esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Tidal Accelerations in the Solar System and in Extrasolar Planetary Systems
## 2 Materials and Methods
### Data  
To simulate the motion of the planets, moons, asteroids, and comets in the solar system, one needs the orbital elements of these objects. The data for the planets and their moons are obtained from the _Solar System Bodies Archive_[7], and the data for the asteroids and comets are gathered from the _JPL Small-Body Database Search Engine_[8]. Although, being of general importance, masses are only listed for a small fraction of asteroids and comets. To give a rough estimate for the missing masses, we calculate the mean density of the known objects, supplying masses using their radius under the assumption of a spherical shape. Based on this mean density and its standard deviation, an estimate for the masses of the other objects is obtained using their radii. The mean density gives a mean mass estimate, and the mean density plus/minus the standard deviation gives a maximal/minimal mass estimate. With these values, the tidal accelerations for each of the three masses (maximum, mean, and minimum) are computed.  
We also include trans-Neptunian objects (TNOs) in our simulation. The data for their physical and orbital properties are taken from various publications listed in the Appendix A in Table A1. For some cases, the given values diverge. We only include TNOs for which the accuracy of the given data seems reasonable: errors of values within a publication <30% or deviations between values of publications <30%. If several value sources for one object were available, we used the mean value of these data. In the column'moons', an asterisk (\") denotes that a moon is known for this TNO but not included in the simulation because of missing or inaccurate data. There are even more multi-body systems, e.g., (42355) Typhon (42355) Typhon I Echidna. Due to a lack of (unambiguous) data (79360), Sila-Nunam (1997 CS29), (38083) Rhadamanthus (1999 HX11), (15810) Arawn (1994 JR1), (28978) Ixion (2001 KX76), (420356) Praamzius (2012 BX85), (53311) Deucalion (1999 HU11), and many more are not included either. If a value is not given in the publications, the data from the JPL database are used or the mass is derived as mentioned above. The mass and radius of the Sun are taken from [9].  
Tidal accelerations are only computed if the radius of the influenced object is larger than 75 km. Since the radius is also part of Equation (1), one also expects low tidal accelerations for these objects. Objects with a radius smaller than 75 km, however, are included in the influencing objects. For comets, we include only the biggest known objects with a radius of around 30 km to test if there could be some tidal potential.  
Data used in the simulation of the extrasolar planetary system TRAPPIST-1 are taken for the central star from [10] for mass and radius and for the planets; from [11] for masses, semi-major axes, and eccentricities; from [12] for mean angular motions and radii; and from [6; 12] for inclinations.
## 4 Discussion
### Effects of eHMI Information Contents, Gap Size and Speed on Gap Acceptance
Compared to the three-level low-medium-high configuration, simplified eHMI configurations--single-level medium, single-level high, two-level low-medium, and two-level medium-high--reduced pedestrians' sensitivity to gap size variations. Interview data suggest that the three-level configuration was preferred due to that it enabled continuous monitoring and assessment of crossing risk, consistent with findings that multi-state feedback in \"intent eHMI\" is preferred by pedestrians [27]. Participants also noted that the three-level eHMI resembled traditional traffic lights, enhancing familiarity and acceptability, as supported by [30]. Moreover, the medium-risk eHMI signal posed particular challenges due to its semantic ambiguity. Participants (e.g., P1, P2, P3, P4, P6, P7, P8, P19) reported difficulty interpreting whether the signal indicated \"safe\" or \"dangerous.\" This aligns with [9], who found that ambiguous signals, such as a yellow \"Caution! Blind Spot\" warning, increased alertness but lacked clear guidance. Participants expressed a preference for unambiguous signals, e.g., \"Safe\" or \"Dangerous\". Due to the lack of clear semantic guidance, medium-risk eHMI particularly requires continuous presentation alongside both low- and high-risk signals. When used continuously with only one of these signals, as in the two-level low-medium and two-level medium-high configurations, pedestrians' sensitivity to variations in gap size is significantly reduced. The lack of significant differences in gap acceptance between the single-level low-risk eHMI and the three-level configuration may stem from the early activation of the low-risk signal, allowing pedestrians sufficient time for rational decision-making. When the low-risk eHMI is deactivated at a TTA of less than 5.1 s, pedestrians may interpret this as an implicit indication that crossing is no longer safe [27]. This reinforces the effectiveness of the single-level low-risk configuration. Additionally, interview data (e.g., P21) indicated that pedestrians primarily relied on the low-risk signal in the two-level low-medium configuration, often making crossing decisions during the low-risk phase without considering subsequent signals. Another reason could be that pedestrians exhibited higher compliance with permissive (low-risk) signals compared to prohibitive (high-risk) signals, consistent with [13]. High-risk signals, typically appearing at TTA less than 3.7 s, often failed to deter pedestrians who had already initiated crossing. Moreover, medium- and high-risk signals induced a sense of urgency [12], potentially increasing perceived waiting time [23] and encouraging violations of prohibitive signals [24; 25].
# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 5 Conclusions  
Machine learning has been under a state of constant evolution in recent years--a good part of this may be attributed to great amounts of training data associated with better infrastructure [1]. However, certain fields and applications do not have this same degree of data availability, be it due to monetary costs, temporal costs, or simple scarcity of data relating to the subject matter [4]. This makes it especially important to know the most relevant features for a given task, as they have an associated cost. For this purpose, and to improve over use of wrapper or filter-based feature selection techniques, an approach using feature extraction to represent interim combinations of features from the original dataset was proposed.  
This approach aims to counter downfalls present in regular feature selection alternatives [22], namely, temporal cost in the case of wrapper methods, and disregard for feature interaction in the case of filter methods. The method operates by generating interim representations of feature combinations, which are then scored and selected using lightweight filter metrics. This structure is designed to mitigate the primary limitations of classical approaches while remaining adaptable across a variety of contexts.  
The computational study demonstrated that the proposed method offers notable improvements in efficiency, particularly when selecting medium-length feature subsets. In these cases, its predictive performance was found to be comparable to that of several state-of-the-art wrapper-based methods, while requiring significantly less time. Statistical analyses using GLS models and [PERSON]'s \(t\)-tests reinforced these observations, highlighting consistent trends across dataset types.  
Designed with modularity in mind, the method allows for user-defined configurations of feature extractors and filter strategies, making it highly adaptable to different modeling scenarios. These characteristics, combined with its empirical performance, suggest that the method can serve as a practical tool for feature selection in constrained environments.
# The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs
## 5 Performance Evaluation  
SLIDE is implemented using the OMNeT++ simulator [26]. A comprehensive performance evaluation for SLIDE using RWP mobility can be found in [6]. The aim of this paper is to validate SLIDE and its usability with different mobility models, or, more specifically, with nonlinear mobility models (GM and EGM).
## 3 Results
### Data Fusion of Downscaled Precipitation with Rain Gauge Observations
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CC** & **RMSE (mm)** & **BIAS (mm)** \\ \hline IMERG\_IDW & 0.754 & 2.395 & 0.126 \\ IMERG\_RBF & 0.758 & 2.482 & 0.144 \\ IMERG\_OK & 0.750 & 2.458 & 0.133 \\ CMORPH\_IDW & 0.729 & 2.559 & 0.222 \\ CMORPH\_RBF & 0.729 & 2.575 & 0.204 \\ CMORPH\_OK & 0.721 & 2.577 & 0.201 \\ ERA5\_IDW & 0.660 & 2.700 & 0.143 \\ ERA5\_RBF & 0.668 & 2.689 & 0.086 \\ ERA5\_OK & 0.656 & 2.713 & 0.105 \\ \hline \hline \end{tabular} Note: IDW represents the calibrated precipitation data using the Inverse distance weight interpolation method, RBF denotes the calibrated precipitation data using the radial basis function interpolation method and OK describes the calibrated precipitation data using the ordinary kriging interpolation method.  
\end{table} 
Table 6: Statistics of rain gauge calibration results using different interpolation methods in GDA for three satellite precipitation downscaling data. Figure 7: The correlation coefficient (CC), bias (BIAS), and root mean square error (RMSE) of the observed, downscaled and calibrated daily precipitation from (**a**) ERA5, (**b**) CMORPH and (**c**) IMERG data. C represents the calibrated precipitation data and O denotes the original satellite precipitation data.
