esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Discussion
### Experiments on VRM
#### 3.1.2. Computation of Aerodynamic Performance  
With the refined grid, the aerodynamic performance of lifting surface computed by nonplanar VRM was compared with wind tunnel test data. The NACA series airfoil data obtained by wind tunnel test are often used as the comparison validation of panel method. The asymmetric NACA-44 series airfoils were chosen for the following experiment. Based on NACA-4424-12 airfoil, a highly swept back, high aspect ratio wing was designed to compare the nonplanar VRM results with wind tunnel test data [33]. Table 3 shows the geometric parameters of the test wing.  
The test airspeed \(V=134m/s\), angle of attack \(\alpha=4.7^{\circ}\) and Reynolds number of \(Re=3.9\times 10^{6}\). In Figure 8, the changes of \(c_{L}\), \(c_{D}\) and quarter chord \(c_{m}\) were compared with the wind tunnel test data. It was found that both planar VRM and nonplanar VRM approximate the test data, while nonplanar VRM had higher accuracy. According to ref [33], the aerodynamic characteristics of seven kinds of wings based on NACA-44 series airfoils were further analyzed (Figure 8 shows the first kind of wing). The mean square error (MSE) of both nonplanar and planar VRM was analyzed as shown in Figure 9. The MSE obtained by planar VRM had been reduced to 0.0254, 0.0073 lower than the MSE of planar VRM. It can be concluded that the nonplanar VRM better approximated the wind tunnel test data. If the nonplanar VRM was applied to the analysis of symmetric airfoils, the nonplanar VRM was simplified to traditional planar VRM.
using a neural network, which handled the optimization calculation [19]. The second paper extended the neurally-controlled equalizer (EQ) method to the widely used third-octave GEQ design [20], where the 31 EQ bands also loosely approximated the bandwidths of human auditory filters [21].  
Compared to our previous conference paper [20], this article presents an improved design method using second-order band filters with a symmetric shape on the logarithmic frequency scale, which was recently proposed for an octave GEQ [22]. The proposed design allows the parametric EQ used as band filters in the GEQ to have a controllable gain at the Nyquist limit, in contrast to the earlier designs that forced the Nyquist gain to one (i.e., 0 dB) [3]. Furthermore, this paper extends both the weighted least-squares (WLS) GEQ design method and the neurally-controlled EQ method to operate in Bark bands, which are a more accurate approximation of the human auditory resolution than the third-octave bands [23].  
Currently, two important metrics in GEQ design are accuracy and computational complexity. Both aspects have an impact when a GEQ is controlled by a computer in automated tasks without a human listener fine-tuning the gains, e.g., when equalizing music to be above a masking threshold in time varying noise [24; 25]. Previous GEQs that are not very accurate (see the measurements, e.g., in [10]) have typically required a human operator to listen to the effect of the command gains as he/she controls them, since the accuracy of different command gain settings, as well as the visual feedback of the command gain sliders can be highly inaccurate. One of the goals of this work was to get the proposed neurally controlled GEQs to provide the accuracy and speed necessary to be used in automated tasks requiring fast updating of the gains, even hundreds of times per second.  
The rest of this paper is organized as follows. Section 2 briefly recapitulates the design of a cascade third-octave GEQ that is approximated with the neural network. Section 3 describes the symmetric parametric EQ filters and the improved GEQ design called the symmetric graphic equalizer (SGE), which is based on an iterative WLS method, and shows how the method is applied to design third-octave and Bark band equalizers. Section 4 discusses the structure and training of a neural network used to update the filter gains rapidly. Section 5 shows the design and computational results and compares them with previous work. Section 6 concludes the paper.
