esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Evaluation Experiments
### Subjective Evaluation Experiments on Sound Image Perception  
In this experiment, participants were asked to indicate the position of the sound image that they perceived. The VSS positions were set to the same positions used in the objective experiments. Band-limited white noise (0-8 kHz) was used as the target signal. The stimuli processed by the comparison patterns were reproduced from the loudspeaker arrays as described in Section 2.1. In contrast, the real monopole sound source was set at the VSS position for the reproduction of the target signal in the comparison pattern 'Real'. Ten subjects (two females and eight males) participated the experiment. The subjects were asked to stand at (0.0 m, 2.5 m), which is the same position as the reference point in Section 2.3, with their eyes covered to avoid being influenced by visual information. The stimuli were presented twice for each comparison pattern and each VSS position, and they were presented in a random order. After listening to each stimulus, the subject was asked to answer the position of the perceived sound image, with four choices corresponding to VSS positions. Therefore, we collected 10 (participants) \(\times\) 4 (VSS positions) \(\times\) 6 (patterns) \(\times\) 2 (times) = 480 answers in this experiment. The answers were used to calculate the correct ratio for distance perception of real sound sources and VSSs.  
The quantitative evaluation results regarding the correct answer ratio are shown in Table 6, and the distribution of the answers is shown in Figure 14. It can be noticed that the proposed method achieves high precision in distance perception when the VSS was constructed far from the listener, i.e., \(r_{\text{S}}\) = (0.0, 0.5) and (0.0, 1.0). When the VSS was constructed near the listener, i.e., \(r_{\text{S}}\) = (0.0, 1.5) and (0.0, 2.0), the proposed method decreased in performance compared with 'PAL' and 'Conv. Hybrid'. This is because these two patterns only employ the PAL array for VSS construction, and the sharp directivity of PAL leads to a high value of DRR, which leads the listener to perceive that the source as closer. The proposed method employs both EDL array and PAL array, so the contribution of PAL is not significant as 'PAL' and 'Conv. Hybrid'.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & \(r_{\text{S}}\) = (0.0, 0.5) & \(r_{\text{S}}\) = (0.0, 1.0) & \(r_{\text{S}}\) = (0.0, 1.5) & \(r_{\text{S}}\) = (0.0, 2.0) & **Average** \\ \hline Real & 85.0 & **60.0** & **60.0** & **100.0** & **76.3** \\ \hline Baseline & 55.0 & 50.0 & 30.0 & 45.0 & 45.0 \\ \hline Conv. PAL & 30.0 & 15.0 & 50.0 & 90.0 & 46.3 \\ \hline Conv. Hybrid & 75.0 & 45.0 & **60.0** & 80.0 & 65.0 \\ \hline Prop.(8.8) & **100.0** & 35.0 & 40.0 & 25.0 & 50.0 \\ \hline Prop.(16.16) & 85.0 & **60.0** & 50.0 & 45.0 & 60.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results on correct answer ratio of distance perception [%].
## 7 Experimental Results and Analysis  
To further prove the feasibility of the TDC-VSG adaptive control strategy, the VSG grid-connected experiments were conducted on the HIL platform and the details of the experimental platform are illustrated in Figure 18. The VSG main circuit topology is integrated into the Field Programmable Gate Array (FPGA) of the HIL real-time simulator, while the control program is executed on the Central Processing Unit (CPU) of the HIL, with an oscilloscope employed for output waveforms. The control parameters for the hardware experiment are as follows: the steady-state damping coefficients for the TVSG control strategy are set to \(D_{\omega}=K_{\omega}=7.6\); the compensation coefficients for the traditional adaptive control are configured as \(K_{\rm J}=0.2\) and \(K_{\rm d}=10\); the compensation coefficient for the TDC-VSG adaptive control is set at \(K_{\rm j}=0.9\), with the steady-state damping coefficient designated as \(D_{\rm T}=17.32\). The specific experimental conditions are set as follows: In Test Condition 1, the reference power \(P_{\rm ref}\) increases from 0 kW to 15 kW at 7.5 s and subsequently decreases to 0 kW at 10 s. In Test Condition 2, the grid frequency drops from 50 Hz to 49.9 Hz.  