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 2 Materials and Methods
### Methodology
#### 2.4.5 Geographically Weighted Random Forest Regression (GWRFR)  
GWRFR is based on the traditional random forest algorithm and can handle spatial non-stationarity [51]. GWRFR is an ensemble learning method recently developed to improve non-spatial models. The main idea behind GWRFR is similar to GWR. GWRFR trains several local models instead of training a global model. However, it includes the power of the random forest regression model, which increases its predictive performance due to its non-parametric nature [50]. Linear models are influenced by outliers, and non-spatial models cannot model spatial heterogeneity [78]. GWRFR can overcome both weaknesses. Recently, GWRFR has been used in different applications such as socioeconomic risk factors analysis [79], type 2 diabetes prevalence analysis [80], and COVID-19 research [81]. GWRFR can be utilized to improve crop yield prediction since crop yield prediction is a spatial problem.
## 5 Conclusions  
This study investigated the displacement distribution and strength requirement of a backfill roof with the basket weave drift layout through numerical simulations conducted with FLAC3D. The main conclusions are summarized as follows:  
(1) The displacement of the backfill roof with the basket weave drift layout exhibits columnar or strip-like distribution patterns. Due to the superposition of displacements, the basket weave drift layout can disperse deformations of the roof. However, this also makes the roof near the intersection of secondary drifts at adjacent layers more susceptible to deformation and failure.  
(2) The strength requirement of the backfill roof increases with drift size. At the Maoping Lead-Zinc Mine, the required UCS of the backfill roof in limit equilibrium for drift sizes of 3.5 m \(\times\) 3.0 m, 4.5 m \(\times\) 4.0 m, and 5.5 m \(\times\) 5.0 m was determined as 1.34 MPa, 1.57 MPa, and 2.38 MPa, respectively.  
(3) The applicability of traditional analytical solutions was evaluated in assessing the strength requirements of the backfill roof with the basket weave drift layout. It shows that traditional analytical solutions tend to yield conservative estimates compared to numerical simulations.  
(4) The iterative search method for determining the strength requirements of a backfill roof can reduce the subjectivity in stability analysis. It provides a valuable methodological reference for backfill design in practice.  
Conceptualization, G.L.; formal analysis, Q.M.; funding acquisition, G.L. and X.Y.; methodology, Q.M.; resources, X.Y.; software, X.Y.; supervision, X.Y. and W.S.; validation, Q.M.; visualization, Q.M.; writing--original draft, Q.M. and G.L.; writing--review and editing, Q.M. and G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deep Earth Probe and Mineral Resources Exploration--National Science and Technology Major Project (Grant No. 2024 ZD1003705) and the Beijing Nova Program (Grant No. 20220484057).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors sincerely thank [PERSON] for his invaluable guidance and support in this research. His expertise and encouragement were instrumental to the work.  
Authors [PERSON], [PERSON], [PERSON] were employed by the company BGRIMM Technology Group. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 4 System Model Description of FBMC OFDM-OQAM and GFDM-OQAM
### Gfdm-OQAM Transceiver
#### 4.2.1.2 Channel Description  
The channel is modelled similarly to the Rayleigh fading channel described in Section 4.1.1.2. However, the channel frequency coefficients are realised such that they have \(L_{ch}\) different values, that is, \(H[\hat{k}]\)= \(\left[H[0],H[1],\ldots,H[L_{c}h-1]\right]\). This is then converted into the time domain channel impulse response \(h(\hat{n})\) through the use of IDFT as:  
\[\begin{split} h(\hat{n})=IDFT[H[\hat{k}]]=\sum_{\hat{k}=0}^{L_{ ch}-1}H[\hat{k}]e^{\frac{2\pi\hat{n}}{N}}\\ \text{for}\ \ \ \ \hat{n}=0,1,\ldots,L_{ch}-1.\end{split} \tag{51}\]
## 3 Experimental Verification
### Measurement and Reconstruction of the Pressure Pulsation in the Pipe  
The dynamic pressure in the pipeline can be calculated by the stress on the pipe's surface tension [31], based on the pressure vessel theory [22].  
\[\sigma_{c}=2\cdot\sigma_{a}=p\cdot\frac{2\cdot D_{\text{int}}^{2}}{D_{ext}^{2 }-D_{\text{int}}^{2}} \tag{8}\]  
where \(\sigma_{c}\) and \(\sigma_{a}\) are the circumferential and axial strains, respectively.  
