esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Bias Issues  
Regarding the first issue of our last _RQ3. What are the issues related to bias, explainability, and privacy of speech-based DL approaches for PD classification?_ in some studies within this systematic review such as [9; 35], PD tends to be diagnosed more accurately in men than in women, which raises concerns about the trustworthiness of the research method [27]. For instance, in [35], higher F-scores are obtained for men compared to women. However, the private datasets employed in that experiment included a higher number of male PWP participants. This suggests that the higher accuracy in diagnosing PD in men may be due to the larger amount of male data available, indicating a demographic bias. Since PD is more prevalent in men than in women [74], this could explain why more men are often included in PD studies.  
A gender bias is also evident in the work of [PERSON] et al. [9]. Using a DAFE approach, the authors demonstrate a noticeable difference in classification performance between men and women with PD, with men being classified more accurately than women. However, women showed a more significant intra-group performance improvement compared to men. The authors highlight that gender differences persist due to factors such as less pronounced brain atrophy and different speech neural circuits in women. Another explanation could be that women's voices typically exhibit greater variability, which can be reduced by Linear Discriminant Analysis (LDA) and Probabilistic Linear Discriminant Analysis (PLDA). The authors employed LDA and PLDA in their X-vector implementation, which may account for the greater improvement in performance for women. Nevertheless, the overall performance remained higher for men. This may be due to the fact that men and women exhibit different symptoms of PD [75]. Men tend to suffer more from rigidity and rapid eye movement behavior disorder, while dyskinesias and depression are more common in women. Additionally, men often show less verbal fluency and greater vocal monotony as a result of PD [76], making it easier to recognize PD in men using speech-based deep learning approaches. This could explain why performance is generally higher for men than for women. Future research should aim to close the performance gap between men and women so that these systems can work reliably for both genders in practice.
## 3 Results and Discussion
As proof of concept, we load the HCPEG matrix with semiconductive PANI (0.8% wt) and the porosity with either GOX or GNWs. The polarizability of PANI can be modified by radicals and peroxides which alter the density of polaron states. We observed that the dielectric properties of the HCPEG matrix are quantitatively altered by the PANI filler (Figure 4e,g). Combining this aspect with the tuneable polarizability of the polyaniline itself makes the as-proposed monolithic porous composite  
Figure 6: Schematic of the replica moulding fabrication process; optical image (**upper**) of the master microneedle array; SEM images (**bottom**) of the porous PEG microneedles array replica and inner structure of one single needle showing the porosity. a stimulus-responsive material. Conveniently, the GOX enzyme loaded into the pores can generate these stimuli which can reach the polyaniline filler thanks to the permeability of the material (Figures 3 and 6), indeed GOX is a peroxidase enzyme generating hydrogen peroxide through its catalytic cycle (Figure 7a). Furthermore, the addition of GNWs to the formulation provides several advantages, such as increased emulsion stability and improved porosity distribution (Figure S2). Moreover, GNWs could be functionalized with enzymes like GOX and homogenize the distribution of the enzyme itself throughout the porosity. It is evident that the choice we made in terms of active fillers was not casual, as well as the microneedles replica molding to test the processability. In fact, the fillers were chosen by having in mind the progression of these composites into microneedles patches for the bloodless glycaemia monitoring by piercing and suctioning fluids from the dermis. Several studies towards this direction are currently ongoing within our group. Despite the specific needs which directed the choices above, the methodology we propose in this contribution is not limited to the mentioned application but has potentialities that go beyond and cover a much wider range, like active filtering and molecular sieving, composites materials for inflow catalysis, and many others.
# Multizone Leak Detection Method for Metal Hose Based on YOLOv5 and OMD-ViBe Algorithm
## 2 Leak Detection Methods  
### Detection Process  
Pressure detection and machine vision are utilized to detect the leakage rate and leak location of the metal hose. Figure 1 shows the diagram of the metal hose leakage detection algorithm, including the leak bubble, recognition, leak location detection and leak rate calculation. To detect the leakage in multiple zones of the metal hose, the total video of the metal hose should be captured and recorded via camera. The ROI region of the metal hose is extracted to decrease the calculation burden based on the YOLOv5 model. The Mosaic image enhancement method balances and enlarges the data to improve the model's generalization capability in YOLOv5. Then, a target area correction is adopted to extract the metal hose area, as shown in Figure 2. Based on the oblique ROI extraction of YOLOv5, the foreground calculation method of the background modeling algorithm is adopted to identify foreground points. Then, centroid coordinates are extracted based on the frequency of foreground points to obtain the location of the leakage area. Finally, e the collected pressure data values are combined with the frequency of foreground points in the leakage area for the multipoint leakage volume calculation.  
