esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Application and Discussion of the Proposed Metric and Visualization
In this way, the random forest regressor is pruned, reducing the computational complexity while keeping the accuracy of the model at a maximum. Three-fold cross-validation was performed to receive validated results. The results show that a random forest regression with a low number of decision trees achieves high coefficients of determination (\(R^{2}>0.98\)). Due to the conditions during the test runs, noise and other disturbances, such as environmental influences are eliminated. Therefore, the data easily reveals the dependencies of the variables. A deeper look at the trade-off of precision (low RMSE) and computation time (low mean-score time) suggests a random forest with 5 trees, a minimum number of 5 samples in one leaf and a maximum depth of 50 (cf. Figure 12). The Pareto front is a matter of subjective perception (cf. Figure 12 gray lines). Another hyper-parameter combination achieving a lower RMSE based on a higher computation time could also be reasonable. This work will not emphasize this since it is not valuable for the discussion of the hypotheses. The trained random forest regression model receives a mean coefficient of determination of 0.99989. This means the model covers almost the whole variance in the training data set. The mean RMSE of 0.00268 in the test data confirms the validity of the model. The prediction of the flow is exact and deviates from the actual flow value only by \(\mp\)0.00268. The feature importance of 0.926 for stroke, 0.001 for pressure and 0.073 for pressure difference show that stroke has the highest impact on the flow by far. This is a reasonable result. A higher stroke increases the flow cross-section, thus influencing the flow significantly. The pressure difference further influences the flow. While keeping the stroke constant, the increase of the pressure difference leads to a higher flow. Pressure \(p\) does not have much influence on the model. Figure 12: Hyper-parameter tuning of the random forest regression based on RMSE and mean-score time. In order to prove the hypothesis, the available training data is manipulated. First, 25% of the training data is randomly deleted (data set A). Consequently, the record completeness is reduced to 75%, while the data set completeness stays constant since the data is deleted randomly without changing the structure of the data. It is expected that the metric and visualization of data set completeness and the accuracy of the prediction model trained based on data set A is similar to the original data set. Second, 25% of the training data is deleted in selected distinct sections of the feature space, deteriorating the data structure (data set B). It is expected that the metric and visualization of data set completeness detect the data quality deficiency by a reduced metric value and that the modal accuracy decreases. The following section discusses the descriptive statistics of the different data sets to analyze the structure in more detail.
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 3 Experiments and Results
### Study Datasets
#### 3.1.2 Self-Collected Dataset  
Although the S3 DIS dataset allows us to evaluate the performance of our method in extracting indoor navigation networks on a large scale, its scenarios are limited to single-floor environments. To address this limitation and better validate our algorithm's performance in network extraction and planning within multi-floor environments, we specifically collected a 3D dataset focused on multi-floor space.  
In this study, we used the HoloLens 2 to acquire 3D data from a multi-floor building named L501. HoloLens 2 is a mixed reality headset developed by Microsoft, and several studies [15] have demonstrated its good capability for indoor mapping. Using our self-collected data, the main goal is to evaluate the algorithm's capability in extracting indoor navigation networks that connect different floors via staircases and elevators.  
Specifically, we conducted data collection and registration floor-by-floor for levels 0 to 4 of the building. Our self-collected data with the scanning trajectory is shown in Figure 11.  
The basic indoor units in our self-collected data include stairwells, elevator lobbies, hallways, and kitchens. Using CloudCompare, we labeled the basic indoor units on each floor with different colors: stairwells in orange, elevator lobbies in yellow, hallways in blue, and kitchens in green, as shown in Figure 12.  
Subsequently, we extracted the navigation elements for each indoor unit according to the rules described in Section 2.1. As shown in Figure 13, using floor 1 as an example, in our self-collected dataset, these elements include doors, virtual doors, staircases, and elevators. Finally, we obtained the semantic point cloud data for navigation network extraction.  
