esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### US Exposure of Human Chondrocytes Cultures Prevented Ultrastruct Changes Induced by OS  
Previous data indicated that US had cytoprotective effects by modulating cell proliferation and by anti-inflammatory effects in non-cancer cells. Most of the genes downregulated by LIUS in non-cancer cells are responsible for cell death and also for inflammatory signal ing pathways correlated with ROS and RNS synthesis [65]. It is not surprising that in our study US reduced MDA and TNF-\(\alpha\) synthesis, concomitantly with increasing chondrocytes' viability and improving chondrocytes' ultrastructure.  
The MDA level assessed in the US-OS group was significantly lower than in the OS group. This result is most likely due to the increased capacity of chondrocytes to neutralize H\({}_{2}\)O\({}_{2}\), thus blocking generation of new ROS and the synthesis of oxidative compounds. Our findings are similar to the results published by [PERSON], who found that US treatment decreased lipid peroxidation and TNF-\(\alpha\) synthesis, concomitantly increasing antioxidant enzymes, CAT and SOD, in muscle injuries [64]. CAT is an important antioxidant enzyme that prevents OS by neutralizing H\({}_{2}\)O\({}_{2}\) and inhibiting OH' and O' synthesis. SOD is the first defense line against the O\({}_{2}\)\({}^{-}\), synthesized by damaged mitochondria under H\({}_{2}\)O\({}_{2}\) action. The antioxidant effects of US were also communicated by [PERSON], who showed that US promoted expression of antioxidant genes GPX3 and GPX7 and decreased expression of prooxidant Nitric Oxide Synthase (NOS) genes [65]. US also influenced synthesis of various antioxidant compounds in plants, like terpenes and antioxidant enzymes [87, 88]. The antioxidant effects of US were shown by [PERSON], who demonstrated that US pretreatment could help plants to eliminate the ROS generated after cadmium and lead exposure, by enhancing activities of CAT, SOD, and glutathione reductase and by reducing MDA and O\({}^{-}\) synthesis [89].  
In our US-OS group the TNF-\(\alpha\) level decreased significantly after US exposure, compared to the OS group. Our results are in accordance with a previous studies in which US exposure reduced the TNF-\(\alpha\) pragmatic level in rabbit OA [41] and tissular TNF-\(\alpha\) synthesis after muscle confusion [64]. In a study published by [PERSON], it was demonstrated that in chondrocytes' cultures biomechanical signals attenuated TNF-\(\alpha\)-induced synthesis of NO, PGE\({}_{2}\), and MMPs [22], molecules involved in chondrocytes and cartilage injury. Similarly, in our experimental model mechanical energy provided by US attenuated OS, TNF-\(\alpha\) synthesis, so alleviating ultrastructural chondrocyte damages.  
In our study the enhanced antioxidant capacity and the decreased TNF-\(\alpha\) level allowed chondrocytes to remain viable and to conserve their ultrastructure in US-OS group, where only a few changes in organelles were observed. In another study [PERSON] demonstrated that US could inhibit expression of several inflammatory markers involved in non-cancer cell death [65].
## 2 Materials and Methods
### SW functions
#### 1 AAA Lumen Segmentation  
_Select First Slice_ (button with a red square in the top): allows the user to select and segment the first slice of the vessel lumen just under the renal arteries, in the axial view. The software detects automatically the right contour when the user draws manually near the lumen area.  
_Select Last Slice_ (button with a red square in the bottom): similar to the previous one, allows the user to select and segment the last slice of the vessel lumen just under the iliac bifurcation. The software detects automatically the right contour when the user draws manually near the lumen area.  
