esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic Emission-Based Method for IFSS Characterization in Single-Fiber Fragmentation Tests
## 3 Results and Discussion
### Statistical Analysis of Fragment Distribution  
Figure 8a displays the Q-Q plots, which demonstrate the expected linear behavior for both the Gamma and Lognormal distributions. This indicates a strong alignment between the empirical data and these theoretical models. In both cases, the data points closely follow the reference line across most of the distribution range, especially in the central quantiles. This suggests that these distributions effectively capture the shape and skewness of the measured fragment length data, see Figure 8b. Minor deviations at the tails are anticipated due to the limited number of extreme values. Although the Weibull distribution is commonly reported in the literature for modeling fragment lengths [28; 35], it did not provide the best fit for the data in this study. The superior performance of the Gamma and Lognormal distributions in the Q-Q plots suggests that these models more accurately represent the observed behavior. Nonetheless, it is important to note that both Gamma and Weibull distributions share key characteristics--such as being defined over positive values and exhibiting positive skewness--which supports the use of Gamma as a valid alternative. The Kolmogorov-Smirnov test supports the observations from the Q-Q plot, revealing that the Gamma and Lognormal distributions have the lowest statistics, at 4.7\(\cdot\)10\({}^{-4}\) and 7.2\(\cdot\)10\({}^{-3}\), respectively. The Gamma distribution was ultimately chosen because it strikes a balance between statistical performance and practical applicability in the context of material mechanics, while also minimizing the risk of model overfitting.  
Table 1 shows the averages of the fragment lengths used in the AE method, calculated using the following two approaches: the arithmetic mean and the fitted gamma distribution proposed method. It can be observed that the average estimated with this method is lower than that obtained with the arithmetic mean.  
Figure 8: Statistical analysis of fragment lengths after SFFTs using optical microscopy. (**a**) Q-Q plot of different distributions applied to the experimental data, where the red dotted line represents the reference line, and the blue cross-shaped points represent the quantiles of the experimental data, (**b**) histogram of the experimental data and fitted probability density functions.
## 2 Mathematical Foundations  
A particular solution of the homogeneous Helmholtz equation for a singular radian frequency \(\omega\) is given by a complex pressure field \(q(\omega)e^{i\mathbf{kx}\cdot\widehat{\mathbf{y}}}\), which corresponds to a plane wave arriving in the direction of a unit vector \(\widehat{\mathbf{y}}\) with an arbitrary complex amplitude \(q\) and wavenumber \(k\). The vector \(\mathbf{x}\) identifies the position where the acoustic pressure is evaluated, and the symbol \".\" represents the scalar product operation. The plane wave approximation is appropriate at a large distance from the acoustic source, where the curvature of the wave can be ignored. In a similar way, an acoustic field that satisfies the homogeneous Helmholtz equation can be represented by means of a Plane Wave Expansion (PWE) as:  
\[p(\mathbf{x},\omega)=\int_{\widehat{\mathbf{y}}\in\Omega}e^{i\mathbf{kx} \cdot\widehat{\mathbf{y}}}q(\widehat{\mathbf{y}},\omega)d\Omega(\widehat{ \mathbf{y}}), \tag{1}\]  
in which \(\mathbf{x}\) is the evaluation point, \(\widehat{\mathbf{y}}\) indicates the different incoming directions of the plane waves, \(q(\widehat{\mathbf{y}},\omega)\) is the amplitude density function and \(\Omega\) is the unitary sphere [19]. The synthesis of acoustic fields based on a plane wave representation is a common approach [16; 17] being adaptable to several audio reproduction techniques. Binaural reproduction is performed by the convolution of the plane waves with the HRTFs according to the direction of arrival [22]. The relation and transformation between the PWE, Ambisonics and wave field synthesis are presented in [23]. Furthermore, mathematical operators to translate [18; 19] and rotate [20] the acoustic field are available, which makes this sound field representation convenient for interactive applications. The disadvantage of this method is that the assumption of plane waves makes the approach suitable for sound fields generated by sources located at a large distance from the listener. In addition, the implementation of an infinite number of plane waves is not feasible, and the use of discrete wave directions generates artefacts in the sound field reconstruction.
