esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Correlations between Parameters
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Cooling Method**} & \multicolumn{4}{c}{**SJ Predictions**} & \multicolumn{2}{c}{**S\({}_{20}\) Predictions**} \\  & **Parameter** & **Average Residuals** & **Average Relative** & **Average Residuals** & **Average Relative** \\  & **(1/10 mm)** & **Errors (\%)** & **(1/10 mm)** & **Errors (\%)** \\ \hline Air & UCS & 1.48 & 6.97 & 3.15 & 5.84 \\  & Elastic modulus & 5.04 & 23.70 & 0.93 & 1.71 \\  & Poisson’s ratio & 1.99 & 9.35 & 1.69 & 3.10 \\  & P-wave velocity & 4.31 & 20.26 & 1.58 & 2.93 \\  & S-wave velocity & 3.68 & 17.33 & 1.65 & 3.37 \\ Water & UCS & 0.90 & 4.48 & 2.87 & 5.33 \\  & Elastic modulus & 2.23 & 11.16 & 2.95 & 5.34 \\  & Poisson’s ratio & 1.05 & 5.26 & 1.99 & 3.72 \\  & P-wave velocity & 0.19 & 0.96 & 1.96 & 3.64 \\  & S-wave velocity & 0.39 & 1.97 & 2.04 & 4.28 \\ \hline \end{tabular}
\end{table} 
Table 4: Average residuals and relative errors for predictions of SJ and \(\mathbb{S}_{20}\) from physical and mechanical variables at the highest temperatures (400, 500, and 600 \({}^{\circ}\)C). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Cooling Method**} & \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**R\({}^{2}\) for SJ Predictions**} & \multicolumn{4}{c}{**R\({}^{2}\) for S\({}_{20}\) Predictions**} \\  & & **Linear** & **Exponential** & **Logarithmic** & **Power** & **Linear** & **Exponential** & **Logarithmic** & **Power** \\ \hline Air & UCS & 0.74 & 0.79 & 0.87 & 0.89 & 0.71 & 0.73 & 0.69 & 0.70 \\  & Elastic modulus & 0.56 & 0.61 & 0.55 & 0.59 & 0.96 & 0.96 & 0.93 & 0.92 \\  & Poisson’s ratio & 0.76 & 0.77 & 0.86 & 0.84 & 0.90 & 0.89 & 0.84 & 0.83 \\  & P-wave velocity & 0.65 & 0.64 & 0.69 & 0.66 & 0.92 & 0.91 & 0.90 & 0.89 \\  & S-wave velocity & 0.74 & 0.71 & 0.77 & 0.72 & 0.89 & 0.88 & 0.86 & 0.84 \\ Water & UCS & 0.63 & 0.43 & 0.72 & 0.51 & 0.69 & 0.67 & 0.77 & 0.75 \\  & Elastic modulus & 0.76 & 0.66 & 0.78 & 0.64 & 0.80 & 0.79 & 0.85 & 0.84 \\  & Poisson’s ratio & 0.85 & 0.69 & 0.80 & 0.62 & 0.90 & 0.89 & 0.83 & 0.81 \\  & P-wave velocity & 0.80 & 0.62 & 0.82 & 0.63 & 0.88 & 0.87 & 0.89 & 0.88 \\  & S-wave velocity & 0.82 & 0.64 & 0.81 & 0.62 & 0.91 & 0.90 & 0.88 & 0.86 \\ \hline \end{tabular}
\end{table} 
Table 3: Coefficients of determination (R\({}^{2}\)) for simple regression curves studied to predict SJ and \(\mathbb{S}_{20}\) from mechanical and normalised (N) physical parameters (UCS, elastic modulus, Poisson’s ratio, P- and S-wave velocities). Figure 10: _Cont._  
Figure 11: _Cont._  
Figure 10: Correlations between variation of SJ and: (**a**) UCS; (**b**) elastic modulus; and (**c**) Poisson’s ratio for air- and water-cooled samples. Figure 11: Correlations between variation of S\({}_{20}\) and: (**a**) UCS; (**b**) elastic modulus; and (**c**) Poisson’s ratio for air- and water-cooled samples. Figure 12: Correlations between variation of SJ and (**a**) P- wave velocity; and (**b**) S-wave velocity for air- and water-cooled samples.