_Segment Lumen_ (button with a highlighted red lumen): this function will start the segmentation of the AAA lumen, considering the volume between the selected first and last slices. The segmentation process is automatic, making use of the Active contours MATLAB function, in particular the Chan-Vese variant. Active contours segmentation algorithm (also called snakes) is an automatic, iterative method where you mark locations in the image by drawing regions, called a seed mask. Active contours grows (or shrinks) these seed shapes to fill the borders of the region in the image. In particular, we used the Chan-Vese variant, which was first designed by [PERSON] and [PERSON] to segment objects without clearly defined boundaries [18]. The Active Contours algorithm was chosen since, as previously reported [19], it is a good option for segmenting images that have varying intensities, low contrast boundaries and noises; moreover, the exact vessel segmentation is not possible by separating vessel and bone regions through only thresholding or region-based approaches [20].  
In our specific case, the software continuously evaluates the centroid of the current detected region and prolongs it to the next slice in order to use it as the starting seed of the new segmentation. In this way, the segmentation process can be completed without requiring other inputs from the user. Since the aorta is quite a cylindrical volume with very little askew sideways, the algorithm works efficiently once the first slice has been correctly segmented. The result of the automatic segmentation can be visually checked by the operator, and overall refinements may be added by manual segmentation.
## 3 Proposed Strongest AoA Components Estimation Algorithm
### RMSE Performance Metric  
To verify the estimation accuracy of our proposed algorithm, we use the RMSE of the strongest AoA estimate as a performance metric. Meanwhile, the best achievable RMSE estimation performance benchmark with \(f_{\text{RF},k}\), \(k\in\{1,\dots,N\}\), can be characterized by deriving the approximated 1-bit CRLB with additive quantization noise model (AQNM) of the antenna array [39].  
The RMSE expression the AoA estimates can be represented as  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(\hat{\theta}_{k,i}-\theta_{k})^{2}}, \tag{12}\]  
where \(\theta_{k}\) represents the desired incidence strongest AoA component for the \(k\)-th user at the BS, and \(\hat{\theta}_{k,i}\) denotes the estimated result. The RMSE of the strongest AoA component estimation proposed in our algorithm is compared to that of conventional schemes in the following simulation section.  
```
0: Quantization threshold \(\tau\) corresponding to \(L_{\min}\) for different SNRs
1: Initialize \(\tau=1\), \(\tau_{m}=0.5\), \(L_{\min}=300\), the number of Monte Carlo simulations \(N=50\),000
2: low SNR regimes
3: A reasonable range for the quantization threshold
4:for\(\tau_{n}=0.3:0.01:1\)
5: Experiment with different noise distributions
6:for\(i=1:N\)
7: Run Algorithm 1 to obtain \(L_{i}\) required for strongest AoA estimation for a certain \(\tau_{i}\) in case 1
8:end
9: Calculate the mean of \(L_{i}\) with different noise distributions
10:\(L_{\text{ave}}=\frac{1}{N}\sum\limits_{i=1}^{N}L_{i}\)
11:if\(L_{\text{ave}}\leqslant L_{\text{min}}\)
12:\(\tau=\tau_{n}\)
13:\(L_{\text{min}}=L_{\text{ave}}\)
14:end
15:end
16:end
17: Medium-to-high SNR regimes
18:for\(i=1:N\)
19: Run Algorithm 1 directly to obtain \(L_{i}\) required for strongest AoA estimation for \(\tau_{m}\) in case 2
20:end
21:\(L_{\min}=L_{\text{ave}}=\frac{1}{N}\sum\limits_{i=1}^{N}L_{i}\)
22:end
```  