Figure 1: Algorithm detection framework.
# A Thorough Investigation of the Dynamic Properties of Granite under Cyclic Loading
## 4 Results and Discussion
### Hysteresis Loops of Specimens
#### 4.1.2 Effect of Loading Frequency  
Figure 8 illustrates the dynamic stress-strain relationship obtained from the tests conducted at different loading frequencies. The hysteresis loop curves of the specimens at high frequencies are less stable, so the hysteresis loops of the specimens at frequencies of 10 and 20 Hz show only stable curves. Additionally, with an increase in loading frequency, the hysteresis loop shape undergoes a transition from pointed leaf-shaped to elliptical, leading to expanded hysteresis loops. This observation indicates that the loading frequency influences the energy dissipation of the specimen, consequently affecting the dynamic damping ratio, which also increases with rising loading frequency. Moreover, it is noteworthy that the impact of loading frequency on the hysteresis loop of the specimen surpasses the influence of confining pressure.
## 2 AE Monitoring at the 105-C Reactor Facility
### Results and Discussion
#### 2.3.4 Remote Monitoring at +48 Level  
A cellular connection was used to remotely operate the wireless acoustic emission data acquisition system. Data from the wireless system was collected between 9 September, 2014 (commencement of test) and 13 November 2014. Due to the loss of power from the solar power/battery system, 10 days of data were lost starting from 11 September 2014. The power was reconnected and the system continued to monitor the connection until 15 October 2014 when a thunderstorm caused a power outage and data was lost for another 13 days. The system continued to collect data afterwards until the data acquisition laptop was damaged on 13 November 2014. The damage was most likely due to moisture and was not repairable.  
As described for the wired system data, the raw data was analyzed and appropriate data filters were used to separate meaningful data from spurious emissions. The limits of the data filters are shown in Table 2. Figure 12 shows acoustic emission activity in terms of amplitude versus time (showing both rain and temperature data) collected between 9 September 2014 and 13 November 2014 from the wireless acoustic emission system. This data set contains a significant number of hits with amplitude exceeding 80 dB. These hits are of relatively high amplitude and may be correlated to ongoing damage.  
One objective of monitoring this location was to assess whether the large vertical crack in the wall is still active. This vertical crack has a width between 0.125 and 0.25 inches with several small hairline cracks extending from it in the horizontal direction. Figure 13 plots the cumulative signal strength (units of pico-Volt seconds) versus time (days) for the collected signals over the monitoring period. An increasing trend in the acoustic emission activity is observed in the figures, which indicates that damage may be progressing at this location.  
To further investigate the trends in this data set, triangulation algorithms were used to investigate if AE events were generated from crack growth. Figure 14 shows the source location results from filtered acoustic emission data. In this figure, each red dot indicates a located acoustic emission event, which means that all four sensors received data with a specified time increme  
Figure 12: (a) Amplitude and temperature versus time for four wireless sensors at the +48 level and (b) rain versus time.  
Figure 13: Cumulative signal strength (pVs) versus time (days) for four wireless sensors at the +48 level.  
was determined based on the characteristic wave speed of the structure, which was experimentally determined during the installation site visit, and the geometry of the sensor grid. Source location from raw data was inconclusive since it showed acoustic emission activity throughout the monitored area. The 6 acoustic emission events from the filtered data set were located in the vicinity of the vertical crack. These results imply that crack growth or friction between crack surfaces was ongoing in this area during the monitoring period.
