esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Studies
### An Experimental Case Study on Complex-Shaped Metallic Turbine Blades
#### 4.1.3 Reference MD Space Based on Healthy and Defective Samples
Classification accuracies of 98.56% and 97.85% were obtained for the training data and testing data, respectively. Compared to the results using the reference MD space based on healthy samples in Section 4.1.1, a classification accuracy quantifies the same percentage of samples being correctly identified in the testing dataset. However, this is referred to as the result of a single trial and cannot be generalized. Thus, statistical classification results of 20 independent runs based on the RF technique are summarized in Table 4.  
A clear enhancement can be found for \(\mathbf{IMCS}_{\mathbf{RF}}^{\mathbf{BPSO}}\) (SMD-R1+SMD-R2), compared to \(\mathbf{IMCS}_{\mathbf{PSO}}^{\mathbf{BPSO}}\) (SMD-R1+SMD-R2), due to the use of nonlinear decision boundaries. However, comparing \(\mathbf{IMCS}_{\mathbf{RF}}^{\mathbf{BPSO}}\) (SMD-R1+SMD-R2) with the classifier constructed with a reference MD space based on healthy samples only (i.e., \(\mathbf{IMCS}_{\mathbf{PSO}}^{\mathbf{BPSO}}\) (SMD-R1)) indicates only a marginal enhancement in training and testing accuracy.
## 2 Federation Deployment Control Protocol  
The FSS concept proposes the establishment of win-win collaborations between satellites to leverage unused satellite resources. In this context, a resource is an abstract object that encapsulates a system capacity, such as memory storage or computational capacity. These resources are consumed or released by the different actions or tasks of the satellite. Therefore, these tasks change the state of the resources over time, making them available during a lapse of time. Federations are established according to the availability of these resources, and the need to make use of them.  
A satellite can offer its remaining resources as services that other satellites can consume. This satellite becomes thus the service provider, and the other satellites that want the service are known as satellite _customers_. These services can only be offered when they are available. For instance, a satellite can offer the service to download data only when it is in contact with the ground station. This situation poses the challenge of how to properly notify the available services. The Opportunistic Service Availability Discovery (OSAD) protocol [15] addresses already this situation by defining a mechanism to publish the services that are available in a satellite. With this publication, the satellites know the available services in the network. The protocol is focused only on defining this publication process. Therefore, no details are presented on how to deploy the federation once the notification is completed.  
The FeDeCoP aims at addressing this technology gap by defining a set of directives to establish a federation. This protocol is based on connection-oriented communications, in which the customer triggers the request of the service (previously published). This communications scheme is necessary to keep a stable federation context or session, which is composed of the resource type and the quantity to be consumed, the status of the shared resources, among other parameters. Typically, connection-oriented protocols are composed of three phases that characterize the entire interaction. In the FeDeCoP case, these phases are known as the negotiation, the _consumption_, and the _closure_ phases.  
Figure 1 shows the state diagram of the FeDeCoP with the three phases and their transitions according to external events. Please note that the _not started_ and the _standby_ states are defined in the implementation of the protocol. They correspond respectively to the initial boot of the protocol, and the state in which the satellite is waiting for a request or a publication. This last state is thus the default mode of the protocol. The details of each phase are presented in the following sections.
## 5 Discussion
### On Classifying Presence Factors  
VR experience designers and developers can utilize the proposed presence factor classification method in different manners. First, it can be used as a tool for designing new VR experiences: from early design iterations onward, the designer can strive to maximize the presence factors in different groups. For example, they can ensure that the hardware quality is adequate for enabling the desired level of realism and that the overall content and technical designs diminish the aspects known to contribute to cybersickness, such as tracking errors, motion-to-photon latency, and screen flickering [55]. Second, the proposed classification method can be used as a tool to prepare the physical environment of the user. Eliminating bright and flickering light sources, removing potential sources of noise, and moving unnecessary physical objects further away are some examples of this approach. Third, the classification can be used as a tool to guide evaluations of VR experiences: the more potential presence factors the experience has, the more likely it is to support presence among its users. The results of an evaluation can inform the development process to improve presence in future versions.  
Many more presence factors may have been discussed in the other VR literature that we did not cover in this study because our literature review was not comprehensive. Moreover, as we focused on examining Four Seasons with a gesture-based UI, other potential presence factors may exist in other VR experiences. For example, the perceived relevance of the VR experience could be identified as a potential presence factor if Four Seasons was evaluated with older adults who might have lived in such a countryside location in their childhood. Moreover, although the proposed classification emerged from the context of gesture-based UI in VR, the static-dynamic and internal-external dimensions are conceptually generic. Similarly, many of the identified potential presence factors may be present in other VR experiences with other UI methods.
# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
## 3 Results and Discussion  
### Comparing Spectral Curves of Different Winter Wheat Cultivars  
The subtle spectral differences between crop species can be detected by hyperspectral imagery, and those differences help to identify crop types [39]. The more obvious the spectral difference, the easier the crop is to distinguish. In this study, the spectral curves of different winter wheat cultivars are presented in Figure 9. It can be seen that the general trends of the six spectral curves are consistent, and there are some differences in some specific bands. From 750 nm to 850 nm, type 6 and type 4 are relatively obvious to distinguish from other winter wheat types, and type 1 is clearer from 450 nm to 700 nm. These distinctions may contribute to higher classification accuracy. In contrast, type 2 and type 3 are confused with each other among all bands, partly because of their similar chemical components and inherent physical characteristics [40]. Based on previous research, both type 2 and type 3 were semi-winter wheat varieties, and their optimal sowing data was around October 10 th [41]. However, in this study, all the winter wheat was sowed on October 30 th to control variables. The postponed sowing data influence their height and leaf chlorophyll content, which might be reflected by their spectral information and resulted in confusion. This hypothesis was supported by [PERSON]'s and [PERSON]'s research that sowing data can affect related agronomic characteristics of winter wheat, with the delay of sowing data, the plant height decreased gradually, and the leaf chlorophyll content increased first and then decreased [41; 42].
###### Abstract  
A propagation of ultrawideband electromagnetic pulses (UWB EMPs) through magnetized plasma has been experimentally studied using a \"gigantic\" coaxial line, which has been developed at IAP RAS for laboratory modeling of ionospheric effects. This coaxial line is 1.4 m in diameter and 10 m in length and is installed inside the chamber of the large-scale Krot plasma device. The line can be filled with rf inductively coupled plasma, magnetized or not. It allows one to explore the propagation of UWB EMPs in plasma along a long path without refraction and divergence and obtain a physical picture of EMP transformation. Under conditions where the duration of the UWB EMP is comparable to the period of electron plasma oscillations (\(f_{p}\)\({}^{-1}\)), the period of cyclotron rotation of electrons (\(f_{c}\)\({}^{-1}\)), or even significantly shorter, a complex of effects of transformation of the waveform and frequency spectrum of the pulse occurs. Without ambient magnetic field, a UWB EMP is distorted due to the effects of the cutoff and frequency dispersion. In dense magnetized plasma, i.e., when \(f_{p}>>f_{c}\), the UWB EMP breaks into two wave packets, the high-frequency one (\(f>f_{p}\)) and low-frequency one (\(f<f_{c}\)). In rare magnetized plasma (\(f_{p}<<f_{c}\)), the cyclotron absorption produces a long train of damped oscillations at a frequency close to the cyclotron frequency \(f_{c}\) following the UWB EMP.  
electronic pulse; magnetized plasma; laboratory simulation; transmission lines +
Footnote †: journal: Applied Physics Letters A  
1
Footnote 1: institutetext: Corresponding author: E-mail: [EMAIL_ADDRESS]
## 4 Results and Discussion
### Influence of the Propagation Medium on AE Signals
For composite or ceramic media, there were fewer excited modes, and they were of higher frequency. The FFT2D of SiC media showed that only two to three modes were excited over the whole frequency range. The acoustic signatures in ceramics were less sensitive to propagation distance, unlike their counterparts in polymer matrices, which were highly dispersive. The viscoelastic nature of the material mainly attenuated the frequencies corresponding to the higher order modes. This consequently affected the content of the signals propagating in the polymer matrix. The energy content of the wave was continuously dissipated. In this case, the propagation distance between AE source and the point of detection had great influence on the frequency content. The representation of the frequency centroid as a function of amplitude (Figure 8c) allowed defining two different groups of materials. We also observed a large scattering of the descriptors for the polymers. This reflected a scattering of the acoustic signature of fiber break in the polymer media in both frequency and amplitude, unlike in the composite and ceramic media where the scattering was less than that of the polymer at this scale. The results obtained with a perfect sensor show that the signature of the carbon fiber break was very different depending on the propagation medium. Moreover, these results show that fiber break did not provide a universal acoustic signature, since the signal emitted by the source was strongly modified by the propagation medium.