# A Novel Real-Time Virtual 3D Object Composition Method for 360\({}^{\circ}\) Video
## 2 Background Theory and Related Studies
### Related Studies  
There have been various studies relating to the 3D composition of virtual objects in 360\({}^{\circ}\) video clips. These studies are based on VR, augmented reality, and mixed reality (MR). Focusing on research on 360\({}^{\circ}\) video, [PERSON] et al. implemented a real-time lighting and material expression of virtual objects, according to the positional change reconstructing the camera trajectory from the captured 360\({}^{\circ}\) video [23]. Furthermore, the proposed MR360 is used to synthesize virtual objects with real background images. However, it is based on a fixed 360\({}^{\circ}\) video, and thus it differs from our proposed method, which contains camera movement [24].  
Similarly, [PERSON] et al. implemented real-time 3D composition using the Unity game engine through a stabilization process after camera tracking [25]. However, camera tracking was based on the captured image. Here, real-time indicates a real-time composition in a 3D program after the tracking process, not during the recording step. Our proposed method is a real-time composition performed at the same time as the video recording.  
We recently proposed a novel system that uses Microsoft HoloLens to track positions precisely for match-moving techniques [11] and studied a virtual camera for making motion-graphics using transformed data from the ZED [26]. In this paper, we propose a stabilized 3D composition system and a pre-visualization system using the ZED based on these previous studies.
###### Abstract  
Soils provide many functions as they represent a habitat for flora and fauna, supply water, nutrient, and anchorage for plant growth and more. They can also be considered as large bioreactors in which many processes occur that involve the consumption and production of different gas species. Soils can be a source and sink for greenhouse gases. During the last decades this topic attracted special attention. Most studies on soil-atmosphere gas fluxes used chamber methods or micro-meteorological methods. Soil gas fluxes can also be calculated from vertical soil gas profiles which can provide additional insights into the underlying processes. We present a design for sampling and measuring soil gas concentration profiles that was developed to facilitate long term monitoring. Long term monitoring requires minimization of the impact of repeated measurements on the plot and also minimization of the routine workload while the quality of the measurement needs to be maintained continuously high. We used permanently installed gas wells that allowed passive gas sampling at different depths. Soil gas monitoring set ups were installed on 13 plots at 6 forest sites in South West Germany between 1998 and 2010. Until now, soil gas was sampled monthly and analysed for CO\({}_{2}\), N\({}_{2}\)O, CH\({}_{4}\), O\({}_{2}\), N\({}_{2}\), Ar, and C\({}_{2}\)H\({}_{4}\) using gas chromatography. We present typical time series and profiles of soil gas concentrations and fluxes of a selected site as an example. We discuss the effect of different calculation approaches and conclude that flux estimates of O\({}_{2}\), CO\({}_{2}\) and CH\({}_{4}\) can be considered as highly reliable, whereas N\({}_{2}\)O flux estimates include a higher uncertainty. We point out the potential of the data and suggest ideas for future research questions for which soil gas monitoring would provide the ideal data basis. Combining and linking the soil gas data with additional environmental data promises new insights and understanding of soil processes.  
**Keywords:** environmental monitoring; soil gas; soil gas profile; soil respiration; carbon dioxide; methane; nitrous oxide +
Footnote †: journal: Applied Sciences
## 3 Experiments
### Experimental Results and Analysis
#### 3.3.5 Computational Cost Analysis  
To evaluate the real-time feasibility of the BBCN model, we analyzed the computational cost of its main components using the NVIDIA Jetson AGX Xavier platform. The average processing time per cycle for each key module is presented in Table 6. The visual perception tasks, particularly semantic segmentation with DeepLab, represent the most computationally intensive part of the pipeline. However, by leveraging GPU acceleration, the total processing time for the entire navigation loop is maintained at approximately 80 ms, which is well within the real-time requirements for robotic navigation at a control frequency of over 10 Hz.
# Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction
## 3 Methodology
### LiDAR Data Acquisition  
LiDAR data acquisition was performed by Terrestrial Laser Scanner which provides data in the form of (X, Y, Z) point cloud. LiDAR data acquisition was done for simulated area and for the RGIPT campus [35]. Further details for data acquisition are provided in Supplementary Section S2.1.