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 5 Experiment  
### Dataset  
In the data generation and mixing part, communication data from five modulation modes, including BPSK, 8 PSK, QAM16, QAM64, PAM4 were generated through the software-defined radio platform GNUradio [47], the sampling rate is 1 MHz, and the code rate is 125 K symbol/s. Referring to the existing research results [48; 49; 50; 51; 52; 53] in Table 1, combined with the actual operating efficiency of the simulation platform, the selected signal-to-noise ratio range was 5-20 dB, and the step size was 2.5 dB. In the simulation, it was assumed that the aliased signals from different sources had the same frequency offset and timing deviation. This article focuses on the influence of the signal-to-noise ratio of different algorithms on the separation effect.  
To meets the long-term dependence of the separation task, each type of signal generated 1000 pure data signals under each signal-to-noise ratio, and each datum contained L = 32,768 sampling points, which was 32.768 ms. First, the amplitude of the pure signal was standardized. Then we used the linear instantaneous mixing model as shown in formula (1) to mix the signals. The signals of five different modulation modes were mixed in pairs to obtain data of 10 mixing modes. In each mixing mode, the signals with the same noise ratio were mixed. The data of the 10 mixed methods were BPSK-16 QAM, 8 PSK-64 QAM,8 PSK-PAM4, 64 QAM-PAM4, BPSK-8 PSK, BPSK-64 QAM, BPSK-PAM4, 8 PSK-16 QAM, 16 QAM-64 QAM, 16 QAM-PAM4, which were used as a mixed data set, with 70,000 samples in total.
## 5 Case Study
### Carbonation Measurements  
Carbonation depth was tested by applying the phenolphthalein indicator solution in the abutments, wings, edge beam and deck. The sketch of measuring the carbonation depth in the abutments and wings can be seen in Figure 4.  
The results of the measurements are presented in Table 12. An associated exposure class was assigned to each measurement location.  
In addition to measuring carbonation depth, properties of reinforcement (such as concrete cover, raster and diameter) were determined using a concrete scanner for non-destructive structural inspection. According to the performed tests and the design documents, the properties of reinforcement in the bridge wings can be obtained as given in Table 13.  
Figure 4: Sketch of measuring carbonation depth \(x_{c}\)(46) on the Weikendorf bridge, for wings (W) and abutments (A).  
These properties are relevant in order to determine the duration of both the initiation and propagation phases. The representation of the measured carbonation depths, as well as the reinforcement properties, can be seen in Figure 5.
# A Survey on Datasets for Emotion Recognition from Vision: Limitations and In-the-Wild Applicability
## 4 Discussion
### Application Scenarios
The robot can extract a child's emotions and react accordingly. For example, suppose the child is sad or even crying. In that case, the robot could respond with emotional reactions to help build positive feelings. In this case, affective robots could be powered with multi-cue emotion recognition models that would help them respond better by understanding the context around them. Models for this task could be trained using EMOTIC to allow context encoding and semantics representation [66]. Of course, one cannot expect a child or a person that must be accompanied to look at the robot all of the time, so techniques must be robust to facial occlusion. **Emotion tracking and mental health. ** Systems that track mood and emotion are more common today, mainly due to the recent discussions regarding the importance of mental health, conditions that have increased recently due to the COVID-19 outbreak, which generated outcomes that could last for years. Although clinicians can give mental support through consultations, patients can sometimes feel uncomfortable exposing their feelings verbally [87]. Telemedicine systems that monitor a patient's well-being could be expanded to also monitor their moods and emotion over time. EMOTIC could also be employed here, given its high context availability and people who are not always facing the camera, to track mood from photos from the gallery and cameras placed in the person's house. For example, the techniques could benefit from a high-level context description to correlate house objects with emotions.
## 5 Results and Discussion
### Impact of DA Observation Types on the Model Performance  
The assimilation runs were conducted for the Set-Up I and II in addition to the Base-Test which assimilated pseudo-Lagrangian drifter data. Set-Up I assimilated the Eulerian data while Set-Up II assimilated both the pseudo-Lagrangian dataset used in the Base-Test and the Eulerian dataset used in Set-Up I. DA performance with respect to the differences between model estimates with and without assimilation was examined. Velocity magnitude for the Free-Run simulation is shown in Figure 8a. The RMSD between Free-Run modelled velocities and DA modelled velocities in the Base-Test, Set-Up I and Set-Up II for all grid points was calculated. The special variation of RMSD for the Base-Test shows that DA remarkably improved the model results of Lagrangian drifter data assimilation, specifically in the sections of the lake where the drifters traversed (Figure 8b). These findings suggest that DA performance is highly dependent on the spatial coverage of drifters. Large RMSD values were observed in a small area in the vicinity of downstream boundary where no observations were available. Errors are likely due to sparse data.  