# An Audio Distortion Dynamic Range Index for Evaluating the Performance of Audio Systems
## 1 Introduction
The current measurement condition specifies the test signal amplitude as \(-\)60 dBFS. However, in practice, distortion is inevitable, just to varying degrees. Therefore, the ultra-low amplitude measurement condition can neither match with the actual distorted working state of DUTs, nor extract its distortion behavior. Secondly, whether consider harmonic and spurious components. In fact, harmonics and spurious components are just different categories of distortion, and both of them can lead to performance degradation of DUTs. The DR in practice cannot avoid distortion. DR measurement should include all the noise, harmonic and spurious, which means DR equals SINAD in this paper. In addition, the SNR, THD, and SINAD indexes are often used together to evaluate the performance of DUTs, but there is a situation that the above indexes of the two DUTs are all equal or very close to each other. This leads to the difficulty of performance comparison. Therefore, an index that can overcome the ambiguities and shortcomings mentioned previously is necessary. This paper proposes a new audio distortion dynamic range (ADDR) index as an effective complement to the existing indexes. The proposedADDR can avoid both the ambiguity occurring in the DR by using the full-scale test signal condition and the controversy during practical use by clarifying that the calculation includes harmonic components in the definition ofADDR. Therefore, the uncertainty of measurement results and the false performance labels of manufacturers will be greatly reduced. Moreover, theADDR index is novel in several ways. First, compared with the existing DR measurement methods, it is more practically applicable because its measurement conditions are closer to the actual distorted usage conditions. Secondly, theADDR index unifies the SINAD and SNR indexes, and it can depict more subdivided performance between SINAD and SNR. Thirdly, theADDRindex comprehensively reflects the impacts of harmonic, spurious, and noise spectral components on DUTs. This helps to compare DUTs if the differences of the traditional indexes of DUTs are not obvious. Finally, as is widely known, automatic speech recognition (ASR) is one of the common applications of audio systems, and its accuracy is tightly related with the fidelity of the corresponding audio system, in this paper, the flexible simulated DUTs with the specified performance characteristics are adopted to verify the higher performance resolution of our ADDR index, as well as its supplementary function to the existing indexes, such as SNR, SINAD, and THD, those are commonly used in performance evaluation of an audio system. The outline of this paper is as follows. Section 2 describes the definition of the proposed ADDR index, to which the critical influence factors are addressed in Section 3. Section 4 reports several experiments to evaluate the proposed ADDR index, followed by the conclusion in Section 5.
## 2 Materials and Methods
### Machine Learning Prediction and Performance Evaluation
#### 2.5.2 Random Forest Algorithm  
Random forest (RF) regression is an ensemble learning algorithm that constructs multiple decision trees and integrates their predictions to perform regression tasks [50]. In the random forest approach, each decision tree is constructed independently and trained on randomly selected sub-samples, which effectively reduces the risk of overfitting. The predictions of multiple decision trees in the random forest are then averaged or weighted averaged to obtain the final regression result.  
The fundamental principles of RF regression are outlined below (as shown in Figure 8).  
Random sample selection: A subset of samples from the original training set is randomly selected to create a sub-sample. This ensures that each decision tree is trained on a different subset of samples, thereby increasing the model's diversity.  
Random feature selection: For each node in every decision tree, when determining the best feature for splitting, only a randomly selected subset of features is considered. This prevents certain features from exerting excessive influence on the overall model, thereby enhancing its robustness.  
Decision tree construction: A decision tree algorithm (such as the CART algorithm) is utilized to construct a decision tree on each sub-sample. During the growth process of the decision tree, the best splitting feature is typically chosen recursively to partition the dataset into subsets with minimal impurity.  
Ensemble prediction: by averaging or weighted averaging the predictions of multiple decision trees, the final regression result is obtained for new input samples.
## 4 Discussion  
This study advances rockfall risk assessment for steep residential slopes by integrating centimeter-resolution LiDAR-derived topography with the six-degree-of-freedom dynamic solver in RAMMS::ROCKFALL. The 3D simulations conducted for WY1-WY6 reveal critical relationships among kinetic energy, velocity, and bounce height--patterns that traditional 2D models fail to capture, particularly with respect to lateral dispersion and energy dissipation through repeated impacts.  
Advancements over Existing Models: Unlike previous studies that relied on simplified geometries or generalized parameters, the LiDAR-RAMMS integration retains detailed micro-topography and enables more reliable reconstruction of rockfall trajectories. This high-resolution modeling improves both spatial accuracy and dynamic fidelity in hazard interpretation.  
Engineering Applications: By converting simulated peak kinetic energy and velocity into ETAG 027:2013 barrier classes, we establish a practical risk matrix to support engineering decisions. The matrix enables targeted selection of flexible barriers (Classes A-C) and prioritizes WY2, WY3, and WY6 for immediate mitigation. Sensitivity analyses, incorporating \(\pm 10\%\) energy and \(\pm 15\%\) bounce variability as well as moisture-induced energy increases, further define conservative design margins for barrier specification and siting.  
Study Limitations: Several limitations remain. The current simulations do not account for block fragmentation, seasonal environmental changes, or site-specific damping calibration, all of which may affect runout distances and energy distribution. Additionally, socio-economic vulnerability indicators were not incorporated, which limits the comprehensiveness of risk prioritization.  
