esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Night Samples Subset
For the daylight samples, these differences were very small. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample Set**} & \multirow{2}{*}{**Feature Set**} & \multirow{2}{*}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Precision \({}^{\star}\)**} & \multirow{2}{*}{**Recall \({}^{\star}\) (\%)**} & \multirow{2}{*}{**Specificity \({}^{\star}\)**} \\  & & & & (\%) & & (\%) \\ \hline \multirow{2}{*}{All} & Original & 69.8 \(\pm\) 0.6 & 68.4 \(\pm\) 0.8 & 73.8 \(\pm\) 1.6 & 65.9 \(\pm\) 1.7 \\  & Reduced & 70.8 \(\pm\) 0.8 & 71.8 \(\pm\) 0.9 & 68.5 \(\pm\) 1.5 & 73.0 \(\pm\) 1.5 \\ \hline \multirow{2}{*}{Day samples} & Original & 80.0 \(\pm\) 2.2 & 80.8 \(\pm\) 3.6 & 78.9 \(\pm\) 3.0 & 81.1 \(\pm\) 4.5 \\  & Reduced & 79.9 \(\pm\) 1.3 & 83.8 \(\pm\) 2.2 & 74.2 \(\pm\) 2.5 & 85.6 \(\pm\) 2.5 \\ \hline \multirow{2}{*}{Night samples} & Original & 65.8 \(\pm\) 0.9 & 89.9 \(\pm\) 3.7 & 35.8 \(\pm\) 3.6 & 95.8 \(\pm\) 2.0 \\  & Reduced & 65.4 \(\pm\) 1.3 & 84.8 \(\pm\) 4.7 & 37.8 \(\pm\) 2.7 & 93.1 \(\pm\) 2.8 \\ \hline \hline \end{tabular}  
* positive class: stressed.  
\end{table} 
Table 3: Summary of the performance of each classification model. Table S2 in the Supplementary Material provides a detailed list of all the features forming the reduced subsets used in these analyses. The Hjorth complexity, GHE and IQR appeared as uncorrelated features for all window lengths and all sample sets. Differently from that, the logarithmic wentropy appeared as an uncorrelated feature at 15 s and 30 min only.
# Preliminary Seismic Hazard Analyses for the Ugandan Region
## 3 Materials and Methods
### Selecting Ground Motion Prediction Equations  
Two GMPEs were selected for use in this study. These GMPEs are applicable to rock sites, can use moment magnitude, and are supported by strong data sets in their regression and calibration [29,30]. One GMPE is a modified version of an earlier GMPE developed by a similar team for rock sites in central and eastern North America as well as the Gulf of Mexico [29,31]. The original version was grounded on the predictions of a stochastic ground-motion model and used local magnitudes ranging from 5.0 to 8.0, however the 2002 version uses moment magnitude. An example of the GMPE results is shown in Figure 1 for \(M_{w}\) = 5.0, 6.0, and 7.0. Figure 1 also shows the GMPE by another GMPE, which is also an extension of a previously developed GMPE by the same researcher [30,32]. One of the differences between the original and newer version is the inclusion of a shear wave velocity in the upper 30 m, \(V_{S30}\) term. For this study, \(V_{S30}\) was assumed to be 760 m/s, representing rock sites. The GMPE results are also shown at for \(M_{w}\) = 5.0, 6.0, and 7.0 and appear to  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Magnitude** & **Distance (km)** & **Time (Days)** \\ \hline
2.5 & 2.9 & 4.2 \\
3.0 & 4.5 & 5.3 \\
3.5 & 6.8 & 7.3 \\
4.0 & 10.3 & 10.9 \\
4.5 & 15.7 & 17.7 \\
5.0 & 24.0 & 30.2 \\
5.5 & 36.5 & 53.5 \\
6.0 & 55.5 & 96.7 \\
6.5 & 84.4 & 176.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Space and time ranges for determining dependent events [26].  
estimate lower PGAs relative to Toro [29] at distances greater than 10 km. Interestingly, at distances greater than 10 km, the PGA results for Idriss [30] at \(M_{w}\) = 7.0 are very similar to the results from Toro [29] at \(M_{w}\) = 6.0.