Figure 19a,b illustrates that the experimental outcomes under given power and grid frequency variations align closely with the simulation results. When \(D_{\omega}=K_{\omega}=7.6\), significant transient oscillations occur within the TVSG control strategy, accompanied by steady-state deviations attributable to damping characteristics during grid frequency variations. The traditional adaptive control strategy significantly mitigates power and frequency oscillations under both disturbances; however, it further exacerbates the steady-state deviation during grid frequency variations. The TDC-VSG control strategy not only suppresses transient oscillations but also solves the issue of steady-state deviation resulting from damping characteristics, exhibiting a faster active response rate under both disturbances. The experimental results confirm the simulation findings, proving the feasibility of the TDC-VSG control strategy.  
Figure 19: Experimental results under different test conditions: (**a**) Test Condition 1; (**b**) Test Condition 2.  
Figure 18: VSG grid-connected experimental platform based on the HIL platform.
# Effects of Nonlinear Damping on Vibrations of Microbeam
## 3 Results  
In this section, the multiple scale method [24; 25; 26], which is widely used to solve weak nonlinear differential equations [27; 28; 29; 30], will be applied to Equation (11). Let \(\widetilde{c}_{1}=\varepsilon^{2}c_{1}\), \(f(t)=\varepsilon f_{1}\cos\left(\Omega_{1}\overline{t}\right)+\varepsilon^{3 }f_{1}\cos\left(\Omega_{2}\overline{t}\right)\), then we obtain:  
\[\ddot{\eta}+\omega^{2}\eta+\varepsilon^{2}c_{1}\dot{\eta}+d\eta^{3}+c_{2}\dot{ \eta}\eta^{2}=\varepsilon f_{1}\cos(\Omega_{1}t)+\varepsilon^{3}f_{2}\cos( \Omega_{2}t) \tag{12}\]  
Suppose  
\[\eta(t;\varepsilon)=\varepsilon\eta_{1}(T_{0},T_{2})+\varepsilon^{3}\eta_{3} (T_{0},T_{2}) \tag{13}\]  
where \(T_{0}=\overline{t}\) and \(T_{2}=\varepsilon^{2}\overline{t}\). Substituting Equation (13) into Equation (12), then equating the coefficients of \(\varepsilon\) and \(\varepsilon^{3}\) on both sides, we obtain:  
\[\varepsilon:\ \frac{\partial^{2}\eta_{1}}{\partial T_{0}^{2}}+\omega^{2}\eta_{1}= \frac{1}{2}f_{1}\exp(i\Omega_{1}T_{0}) \tag{14}\]  
\[\varepsilon^{3}:\frac{\partial^{2}\eta_{3}}{\partial T_{0}^{2}}+\omega^{2}\eta _{3}=-2\frac{\partial}{\partial T_{0}}\left(\frac{\partial\eta_{1}}{\partial T _{2}}\right) \tag{15}\]  
The solution of Equation (14) can be written as:  
\[\eta_{1}=A(T_{2})\exp(i\omega T_{0})+\Lambda\exp(i\Omega_{1}T_{0})+cc \tag{16}\]  
where \(\Lambda=\frac{1}{2}f_{1}\left(\omega^{2}-\Omega_{1}^{2}\right)^{-1}\). Substituting Equation (16) into Equation (15), we obtain:  
\[\begin{array}{c}\frac{\partial^{2}\eta_{3}}{\partial T_{0}^{2}}+\omega^{2} \eta_{3}=\frac{1}{2}f_{2}\exp(i\ \Omega_{2}T_{0})-[i2\omega(A^{\prime}+c_{1}A)\\ +(i2c_{2}\omega+6d)\Lambda^{2}A+(ic_{2}\omega+3d)A^{2}\overline{A}]\exp(i\ \omega T_{0})+NST\end{array} \tag{17}\]  
where \(NST\) denotes non-secular terms [26], and the prime indicates differentiation with respect to \(T_{2}\) for simplicity.
## 2 Materials and Methods
### Training Protocol and Hyperparameters Optimization  
Since the numerosity of the dataset is limited, we performed repeated stratified cross-validation (CV). In particular, we repeated the CV five times, with the test set size constituting 20% of the dataset, so that the test sets in each repetition would be independent. The test set was employed for final evaluation, whereas the rest of the dataset was used for a 10-fold CV, according to which we further divided the data into training and validation sets. These splits were performed in a stratified fashion so that the class balance would be preserved. At this point, some of the classifier's hyperparameters were optimized using the software Optuna (version 4.2.0) [22], with classification accuracy being the metric to be optimized. This was achieved by performing multiple trials. We performed 100 trials per fold to ensure that the optimization method would converge to the best set of hyperparameters.  