Future Work: Future research should integrate multi-temporal UAV-LiDAR datasets for time-series monitoring of slope evolution. Coupling discrete fracture network (DFN)-based fragmentation models and Monte Carlo uncertainty frameworks with real-timegeotechnical sensor inputs (e.g., InSAR, ground-based radar, etc.) would enable dynamic forecasting of rockfall hazards with probabilistic confidence.  
Conclusion of the Discussion: In summary, this discussion highlights the methodological innovations, practical benefits for engineering decision making, and critical avenues for future refinement. The proposed LiDAR-simulation framework offers a robust foundation for improving rockfall hazard management in complex mountainous environments.
## 2 Overview of Positioning Principles Using UWB and Wi-Fi RTT Radio Signals
### Ranging Error Optimization  
The TOF, TOA, and TDOA ranging methods can all result in significant ranging inaccuracies due to hardware, environmental factors, or signal delay errors and communication errors during signal transmission. To reduce the impact of system errors on ranging measurements, it can be determined by averaging multiple statistical measurement results [28, 29]. In addition, there are generally two methods to correct these system errors, including hardware compensation and data compensation. Hardware compensation involves extending the signal propagation time in the hardware to offset system errors, which can reduce ranging errors due to relative position changes between the router and mobile device to a certain degree. However, this method may introduce errors over time with hardware aging and poses challenges related to device storage and usage [30]. On the other hand, data compensation corrects the systematic errors during data processing, and it provides stable correction performance without additional costs. Although data  
Figure 5: Illustration of (**a**) TOA and (**b**) TDOA positioning principles.  
compensation may struggle to compensate for errors caused by relative position changes, it provides consistent and reliable correction performance.  
The geometric positioning principles of both UWB and Wi-Fi RTT require multiple base stations. The geometric layout of these base stations may also affect the ranging errors. For complex NLOS situations, when optimizing the layout of UWB base stations, researchers often use indicators such as dilution of precision (DOP) or geometric DOP (GDOP) to evaluate positioning accuracy and enhance the performance of UWB systems [31]. Using DOP as an evaluation indicator for base station placement, a significant correlation between DOP and positioning accuracy was confirmed [32]. The effect of antenna direction on positioning accuracy was further studied and demonstrated through multi-base station simulation [33]. A well-designed layout can significantly improve accuracy, especially when the distance between the target area and each base station is uniformly distributed. Refs. [34, 35] performed GDOP simulations using small UWB antennas in various typical layouts. The algorithm based on position DOP (PDOP) optimizes the deployment of UWB base stations, breaking through the limitation of only studying typical layout methods [36], and proposes a three-stage UWB base station layout design for underground parking lots, providing a scientific process reference for underground parking lots [37]. Mathematical modeling and genetic algorithms are used to optimize PDOP and base station sharing value as the objective function, providing an innovative method for deploying UWB positioning networks. It improves positioning accuracy and is also applicable to different environments.
## 3 Model Test Verification
### Influence of Bearing Plate Position  
When the horizontal load at the top of the pile is 50 N, the diameter of the bearing plate is fixed (55 mm), and the position of the bearing plate from the ground is changed to 25 cm, 20 cm, and 15 cm; then, the comparison of pile displacement and bending moment is shown in Figures 10 and 11.  
When the bearing plate is 0.15 m, 0.20 m, and 0.25 m away from the ground, the horizontal displacement at the ground of the squeezed branch pile gradually increases, and the horizontal displacement values at the ground are 4.33 mm, 4.41 mm, and 4.45 mm, respectively. Compared with the ZPW-25 pile with the largest buried depth of the bearing plate, its deformation resistance is increased by 3% and 1%, respectively. It can be seen that the closer the bearing plate is to the ground, the more it can play a role, the smaller the deformation at the ground, and the higher the corresponding horizontal bearing capacity. However, with the deepening of the position, the role of the bearing disk is gradually less obvious. That is, the lower the bearing plate is set, the smaller the lateral resistance to the soil. By observing Figure 11, it can be seen that the bending moments of piles with different branches vary significantly at the setting of the plate, and the changes are intensified before and after the curve trend near the bearing plate. The ZPW-15 curve in Figure 11 shows that the bending moment of the pile body changes slowly at 0.15 m, that is, at the bearing plate, but the curve changes abruptly above and below 0.15 m. In the ZPW-25 curve, due to  
Figure 9: Bending moment of pile body with different bearing plate diameters.  
the lower position of the bearing plate, the lateral movement and rotation angle of the pile body are small, and the resistance of the bearing plate to the bending moment cannot be fully exerted. Therefore, in order to make the bearing plate play the maximum role in the actual project, the plate should be placed as close to the ground as possible.