The spatial variation of RMSD (i.e., the deviation between Free-Run and Set-Up I) for the assimilated velocity was then calculated. This statistical measure shows that a single sensor at a certain location can contribute to improvement in the model results through DA (Figure 8c). We found distinct differences between the model before and after assimilation. The difference between the assimilated Lagrangian and Eulerian velocity (Set-Up II) and Base-Test velocity, suggests that including both sets of data (i.e., Lagrangian and Eulerian) concurrently results in larger RMSD in the domain compared to when the Base-Test or Set-Up I is examined independently (Figure 8d). The model is strongly affected by DA as observed from the three cases examined, although the impacts are not identical in terms of magnitude or with respect to location. RMSE and R\({}^{2}\) values before and after DA for Lagrangian, Eulerian and Both Lagrangian and Eulerian DA indicate significant improvements over the Free-Run simulation (Table 4 and Figure 9).  
Figure 8: DA effect on velocity magnitude in the entire domain. (**a**) Model without assimilation, (**b**) the difference between model estimates with and without assimilation according to Base-Test configuration, (**c**) using configuration Set-Up I and (**d**) Set-Up II.
## 4 Methodology
### Spectral Analysis  
With the help of spectral analysis of the gravity data, average depths of underground layers can be calculated without requiring any geological or density information about the region.  
The radial amplitude value (Equation (1)) is obtained by calculating the amplitude spectrum obtained as a result of the two-dimensional Fourier transform around a circle with the center point of \((k_{x}=k_{y}=0)\) and radius of \(k_{r}\) (Equation (2)).  
\[A=|F|=\sqrt{\text{Re}(F)^{2}+\text{Im}(F)^{2}} \tag{1}\]  
\[k_{r}=\sqrt{k_{x}^{2}+k_{y}^{2}} \tag{2}\]  
The amplitude spectrum is generally used to calculate the depths of the upper boundaries of potential field sources [47]. Therefore, the natural logarithm of the radial amplitude spectrum was taken and plotted on a graph against the wave numbers. Lines were drawn manually on the decreasing-amplitude curve, and the slopes of these lines gave the average depth of the layers (Figure 4). Fourpot 1.3b application was used for the analysis [48].  
On the basis of the critical wave numbers obtained as a result of spectral analysis, the average lower and upper boundary wavelengths of the layer of interest for the study area were calculated to be 3.22 km and 18.5 km (Equations (3) and (4)).  
\[\begin{array}{c}k_{c_{1}}=0.34\text{ rad}/\text{km}\\ \lambda_{c_{1}}=\frac{2\pi}{k_{c_{1}}}=\frac{2\pi}{0.34}=18.5\text{ km}\\ \end{array} \tag{3}\]  
\[\begin{array}{c}k_{c_{2}}=1.95\text{ rad}/\text{km}\\ \lambda_{c_{2}}=\frac{2\pi}{k_{c_{2}}}=\frac{2\pi}{1.95}=3.22\text{ km}\\ \end{array} \tag{4}\]
## 3 Results  
### Comparison among the Classification Methods  
To verify the effects of transposed convolution, we compared four classification methods: 2D CNN with transposed convolution (proposed method), 1D CNN, 2D CNN for time-series using electrode data and 2D CNN for using STFT data.  
Tables 5 and 6 display the comparisons among the results for the four network types. The \(p\)-value indicates the results of the Wilcoxon signed-rank test.  
The means and standard deviations (SD) of the accuracies of 100 times in total (20 times 5-fold cross-validations) for each subject are displayed in Table 5. For cross-validation, the average accuracy across subjects was approximately 0.77 when transposed convolution was used, and 0.42 to 0.65 in the other cases. When calculating the information transfer rate (ITR) [8], the results were as follows: 17.30 for the model using transposed convolution, 10.36 for the 1D CNN, 5.50 for the 2D CNN using STFT, and 2.18 for the 2D CNN using time-series electrode data.  