The optimized classifier was then trained, and the final evaluation was performed using the test set. The metrics employed to evaluate the performance of the model were accuracy, precision, recall, f1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC). Figure 1 depicts a diagram of the training protocol.  
To better highlight the differences between healthy controls and patients affected by one of the diseases, we performed multiple classification tasks that included the following:  
* Multi-class classification;
* Binary classification between healthy and affected by any disease. In this case, the class labels PD, CA, and HSP were replaced with a common label, and we labeled this case as _HC vs. rest_;
* Binary classification between healthy and affected by disease. In this case, only the data belonging to the class HC and one of the other classes were provided to the model. We performed all the possible binary classifications, i.e., _HC vs. PD_, _HC vs. CA_, and _HC vs. HSP_.  
Since the dataset includes subjects of age ranging from 21 to 79 years, we decided to use this information to improve the robustness of our results. Indeed, even the healthy older population shows differences in spatial, temporal, and kinematic gait parameters with respect to the younger population [23; 24]. Considering the classification tasks only involving a neurodegenerative disease, since the age range differed for every disease, we performed the classification using the subset of the controls whose age was inside the age  
Figure 1: Representation of the training protocol. The full dataset is split so that 20% of it is used as test set, and the remaining 80% is used for the cross-validation. Inside, the cross-validation (red rectangle) is further split with the same ratio into validation and training sets. The classifier hyperparameters are then optimized with Optuna (blue rectangle).  
range of the subjects affected by the disease. In this way, we avoided introducing additional variability to the gait parameters that could be predominantly age-related.
# Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
## 2 Data and Methods
### Comparison of Ice Velocity Products
#### 3.3.3 Ice Velocity Precision  
To assess the precision of the velocity products using independent observations, we form individual mosaics for each of the processed cycles and each sensor including historic data, such as ENVISAT ASAR, ALOS/PALSAR, as well as RADARSAT-2 data from 2009-2011 (Figure 5). We use the standard deviation derived for each pixel of the mosaics as an estimation of the precision of one ice velocity measurement. Figure 6a shows the distributions of this standard deviation for each mosaic made from a specific sensor and repeat cycle. To be statistically significant, we only keep pixels with more than 10 measurements and for which the absolute speed is less than 300 m/year (to minimize to the influence of ice flow changes). We find that the most probable standard deviation for the Landsat mosaics using the 16, 32, 48, 64, 80 and 368-day cycles are respectively 17, 11, 7, 6, 5 and 1 m/year, which provides a direct estimation of the precision of the ice velocity measurement for an individual scene. By converting this precision in ice displacement (m/year) into offset (pixel), we find that precision of the image correlation ranges between 0.05 and 0.07 pixels, or twice lower than the default precision in Table 1.  
Figure 4: Ice speed of Zachariae Isstrum between 1992 and 2017 (**A**) and between 2014 and 2017 (**B**) from multiple sensors. Landsat prior to 2013 were downloaded from NSIDC [37].  
The same estimations for the C-Band SAR sensors reveal that the nominal standard deviation is lower for similar repeat cycles (Figure 6b). Sentinel-1 with 6, 12 and 24-day cycles, RADARSAT-2 with 24-day cycle, and ENVISAT/ASAR with 35-day cycle, have standard deviations in the range of 17, 8, 5, 4 and 3 m/year, respectively. It is difficult to assess exactly the corresponding error of image cross-correlation as the native pixel of the SAR observations are not square, but we estimate this value to be between 0.02 and 0.05 pixel depending on the range or azimuth resolution chosen (see Table 1).  
The slightly more precise cross-correlation results from C-Band SARs may be attributed to the better spatial resolution of the raw data. It is also possible that the tracking of image speckle in SAR data is more efficient than surface features in Landsat-8 data because of the availability of amplitude and phase that allow more precise image interpolation.  
The precision from the only L-Band sensor, ALOS/PALSAR, is larger (8 m/year) than expected based on its repeat cycle and pixel resolution because the data from this sensor are impacted by the ionosphere more than data acquired at the C-band frequency [22; 39].  