## 2 Results
### Analysis of Real Samples  
When the best parameters of the extraction were established, the analysis of commercial plum brandy, pear brandy, and apple brandy was performed to determine the VSCs content in samples. Before the extraction, the 10 mL of diluted samples (to 2.5% _v/v_ ethanol content) were placed in a 20 mL vial along with 2 g of NaCl and 0.1 g EDTA and 10 \(\upmu\)L of IS (thiophene). The SPME procedure was conducted with use of DVB/CAR/PDMS fiber at 35 \({}^{\circ}\)C for 30 min. Each sample was analyzed in triplicate in both SCAN and SIM mode. The chromatograms obtained in SCAN mode were used to identify the compounds by means of retention time and mass spectra and to perform the deconvolution of obtained GC-MS spectra and compare them with entries of the in-house compound library for VSCs. The results of analysis are presented in Table 4. Among 20 analyzed VSCs, only 10 were present in authentic samples of brandy of different origins, and only thiophenol was detected in every studied sample. In pear brandy, 6 different VSCs were detected, and among them, 2-thiophenecarboxaldehyde was present in the largest quantity (3.39 \(\upmu\)g/L), followed by 1-butanethiol (1.17 \(\upmu\)g/L). Moreover, pear brandy contained trace levels of dipropyl and dibutyl sulfide and thiophenol. In the analyzed apple brandy, only 4 VSCs were detected; however, the concentration of 2-methyltetrahydrofuraniophene-3-one was relatively high, and for that reason, the total concentration of VSCs found in this sample was the highest and reached almost 82 \(\upmu\)g/L. The lowest concentration of compounds of interests was found in plum brandy, where only dimethyl disulfide was found in more than trace concentration. Very few authors have analyzed the presence of VSCs in spirit beverages. [PERSON] et al. (2010) analyzed freshly distilled Calvados and Cognac samples with use of sulfur chemiluminescence detector. They found that 3-thiophenecarboxaldehyde and 2-thiophenecarboxaldehyde are the compounds present in highest quantities, whereas Cognac contains 5-6 folds more of them than Calvados. The authors also found significant concentrations of ethyl propyl sulfide, ethyl propyl disulfide, and dibutyl sulfide. [PERSON] et al. (2010) studied the presence of dimethyl sulfide, methyl propyl sulfide, and dimethyl disulfide in different fermented beverages, including whisky. Four different samples of whisky were tested and in three of them, authors found from 0.66 to 1.18 ng/mL of dimethyl disulfide; in the fourth sample methyl propyl sulfide was determined (0.28 ng/mL).
###### Abstract  
This study investigated the influence of saccadic eye movements and emotions on real and animated faces to enhance a detailed perception of facial information. Considering the cross-cultural differences in facial features, animated faces also influence visual preferences due to their unique visual appeal. Since there are differences in facial features across cultures, animated faces can also impact visual preferences due to their unique visual appeal. This study involved 60 participants and utilized four stimulus conditions, including real and animated faces from America and Japan. A five-point Likert scale was employed to measure participants' subjective emotional pleasure and arousal levels, while eye-tracking equipment was used to analyze participants' saccadic eye movements. The results revealed that, in comparison to Japanese real faces (JT), American real faces (AT) exhibited significantly greater average saccadic distances (AT: 113.03 \(\pm\) 46.26, If: 110.78 \(\pm\) 45.55, \(p\) \(<\) 0.05). However, there was no statistically significant difference in the average saccadic distance between Japanese and American animated faces (109.88 \(\pm\) 45.73 and 110.85 \(\pm\) 45.31, respectively). Additionally, evaluating the mediating role of pleasure and arousal levels among these facial stimuli did not impact saccadic behavior. Our observations suggest that cultural factors yield a pronounced impact on saccadic behavior within real-life scenarios. However, these cultural nuances appear to attenuate in virtual and abstract animated environments. This study's findings shed new light on the dynamic relationship between saccadic eye movements, emotions, and cultural dimensions, unraveling complexities in facial perception across diverse cultural landscapes. The implications of our research extend to animation, serving as a catalyst for further exploration in this burgeoning field.  