## 3 Results
### Intercomparison between UTOPIA and NOAH Simulations Initialized with GLDAS
#### 3.3.3 Latent Heat Flux (LHF)  
LHF, in terms of differences among mean and median values, shows similar results between the two models and the two versions of GLDAS, with mean differences of about 0-15 W m\({}^{-2}\) (Tables 18-20 and Supplementary Figures S15-S17) with respect to SHF. The larger range of UTOPIA simulations (about 60-130 W m\({}^{-2}\)) and the more extreme values of extremes (for UTOPIA, minima are about \(-2\)-20 W m\({}^{-2}\) lower, and maxima about 40-120 W m\({}^{-2}\) higher) indicate a situation similar to that for SHF, but with larger values and weaker deviations. In particular, the LHF maxima reached by UTOPIA sometimes exceed net radiation (in EXP2b and sometimes EXP3b), causing SHF to be negative in those occasions. This is not occurring for NOAH. Also, regarding the two versions of GLDAS, conclusions are similar to those for the SHF variable (often, but not always, GLDAS-2.1-based mean values exceed those based on GLDAS-2.0, while ranges of GLDAS-2.1 simulations are larger than those of GLDAS-2.0 ones). Thus, also for LHF, we can conclude that perhaps both models capture the mean value of daily means and can be considered able to get the climatology of the sites, but individual NOAH daily values may be considered more accurate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Mean** & **Standard Deviation** & **Median** & **Min** & **Max** & **Range** \\ \hline EXP2b & 16.6 & 41.2 & 2.0 & \(-79.0\) & 151.1 & 230.1 \\ EXP4 & 28.2 & 29.0 & 27.1 & \(-45.4\) & 139.4 & 184.8 \\ EXP3b & 14.2 & 29.0 & 5.6 & \(-55.1\) & 112.7 & 167.8 \\ EXP5 & 28.2 & 25.6 & 30.9 & \(-57.5\) & 83.8 & 141.3 \\ \hline \hline \end{tabular}
\end{table}
Table 15: SHF (W m\({}^{-2}\)) simulated by UTOPIA and NOAH, initialized with GLDAS, at the CF station.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Mean** & **Standard Deviation** & **Median** & **Min** & **Max** & **Range** \\ \hline EXP2b & 16.0 & 37.5 & 4.9 & \(-99.5\) & 150.9 & 250.4 \\ EXP4 & 17.4 & 20.4 & 14.3 & \(-36.4\) & 94.5 & 130.9 \\ EXP3b & 11.1 & 27.0 & 3.3 & \(-59.6\) & 110.3 & 169.9 \\ EXP5 & 12.2 & 17.8 & 13.2 & \(-42.7\) & 51.1 & 93.8 \\ \hline \hline \end{tabular}
\end{table}
Table 17: SHF (W m\({}^{-2}\)) simulated by UTOPIA and NOAH, initialized with GLDAS, at the FB station.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Mean** & **Standard Deviation** & **Median** & **Min** & **Max** & **Range** \\ \hline EXP2b & 16.8 & 61.0 & 40.0 & \(-17.8\) & 278.1 & 295.9 \\ EXP4 & 54.8 & 47.9 & 38.2 & \(-6.2\) & 179.5 & 185.7 \\ EXP3b & 54.6 & 41.2 & 44.5 & \(-8.7\) & 174.5 & 183.2 \\ EXP5 & 42.9 & 28.0 & 38.0 & \(-9.3\) & 110.0 & 119.3 \\ \hline \hline \end{tabular}
\end{table}
Table 18: LHF (W m\({}^{-2}\)) simulated by UTOPIA and NOAH, initialized with GLDAS, at the CF station.
## 6 Verification
### Basic Parameter Settings of Each Model  
For anomaly detection and classification using the sHDP-VAR-HMM with a first-order auto-regressive Gaussian likelihood, each state \(k\) has two parameters to be defined: the regression coefficient matrix \(A_{k}\) and the precision matrix \(\Lambda_{k}\). The conjugate prior is the MNIW, for which four parameters \(\
u,\Delta,V,M\) are assigned values in a fashion similar to the _Motion-Capture-Dataset_ in [53]. In order to guarantee the prior has a valid mean, the degrees-of-freedom variable is set with \(\
u=d+2\) and we set \(\Delta\) by constraining the mean or expectation of the covariance matrix \(\mathbb{E}[\Lambda_{k}^{-1}]\) under the Wishart prior in Equation (11).  