## 3 Design Constraints for Routing in Wireless Sensor Networks  
Due to the reduced computing, radio and battery resources of sensors, routing protocols in wireless sensor networks are expected to fulfill the following requirements [5]:  
* _Autonomy_: The assumption of a dedicated unit that controls the radio and routing resources does not stand in wireless sensor networks as it could be an easy point of attack. Since there will not be any centralized entity to make the routing decision, the routing procedures are transferred to the network nodes.
* _Energy Efficiency_: Routing protocols should prolong network lifetime while maintaining a good grade of connectivity to allow the communication between nodes. It is important to note that the battery replacement in the sensors is infeasible since most of the sensors are randomly placed. Under some circumstances, the sensors are not even reachable. For instance, in wireless underground sensor networks, some devices are buried to make them able to sense the soil [6].
* _Scalability_: Wireless sensor networks are composed of hundred of nodes so routing protocols should work with this amount of nodes.
* _Resilience_: Sensors may unpredictably stop operating due to environmental reasons or to the battery consumption. Routing protocols should cope with this eventuality so when a current-in-use node fails, an alternative route could be discovered.
* _Device Heterogeneity_: Although most of the civil applications of wireless sensor network rely on homogenous nodes, the introduction of different kinds of sensors could report significant benefits. The use of nodes with different processors, transceivers, power units or sensing components may improve the characteristics of the network. Among other, the scalability of the network, theenergy drainage or the bandwidth are potential candidates to benefit from the heterogeneity of nodes [7].
* _Mobility Adaptability_: The different applications of wireless sensor networks could demand nodes to cope with their own mobility, the mobility of the sink or the mobility of the event to sense. Routing protocols should render appropriate support for these movements.
## 3 Experiments
### Quantitative Metrics  
To comprehensively evaluate the superior performance of our proposed method, we used six quantitative metrics in our experiments. These include the peak signal-to-noise ratio (PSNR) [22] based on an error-sensitive quality metric; an index to evaluate the mean square error between the fused image and the reference image (ERGAS) [35]; the spectral angle mapper (SAM) [12] to evaluate the spectral angle shift of the fused image; and the universal quality index (UIQI) [36] and the structural similarity (SSIM) [37], the last of which is an important evaluation metric for evaluating the time required for fusion.  
The first five metrics evaluate the similarity between the fused image and the target image from several aspects, and the better the result of the metrics, the better the effect of the fusion. A higher similarity between the fused image and the target image indicates that the LR-HSI obtained after spatial blurring downsampling achieves better resolution enhancement after fusion.
###### Abstract  
This paper presents a static unitary detector (STUD)-based laser detection and ranging (LADAR) sensor with a 16-to-1 transimpedance-combining amplifier for high spatial resolution three-dimensional (3-D) applications. In order to readout the large size of a photodetector for better results of 3-D information without any reduction of the bandwidth, the partitioning photosensitive cell method is embedded in a 16-to-1 transimpedance-combining amplifier. The effective number of partitioning photosensitive cells and signal-combining stages are selected based on the analysis of the partitioning photosensitive cell method for the optimum performance of a transimpedance-combining amplifier. A prototype chip is fabricated in a 0.18-\(\upmu\)m CMOS technology. The input referred noise is 41.9 pA/\(\upmu\)Hz with a bandwidth of 230 MHz and a transimpedance gain of 70.4 dB-\(\Omega\). The total power consumption of the prototype chip is approximately 86 mW from a 1.8-V supply, and the TICA consumes approximately 15.4 mW of it.  