Figure 10: The three areas of the S3 DIS dataset used in this study: (**a**) Area_1, (**b**) Area_4, and (**c**) Area_6. All images are screenshots from the original dataset and the ceilings are removed for a better visualization.
# Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications
## 5 Simulations with Measured Data
### Simulation Environments  
For simulations, we considered a sensor network that consists of multiple sensors and a single access point (AP) as described in Figure 12. We assumed that each sensor and the AP were equipped with a single and \(M\) antennas, respectively. We considered the TDMA in a sensor node scheduling to avoid interference issue and guarantee the reliability of wireless link. For modulations, we considered quadrature phase-shift keying (QPSK) and 16-level quadrature amplitude modulation (16-QAM). In addition, we set multiple antenna configurations for the AP: \(M\in\{1,2,4\}\). For references, we plot the results under the assumption of independent Rayleigh fading channel.  
During uplink transmissions (data transmission from the sensor to the AP), we considered a maximum ratio combining (MRC) scheme to improve receive diversity [20]. Similarly, we considered a space-time block coding (STBC) scheme to achieve transmit diversity during downlink transmissions  
Figure 11: Path loss measurements at 4.3 GHz.  
Figure 12: The wireless network model considered during simulation (e.g., the number of sensors: 3).  
(data transmission from the AP to the sensor) [21]. Especially, we considered the following STBC matrix for \(M=2\) and \(M=4\)[22; 23]:  
\[\begin{split} C_{2}^{\text{STBC}}&=\begin{bmatrix}x_{1}& -x_{2}^{*}\\ x_{2}&x_{1}^{*}\end{bmatrix},\\ C_{4}^{\text{STBC}}&=\begin{bmatrix}x_{1}&-x_{2}^{*}&-x_{3}^{*}&0\\ x_{2}&x_{1}^{*}&0&-x_{3}^{*}\\ x_{3}&0&x_{1}^{*}&x_{2}^{*}\\ 0&x_{3}&-x_{2}&x_{1}\end{bmatrix},\end{split} \tag{8}\]  
where column and row indexes correspond to time-slot and transmit antenna, respectively, and \(x_{n}\) denotes transmit symbol at \(n\) time-slot.
###### Abstract  
Equivalent circuit models are a hot research topic in the field of lithium-ion batteries for electric vehicles, and scholars have proposed a variety of equivalent circuit models, from simple to complex. On one hand, a simple model cannot simulate the dynamic characteristics of batteries; on the other hand, it is difficult to apply a complex model to a real-time system. At present, there are few systematic comparative studies on equivalent circuit models of lithium-ion batteries. The representative first-order resistor-capacitor (RC) model and second-order RC model commonly used in the literature are studied comparatively in this paper. Firstly, the parameters of the two models are identified experimentally; secondly, the simulation model is built in Matlab/Simulink environment, and finally the output precision of these two models is verified by the actual data. The results show that in the constant current condition, the maximum error of the first-order RC model is 1.65% and the maximum error for the second-order RC model is 1.22%. In urban dynamometer driving schedule (UDDS) condition, the maximum error of the first-order RC model is 1.88%, and for the second-order RC model the maximum error is 1.69%. This is of great instructional significance to the application in practical battery management systems for the equivalent circuit model of lithium-ion batteries of electric vehicles.  
Keywords: lithium-ion batteries; equivalent circuit model; electric vehicles; parameter identification; battery management system +
Footnote †: journal: Applied Sciences
# A Review on Local Failure Probability Sensitivity Analysis
## 4 Local RSA in the Standard Normal Space
### Sensitivity with Direct Simulation Method  
Surface integrals cannot be directly estimated with Monte Carlo methods. Nevertheless, there are special contexts in which the derivative of the domain integral \(P_{f}\) is still a domain integral, in the standard normal space. Indeed, if the failure probability is estimated in the directional sampling framework or in the line sampling framework, the failure domain indicator function is no longer explicit in the definition of the failure probability integral. Consequently, the differentiation of the probability integral results in a domain integral as well, which can be estimated with MC methods.  