Figure 5: Antarctic ice speed maps for each sensor and each cycle separately. The ice speed is coded on a logarithmic scale overlaid on MOA background mosaic.
###### Abstract  
Soil-rock mixture is a common geo-material found in natural deposit slopes and various constructions, such as tunnels, hydropower stations, and subgrades. The complex mechanical characteristics of soil-rock mixture arise from its multi-phase compositions and cooperative interactions. This paper investigated the mechanical properties of soil-rock mixture, focusing on the influence of rock content, and soil-rock interface strength was discussed. Specimens with varying rock contents were subjected to uniaxial compression tests. The results indicated that rock content, as a key structural parameter, significantly controls the crack propagation trends. As rock content increases, the initial structure of the soil matrix is damaged, leading to the formation of a weak-strength soil-rock interface. The failure mode transitions from longitudinal cracking to multiple shear fractures. To analyze the strength of the soil-rock interface from a mesoscopic perspective, simulations of soil-rock mixture specimens with irregular rock blocks were conducted using the particle discrete element method (PDEM). At the meso-scale, the specimen with 30% rock content exhibited a complex particle displacement distribution, with differences in the direction and magnitude of displacement between soil and rock particles being critical to the failure modes of the specimen. As the soil-rock interface strength increased from 0.1 to 0.9, the distribution of force chains within the specimen shifted from a centralized to a more uniform distribution, and the thickness of force chains became increasingly uniform. The strength responses of the soil-rock mixture under uniaxial compression condition were discussed, revealing that the uniaxial compression strength (UCS) of soil-rock mixture decreases exponentially with increasing rock content. An estimation formula was developed to characterize the UCS of soil-rock mixture in relation to rock content and interface strength. The findings from both the experiments and simulations can provide valuable insights for evaluating the stability of deposit slopes and other constructions involving soil-rock mixture.  
soil-rock mixture; uniaxial compression test; uniaxial compressive strength; irregular rock block; failure mechanism +
Footnote †: journal: Applied sciences
## 5 Discussion and Conclusions
Although the model has high accuracy, there are still some limitations. The data distribution likely does not represent the full range of actual data distribution that may be encountered when probing different natural subaquatic environments because of the range of environmental parameters that may be encountered (e.g., different types of oil, different degrees of weathering, different natural organic matter content, and natural biofilmformation). Thus, the domain gap between the lab data during training and the video from actual field samples may influence the accuracy. Future work can overcome these challenges by enlarging the oil sheen dataset collected with different natural conditions, including a range of videos and images taken for field sheen events using the OS-SS. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12178865/s1](https://www.mdpi.com/article/10.3390/app12178865/s1)]([https://www.mdpi.com/article/10.3390/app12178865/s1](https://www.mdpi.com/article/10.3390/app12178865/s1)), Table S1: Input data summary table. Table S2: OS-Net performance under different data augmentation methods. Table S3: OS-Net hyperparameters tuning grid search values. Figure S1: VOS-Net prediction results. The X axis shows the prediction index with time (1 prediction/s) and the Y axis shows the VOS-Net prediction result. The black line represents the region where there was oil sheen present. Ground truth labels are shown at the top of each region. J.D., P.B., N.S. and T.P.H. ; Data curation, K.S. ; Formal analysis, J.D., J.S. and Y.G. ; Funding acquisition, P.B., N.S., T.P.H. and G.V.L. ; Investigation, K.S. and G.V.L. ; Methodology, J.D., K.S. and G.V.L. ; Supervision, J.S. ; Validation, J.D. and Y.G. ; Writing--review & editing, K.S., J.S., Y.G., P.B., N.S., T.P.H. and G.V.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Chevron. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to agreements in place with the study sponsor. The authors declare no conflict of interest.