a 2023 13 1108 11  
Building upon visual observations, rapid eye movements become crucial in their interaction with specific stimuli, especially those related to social interactions [8; 9]. It can be said that among numerous visual stimuli, the brief visual input from facial stimuli enables us to understand social interactions and make basic judgments about others based on their facial expressions and features, ranging from an individual's membership in social categories (e.g., age, gender, race) to their mental and emotional states [10; 11; 12]. Therefore, faces constitute a distinct and intricate category of stimuli, distinct from other complex stimulus categories. In terms of the gaze perception of faces, different mechanisms support it. Larger reactive saccades are typically observed during gaze, representing coarse visual processing, while smaller spontaneous saccades occur when we attempt to focus on specific points of interest, representing finer visual processing [13; 14].  
In the following review of the relevant literature, we divide it into three parts: eye movement differences in facial races, the unique visual attractiveness of animated faces, and the impact of pleasant arousal on eye movements.
## 4 Results
### Performance Comparisons between DPGA and Grid-Search Approaches
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**(a)**FD001 dataset} \\ \hline
**FFE Algo.** & **FFE Para.** & **FFE Algo.** & **RFE Para.** & **PCA Flag** & **PCA Para.** & **LM Para.** \\ \hline CMA & \(n_{MAK}=10\) & PCA, LSA & \(n_{PC}=1\) & TRUE & \(\epsilon=91.649\) & \(s_{\text{FWNO}}=alam\)\(\wedge\)\(n_{\text{HN}}=11\)\(\wedge\)\(\text{def}_{\text{HL}}=tanh\) \\ CMA & \(n_{MAK}=10\) & LSA, FAG & \(n_{PCA}=2\) & TRUE & \(\epsilon=91.649\) & \(s_{\text{FWNO}}=alam\)\(\wedge\)\(n_{\text{HN}}=11\)\(\wedge\)\(\text{def}_{\text{HL}}=tanh\) \\ CMA & \(n_{MAK}=10\) & LSA, FAG & \(n_{PCA}=2\) & TRUE & \(\epsilon=91.649\) & \(s_{\text{FWNO}}=alam\)\(\wedge\)\(n_{\text{HN}}=10\)\(\wedge\)\(\text{def}_{\text{HL}}=tanh\) \\ CMA & \(n_{MAK}=10\) & LSA, FAG & \(n_{PCA}=1\) & TRUE & \(\epsilon=98.517\) & kNN: \(n_{\text{HN}}=7\)\(\wedge\)\(\text{def}_{\text{HN}}=uni\)\(form\) \\ SMA, CMA, LSA & \(n_{MA}=6\) & PCA & \(n_{PCA}=1\) & TRUE & \(\epsilon=98.394\) & kNN: \(n_{\text{HN}}=9\)\(\wedge\)\(\text{def}_{\text{HN}}=distance\) \\ CSA, ES & \(n_{MA}=6\) & None & None & TRUE & \(\epsilon=99.012\) & SVM: \(C=5\) \\ CMA, ES & \(n_{MA}=9\) & FAG & \(n_{PCA}=2\) & TRUE & \(\epsilon=99.477\) & RF: \(n_{\text{HW}}=4\) \\ \hline \multicolumn{6}{c}{**(b)**FD002 dataset} \\ \hline
**FFE Algo.** & **FFE Para.** & **RFE Algo.** & **RFE Para.** & **PCA Flag** & **PCA Para.** & **LM Para.