The sensitivity analysis in both sampling frameworks is detailed in the following sections, where no assumption on the nature of \(\mathbf{s}\) is made. In both frameworks, the observations generated to compute the failure probability estimate can be reused to compute the failure probability sensitivity estimate.
# Improving GNSS PPP Convergence: The Case of Atmospheric-Constrained, Multi-GNSS PPP-AR
###### Abstract  
GNSS positioning performance has been shown to improve with the ingestion of data from Global Ionospheric Maps (GIMs) and tropospheric zenith path delays, which are produced by, e.g., the International GNSS Service (IGS). For both dual- and triple-frequency Precise Point Positioning (PPP) processing, the significance of GIM and tropospheric products in processing is not obvious in the quality of the solution after a few hours. However, constraining the atmosphere improves PPP initialization and solution convergence in the first few minutes of processing. The general research question to be answered is whether there is any significant benefit in constraining the atmosphere in multi-frequency PPP? A key related question is: regarding time and position accuracy, how close are we to RTK performance in the age of multi-GNSS PPP-AR? To address these questions, this paper provides insight into the conceptual analyses of atmospheric GNSS PPP constraints. Dual- and triple-frequency scenarios were investigated. Over 60% improvement in convergence time was observed when atmospheric constraints are applied to a dual-frequency multi-GNSS PPP-AR solution. Future work would involve employing the constraints to improve low-cost PPP solutions.  
GNSS; Precise Point Positioning; Global Ionospheric Maps; ionosphere; troposphere; convergence +
Footnote †: journal: Sensors
# Automatic Processing of Historical Japanese Mathematics (Wasan) Documents
## 2 Materials and Methods
### Classification of Kanji Images Using DL Networks
#### 2.6.2 DL Networks Tested  
In order to study the performance of DL networks when used with these two datasets, we have considered five different deep learning architectures as defined on the FastAI Library [45].  
1. Alexnet [46] is one of the first widely used convolutional neural networks, composed of eight layers (five convolutional layers sometimes followed by max-pooling layers and three fully connected layers). This network was the one that started the current DL trend after outperforming the current state-of-the-art method on the ImageNet data set by a large margin.  
2. Squeezenet [47] uses so-called squeeze filters, including a point-wise filter to reduce the number of necessary parameters. A similar accuracy to Alexnet was claimed with fewer parameters.
3. VGG [21] represents an evolution of the Alexnet network that allowed for an increased number of layers (16 in the version considered in our work) by using smaller convolutional filters.
4. Resnet [48] is one of the first DL architectures to allow a higher number of layers (and, thus, \"deeper\" networks) by including blocks composed of convolution, batch normalization and ReLU. In the current work, a version with 50 layers was used.
5. Densenet [49] is another evolution of the resnet network that uses a larger number of connections between layers to claim increased parameter efficiency and better feature propagation that allows them to work with even more layers (121 in this work).  
Taking into account the fact that we had a very large number of classes and a limited number of example for each of them, we decided to use the transfer learning property of deep learning networks to improve our kanji classification results. Taking into account previous practical experiences with these networks [50], we initialized all these DL classifiers using Imagenet weights [46]. Their final layers were then substituted by a linear layer with our number of classes. This final layer was followed by a softmax activation function. The networks thus constructed were trained using the manually annotated tree points and information of the tree health classes annotated in the orthomosaic. Initially, all the data were considered together and randomly divided into two subsets of 80% for training and 20% for testing.
# A Survey on Satellite Communication System Security
## 2 Existing Surveys and Motivation
### Paper Organization  
The organization of this paper is illustrated in Figure 2. Section 3 addresses the background of SCSs and their environment. Section 4 introduces security attacks in SCSs. Section 5 describes the security defense method for attacks on SCSs. Section 6 presents security policies and strategies in space and compares each country. In Section 7, future directions for research are provided. Lastly, we summarize our research in the conclusion and provide the abbreviations that are used in this paper.