## 3 Results and Discussion  
### Indirect Measurement of Viscosity  
In this study, we aim to study the deviation in the swimming ability of ubiquitous microorganisms (Paramecia and Euglena) in contaminated water due to the presence of emerging pollutants Fe and Zn. The method is based on a cost-effective implementation that permits the assessment of water quality by evaluating the collective influence of heavy metal pollutants on the swimming ability of aquatic microorganisms. Additionally, we have measured the swimming ability of Paramecia in solutions of different viscosity, \(\eta\), to see how their swimming behavior changes with increased drag. Since the relationship between an environment's viscosity and the swimming ability of Paramecia has previously been measured numerous times [15; 16], we therefore used this study to correlate the proposed/novel biospeckle technique to earlier findings before applying it to the actual water quality assessment.  
The advantage of selected organisms is that in addition to being common in nature, their swimming ability is tremendously and significantly influenced by the viscosity of their habitats [17].  
The cross-correlation function as a function of time in different viscosity solutions is shown in Figure 3. The graph indicates that the width of the cross-correlation function spreads out as the viscosity of the media increases, indicating the slow movement of plankton under different viscous solutions. In the control group (1 cP methylcellulose), Paramecia exhibited their typical fast and erratic swimming patterns. At 2.5 cP methylcellulose, a slight reduction in speed was noted, but the Paramecia still managed to navigate relatively well. However, at viscosity concentrations of 2.5 cP and above, the Paramecia's swimming speed was markedly reduced, and their movements became more sluggish and less coordinated. At the highest viscosity concentration (8 and 12 cP), Paramecia struggled to move at all, often appearing to be trapped in the viscous medium. The cross-correlation time at 2.5, 5, and 8 cP was observed to be approximately 2, 4, and 4.5 times larger than the control, respectively. As reported in previous studies, the swimming ability of Paramecia is strongly affected by the viscosity in the range of 1-5 cP, showing an average relative drop of 29.2% in the correlation time at 1 cP per increment. However, on a further increase in the viscosity range 5-12.5 cP, the Paramecia movement was completely hindered, and the correlation time was observed to be the same as 5 cP. This reduction in swimming ability at higher methylcellulose concentrations can be attributed to the increased resistance against the ciliary beat, which Paramecia are unable to overcome. The tendency of the presented data with the previous studies regarding the swimming behavior of Paramecia and their dependency on the viscosity of the surrounding media is noteworthy and suggests the suitability of the swimming behavior as a strong indicator for general use.
## 2 Methodology
### Data Collection and Pre-Processing  
The study was conducted following the approval of the institutional ethics committee at Kasturba Medical College, Manipal. Data were collected from 25 male patients aged between 30 and 70, focusing on the lumbar vertebrae for testing purpose. This testing dataset provided a practical evaluation of our model's performance in clinical scenarios. For training and validation of the Res U-Net model 214 CT scans were obtained from the open-source VerSe'20 dataset. This dataset provided a comprehensive and diverse set of images which is crucial for achieving high model accuracy. The use of such a large dataset allowed the model to learn from various anatomical variations and imaging conditions thereby enhancing its generalizability. The training data were split into 171 scans for training (80% of the dataset) and 43 scans for validation (20% of the dataset).  
The pre-processing process involved several steps to prepare the CT images for analysis [26]. Initially, all scans were reviewed for quality by excluding those with missing slices. Noise reduction was performed using a Gaussian filter to preserve edge integrity which was followed by histogram equalization to enhance image contrast. Intensity normalization to a standard range (0-255) was conducted to mitigate variations due to differing scanning conditions. Segmentation of the vertebral bodies was achieved through a combination of thresholding and region-growing algorithms by ensuring precise delineation of the vertebral boundaries. Key anatomical and texture features were then extracted by including vertebral body dimensions. Segmentation of the vertebral bodies was achieved through a combination of [PERSON]'s thresholding method and a region-growing algorithm. [PERSON]'s method was used to find the optimal threshold value for separating the vertebrae from surrounding tissue, with a threshold value of 220 Hounsfield units. The region-growing algorithm was employed to precisely delineate the vertebral boundaries by expanding from seed points in the vertebral regions. The key anatomical features extracted included vertebral body dimensions such as height, width, and depth, as well as texture features related to bone density.
