pressure distribution [35] from which, in turn, blade loads can be readily evaluated.
# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
## 3 Results
### Original Results and Validations Using Ground Observations at Annual and Monthly Scales
GPROF2014 was Bayesian in nature, and used \"dynamic\" detection threshold values, compared with GPROF2010 of regression  
Figure 4: Scatterplots of the validation between ground observations and (**a**) the original TMPA data at a spatial resolution of 0.25\({}^{\circ}\); (**b**) the resampled IMERG data at a spatial resolution of 0.25\({}^{\circ}\); and (**c**) the original IMERG data at a spatial resolution of 0.1\({}^{\circ}\), at the annual scale. Figure 5: Monthly mean precipitation from 113 rain gauges (gray bar), the original TMPA (blue line), the resampled IMERG (green line) and the original IMERG (red line) for the TP from January to December 2015.  
and relative \"static\" detection threshold values. Therefore, GPROF2014 resulted in much less \"false\" alarm. During wet seasons, both TMPA and IMERG corresponded well with ground observations with R\({}^{2}\) values approximately 0.70. IMERG was still more accurate with bias \(\sim\) 10%, MAE \(\sim\) 22 mm and RMSE \(\sim\) 32 mm, than TMPA (bias \(\sim\) 20%, MAE \(\sim\) 25 mm, RMSE \(\sim\) 37 mm). Regrading to the resampled IMERG data at 0.25\({}^{\circ}\) resolution, the statistical results were very close to those of the original IMERG at 0.1\({}^{\circ}\) and thus provided more accurate precipitation estimates than TMPA at 0.25\({}^{\circ}\). Based on these evaluations, we concluded that IMERG data were better than TMPA data in terms of accuracy, with anomalies removed at the annual scale.
# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
###### Abstract  
The increasing complexity of the UAV aerodynamic design, imposed by novel configurations and requirements, has highlighted the need for efficient tools for high-fidelity simulation, especially for optimization purposes. The current work presents an automated CFD framework, tailored for fixed-wing UAVs, designed to streamline the geometry generation of wings, mesh creation, and simulation execution into a Python-based pipeline. The framework employs a parameterized meshing module capable of handling a broad range of wing geometries within an extensive design space, thereby reducing manual effort and achieving pre-processing times in the order of five minutes. Incorporating GPU-enabled solvers and high-performance computing environments allows for rapid and scalable aerodynamic evaluations. An automated methodology for assessing the CFD results is presented, addressing the discretization and iterative errors, as well as grid resolution, especially near wall surfaces. Comparisons with the results produced by a specialized mechanical engineer with over five years of experience in aircraft-related CFD indicate high accuracy, with deviations below 3% for key aerodynamic metrics. A large-scale deployment further demonstrates consistency across diverse wing samples. A Bayesian Optimization case study then illustrates the framework's utility, identifying a wing design with an 8% improvement in the lift-to-drag ratio, while maintaining an average \(y^{+}\) value below 1 along the surface. Overall, the proposed approach streamlines fixed-wing UAV design processes and supports advanced aerodynamic optimization and data generation.  
CFD automation; fixed-wing UAV; wing design; uncertainty; optimization +
Footnote †: journal: _Algorithms_  
1
## 3 Proposed Method
In the first training phase, speaker \(A\)'s waveform was fed into the generator \(G_{A\to B}\) to generate speaker \(B\)'s speech. Using this converted waveform \(w^{A\to B}\), the generator \(G_{A\to B}\) and discriminator \(D_{B}\) were trained. Also, the generator \(G_{B\to A}\) and discriminator \(D_{A}\) were trained in a similar fashion by swapping the source and target speakers. In the second phase, the converted waveform \(w^{B\to A}\) from the first phase was fed into the generator \(G_{A\to B}\) to generate waveform \(w^{B\to A\to B}\). Then, the generator \(G_{A\to B}\), discriminator \(D_{B}\), \(MSD_{B}\), and \(MPD_{B}\) were trained. Similarly, the generator \(G_{B\to A}\), discriminator \(D_{A}\), \(MSD_{A}\), and \(MPD_{A}\) were trained by swapping the source and target speakers. These two phases were repeated until the model converged. It should be noted that the first phase was completely unsupervised learning using non-parallel data, that is, \(w^{A}\) and \(w^{B}\) were not the same transcription utterances. On the other hand, since the second phase required parallel data to compute the feature-matching loss, the pseudo-parallel data, that is, \(w^{A}\) and \(w^{A\to B}\) (as well as \(w^{B}\) and \(w^{B\to A}\)), which were the same transcription utterances from different speakers, were utilized.