** \\ \hline SMA, CMA, & \(n_{MA}=4\) & PCA, LSA, FAG, & \(n_{PCA}=2\) & TRUE & \(\epsilon=99.456\) & \(s_{\text{FWNO}}=adam\)\(\wedge\)\(n_{\text{HN}}=6\)\(\wedge\)\(\text{def}_{\text{HL}}=relu\) \\ EMA & \(n_{MAK}=9\) & FAG & \(n_{PCA}=3\) & TRUE & \(\epsilon=99.980\) & kNN: \(n_{\text{HN}}=7\)\(\wedge\)\(\text{def}_{\text{HL}}=uni\)\(form\) \\ CMA & \(n_{MAK}=10\) & PCA & \(n_{PCA}=1\) & TRUE & \(\epsilon=98.771\) & SVM: \(C=4\) \\ CMA & \(n_{MAK}=9\) & PCA & \(n_{PCA}=1\) & TRUE & \(\epsilon=98.771\) & SVM: \(C=4\) \\ SMA, EMA & \(n_{MAK}=10\) & PCA & \(n_{PCA}=3\) & TRUE & \(\epsilon=93.179\) & RF: \(n_{\text{HW}}=5\) \\ SMA, CMA & \(n_{MAK}=9\) & FAG & \(n_{PCA}=2\) & TRUE & \(\epsilon=93.179\) & RF: \(n_{\text{HW}}=5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Best solutions of our DPGA approach in RUL prediction datasets. (**a**–**d**) Results of the sub-dataset FD001, FD002, FD003, and FD004, respectively, in the NASA C-MAPSS dataset. Bold values denote the minority cases in all sub-datasets.
## 2 Materials and Methods
### Online Monitoring of PD Using the AE Method
#### 2.2.2 Parameters and Functions of the PD Monitoring System Using the AE Method  
The PDtracker Portable system (Figure 2a), developed by Poznan University of Technology, was used for online PD monitoring using the acoustic emission method. The system enables simultaneous sampling on 8 analog inputs with a maximum resolution of 12 bits and a speed of up to 20 MS/s (millions of samples per second). Piezoelectric transducers of type A6890 (Poznan University of Technology, Poland) (Figure 2b) were used to record AE pulses, which are optimized for the detection of PD in oil-paper insulation. The A6890 transducer has two piezoelectric disks with opposite polarization directions and different heights. This fully differential design allows this transducer to obtain (i) high peak sensitivity (\(-61.1\) dB ref. V/ubar), (ii) two resonant frequencies for longitudinal acoustic waves (68.7 kHz and 89 kHz), which are characteristic of surface discharge and discharges in transformer oil, respectively, (iii) a wide bandwidth from 20 to 100 kHz (Figure 2c), and (iv) low noise (removal of the common-mode noise by the differential preamplifier) [35]. A differential preamplifier with a gain of 40-60 dB and an active band-pass filter (20-500 kHz) is integrated with the piezoelectric transducer in a waterproof housing equipped with four magnetic holders. The PDtracker Portable system software (version 1.6) was developed in the NI LabVIEW 2019 environment. One of the main tasks of the software is to determine, in real time, five parameters of AE pulses from PDs, i.e., the number of pulses per minute, the amplitude and average energy of pulses, and the amplitude and maximum energy of pulses.
## 3 Implementation of X-IoCA for SAR Missions: SAR-IoCA
### Internet of Robotics Things
#### 3.1.1 Development of Ad Hoc 5G Sensors and an ROS-Based Distributed Network  
We developed ad hoc 5G sensor nodes on commercial hardware (Huawei P40 Pro 5G), taking advantage of the portability and lightness of a smartphone, which can be installed on a UAV, a UGV, or carried by a human agent. In them, two functionalities were included for:  
* Transmitting, in real time, the measurements captured by the smartphone's internal sensors (camera, microphone, IMU, and GPS) by implementing various ROS nodes in the smartphone. These ROS nodes are able to publish the information through the 5G network, for which an Android application was developed and shared with the community [39].