LADAR receiver; Static unitary detector (STUD); Partitioning photosensitive cell (PFC) method; Transimpedance combining amplifier (TICA); Three-dimensional (3-D) imaging +
Footnote †: journal: Applied
## 5 Discussion
### Significance of Anomaly Detection Approach  
Our anomaly detection system demonstrates promising capabilities for identifying malicious sensor manipulations in real time. The RGB camera anomaly detection model achieved excellent discrimination between normal operation and attack conditions, with attack scenarios consistently producing anomaly scores significantly higher than baseline. This clear separation suggests that anomaly detection could serve as a reliable trigger for fallback mechanisms when primary defenses fail [7].  
Interestingly, the DVS camera anomaly detection model exhibited different characteristics, reflecting the unique properties of event-based vision. The higher baseline variability in DVS anomaly scores indicates that event-based sensors may require more sophisticated detection algorithms that account for their asynchronous nature [28]. Nevertheless, the model successfully identified abnormal patterns during attacks, particularly when defenses were not active. Further details on the training process and the observed loss plateau indicating optimal understanding of the training data can be found in Appendix A.  
These findings support the potential of anomaly detection as a foundational component of a multi-layered security approach [44]. By continuously monitoring sensor inputs for deviations from expected patterns, autonomous vehicles could rapidly identify potential attacks and implement appropriate countermeasures before safety is compromised.
## 2 Proposed EPP-Based Beam Scanning Approaches
### Test case (5): \(R=0.5\)
#### 2.2.3 Comparison between EPP/BSA and SLL/EPP-BSA  
Based on prior simulations, we can conclude that the proposed EPP-BSA provides wider scanning ranges than the proposed SLL/EPP-BSA but with much higher SLLs at the endpoints of the scanning ranges. In this regard, the proposed SLL/EPP-BSA may be useful in interference-sensitive applications that require small scanning sectors.
## 1 Introduction
As the coupling between negative and positive sequence is very small, off-diagonal elements of the impedance matrix can be neglected for stability analysis [5; 20]. In the dq domain, the three-phase converter is disturbed in the dq frame, where three-phase variables can be transformed into the dq domain by the Park's transformation. Similar to the sequence domain, the small-signal impedance/admittance of the converter can be written by a \(2\times 2\) impedance/admittance matrix [21; 22]. In the dq domain, there is a high-coupling between d-axis and q-axis. Therefore, for the stability analysis, off-diagonal impedance matrix elements cannot be neglected like they can in the sequence domain [23; 24; 25]. As the converter controller is usually implemented in the dq frame, dq domain modelling is simpler and more efficient for the controller design compared to the sequence domain modelling. However, for a stability analysis of the whole system, dq domain impedance-based modelling is a more complex method because of high-coupling between the d-axis and q-axis. As the impedance data of the grid is usually available in the positive/negative sequence domain and the sequence domain impedance matrix is almost decupled, it is more efficient to do a stability analysis of a large system in the sequence domain. In order to have both the advantages of dq domain and sequence domain modelling, this paper presents a systematic and modular method, where the wind turbine is modelled in the dq domain and where stability analysis of the wind power plant is analysed in the sequence domain. In fact, for stability analysis, dq domain impedance is transferred to the sequence domain using a transformation matrix. The equivalent positive sequence impedance is used to analyse the wind power plant stability based on the system poles and based on Nyquist criterion of the open-loop system. The presented method is scalable and adaptable to different scales of the power system along with a low computational burden. In the following section, a wind turbine with full-scale converter configuration is considered to be modelled. The dynamics of the PLL and the time delay, which have been usually neglected in the literature on wind power plant stability analysis [24], are also included to have a more accurate model. Then, a 108 MW wind power plant connected to a weak grid is analysed under different conditions.