Table 6 displays the average and SD of the learning accuracy using all training data and evaluating the unseen test data 100 times. When the unseen data were classified, the average accuracy was approximately 0.71 when transposed convolution was used and 0.43 to 0.62 in the other cases. When calculating the information transfer rate (ITR), the results were as follows: 13.82 for the model using transposed convolution, 9.08 for the 1D CNN, 4.23 for the 2D CNN using STFT, and 2.27 for the 2D CNN using time-series electrode data. The classification accuracy of each subject was the highest when transposed convolution was used in both cases. The accuracy of the proposed method was significantly higher (\(p<0.05\)) than that of the other methods.  
These results indicate that by expanding the feature dimension with transposed convolution, improved features are automatically extracted.
## 3 Underwater Routing Protocols
### Spr-Dlp  
The sector-based routing with destination location prediction (SBR-DLP) protocol makes the destination node mobile rather than fixed at the surface of the water [16]. This makes all nodes know only their position information and predict the predefined mobility pattern of the destination node. A node does not need to know the position information of its neighbors. A source node that has data to send broadcasts a control message to its one-hop neighbors that contains its position information and packet ID. Upon receiving the control packet, every neighbor node replies to the source node only if it is closer to the destination node (the predefined position of the destination) than the source node. The packet collision is overcome by dividing the transmission range of the source node into sectors. Nodes in the sector bisected by the straight line from the source to the destination are prioritized to send replies to the source node. The protocol achieves a significant reception of packets at the sink when nodes are mobile. However, it has high delay because the mobile nodes do not prioritize the nodes that have data packets ready for transmission. Instead, the mobile nodes move with their predefined paths irrespective of which node has data packets ready for transmission.
## 5 Results and Discussion  
The performances of PRDFS and BRDFS were comparatively tested on 11 games with an adversary model. Each game was conducted with different percentages of malicious nodes. The duration of each game was 1000-time steps. The simulation results provide interesting insight into the comparative efficacy of PRDFS and BRDFS.  
The Comparative Performance of PRDFS and BRDFS in Terms of Demand Forecasting Resilience to Stealth Attacks  
Major Findings  
* PRDFS exhibits an almost perfect mean resilience value of \(\mathbf{\sim}\mathbf{0.99}\) over the time frame of the game (i.e., 1000-time steps) regardless of the percentage of the nodes compromised by the adversary (Figure 7). This is mainly because it detects and recovers all compromised nodes at the very early stages of games (within first 30-time steps) (Figure 10).
* During the first 40-time steps of games while the attack is still occurring or just ended, the mean demand forecasting resilience of PRDFS slightly decreases but it never goes below 0.8 (its resilience ranges from 0.82 to 1), regardless of the percentage of the malicious nodes (Figure 7). In contrast, the mean demand forecasting resilience sharply decreases in BRDFS over the simulation period (i.e., 1000 time-steps) as the percentage of malicious nodes increases (Figure 7).
* In PRDFS, the interval demand forecast accuracy demonstrates greater resilience than the point demand forecast accuracy, while the opposite trend is observed in BRDFS (Figure 7). In BRDFS, the interval demand forecast accuracy has weaker resilience compared to the point demand forecast accuracy and the trend becomes more pronounced as the percentage of malicious nodes increases. This is mainly because BRDFS fails to detect most malicious nodes (Figure 11). All colluding malicious nodes report similar measurements and thereby artificially reduce the uncertainty around the mean estimate, which results in low interval demand forecast accuracy and yields a very high FPR in detecting malicious nodes.  
We attribute the high resilience of PRDFS in the face of an attack to the combined effect of the strong performances from its modules: SIDS, TMS, and DFS. Sections 5.2-5.4 discuss the comparative performances of the individual subsystems.
# Reference Data by Player Position for an Ice Hockey-Specific Complex Test
## 2 Materials and Methods
### Study Design and Methodology  
This cross-sectional study utilized data from the IHCT over six seasons (2015-2020) in the 3 rd German ice hockey league (three teams). Only male field players (forwards and defenders) were included in the investigation. Before testing, all participants were carefully familiarized with the testing protocol of the IHCT (sketch, instructional video, demonstration on ice; Figure 1).  