* Measuring and monitoring the health status of human agents by means of specialized sensors linked via Bluetooth to the smartphone. Various physiological parameters can be captured, such as electrodermal activity (EDA), electrocardiogram (ECG), blood oxygen levels, or lung capacity. This information could be useful to detect and evaluate the stress of the subjects (e.g., the rescuers) wearing the sensors [49; 50; 51].  
Moreover, a ROS-based distributed network was integrated into the 5G pilot network, such that each CPE attached to the agents or MEC centers, is capable of transmitting large amounts of information (given the high bandwidth of the network) with others via a subscription/publication method to topics through the WAN. The ROS nodes were distributed between edge, fog, and cloud, resulting in a flow of messages between multiple public IP addresses, while the ROS master node is launched in a virtual machine in the cloud. The part of the computational fog located in the operation area is constituted by the computers onboard the robots. Onboard cameras and the smartphones attached to the robots provide vision and sound from the ground (UGVs) and from the air (UAVs), completing a low-latency information system over the area of operations. Thanks to IoCA architecture, a wide range of information is accessible from both MEC centers.
## 4 Analysis and Verification of Rolling Bearing Fault Diagnosis Tests
### Comparison Test of Diagnostic Models
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **Avg/\%** & **Max/\%** & **Min/\%** & **Std/\%** & **95\% Confidence Intervals/\%** \\ \hline
1-VMD-PE-LSTM & 86.25 & 91.67 & 78.13 & 4.1615 & (89.39,83.11) \\
2-VMD-PE-IGTO-LSTM & 88.13 & 90.63 & 84.38 & 2.0412 & (89.66,86.59) \\
3-GTO-VMD-PE-LSTM & 90.42 & 92.71 & 88.54 & 1.2148 & (91.33,89.50) \\
4-IGTO-VMD-PE-LSTM & 91.98 & 93.75 & 89.58 & 1.3217 & (92.98,90.98) \\
5-IGTO-VMD-PE-GWO-LSTM & 92.81 & 94.79 & 90.63 & 1.5059 & (93.95,91.68) \\
6-IGTO-VMD-PE-PSO-LSTM & 93.33 & 95.83 & 90.63 & 1.6271 & (94.56,92.11) \\
7-IGTO-VMD-PE-GTO-LSTM & 94.48 & 96.88 & 92.71 & 1.3217 & (95.48,93.48) \\
8-IGTO-VMD-PE-GTO-LSTM & 95.00 & 97.92 & 92.71 & 1.5309 & (96.15,93.85) \\
9-IGTO-VMD-PE-IGTO-LSTM & 96.67 & 98.96 & 94.79 & 1.2148 & (97.58,95.75) \\ \hline \end{tabular}
\end{table} 
Table 6: Results of the testing phase for CWRU dataset. As can be seen from Table 7, the IGTO-VMD-MSE-IGTO-LSTM model has an average precision of 0.9804, an average recall of 0.9804, and an average F1 score of 0.9804, which all reach the highest value among all compared models; therefore, the proposed model in this paper exhibits excellent classification performance, especially in the recognition of multiple fault categories and is a model worth prioritizing and applying in fault diagnosis tasks.