\[\mathbb{E}[\Lambda_{k}^{-1}]=s_{F}\sum_{n=1}^{N}\sum_{t=1}^{T_{n}}(y_{t}- \overline{y})(y_{t}-\overline{y})^{T}. \tag{37}\]  
Assume that we record \(N\) sequential data for each skill and the length of sequence \(n\in N\) is \(T_{n}\). Thus, we can easily define the parameter \(\Delta\) accordingly as  
\[\Delta=(\
u-d-1)\mathbb{E}[\Lambda_{k}^{-1}]. \tag{38}\]  
Specifically, we placed a nominal prior on the mean parameter with mean equal to the empirical mean and expected covariance equal to a scalar \(s_{F}\) times the empirical covariance, here \(s_{F}=1.0\). This setting is motivated by the fact that the covariance is computed from pooling all the data and it tends to overestimate mode-specific covariances. A value slightly less than or equal to 1 of the constants in the scale matrix mitigates the overestimation. Also, setting the prior from the data can move the distribution mass to reasonable parameter space values. The mean matrix \(M\) and \(V\) are set such that the mass of the MN distribution is centered around stable dynamic matrices while allowing variability in the matrix values (see Section 3.2 for details).  
\[M =\mathbf{0}_{d},\] \[V =1.0*\mathbf{I}_{d}. \tag{39}\]  
where \(\mathbf{0}_{d}\) and \(\mathbf{I}_{d}\) are the matrix of all zeros and the identity matrix, respectively, each of size \(d\times d\). For the concentration parameters, a \(Gamma(a,b)\) prior is set on HDP concentration parameters \(\alpha\). A \(Beta(c,d)\) prior is set on the self-transition parameter \(\mu\) and the degree of self-transition bias \(\kappa\) is set to 50. We choose a weekly informative setting and choose \(a=0.5,b=5,c=1,d=5\). Specifically, the initial transition proportion parameter is set \(\mu\sim Beta(1,10)\). The Split-Merge Monte Carlo method  
Figure 6: Multimodal signals of a nominal experiment with six skills.  
sampling parameter maximum iterations is set to 1000. The truncation states number is set to \(K=5\) for anomaly detection, and \(K=10\) for anomaly classification. Then, the HDP-HMM model of each anomaly class or nominal skill is trained throughout the procedure, as defined in Algorithm 1. Note that, as with pre-processing, we also use the same model parameters in training and testing as well as in other baseline approaches.
## Sensory and Instrumental Flavor Changes in Green Tea Brewed Multiple Times  
**[PERSON] 1, [PERSON] 2,* and [PERSON] 2**  
* Department of Food Science and Nutrition, Pusan National University, 30 Jangjeon-Dong, Geumjeong-gu, Busan 609 735, Korea; E-Mail: [EMAIL_ADDRESS]
* Sensory Analysis Center, Kansas State University, Manhattan, KS 66506-1407, USA; E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-785-532-0162.  
_Received: 16 August 2013; in revised form: 31 October 2013 / Accepted: 18 November 2013 / Published: 29 November 2013_  
**Abstract:** Green teas in leaf form are brewed multiple times, a common selling point. However, the flavor changes, both sensory and volatile compounds, of green teas that have been brewed multiple times are unknown. The objectives of this study were to determine how the aroma and flavor of green teas change as they are brewed multiple times, to determine if a relationship exists between green tea flavors and green tea volatile compounds, and to suggest the number of times that green tea leaves can be brewed. The first and second brews of the green tea samples provided similar flavor intensities. The third and fourth brews provided milder flavors and lower bitterness and astringency when measured using descriptive sensory analysis. In the brewed liquor of green tea mostly linalool, nonanal, geraniol, jasmone, and \(\beta\)-ionone volatile compounds were present at low levels (using gas chromatography-mass spectrometry). The geraniol, linalool, and linalool oxide compounds in green tea may contribute to the floral/perfumy flavor. Green teas in leaf form may be brewed up to four times: the first two brews providing stronger flavor, bitterness, and astringency whereas the third and fourth brews will provide milder flavor, bitterness, and astringency.  