# Unmanned Aerial Vehicle Depth Inversion to Monitor River-Mouth Bar Dynamics
## 4 Results  
### Estimated Bathymetry  
We estimated the coastal bathymetry for each case using the method described in Section 3. The record length of the video differs in the four cases listed in Table 2. A previous study suggested that the estimation accuracy would not change with signal length when the signal is sufficiently long (>5 min) [19]. Therefore, we analyzed the intensity signal of the whole video length for each case in Table 2; we will discuss how the video length affects the estimation accuracy in 5.2. The depth inversion was performed on a regular grid with 2-m grid spacing, and the wave parameters were computed at each grid point with eight reference points (\(N=8\)) along a circumference with \(r=20\) m; we will later discuss the estimation sensitivity to the two parameters. Then, we estimated water depth from the dispersion relationship, except at grid points where the signal time lag did not vary sinusoidally along the circumference. The exception occurs in the land area, surf zones, and  
Figure 8: Spatial distributions of (**a**) the representative wave period and (**b**) the mean cross-correlation coefficient.  
areas where the wave signal is weaker than noise signals, or where two wave components of similar magnitudes superpose in the signal. Finally, we created a contour map from the gridded water depth data for each case after applying a Gaussian filter of size \(5\times 5\).  
Figure 9 shows the contour maps of estimated water depth for the four cases. We successfully obtained the continuous distribution of water depth in the entire domain, except inside the surf zone. The result in Case 1 shows alongshore uniform bathymetry with an increasing seabed slope to the shore. In contrast, the depth map of Case 2 reveals a milder beach slope due to the effect of detached breakwaters. The depth inversion did not work in the areas behind the breakwaters, where diffracted waves in opposite directions superposed to form combined wave fields. Cases 3 and 4 detected the deltaic bar in front of the river mouth in different forms. However, wave breaking in front of the river mouth limited the depth inversion in the vicinity of the river mouth. Comparing the two results suggests that high waves during the typhoon passage caused erosion in nearshore areas along the sand spit, whereas a significant amount of sediment accumulated in the deeper area. Since Case 4 was observed more than 10 days after the event, a crescenti bar was forming by wave-induced sediment transport in front of the river mouth. These results confirm that the present method works in various coastal situations and can grasp the morphological evolution of a river-mouth bar.
# Double-Layer Compressive Sensing Based Efficient DOA Estimation in WSAN with Block Data Loss
## 4 Experimental Results
### Simulation Settings
To clearly compare the DOA estimation performance for CS-DOA and DCS-DOA under different packet sizes, the DOA RMSE and the detection frequency results are shown in Figures 12 and 13, where each point is based on an average of the results obtained from 500 independent simulation runs. DCS-DOA is denoted by DCS-DOA(1) if the projection matrix at each array sensor is a dimension-reduced permutation matrix, and it is denoted by DCS-DOA(2) if the projection matrix is constructed from a random Gaussian matrix. Figure 11: The comparison of DOA spatial spectrum between CS-DOA and DCS-DOA with packet size = 64. Figure 12: The comparison of DOA RMSE between CS-DOA and DCS-DOA under different packet sizes. According to the simulation results, whatever the packet size, the DOA estimation performance achieved by DCS-DOA has obvious advantages over CS-DOA. Additionally, the DOA estimation RMSE and detection frequency for DCS-DOA is no longer affected by block data loss, as shown in Figure 14. Therefore, we can exploit a larger packet size to achieve a higher data transmission efficiency. The performances achieved by DCS-DOA(1) and DCS-DOA(2) are verified to be very close to each other. Considering that the storage, fetch and processing of a permutation matrix is simpler and less energy-consuming, the projection matrix is usually constructed from a permutation matrix. In the next simulations, we use DCS-DOA(1) as a representative, simply denoted by DCS-DOA.
## 3 Results
### Principal Component Analysis (PCA)  
In the study, PCA showed that there were differences among the different pepper species. The PCA score graph of the first two PCs explained 45.9% of the total variance, and the results showed the corresponding load graph (Figure 3B). It can be seen that the content characteristics of metabolite could be clearly distinguished for _C. chinense_ (S22, S23, S24, S26), _C. frutsccens_ (S19, S20, S61), and _C. annuum_. The _C. frutsccens_ (S19, S20, S61) and _C. annuum_ was highly similar in metabolite traits. Some _C. chinense_ accessions (S23, S24) had a positive effect on PC1 and a negative effect on PC2. These accessions were characterized by a high concentration of capsaicin, dihydrocapsaicin, and \(\beta\)-carotene. Many content features were negative values as shown in the PC1 of Figure 3B, including ascorbic acid, total soluble solids, titratable acid, total soluble protein, C18:2n6c, total polyunsaturated fatty acids, and zeaxanthin, but the rest of the features in this study were positive values. The contents of \(\beta\)-carotene and capsanthin were positively correlated with those of C12:0, C14:0, and other fatty acids. Moreover, the above law of correlation was conformed to the concentrations of capsaicin, dihydrocapsaicin, and nordihydrocapsaicin with that of the C12:0, C14:0, C15:0, and C15:1 fatty acids (\(p\)\(<\) 0.05). The concentration of total monounsaturated fatty acids was significant positively correlated with that of C18:1n9c (\(p\)\(<\) 0.05), and the same as the total polyunsaturated fatty acids with that of C18:2n6c (\(p\)\(<\) 0.05) (Table 2, Figure 2).  