All players were assessed on the same day, and the tests were performed in the same order. The order of the different tasks had no influence on the intrarater reliability of the tasks and parameters. [PERSON] et al. [17] found no differences for the last skating tasks (weave agility without and with the puck). Only the 10 m backward sprint without a puck displayed a markedly reduced reliability (ICC = 0.54, coefficient of variation (CV) = 9.2%). Reliability was slightly reduced for three parameters (10 m sprint with puck, slap shot 3 before test, slap shot 1 after test) (ICC: 0.60-0.73; CV: 5.0-8.3%) [17].  
The preparation of the test (nutrition, warm-up, instructions) was standardized and has already been published [10; 17].  
Figure 1: Schematic diagram of the ice hockey-specific complex test (IHCT) and description of the test sequences [10].  
The purpose of the IHCT (Figure 1) is to imitate essential match related ice hockey-specific demands and skills in a sequence of different on-ice tests using a complex test design [10]. The IHCT comprises the following five actions:  
* shots on goal at the start of the test,
* a sprint test (without/with puck, forward/backward),
* transition agility test,
* weave agility test,
* shots on goal at the end of the test.  
A detailed description regarding action sequences (e.g., explanation of the test selection, load, and recovery time) and parameters (e.g., measurement systems, reliability) has been previously published in [PERSON] et al. [10; 17].  
To ensure strong interrater reliability, the same investigators (R.S.; S.S.) performed all the test measurements.  
Heart rate was measured using a real-time monitoring system (Polar Team Pro System; Polar Electro GmbH, Buttelborn, Germany). Lactate concentrations were determined by an enzymatic analyzer (Super GL compact; Dr. Muller Geratebau GmbH, Freital, Germany). Capillary blood samples were taken from the athlete's ear lobe before warm-up, and at 2, 6, and 10 min after finishing the test.
## 6 Conclusions  
In order to improve the shortcomings of FSDAF, this study developed the Enhanced FSDAF (EFSDAF) to accurately monitor dynamic changes of land surface. EFSDAF improved FSDAF in the following aspects: (1) unlike existing spatiotemporal fusion methods of unmixing based (e.g., STRUM, FSDAF), EFSDAF considered the mixed pixels of fine resolution images (Landsat), and the fusion results by EFSDAF takes into account intra-class variability and can accurately reserve the spatial details of land surface; (2) the differences between the coarse resolution images and the fine resolution images were adjusted; (3) a new residual index (_RI_) was proposed to guide the residual distribution, which improved the fusion accuracy in the abrupt change area. Experimental results demonstrated that:  
1. EFSDAF can accurately monitor both the gradual change and abrupt change events. More importantly, EFSDAF can reserve more spatial details of land surface and has a stronger robustness than FSDAF.
2. EFSDAF can monitor more information of land cover change than FSDAF by introducing a new residual index (_RI_) to guide residual distribution because the proposed _RI_ considers the actual source of residual.  
Figure 10: Differences comparison between the predicted image by EFSDAF and input images in dataset 2. From left to right: (**a**) Landsat image at \(T_{P}\); (**b**) Landsat-like image by EFSDAF at \(T_{P}\); (**c**) MODIS image at \(T_{P}\); (**d**) MODIS image at \(T_{B}\).  
EFSDAF is an automated fusion method because it does not need additional input parameters, which has great potential to monitor long-term dynamic changes of land surface using high spatiotemporal images.  
In addition, we also expect that EFSDAF can be applied to other products of remote sensing and other satellite sensors.  
[PERSON] designed and developed the EFSDAF code and wrote the manuscript. [PERSON] directed and revised the manuscript. [PERSON] and [PERSON] processed the experimental data. [PERSON] and [PERSON] made the Figures and Charts. [PERSON] revised the manuscript.  
This study was financially supported by National Natural Science Foundation of China [Grant number 41971387 and Grant number 41071271], Shaanxi Province Natural Science Foundation [Grant number 2015]M4132], Strategic Priority Research Program of Chinese Academy of Sciences, China [Grant number XDA2004030201], and Shaanxi Key Laboratory of Ecology and Environment of River Wetland [Grant number SXSD201701].  
The authors are grateful to [PERSON]. [PERSON] for sharing test data in the remote sensing community and thank [PERSON] for sharing the spectra of Global SVD Endmembers. Thanks to the FSDAF code shared by [PERSON] and the STRUM code provided by [PERSON]. Most of all, I want to thank my girlfriend [PERSON] for the constant support and encouragement.  
The authors declare no conflict of interest.