###### Abstract  
The properties of Hardware-in-the-Loop (HIL) for the development of controllers, together with electronic emulation of physical process by Digital Twins (DT) significantly enhance the optimization of design and implementation in nonlinear control applications. The study emphasizes the use of the Raspberry Pi (RBP), a low-cost and portable electronic board for two interrelated goals: (a) the Electronic Control Unit (ECU-RBP) implementing a Lyapunov-based Controller (LBC) for nonlinear trajectory tracking of P3 DX wheeled robots, and (b) the Digital Twin (DT-RPB) emulating the real robot behavior, which is remotely connected to the control unit. ECU-RBP, DT-RBP and real robot are connected as nodes within the same wireless network, enhancing interaction between the three physical elements. The development process is supported by the Matlab/Simulink environment and the associated packages for the specified electronic board. Following testing of the real robot from the ECU-RBP in an open loop, the model is identified and integrated into the DT-RBP to replicate its functionality. The LBC solution, which has also been validated through simulation, is implemented in the ECU-RBP to examine the closed-loop control according to the HIL strategy. Finally, the study evaluates the effectiveness of the HIL approach by comparing the results obtained from the application of the LBC, as implemented in the ECU-RBP to both the real robot and its DT.  
hardware in the loop; electronic control unit; digital twin; Raspberry Pi; Lyapunov-based control; nonlinear trajectory tracking; P3-DX robot; wireless network control +
Footnote †: journal: _actautators_  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000  
including the automotive sector [1; 2; 3], academia [4; 5; 6], and power electronics [7; 8; 9] where practical validation of theoretical concepts is essential for education and prototyping.  
In parallel with the evolution of HIL strategies, the concept of the Digital Twin (DT) has gained considerable interest. A DT can be defined depending on the context of application: education and training, service and maintenance, or production planning and control. Basically, it is a virtual representation or model of a physical object or process obtained from real-world data. More precisely, the physical entity and its virtual implementation are interconnected through a synchronized connection allowing bidirectional data exchange to complete the DT concept [10; 11; 12].  
DT is currently a crucial element of smart manufacturing, cyber-physical systems, and Industry 4.0 architectures. Recent reviews and surveys highlight their expanding role across sectors, underlining their utility for system monitoring, forecasting, and control validation [13; 14; 15; 16]. Despite the rapid progress in both HIL and DT technologies, their combined use in nonlinear control applications on low-cost, embedded platforms remain underexplored. Consequently, the paper focuses on the electronic implementation of HIL-DT for robotic applications.
# A Review of Unmanned Aerial Vehicle Based Antenna and Propagation Measurements
## 2 UAV-Based Measurements
### UAV-Based Near-Field Measurements
In [45], a Mikrokopter equipped with a signal generator and a short monopole was employed to measure a 6 m parabolic dish fed by a dual-polarized LPDA operating in the frequency range of 300 MHz to 3 GHz. In this UAV model, the ground plate was made of aluminum, and to minimize the impact of the UAV body on the antenna, a mesh structure was placed between the frame of the UAV and the antenna. Similarly, the UAV propellers can generate harmonics that are influenced by the propellers' rotations per minute (RFM) and their dimensions. The Doppler spectrum and harmonics [46] generated by the propellers can be studied using the double-edge diffraction model [47]. It was observed that when an antenna mounted on a UAV transmits signals while the propellers rotate, the signal received by the AUT exhibits a Doppler effect. The Doppler effect, harmonics, and  
Figure 11: UAV–based measurements in radiating near field. scattering experienced by EM waves from an antenna lead to a drop in the power received by the AUT, which affects the radiation pattern of the AUT. The effects of propellers can be avoided by placing the antenna on the UAV at a location which is far from the propellers. Other approaches, such as using fiber glass material instead of carbon fiber for the propellers, are validated in [48; 49]. All the measurements of the radiation patterns in this case were performed at 328.5 MHz.
## 3 Methods
### Training and Blind Test  
Statistical models are usually optimized through a calibration/validation process for parameter adjustment using a training dataset extracted from the whole database. Data that were not included in the training dataset were used for blind testing of the optimized model. We constructed 16,125 matchups for the ML-based daily ET\({}_{0}\) modeling in the croplands of South Korea for the period between March and November of 2013-2019. We first divided the whole matchups into 80% (12,941 records) for training and 20% (3184 records) for the blind test. This sampling was performed with consideration of the monthly distribution of data so that the ML-based ET\({}_{0}\) model could learn from the temporally representative dataset. Using the training dataset, we first conducted a 10-fold calibration/validation to optimize the ML-based ET\({}_{0}\) model (Figure 4). Then, we conducted a blind test using the remaining data and derived the accuracy statistics, including mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (CC).  
Figure 3: The changes in the root mean square error (RMSE) according to the number of trees in the calibration/validation process of the random forest (RF) modeling. The RMSEs almost converged when the number of trees is around 100.