According to [PERSON]'s law for linearly elastic isotropic materials, the inner pressure can be calculated by the measured strain using the following functions:  
\[\left\{\begin{array}{l}\sigma_{a}=\frac{E}{1-v^{2}}\cdot(\varepsilon_{a}+v \cdot\varepsilon_{c})\\ \sigma_{c}=\frac{E}{1-v^{2}}\cdot(\varepsilon_{c}+v\cdot\varepsilon_{a})\\ \end{array}\right. \tag{9}\]  
where \(E\) is [PERSON]'s modulus, \(\varepsilon_{c}\) and \(\varepsilon_{a}\) are the circumferential and axial stress, respectively, \(v\) is [PERSON]'s ratio,  
Combining Equations (8) and (9), the internal dynamic pressure can be reconstructed by the axial and circumferential strains measured by the strain gauges.  
\[p=\frac{D_{ext}^{2}-D_{\text{int}}^{2}}{2\cdot D_{\text{int}}^{2}}\cdot\frac {E}{1-v^{2}}\cdot(\varepsilon_{c}+v\cdot\varepsilon_{a}) \tag{10}\]
## 4 Results and Discussion
### Data Refinement
#### 4.4.2 Data Quantization  
In this section, different data quantization approaches are presented.  
1. **Selection bit(s).** The combination of data thresholding and selection bit method would reduce the amount of data needed for acceptance/rejection decision even more. According to performance metrics shown in Table 13, for a threshold value 0.1, there is a drop in accuracy for both models, while a threshold value of 0.5 followed by selection bits method for QCNN gives results comparable to those achieved with complete full-precision data.
2. **Uniform quantizer.** A mandatory step to use uniform quantization is capping. Otherwise, as there is a wide range of values used for image presentation, most of them would become 0 after applied quantization. Figure 15 shows model accuracies and FNR depending on the capping value applied before uniform quantization.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Threshold** & **acc** & **prec** & **rec** & **f1** & **AUC** & **fpr** & **fnr** \\ \hline
0.1 & 0.9611 & **0.9615** & 0.9607 & 0.9611 & **0.9832** & **0.0385** & 0.0393 \\
0.1 & 0.9302 & 0.9029 & 0.9641 & 0.9325 & 0.9752 & 0.1037 & 0.0359 \\
0.5 & **0.9659** & 0.9611 & **0.9711** & **0.9661** & 0.9784 & 0.0393 & **0.0289** \\
0.5 & **0.9676** & **0.9664** & **0.9689** & **0.9676** & **0.9839** & **0.0337** & **0.0311** \\ \hline \hline \end{tabular}
\end{table}
Table 13: The results of the selection bit method applied after thresholding, for the QCNN and QMLP (gray background) model.  
Figure 15: Model accuracy and FNR as a function of data bit width and capping value.  
According to Figure 13, for a lower number of chosen bit widths (2-4) for data quantization, both QCNN and QMLP achieve better results for accuracy and FNR. Capping values 2 and 4 give better results than 8 and 16; hence 4 is chosen to be applied as the capping value whenever uniform quantization is used, and results are given in Table 14.  
According to Table 14, models with 2 and 3 input data bit width outperform models with wider data precision.  
3. **Non-uniform quantizer.** The models based on non-uniformly quantized data with results in Table 15 show somewhat lower performances than models based on 2-bit uniformly quantized input.
# Identification of a Human-Structure Interaction Model on an Ultra-Lightweight FRP Footbridge
## 4 Numerical Study
### Finite Element Model  
A linear elastic FE model of the structure, displayed in Figure 7a, was developed in Abaqus [36] based on the information presented in Section 2. Stringers, cross-beams, handrail poles, and deck panels were modeled with node reduced integration shell elements (S4R). Whereas solid elements C3D8R were used to model the concrete blocks at both ends of the footbridge. Also, top and bottom flanges of the stringers were defined as shell composite layouts composed of two plies. One layer corresponds to the GFRP laminate, and the other one refers to the CFRP strip.  
Stringers, cross-beams, and handrail poles were joined together using the tie constraint option. The connections between the stringers' top flange and the bottom part of the deck were defined employing connector elements type CONN3D2, every 25 mm along the stringers' length. Additionally, the stringers were connected to the concrete blocks by using the embedded element technique, so the solid elements were hosts for the shell elements. The size of the mesh for the different elements was 30 mm by 30 mm.  