## 2 Inter-DCI Link Modeling and OP Estimation
### EDFA and Optical Filter Modeling  
We model the SC-EDFA by a block with flat power gain \(g\) and addition of ASE noise. The EDFA gain \(g\) is set to fully compensate the losses introduced by the MCF and CDC module, and the ASE noise is modeled as additive white Gaussian noise with power spectral density, per polarization mode, given by \(S_{ASE}=0.5F_{n}(g-1)hv_{o}\), where \(hv_{o}\) is the photon energy, and \(F_{n}\) is the amplifier noise figure [39]. This model assumes a flat gain and noise figure as typically encountered in relatively narrow optical bandwidths [40] as those considered after optical filtering in this work.  
The optical filter is modeled by a 4 th order super Gaussian (SG) filter. The SG filter has been shown to model accurately the optical filtering effect in comparison with experimental results, and is widely adopted in the literature [41; 42]. The transfer function of the \(i\)-th order SG filter is given by  
\[H_{o}(f)=\frac{1}{\sqrt{i_{L}}}\exp\left[-\left(\frac{2|f-f_{o}|}{B_{o}}\right) ^{2i}\ln\sqrt{2}\right] \tag{6}\]  
where \(f_{o}\) is the optical filter central frequency, \(i_{L}\) is the insertion loss in linear units, and \(B_{o}\) is the optical filter bandwidth at \(-3\) dB.
# Analysis of _In Vivo_ Plant Volatiles Using Active Sampling and TD-GC\(\times\)GC-TOFMS
## 4 Discussion
### Validation of In Vivo Active Sampling Using Multiple Plant Species  
As displayed in Figure 2, the _in vivo_ active sampling methodology followed by TD is applicable to a wide range of plant species and can likely be applied to most plant species, demonstrating its versatility. Monoterpenes, sesquiterpenes, benzenoids, alcohols, and aldehydes were some of the many classes detected using the _in vivo_ plant sampling system (Table 2). The compounds highlighted in Table 2 were selected as they are known to play significant roles in plant defense and communication. For example, terpenes such as limonene attract pollinators, and \(\alpha\)-pinene can repel pests [50]. Benzaldehyde is known to be involved in various plant functions performing as a pollinator attractant and an antifungal compound [51]. Furthermore, C\({}_{6}\)-aldehydes and hexenyl acetate greatly influence insect-plant interactions and play valuable roles in plant defense [52].  
The compounds collected by the _in vivo_ active sampling system are comparable to other currently used techniques. For example, the essential oil extraction of Mojito mint leaves followed by liquid injection yields limonene and eucalypt, which are both present in high abundances using the _in vivo_ active sampling system [53; 54]. SPME-GC-MS analysis of homogenized blueberry samples detected several volatile compounds, including benzaldyde and 2-hexen-1-ol (E), which are both detected when sampling _in vivo_[55]. In another study conducted by [PERSON] et al. using SPME-GC\(\times\)GC-TOFMS, they detected several of the same compounds from Little Napoli tomato plant leaves as the technique described in this paper, including hexanal and \(\gamma\)-terpinene [56]. Additionally, the results observed from collecting headspace plant volatiles using the described _in vivo_ plant sampling system for Sugar Rush tomato are comparable to those found by [PERSON] et al. by purging and trapping on Tenax TA resin followed by solvent elution [57].