**Keywords:** green tea; multiple brews; flavor; sensory; volatile compounds
# Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis
## 2 Related Work
### Machine Learning-Based Methods  
In recent years, due to the widespread availability of online images and the establishment of labeled databases, machine learning-based methods have been widely explored. For example, in 2020, [PERSON] et al. [22] proposed a semi-supervised viewpoint estimation method that can learn to infer viewpoint information from unlabeled image pairs, where two images differ by viewpoint change. [PERSON] et al. [23] presented five key insights that should be taken into consideration when designing a CNN that solves the problem. Based on these insights, the paper proposes a network in which (i) the architecture jointly solves detection, classification, and viewpoint estimation; (ii) new types of data are added and trained on; And (iii) a novel loss function, which takes into account both the geometry of the problem and the new types of data, is proposed. In 2019, [PERSON] et al. [14] proposed a viewpoint estimation method based on Convolutional Neural Networks (CNNs) for volume visualization. In 2021, [PERSON] et al. [24] proposed a self-supervised viewpoint estimation method that infers viewpoints from live camera images. This approach relies on a 3D model of the object and does not require manual data annotation or real images as input. In 2020, [PERSON] et al. [25] developed a cylindrical convolutional network to estimate viewpoint.  
A commendable viewpoint is that 3D models have the potential to generate a large number of images of high variation, which can be well exploited by deep CNNs with a high learning capacity [26]. Some work builds an image database with viewpoint labels before performing image-based viewpoint estimation on a given object. In 2015, [PERSON] et al. [26] proposed a scalable and overfit-resistant image synthesis pipeline, together with a novel CNN specifically tailored for the viewpoint estimation task. In 2016, [PERSON] et al. [27] built a CNN model to estimate object viewpoint from a single RGB image after generating a synthetic RGB image dataset with viewpoint annotations and extracting powerful features for the estimation. Several representative CNN models are listed in Table 2 as examples of these machine learning-based methods. Table 2 shows that these methods have established datasets, but unfortunately, these data have not been made public.  
We have established a new dataset and made it public in this paper. In addition, unlike these references, we also provide a viewpoint sampling method to facilitate readers in establishing new datasets.
## 3 Communication System Design
### Demodulator Design  
The data demodulation is dependent on the envelope characteristics of the primary and secondary side currents, therefore, the demodulators on both sides can be designed with a similar structure. According to the functionality, the demodulator circuit shown in Figure 10 can be divided into four parts: current to voltage (\(I/V\)) converter, precision full-wave rectifier, passive third-order low-pass filter and comparator. For the \(I/V\) converter, its input voltage \(V_{\rm in}\) is determined by the ratio of high-frequency current sensor and the value of \(R_{\rm x}\), and the transduction gain can be expressed as \(G_{\rm iv}=KR_{\rm x}i_{\rm s}\). In practice, the variation of voltage of current sensor is too small, and it is easily  
Figure 9: Key waveforms of the S-BAR and modulator during the modulation of bit “1” and bit “0”.  
overwhelmed by the threshold voltage of diode. Then, the precision full-wave rectifier will compensate this voltage, and the output voltage on rectifier is given by  
\[V_{\text{rec,dc}}=\frac{4V_{\text{max}}}{\pi}\left[\frac{1}{2}+\frac{1}{1\times 3 }\cos\left(2\omega t\right)-\frac{1}{3\times 5}\cos\left(4\omega t\right)+\frac{1}{5 \times 7}\cos\left(6\omega t\right)-...\right]. \tag{23}\]  
where, \(V_{\text{max}}\) is the maximum value of \(V_{\text{rec,dc}}\). It can be clearly seen from (23) that the voltage \(V_{\text{rec,dc}}\) consists of even-order components and DC component. In order to eliminate these components, a third-order low-pass filter is used.  
Notably, the voltage from demodulator should be designed high enough for the comparator, and the SNR should be increased to improve communication performance. Ignoring the non-linear characteristics of current sensor and diodes, the transfer function of demodulator is given by  
\[G(s)=G_{\text{iv}}\frac{R_{6}}{C_{1}C_{2}L_{1}R_{5}R_{6}s^{3}+(C_{1}L_{1}R_{5} +C_{2}L_{1}R_{6})s^{2}+(C_{1}R_{5}R_{6}+C_{2}R_{5}R_{6}+L_{1})s+(R_{5}+R_{6})}. \tag{24}\]  
By selecting appropriate component value, the Bode plot of demodulator is calculated by MATLAB, as shown in Figure 11. It has low magnitude attenuation in the low frequency domain, so it can extract low frequency current fluctuations that carry information. Meanwhile, the fundamental and high-order components are blocked at the cut-off frequency (\(f_{\text{c}}\)=200 kHz).