To represent a simply supported structure, displacements of two areas of 0.20 m by 0.15 m at the bottom of one concrete block were constrained in the longitudinal, vertical and transversal (x, y and z) direction. Whilst vertical (y) and transversal (z) displacements of two similar areas at the bottom of the other block were constrained. Finally, an additional uniformly distributed non-structural mass of 2.0 kg/m\({}^{2}\) over the bridge deck was assumed to account for the L profiles, washers, nuts and bolts omitted in the model. Mechanical properties of the FRP profiles and the other materials employed to develop the numerical model are presented in Table 3.  
Figure 6: Experimental results for a single pedestrian walking at 1.9 Hz: (**a**) Acceleration reponse at midspan when the Test Subject 2 crossed the bridge. (**b**) Response in the frequency domain. (**c**) Mean 1s RMS acceleration and its dispersion considering 18 records.
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 3 Materials and Methods
### Sample  
In this phase, the initial dataset is selected. At this point, the researcher can choose to work with the entire dataset, or they can choose to select a representative sample of the original dataset. The data came from two public datasets, InSDN [30] and ISCX-VPN-NoVPN [31]. The InSDN dataset is developed over a specific SDN network to generate regular and attack traffic, essential for evaluating performance or creating new intrusion detection systems. The logs for each traffic type are in CSV files, which are approximately 1.88 GB and 3.58 GB in size, respectively. The ISCX-VPN-NoVPN dataset has different kinds of application traffic, allowing other aspects and scenarios to be addressed. Its files are in PCAP format, indicating the data is unprocessed. The data are divided into five zip files; three files are nonVPN, and two are VPN, totaling 28.00 GB of raw data.  
The network flows from the PCAP files obtained from two datasets were extracted using the CICFlowMeter tool [31]. The tool, developed by the Canadian Institute for Cybersecurity (CIC) at the University of New Brunswick, Canada, is written in Java. It can extract time-based features from network flows, which are essential for calculating time-related statistics. The generated flows are computed bidirectionally. In the study conducted by [32], the tool's output was configured with a flow timeout of 15 s, extracting 83 features in CSV file format. The study [33] also used the time-base features of the InSDN dataset. Based on this approach, Table 2 presents the details of the datasets after processing the PCAP files with a flow timeout of 15 s.
## 2 Experimental
### Stabilization Procedure  
The as-received Sewage-MSWI FA samples were stabilized following a very recent proposed technology based on the use of MSWI-BA deriving from the corresponding combustion line to stabilize FA [4]--for each FA, the procedure (defined procedure a) involved the mix of about 130 g of FA with of 20 g of BA, about 30 g of CFA, and about 40 g of FGD [4; 13] (see Table 1). Another procedure, with the same amount of ashes reported for procedure a)), but that did not involve the BA addition, was also realized (defined procedure b)). Procedure b) was selected to have a comparative procedure suitable to allow us to evaluate the BA role in the stabilization. In this case, all samples exhibited a relative weight percentage, already used for similar stabilization technology [5], as follows--65% FA, 15% CFA and 20% FGD. For both stabilization procedures, all powders were carefully mixed before adding approx. 200 mL of milliQ water (Millipore DirectQ-5 TM, Millipore S.A.S., 67120, Molsheim, France); then they were additionally mixed for 20 min. The obtained eco-materials were aged in laboratory for 3 months at room temperature.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Line**} & \multirow{2}{*}{**Procedure**} & \multicolumn{4}{c}{**Mass (g)**} & \multicolumn{2}{c}{**V (mL)**} \\ \cline{3-6}  & & **Sewage-MSWI FA** & **CFA** & **FGD** & **Sewage-MSWI BA** & **H\({}_{2}\)O** \\ \hline \multirow{2}{*}{\(1\)} & a) & 130.2 & 31 & 40.8 & 20.1 & 200 \\  & b) & 130.6 & 30.4 & 40.7 & - & 200 \\ \hline \multirow{2}{*}{\(2\)} & a) & 130 & 30.8 & 40 & 20 & 200 \\  & b) & 130.1 & 30.3 & 40.4 & - & 200 \\ \hline \multirow{2}{*}{\(3\)} & a) & 130.2 & 30.1 & 40.8 & 20 & 200 \\  & b) & 130.1 & 30 & 40.3 & - & 200 \\ \hline \multicol