**Algorithm 2** Monte Carlo Simulation for \(L_{\min}\)
## 3 Results
### Ablation Experiment and Comparison with Traditional Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Period** & **Type** & **Proposed** & **Degraded I** & **Degraded II** & **a** & **b** & **c** \\ \hline \multirow{4}{*}{2015–2020} & FOM & 0.1744 & 0.1588 & 0.0584 & 0.1661 & 0.1595 & 0.1608 \\  & Kappa & 0.9684 & 0.9668 & 0.9596 & 0.9678 & 0.9670 & 0.9671 \\  & Accuracy & 0.9829 & 0.9821 & 0.9771 & 0.9827 & 0.9820 & 0.9821 \\  & F1 & 0.9641 & 0.9585 & 0.9578 & 0.9626 & 0.9590 & 0.9595 \\  & Hamming loss & 0.0171 & 0.0179 & 0.0229 & 0.0172 & 0.0179 & 0.0178 \\ \hline \multirow{4}{*}{2010–2020} & FOM & 0.4662 & 0.2774 & 0.2183 & 0.4535 & 0.2793 & 0.2741 \\  & Kappa & 0.9599 & 0.9388 & 0.9300 & 0.9591 & 0.9392 & 0.9386 \\ \cline{1-1}  & Accuracy & 0.9785 & 0.8017 & 0.9625 & 0.9781 & 0.9675 & 0.9671 \\ \cline{1-1}  & F1 & 0.8113 & 0.8017 & 0.7862 & 0.8100 & 0.8033 & 0.8022 \\ \cline{1-1}  & Hamming loss & 0.0214 & 0.0326 & 0.0374 & 0.0218 & 0.0325 & 0.0328 \\ \hline \hline \end{tabular}  
2010–2020 indicates that the land use/cover pattern in 2020 is simulated based on 2010 land use/cover data and multiple spatial variables. The process is similar to the 2015 data used in the article to simulate the land use pattern in 2020.  
\end{table} 
Table 4: Comparison of the simulated result using the proposed model, Degraded I model, Degraded II model, a model, b model, and c model. is about 19 times that of the CA model based on the HQSSIA, which verifies the above conjecture and analysis (Table 5).
## 4 Dataset and Evaluation Metrics
### Evaluation Metrics  
To evaluate the portfolios, we use three performance metrics that are commonly used in finance, including annual volatility, a Sharpe ratio and portfolio turnover. The annual  
\begin{table}
\begin{tabular}{l r r r r r r r} \hline \hline  & **2013** & **2014** & **2015** & **2016** & **2017** & **2018** & **2019** \\ \hline Minimum number of stocks & 304 & 302 & 303 & 308 & 324 & 350 & 380 \\ Maximum number of stocks & 316 & 310 & 313 & 324 & 350 & 380 & 387 \\ Average daily return & 16 & 13 & 6.23 & 5.26 & 10.44 & \(-\)1.79 & 2.42 \\ Standard deviation & 815.88 & 276.2 & 354.13 & 283.13 & 255.54 & 275.92 & 249.2 \\ Average volume & 4.54 & 9.03 & 8.12 & 8.88 & 11.84 & 11.19 & 9.66 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical summary of the historical data on the HOSE exchange from 2013 to 2019.  
volatility (\(\sigma\)) is a standard deviation of portfolio returns which indicate a risk of that portfolio. The volatility in the context of the GMVP, lower is better. The Sharpe ratio is developed by [PERSON] [23] to describe the profit of an investment compared to its risk, it is defined as follows:  
\[\text{Sharpe ratio}=\frac{R-R_{f}}{\sigma}, \tag{4}\]  
where \(R\) is the return of a portfolio and \(R_{f}\) is a risk-free rate. For \(R_{f}\), we use Vietnam's 10-year government bond in the same period. A portfolio with a higher Sharpe ratio is a better portfolio.  
The portfolio turnover measures a level of stability of the portfolio weights over the investment horizon [24]. In other words, it measures a fluctuation of portfolio weights. In the scope of the GMVP, we prefer a portfolio with a lower portfolio turnover. The formula of the portfolio turnover is as follows:  
\[\text{Portfolio Turnover}=\frac{1}{T-1}\sum_{t=1}^{T-1}\sum_{i=1}^{N}\left(| \mathbf{w}_{t+1,i}-\mathbf{w}_{t,i}|\right) \tag{5}\]  
where \(\mathbf{w}_{t,i}\) is a weight of asset \(i\) at date \(t\). A maximum value for the Portfolio Turnover from Equation (5) is two: in other words, 200% changes from a portfolio weights to another completely different weights, and a minimum value is 0%.  