Figure 3: Principal component analysis of quality indicators of 75 pepper germplasm resources. (**A**) The PCA score plot in the space of the first 2 PCs explaining 45.9% of the total variance, and (**B**) the corresponding loadings plot. Sample number as shown in Table 1.
## 3 RBER Estimation
To realize this, a threshold RBER \(BER_{th}\) can be set to trigger switching between hard-decision and soft-decision decoding statuses that hard-decision decoding is applied when \(\overline{BER_{est}}\)<\(BER_{th}\), and the soft-decision decoding is used otherwise. The method can also be used for cell wearing monitoring. The RBER will be rising when NAND flash cells are gradually wearing out as the number of P/E cycles increasing, so the long-term recorded \(\overline{BER_{est}}\)s can reflect the cell wearing condition in NAND flash. A block is possibly being worn out when its \(\overline{BER_{est}}\) reaches a certain threshold, and the controller should be informed to execute error management strategies or migrant the data to other blocks. Figure 8: Probability density of estimated BER at actual (**a**) RBER = 0.01 and (**b**) RBER = 0.1 when using 1/2 gray coding modulation, balanced gray coding modulation, and with interleaving. Figure 7: Mean square errors between the actual raw bit error rate (RBER) in NAND flash and the RBER estimated with the true-value sequence of different lengths, tested in RBER = (0, 0.1] with \(N=16\). Figure 9: The linear fit of the RBER estimated with \(L=256\) vs. the actual RBER, where the coefficient of determination \(R^{2}=0.992\).
## Abbreviations  
Full form  
Photonic integrated circuit  
Electronic integrated circuit  
EIC  
Waveguide  
Electron beam lithography  
EBL  
Nanopimprint lithography  
NIL  
Electro-optic  
EO  
Thermo-optic  
Tom  
Complementary metal oxide semiconductor  
CMOS  
Artificial intelligence  
AI  
Artificial neural network  
ANN  
Machine learning  
ML  
Indium phosphide  
InP  
Gallium arsenide  
GaAs  
Silicon nitride  
Si\({}_{3}\)N\({}_{4}\)  
Silicon-on-insulator  
SOI  
Extinction ratio  
ER  
Phase change material  
PCM  
Germanium-Tin  
GeSn  
Photodetector  
PD
## 2 Materials and Methods
### Test Substance Analysis by High-Performance Liquid Chromatography  
The content of atractylenolide III in the root extracts of _A. japonica_ (AJ extracts) was measured using an Agilent 1260 Infinity II high-performance liquid chromatography (HPLC) system (Agilent Technologies, Inc., Santa Clara, CA, USA). This system featured a UV-visible detector and employed a Unison US-C\({}_{18}\) column (dimensions: 4.6 mm \(\times\) 250 mm, particle size: 5 \(\upmu\)m; PeakmanSP Co., Ltd., Hanam, Republic of Korea). Both the AJ extract and the standard atractylenolide III were dissolved in methanol and subsequently filtered using a 0.45 \(\upmu\)m filter disc. The column temperature was kept at a constant 25 \({}^{\circ}\)C throughout the analysis, with the detection of atractylenolide III occurring at 224 nm wavelength. The mobile phase consisted of acetonitrile and 0.05% trifluoroacetic acid. Detailed HPLC analytical conditions are listed in Table S1. For calibration, standard atractylenolide III (BF-A2011) obtained from Biofron Inc., Fullerton, CA, USA, was employed as a reference. The sample was injected at a volume of 10 \(\upmu\)L, and the flow rate was set at 1.0 mL/min. Quantification was performed by comparing the relative peak area to that of the standard, allowing for determination based on peak area and concentration relationships.