# A Bibliometric Analysis and Visualization of In-Vehicle Communication Protocols
## 5 Conclusions  
This paper offers a detailed bibliometric and co-citation analysis of the existing literature on connected vehicles, vehicular networks, and the intersections of cybersecurity. The results point to a deep and developing research field, noting the foundational work of important figures such as [PERSON] and [PERSON]. The author and source clustering showcase particular focus areas such as disruption detection, in-vehicle communication protocols, vehicular machine learning, and telematics security. Critical research is conducted in these fields, and Ad Hoc _Networks_, _ACM Computing Surveys_, and _Accident Analysis & Prevention_ serve as platforms for new theories that are empirically grounded and valuable to practice. Such journals note the impact of their publications. The country analysis illustrates the prominence of China, the US, and Australia, which indicates the global interest and collaboration in the security issues of connected mobility. The dynamic and interdisciplinary nature of the discipline is further evident through the caliber of some of its other conferences, including the _IEEE Symposium on Security and Privacy_ and the _IEEE Intelligent Vehicles Symposium_.  
The findings show a notable change in scholarly attention from the more traditional aspects of concern, such as security protocols and architectures, to more comprehensive AI-driven and user-centric methods. This change aligns with the industrial evolution of connected vehicle technologies and the more sophisticated security challenges [24].  
In contrast with recent narrative reviews, this study not only confirms dominant trends but also visualizes and exposes emerging, under-represented themes in the ITS research ecosystem.  
In general, this study illuminates the particular aspects of the discourse within the field, which are likely of interest to researchers, policymakers, and technology developers. Mapping described theories, contributors, and knowledge clusters highlight strengths while laying the groundwork for future research. Connected vehicles are expected to be integrated into the innovative city framework; therefore, concentrated academic attention will be needed to ensure that the vehicles are secure, efficient, and ethically used within the transport system.
## 4 Discussion
### Acoustic Parameters
#### 4.1.3 C\({}_{80}\) and D\({}_{50}\)  
C\({}_{80}\) and D\({}_{50}\) parameters are an indication of balance between early- and late-arriving energy as ISO 3382-1:2009 states. The clarity measure C\({}_{80}\) describes the temporal transparency of musical performances and is calculated from the tenfold logarithm of the ratio between the sound energy arriving at a reception measuring position up to 80 ms after the arrival of the direct sound and the following sound energy. D\({}_{50}\) (\"definition\" or \"Deutlichkeit\") is sometimes used for speech conditions and results from the ratio between the sound energy arriving at the reception measuring position up to a delay time of 50 ms after the arrival of the direct sound and the entire energy.  
Results for C\({}_{80}\) and D\({}_{50}\) (Figures 7 and 9, Tables 1 and 2) for all measurements (12 positions method) compared with measurements with a dodecahedron speaker show a mean absolute error of less than 1.7 dB. For D\({}_{50}\) the results show a mean absolute error of 10%. These results are satisfactory but not as much as the results for the RT.  
Again, a possible explanation might be that the differences of the sound fields created between the dodecahedron speaker and the proposed method are more profound in the early stages of the impulse response measurements created for each case. Hence, since C\({}_{80}\) and D\({}_{50}\) parameters are an indication of early-to-late arriving sound energy, these differences affect the measurement of C\({}_{80}\) and D\({}_{50}\) more than the measurement of RT.  
Furthermore, there seems to be a deviation of the results at high frequency range, which should be due to the different speaker directivity compared to the dodecahedron speaker. More on this will be discussed in the next section.
# A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis +
## 2 Literature Review
However, one of the disadvantages of using thermal imagers is that the detection cannot be done if leaked gas has the same temperature as the surrounding environment [17]. In recent years, many studies focused on the use of drones and mobile robots for gas leak detection applications. [PERSON] et al. (2020) used a microcontroller-based gas sensor array together with an imaging system installed on a drone for the sole purpose of localizing \(CH_{4}\) emission hotspots [18]. Some studies employed measuring instruments for gas leak detection installed on an aerial platform as a candidate solution for gas leak detection. [PERSON] (2020) presented an aerial near-infrared laser sensor platform creating quantitative plume images by scanning laser beans for locating and visualizing methane gas emissions [19]. The study demonstrates that the introduced active laser-based QGI technology is more sensitive, quantitative, and independent of ambient conditions than passive thermal infrared optical gas imaging systems. Another study utilized a modified version of an open-path tunable laser diode absorption spectrometer installed on a fix-wing drone for locating the methane gas emissions in upstream natural gas networks [20]. In addition, in the study conducted by BAM Federal Institute of Materials Research and Testing in collaboration with AirRobot, a micro-UAV platform which is capable of measuring hazardous gas concentrations and locating the sources were presented that uses five gas detectors embedded in an aerial platform [21]. One of the popular approaches for drone-enabled gas leak detection is the use of optical imagers like thermal imaging systems for aerial platforms. [PERSON] et al (2020) introduced a real-time automated technique for hydrocarbon gas leak detection. They used Faster R-CNN neural network integrated with optical gas imaging technology [22]. Due to the real-time capability of the algorithm, it can be possible to embed the technique in  
Figure 1: The categories of gas leak detection approaches and their subcategories. an aerial platform for instant detection and reporting. [PERSON] et al. (2020) addressed the visualization of methane gas leak detection by using an LWIR Cryogenic multispectral infrared camera installed on a Hexarotor drone [23]. One study investigates a comparison study between a few approaches for gas leak detection to provide a more comprehensive analysis of available methods [24]. They employed different gas sensors and a thermal camera installed on a Quadrotor for the experiment. The integrated system provides a gas distribution map of the investigated area. [PERSON] et al. (2009) proposed an automated system for detection of pressured air and gas leaks [17]. The system takes advantage of infrared thermography embedded in a mobile robotic platform to conduct automated routine inspections of facilities.
# Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
## 1 Introduction  
Despite the development of sensing and computer technology, autonomous driving systems, like mobile robots and autonomous vehicles, have received considerable academic attention and have seen an increase in commercialization. In logistics services, mobile robots are used as logistics robots, which are in charge of moving goods as well as in several other industries, including food delivery, restaurant and cafe service, informational guidance, and airport cleaning [1]. Modern intralogistics systems increasingly use autonomous mobile robots (AMRs) rather [2] than autonomously guided vehicles (AGVs). AMRs, unlike AGVs, may move in open spaces without needing extra floor markers such as painted lines/magnetic tapes, which are most frequently utilized when more flexibility is needed in jobs involving cargo movement. Even though they are free to move around, to improve the safety and efficiency of their operation, it is frequently necessary to restrict the areas in which they are allowed to move [3]. Using DRL for mobile robot indoor path planning has become a prominent trend in recent years, and it has done so with great success. However, given the context of restricted computational resources, the extensive application of DRL-based path planning to mobile robots has been severely constrained by the excessively lengthy training period. Map-free advantages, strong learning capacity, less reliance on sensor accuracy, etc. are all provided by DRL. In order to train DRL-capable robots, a 3D simulation environment with a physics engine is often needed. Physical laws in this case constrain the robot's ability to move. Based on the interactive trial-and-error process used in its execution, training takes a lot longer. As a result, the method struggles to converge to the intended targets in complicated contexts or activities with randomly initialized network parameters. For instance, the weights and biases may worsen the DRL-enabled path planning's performance [3]. Furthermore, most DRL frameworks constantlyexecute actions whenever impacts from observation states are deduced. Because those actions are frequently localized, this may restrict and limit the potential of a mobile robot to make an appropriate global judgment.
## Materials and Methods
### Algorithms
#### 2.4.4 CA Collision Detection  
After receiving traffic information about aircraft of interest as resulting from the CA coarse filtering processing, the collision detection function checks for potential collision conditions between the ownship and each considered aircraft. The collision detection is based on the calculation of possible breach of spherical collision volume by the ownship with respect to all the surrounding traffic vehicles: a pair-wise check is implemented for each and all the surrounding traffic vehicles that are provided by the dedicated CA coarse filtering functionality, and the collision volume breach is considered only if occurring within the specified emergency time horizon (greater than the tactical time horizon of the SA functionality) that is considered the collision avoidance look ahead time by the ASACAS. The output of this functionality is a list of potentially colliding vehicles with associated collision geometry relevant data.  
For the collision avoidance functionality, a suitable safety bubble that is smaller than the separation assurance volume is considered. Based on the near mid-air collision definition by the Federal Aviation Administration (FAA) [32], which refers to the possibility of collision during an encounter in which the aircraft are within 500 ft of each other, the safety volume for the collision avoidance functionality implemented by the ASACAS is a spherical volume of airspace centered on the aircraft with a radius of 500 ft, as represented in Figure 4, where the relation with the separation assurance volume is also indicated, even if not in scale.  
In addition to such collision avoidance volume nominal dimensions, a suitable extra-size is applied due to the uncertainties affecting sensor measurements and aircraft maneuvering. This additional extra-size is obtained by means of an incremental sizing factor that increases collision volume dimensions according to the closure rate between the ownship and the involved threat. Therefore, the collision avoidance safety volume is obtained as a sum of the nominal (minimum) size and the closure rate-dependent calculated extra-size, resulting in a variable overall size due to the variable closure rate between the considered aircraft.