# Numerical Simulation of Seakeeping Performance on the Preliminary Design of a Semi-Planing Craft
## 3 Data Analysis
### Full-Scale Experiments  
This study performed full-scale experiments of the series 62 craft at 7, 14, and 33 knots and measured the heaving, rolling, and pitching motion every 30 degrees at wave heading angles ranging from 0 (head sea) to 180 degrees (following sea). The measured data were compared with the simulation data. Since the significant wave height (\(H_{1/3}\)) ranged from 1.2 to 1.4 m, the average cycle (\(\overline{T}\)) ranged from 4.2 to 4.3 s, and the sea state ranged from 2 to 3 during the three experiments, a significant wave height, average period, and sea state of 1.3 m, 4.2 s, and 3 were selected. The results generated were compared with the simulation data in terms of the heaving, rolling, and pitching motion responses for each wave angle.  
The full-scale sea trials were measured at Zuoying harbor for four days in 2003. Each sea trial for an individual heading angle under a specific sailing speed was three minutes. The statistical approach used for significant motion, which is the average value of the highest one-third of all measured results, was based on the zero-up crossing method. The statistical convergence of measured data was achieved using Rayleigh distribution within a 95% confidence interval.  
Comparisons for the three different motion responses are detailed as follows:
###### Abstract  
The deposition of aerosol particles has a significant impact on the output capacity of photovoltaic modules. Therefore, studying the deposition characteristics of aerosol particles on photovoltaic modules is of great importance for improving their output capacity. Particle morphology is one of the important parameters affecting the deposition characteristics of aerosol particles. This study introduces the spherical coefficient as a quantification method for characterizing the morphology of aerosol particles. Numerical simulations using FLUENT 2022 software were conducted to investigate the influence of the spherical coefficient on the deposition characteristics of aerosol particles on photovoltaic modules. The reliability of the numerical simulations was further validated through experimental studies. Based on the research, the following conclusions can be drawn: the airflow velocity near the surface of the photovoltaic panel increases from bottom to top, with the lowest wind speed recorded near the ground at a minimum value of 2.2 m/s and a maximum value of 3.89 m/s. The air pressure near the surface of the photovoltaic panel shows a decreasing trend from bottom to top, with the highest pressure recorded near the ground at a maximum value of 10 pa and a minimum value ranging from 3.33-5.56 pa. During the deposition process, the accumulation of particles increases with an increase in the sphericity factor. Furthermore, as the sphericity factor gradually increases, the distribution of particles on the surface of the photovoltaic panel becomes more dispersed, covering the entire surface.  
PV modules; aerosol particles; spherical coefficient; deposition characteristics; numerical simulation +
Footnote †: journal: Applied Sciences
## 5 Simulation Results  
In this section, some simulations and discussions are illustrated. We verify the proposed approach of UFMC and UFMC-CDMA RadCom systems and compare it with that of an OFDM RadCom system. First, the performance of the radar system is examined and compared with that of OFDM. Afterwards, UFMC and UFMC-CDMA communication systems are evaluated over different channels and compared with OFDM. Spreading sequences and filter-length simulations are provided for both radar and communication to find how they either degrade or enhance overall performances. All simulated scenarios were performed in MATLAB.
# Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance
## 5 Case Analysis
### Results of Electric Vehicles Route Planning Optimization
#### 5.2.1 Electric Vehicle Travel Paths  
Taking the first segment of the travel chain of vehicle 8, which does not trigger charging demand, as an example, the travel time is 6 am, and its O-D nodes are 29-11. As shown in Figure 10, the shortest distance travel route without guidance is (29-30-26-20-16-17-11). In the shortest travel time guidance path, based on real-time road congestion, the optimal travel path is (29-30-31-18-17-11).  
Figure 10: Comparison of EV travel paths.  
Figure 9: Temporal distribution of EVs travel.  
The road impedance data for the corresponding segments of the two paths are shown in Figure 11.  
The segment of 30-26-20-16-17 has the same length as the segment of 30-31-18-17. However, the driving path guided by time avoids driving through relatively more congested residential areas and commercial districts, reducing the number of segments and decreasing the time cost for the driver by 18%. Additionally, the increased driving speed on this path reduces the energy consumption of the vehicle. The comparison is shown in Table 2.