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 2 Materials and Methods
### Performance Metrics
#### 2.4.4 Delay  
Delay is the time to transfer packets from the source packet to its destination that passes through a series of nodes [29]. Because the delay is related to time, it is stated  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Code** & **REEQ_RETRIES Value** & **MAX_REEQ_TIMEOUT Value** \\ \hline
1 & AODV 1 & 3 (default) & 10 (default) \\
2 & AODV 2 & 2 & 10 \\
3 & AODV 3 & 4 & 10 \\
4 & AODV 4 & 3 & 5 \\
5 & AODV 5 & 3 & 6 \\
6 & AODV 6 & 3 & 7 \\
7 & AODV 7 & 3 & 8 \\
8 & AODV 8 & 3 & 9 \\
9 & AODV 9 & 3 & 11 \\
10 & AODV 10 & 3 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: REEQ_RETRIES and MAX_RREQ_TIMEOUT variation of AODV protocol.  
in millisecond units. Several factors support this delay, namely, network congestion, overutilized servers, and distance between connection points on the network. A more excessive load on a network, with the connection distance being far away allows for a high delay value. Delay is one parameter for assessing QoS. The smaller the delay value, the better the quality of a network. The average delay can be calculated using Equation (4).  
\[\text{Delay}=\frac{\text{time packet received}-\text{time packet sent}}{\text{total package received}}. \tag{4}\]
# Bioactive Compounds from Herbal Medicine Targeting Multiple Myeloma
## 3 Flavonoids in Multiple Myeloma
### Isoliquiritigenin  
Isoliquiritigenin (20,40,4-trihydroxychalcone), a bioactive compound with a chalcone structure, was isolated from licorice root (Figure 17) [399].  
Isoliquiritigenin has various biological activities, such as antioxidant, antiviral, antiinflammatory, neuroprotective, antidiabetic, chemoprerevinety and antitumor activity [399].  
Isoliquiritigenin has been shown to have anticarcinogenic activity in different cancers, such as breast cancer [400], colon cancer [401], ovarian cancer [402], acute myeloid leukemia [403], and melanoma [404].  
[PERSON] et al. [405] studied the effect of isoliquiritigenin on MM cells, both in vitro and in vivo. Isoliquiritigenin inhibited the growth of MM cells and induced their apoptosis. In MM xenograft models, isoliquiritigenin showed significant antitumor activity and potentiated the anti-MM activity of Adriamycin. Isoliquiritigenin downregulated IL-6 expression and reduced levels of phosphorylated ERK and STAT3. Isoliquiritigenin inhibited phosphorylation levels of ERK and STAT3 induced by recombinant human IL-6, which are critical signaling proteins in IL-6 signaling regulation networks.
# Reducing Videoconferencing Fatigue through Facial Emotion Recognition
## 3 Data and Methods  
### Experimental Setup  
We collected data from a virtual seminar called Collaborative Innovation Networks (COINs 2020) [43], which involved three instructors and 35 students from MIT, the University of Cologne and the University of Bamberg. Students formed virtual teams with three to five participants from different locations, resulting in a total of eight teams, each of which investigated a given complex business topic independently. Afterwards, the seminar was organized as a virtual meeting by using Zoom ([[https://zoom.us/](https://zoom.us/)]([https://zoom.us/](https://zoom.us/)), accessed on 10 May 2021) which was held every two weeks from 6 April 2020 to 14 July 2020. In each meeting, each team gave a PowerPoint-supported presentation of 10-15 min in rotating order, reporting the current progress of the team project to the audiences, i.e., the other teams and instructors.  
During the virtual meeting, both the speakers and audiences were asked to keep the built-in camera on their local devices active to ensure that the faces would clearly appear on the video. We then recorded each meeting using Zoom's built-in recording function. In order to capture all the participant's faces, we recorded the video in Zoom's Gallery View, which can display up to 49 thumbnails in a grid pattern on a single screen. The recorded videos are the main analytical material used in this work.  
After each presentation, the audience was asked to rate the overall performance by using a pre-designed anonymized poll published in a Google Form. Specifically, individuals were asked to answer \"How many points will you give this presentation\" on a numeric rating scale between 1 and 5, with 5 being the maximum number of points for the given presentation. Moreover, we provided an additional option, \"I am a speaker\", in the poll to prevent subjective scoring by the speaker. After collecting the poll data, we denoted the collective score y for a presentation as the mean value from all audiences. We deem this as a reasonable method to align the poll score with the presentation and most importantly, to reduce the bias of an individual's subjective evaluation. These ground-truth collective scores are used as the dependent variable for further analysis.