Only out-of-sample results are reported for those evaluation metrics. We also report \(p\)-value for each pair of covariance estimators for all portfolio metrics in an Appendix section. Particularly for the Sharpe ratio, we use a bootstrapping method which is suggested by [PERSON] and [PERSON] [25] to do a robust performance hypothesis testing.
# Evaluating Litter Yield and Decomposition for Re-Vegetated Mangroves in a Subtropical Mudflat
## 2 Materials and Methods
### Field Investigation and Sample Collection
#### 2.2.2 Litter Decomposition Experiment  
A field experiment was also conducted during the period from September 2013 to August 2014 to examine the decomposition of litters for the three dominant mangrove species. Prior to the experiment, samples of fresh branches and leaves for each of the three species were collected from the study site. In the laboratory, the fresh litter samples were oven-dried at 60 \({}^{\circ}\)C until constant weight. A tightly fastened nylon mesh bag with a pore size of 1 mm was used to hold a mixture of leaf sample (10 g) and branch sample (10 g). For each of the 10 m \(\times\) 10 m plots, three random locations were used, representing three replicates within the experimental plot. For each location within the plot, 12 bags with the loaded leaves and branches were laid on the ground surface.  
During the period of the field experiment, one of the 12 litter sample bags was removed at the end of each month and transported to the laboratory to determine the weight loss.
# Ultra-Generalized Continuous Class F Power Amplifier with Finite Third-Harmonic Load Impedance
## 2 Analysis of the Proposed UCCF Mode
### Brain Current Waveform of the Proposed UCCF Mode
The origin of the coordinates represents the continuous class F mode (\(\alpha=\pi\) and \(pcr=1\)). The horizontal axis represents the GCCF mode with \(\alpha\) values varying from \(\pi\) to \(2\pi\) (\(pcr=1\)). The vertical axis represents the PCC-CF\({}_{3}\) mode with \(pcr\) values varying from 1 to 2 (\(\alpha=\pi\)). The color depth in the graph represents the magnitude of the current component. As shown in Figure 5a, \(I_{dc}\) increases with increases in \(pcr\) and \(\alpha\). Figure 5b shows that when \(pcr\) is held constant, \(I_{1}\) initially increases and then decreases with an increase in \(\alpha\). Figure 5c shows that \(I_{2}\) decreases with increases in \(pcr\) and \(\alpha\). Figure 5d shows that when \(pcr\) is held constant, \(I_{3}\) initially decreases and then increases with an increase in \(\alpha\). The blue area in the upper left corner of Figure 5d shows that \(I_{3}\) has reached its minimum area. The minimum value of \(I_{3}\) in the UCCF mode is \(-0.179\), and this occurs when \(pcr\) is equal to 2 and \(\alpha\) is equal to \(1.184\pi\). In this region, \(I_{3}\) is smaller than the horizontal and vertical axes, which indicates that the UCCF mode achieves a smaller \(I_{3}\) compared to the GCCF and PCC-CF\({}_{3}\) modes.
# Digital Twin Conceptual Model within the Context of Internet of Things
## 5 Virtual Space Layer
### Data Modeling  
The data modeling sublayer builds dynamic 3D models of the physical object or system that is being monitored. As the physical object is going through changes, the sensors in the PSL capture them and send them to the virtual layer, specifically to the data aggregation sublayer, where it is stored in data repositories. The data modeling sublayer will then read the updated data from the data repositories and dynamically create 3D models that take new data into consideration and reflect the current state of the physical object or system. Additionally, the model is extended with the physical information and temporal and contextual aspects of the machine relative to its operating environment such as the status of the machine, location, temperature, pressure, movement, process flow, and energy consumption. This builds the model to be more dynamic and robust in close interaction with the real world. The 3D modeling software that creates models and maps programmatically is particularly useful for this purpose. There are many programmatic modeling software types available, such as  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Structured** & **Semi-Structured** & **Unstructured** \\ \hline Sensors data (Row numerical data) & JSON (Strings) & 2D and 3D models \\ Historical data (Tables) & XML (Text file) & Images, videos and audio gathered \\ Bill of Materials & Trees, graphs, charts & Images, multimedia \\ \hline \hline \end{tabular}
\end{table}
Table 2: Types of digital twin data collected at aggregation layer.  