# Elevation and Mass Changes of the Southern Patagonia Icefield Derived from TanDEM-X and SRTM Data
## 2 Materials and Methods  
### Data  
From 11-22 February 2000, the Shuttle Radar Topography Mission of the National Aeronautics and Space Administration (NASA) acquired a near global data coverage outside the polar regions with a bi-static C-band SAR interferometer [18]. We used the void-filled LP DAAC NASA Version 3 SRTM DEM with a ground resolution of 1 arcsecond, resampled to the local UTM zone 18S as reference dataset. DEMs of subsequent dates were derived from data of the satellite constellation of the ongoing TerraSAR-X and TanDEM-X (further referred to as TDX) missions. The two satellites, operated jointly by the German Aerospace Center (DLR) and ASTRIUM Defense and Space GmbH, fly in a close helix orbit formation. As such, they form a bi-static or pursuit monostatic SAR interferometer with both satellites acquiring data. The system has a swath width of 30 km and enables a ground resolution of 0.4 arcsecond. The TDX constellation is acquiring data since 2010 [19]. The datasets are provided as a pair of Single Look Complex scenes of an average scan-time of seven seconds in along-track direction. To cover our study area, multiple scenes along track and across orbits had to be mosaicked (see Supplementary Materials Figure S1). Table 1 summarizes the major characteristics of the TDX data sets used. For the total coverage of the SPI in the austral summer 2015/16 we used 23 scenes. For the southern tip of SPI a repeat coverage was available, consisting of 4/4-5 scenes from two relative orbits (summer 2011/12, autumn 2014, winter 2016) and seven scenes from three relative orbits (summer 2015/16) (see Supplementary Materials Figure S1). All data was acquired in descending path direction and with similar incident angles facilitating comparable viewing geometries.  
We used optical satellite data to derive raster masks for the DEM post-processing and to refine the glacier catchments of the Randolph Glacier Inventory Version 5.0 [20]. The respective Landsat-5 and Landsat-8 data is listed in supplemental Table S1. Data was chosen as a trade-off between most adequate acquisition date and minimum cloud cover.
## 2 State of the Art
### Time-Variant Techniques  
Most of the time-variant approaches are based on short time Fourier transform [39], quadratic time-frequency distributions [40], and TV-AR modeling [13; 22; 41]. Besides, continuous wavelet transform has been used for a spectral-based analysis of the FHR and UC signals [42], and discrete wavelet transform has been employed to study the transient behavior of the UC events [43].
## 3 X-ray Micro-CT Scanning Test
### Cross Sectional Porosity  
For each slice from CT scanning, the cross sectional porosity, which is defined as the ratio between the number of pixels representing porosity and the overall number of pixels representing the cross section, can be evaluated. As illustrated in Figure 4, a layer by layer cross-sectional porosity analysis was performed on each two-dimensional section along the \(Z\) axis. It is worth noting that the cross sections were not ideally circular for the carbonate samples due to the surface open pores. Therefore, in the calculation of cross sectional porosity, the overall area of the cross section was determined using a circular fitting profile, whose diameter was determined to be the average distance between pairs of centrosymmetric (with regard to the Z axis) pixels on the actual profile of the cross section except the open pores.  
Now that the number of obtained cross sections are different for the carbonate and sandstone samples due to different resolutions of scanning, around 800 cross sections that were equally spaced along the Z axis were chosen for each sample for the evaluation of the cross-sectional porosity. Figure 5 illustrates the distribution of cross-sectional porosity along the Z axis for all the samples. Note that the cross sections in all the samples are indexed consistently so that the cross sections with the same index share approximately the same height in the samples.  
As is shown in Figure 5, much higher cross-sectional porosity was observed for the carbonate samples compared to the sandstone sample. Moreover, the deviation of cross-sectional porosity along the Z axis was more severe for the carbonate samples. This demonstrates the complex forming process of carbonate rocks, as the randomness of the sedimentary particulates and the heterogeneity of the diagenesis process result in anisotropic pore structures. In contrast, small and less deviative cross-sectional porosity was observed for the sandstone sample, indicating relatively uniform and dense internal structure. It is noteworthy that the peaks in the distribution of cross-sectional porosity of Figure 5 imply large pores that span multiple adjacent cross sections. Therefore, how the cross-sectional porosity evolves along the Z axis reflects the change of porosity inside the specimen, which can identify large pores in the sample in a non-disruptive manner.  
Figure 4: Illustration of cross sections along the Z axis for the cylindrical test samples.