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 6 Discussion
Due to the sensitive nature of this material, external validation with public or third-party datasets is not allowed, and data sharing is prohibited under national security regulations. While this limits conventional scientific replication, such restrictions are standard in military research involving operational defense data. Despite efforts to balance and segment the dataset, its overall scale remains modest. The inclusion of a wider range of vessels, environments, and operational profiles would likely improve the robustness and generalizability of the classifiers. Additionally, deep learning models were not considered in this study due to the limited volume of labeled data, although they may offer advantages when larger datasets become available. This study assumed that each acoustic recording corresponded to a single vessel and was acquired during a dedicated and controlled run. Scenarios involving overlapping acoustic signals from two or more vessels operating simultaneously in the same maritime \begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline CNN ([PERSON] et al., 2018) [36] & 94.0 \\ CNN ([PERSON] et al., 2012) [35] & 89.8 \\ Proposed XGBoost with Lofar (w/ Dec) & 99.7 \\ Proposed XGBoost with PCA (w/o Dec) & 99.4 \\ Proposed XGBoost with NLPCA (w/ Dec) & 99.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of classification accuracy between CNN-based methods and the proposed XGBoost models. region were not considered. This represents a limitation, as real-world passive SoNaR operations frequently encounter multi-source environments. This absence of spatial data may suggest an erroneous assumption that spatial resolution is irrelevant to classification tasks. It is important to note, however, that in operational contexts, vessel-specific acoustic signatures can sometimes be isolated through the integration of additional intelligence sources. For instance, a hydrophone positioned at the entrance of a port, when used in conjunction with historical cruise data from maritime traffic systems, may enable the independent attribution of signals to individual vessels. Nonetheless, these intelligence-based methods depend heavily on classified operational procedures and surveillance infrastructure. A detailed examination of such techniques falls outside the scope of this work, which focuses strictly on the technical classification of non-overlapping, single-source acoustic signals under controlled conditions. Future work may focus on expanding the dataset to include additional ship classes and environmental conditions, applying advanced feature extraction and domain adaptation methods to enhance the robustness, and assessing the deployment of trained models in real-time onboard SoNaR systems. These efforts aim to support the development of intelligent, passive maritime surveillance systems that strengthen naval situational awareness and operational readiness.
# Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
## 3 System Design and Methodology
### Methodology
#### 3.2.3 GNSS Spoofing  
A spoofing attack was carried out using a software-defined GPS signal simulator, GPS-SDR-SIM, to manipulate the position and timing of a target receiver. GPS-SDR-SIM generates realistic GPS baseband signals that can be converted into RF signals using SDR devices, making it ideal for experimental manipulation and spoofing of GPS signals.  
Figure 4: GPS BPSK jamming script using GNU radio.  
The simulator supports user-defined motion trajectories specified through input sources such as CSV files. In this work, the SDR was used to broadcast simulated GPS signals. A 10 Hz user motion sampling rate was applied, allowing for accurate tracking of dynamic movements without overwhelming the processing capacity and providing precise motion simulation. The simulator requires GPS broadcast ephemeris files to create GPS signals that allow pseudorange and Doppler shift calculations for each visible satellite. These calculations generate digitized in-phase (I) and quadrature-phase (Q) samples that form the basis of the spoofed GPS signal. This process allows controlled GPS spoofing tests and vulnerability assessments.
## 3 Results and Discussions
### Results and Discussions for Conflict Detection  
After validating the proposed trajectory prediction approach, the conflict detection algorithm is also tested based on the replaying data and simulated conflicts. The flight trajectory in the next 3 min is predicted to illustrate the positional distribution and further support the flight conflict detection. To improve the prediction accuracy, the trajectory of test flights is predicted at each updating interval, of about 4 s. The experimental results are summarized in Table 1, in which a baseline approach is also implemented based on a probabilistic trajectory prediction algorithm [4]. The experimental purpose is to minimize the FA in the case of detecting all real and simulated conflicts. The values in the \"Results\" column indicate that 15 and 26 false alarms are detected when performing the approach to detect all  
Figure 14: Experimental results for a decelerating flight.  
Figure 15: The predicting flight time and its CI with respect to the flight speed (the dots in a different color are extracted from historical trajectories).  
53 conflicts. It can be seen from the results that the proposed conflict detection approach can obtain a lower FA (22.06 vs. 32.91) compared to the baseline approach. Moreover, the proposed approach provides more prewarning time (+5.7 s) to concerned controllers for dealing with the emergency. Thus, the experimental results validate the effectiveness and efficiency of the proposed approach for the conflict detection task in air traffic studies.