Google Sketchup [19], where the modeling process can be automated using the Ruby API. Another option available is Simio Simulation Software [20], which builds base 3D models of the physical object or system using design specifications, BOM, and historic data, and then dynamically updates the model based on sensor readings from the PSL.
# Low-Cost, Compact, and Rapid Bio-Impedance Spectrometer with Real-Time Bode and Nyquist Plots
## 2 Materials and Methods
### Unit of AC Current Source  
The BIS instrumentation system requires a pure sine current signal with a selectable frequency and amplitude. For this purpose, the unit of AC current source is developed using two modules, i.e., the voltage generator and the voltage to current (_V/I_) converter. The voltage generator is serving to produce a sine voltage in the frequency range of 10 Hz to 10 MHz, while the _V/I_ converter functions to change the voltage source into current source at the related frequency, with selectable current amplitude of the 1 uA, 10 uA, 100 uA, and 1 mA.
###### Abstract  
This work presents a novel shot boundary detection (SBD) method based on the Place-centric deep network (PlaceNet), with the aim of using video shots and image queries for video searching (VS) and fingerprint detection. The SBD method has three stages. In the first stage, we employed Local Binary Pattern-Singular Value Decomposition (LBP-SVD) features for candidate shot boundaries selection. In the second stage, we used the PlaceNet to select the shot boundary by semantic labels. In the third stage, we used the Scale-Invariant Feature Transform (SIFT) descriptor to eliminate falsely detected boundaries. The experimental results show that our SBD method is effective on a series of SBD datasets. In addition, video searching experiments are conducted by using one query image instead of video sequences. The results under several image transitions by using shot fingerprints have shown good precision.  
video searching; video fingerprint; shot boundary detection; PlaceNet; image query; LBP-SVD; SIFT +
Footnote †: journal: Applied  
Article  
Video Searching and Fingerprint Detection by Using the Image Query and PlaceNet-Based Shot Boundary Detection Method
# Instability of High Liquid Limit Soil Slope for the Expressway Induced by Rainfall
###### Abstract  
The instability of high liquid limit soil slope is a common engineering problem in highway construction. This study focused on the slope at section K79 + 880 of the Guang-Le Expressway in Guangdong Province, China. In order to reduce the landslide and learn the mechanism of the high liquid limited soil slope, the effects of different rainfall intensities and rainfall time on the high liquid limit soil slope were studied by in-site monitoring and numerical simulation. According to the characteristics of the slope deformation and the monitoring data, the slope landslide is divided into three grades. Numerical simulation results show that the influence of rainfall intensity on the safety factor of high liquid limit soil slope is greater than that of rainfall time. At the slope top, the cumulative changes of Mises stress in Group B and Group C were \(-2.19\) kPa and \(-2.91\) kPa, respectively, and the cumulative decreases were \(-7.22\%\) and \(-9.60\%\), respectively. At the slope bottom, the cumulative changes of Mises stress in Group B and Group C were \(-2.05\) kPa and \(-4.32\) kPa, respectively, and the cumulative decreases were \(-4.50\%\) and \(-9.48\%\), respectively. With the increase of rainfall for \(24\) h, the safety factor of Group C decreased by an average of \(0.0408\), and with the increase of rainfall time, the safety factor increased, and the safety factor of \(96\) h of rainfall in Group C was \(0.1249\) lower than that of \(24\) h of rainfall. The greater the rainfall intensity, the greater the change of matrix suction at the top of the slope, and the more prone the top of the slope to shallow landslides. For the high liquid limit soil slope, slope instability phenomena such as surface flow collapse occur easily and have little impact on the deep sliding surface. The dimensionless displacement coefficient K is proposed to quantify the landslide displacement under different rainfall. A general method is proposed to measure the cumulative displacement of the K79 + 880 slope where the sensor is not placed.  
unsaturated soil; rainfall; slope instability; numerical simulation; in-site monitor Article
## 2 Materials and Methods
### Experimental Setup  
We recruited participants over the age of 60 and those who had sustained lower-limb injuries in the past 6 months, suffered severe medical conditions, or had a history of such conditions, and individuals with cognitive impairments were not excluded from this study. In total, 20 volunteers (age: 65.6 \(\pm\) 6.2 years, height: 163.2 \(\pm\) 7.5 cm, weight: 64.3 \(\pm\) 9.6 kg, 13 males and 7 females) participated in this study. Each subject performed two rollator gait conditions: with and without the passive gait assist device. Data for both conditions were obtained using a treadmill set at a self-selected comfortable speed. The self-selected speed was chosen by increasing the treadmill speed up to the point at which the participant felt that it matched their typical gait speed. In order to investigate whether our system reduced plantar and dorsiflexor demands, EMG signals were obtained from the tibialis anterior (TA) and soleus (SL) using wireless EMG sensors (Trigno wireless system, Delsys Inc., Natick, MA, USA). In addition, foot pressure insoles (PedarX, Novel GmbH, Munich, Germany) were used to identify heel contact (HC) and toe-off (TO) events such that EMG signals could be assessed according to the stance and swing phase. In order to utilize the HC and TO events, the plantar force data and EMG data needed to be synchronized. This was carried out by applying an impact force to the sole of the shoe, which caused a sharp signal to appear in the plantar force data and the IMU sensor, which co-exists within the EMG sensor. The EMG and foot pressure signals were obtained at a sampling frequency of 1777 and 74 Hz, respectively. The size of the shoe and insoles were determined according to the subject's foot size.  
The participants provided a signed written consent form, and the research ethics of human experiments was ensured by conducting the experiments within the contents approved by the Institutional Review Board of Korea University (IRB No. 2022-0399-01).  
Figure 2: User wearing a size 9 (270 mm) shoe participating in treadmill gait movement (**a**) Image of a subject using the passive gait assist system. (**b**) The pulling force of the elastic cord distributed among straps 1 and 2.
## 9 Optimization
### Well Spacing Stimulation Design Optimization  
To optimize both estimated ultimate recovery (EUR) and cost efficiency, it is crucial to fine-tune well spacing and stimulation parameters. The optimization process encompasses petrophysical and geomechanical assessments, hydraulic fracturing simulations, and reservoir modeling. In the last ten years, there has been a shift in stimulation strategies from using more viscous fluids and broader cluster intervals to adopting less viscous fluids with narrower cluster spacing.  
[PERSON] et al. conducted an optimization study, focusing on well stimulation parameters such as proppant and fluid volumes, cluster intervals, and fluid categories. Some wells employed high-density completion (HDC) strategies, which involved increased proppant and fluid volumes, along with reduced cluster spacing. Cloud-based computational resources facilitated the parallel execution of numerous simulations. Extensive data collection efforts were undertaken to gauge well performance, incorporating metrics like liquid tracers, production pressures, and treatment pressures. A design of experiments (DoE) approach was employed to assess the influence of well spacing and stimulation parameters. Utilizing cloud computing, the analysis reduced the computational time from three months to one week. The optimization process led to a 50-100% increase in estimated ultimate recovery (EUR) and a 10-30% reduction in per-unit development costs [78].