esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Proteomic Analysis of Potential Signaling Pathways in MCAO Rats Treated with LA-1  
To further investigate the molecular mechanisms underlying the neuroprotective effects of LA-1 in the MCAO model, we performed proteomic analysis on the ischemic side (right hemisphere) of brain tissues, both with and without LA-1 treatment. After rigorous data filtering to eliminate low-scoring spectra and removal of redundant proteins, a total of 4143 proteins were identified and quantified. Principal Component Analysis (PCA) on the proteomic data revealed distinct protein expression patterns among the different groups (Figure 7A). After differential expression analysis, proteins meeting the criteria of \(p<0.05\) and Fold change > 1.5 were considered differentially expressed proteins (Figure 7B). Compared to the sham group, 311 differentially expressed proteins were identified from the MCAO group, including the protein levels of 40 were increased and 272 were decreased. Between the two cerebral ischemia-reperfusion groups, LA-1 treatment resulted in the protein levels of 122 increased and 38 decreased when compared to the non-treatment group. Heat map displayed the normalized abundance of identified differential proteins (Figure 7C). We further screened out genes without corresponding protein names and performed a Venn plot. The Venn diagram illustrates the overlap of differentially expressed proteins between the comparisons: sham vs. MCAO and MCAO vs. LA-1. A total of 44 overlapping differential proteins were identified from both comparisons.  
Figure 7: Identification of differentially expressed proteins from ischemic penumbra. (**A**) PCA of gene expression in Sham, MCAO, and LA-1-treated groups. (**B**) The proteome profiles in MCAO groupcompared with the Sham group, LA-1-treated group compared with the MCAO group. (**C**) The heatmap of DEPs in MCAO group compared with the Sham group, LA-1-treated group compared with the MCAO group, and the differential protein overlap: Sham vs. MCAO and MCAO vs. LA-1. (**D**) GO BP results of DEPs in MCAO group compared with the Sham group. (**E**) GO BP results of DEPs in LA-1-treated group compared with the MCAO group. (**F**) KEGG enrichment of DEPs in MCAO group compared with the Sham group. (**G**) KEGG enrichment of DEPs in LA-1-treated group compared with the MCAO group.  
To reveal the potential cellular pathways involved in cerebral ischemia-reperfusion injury, we performed functional enrichment analysis on the identified differential proteins from each group (Figure 7D,E). The results from Gene Ontology (GO) enrichment analysis displayed that the proteins affected by ischemia-reperfusion are involved in synapse functions, neuron projection and intracellular transport (Figure 7D), while the proteins regulated by LA-1 are involved in PI3K and autophagy related pathways (Figure 7E). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation analyses revealed that endocytosis and Parkinson disease related pathways are associated with ischemia-reperfusion (Figure 7F) while the proteins regulated by LA-1 may participate in neurodegeneration-multiple diseases (Figure 7G).
# Low-Cost Biochar Adsorbents for Water Purification Including Microplastics Removal
## 3 Results  
### Yields, Surface Areas, and Porosities  
The produced AC were characterized with respect to yield, elemental composition, surface area, and porosity (Tables 3 and 4). The surface area of pine bark biochar was most affected by increasing the gas flow rate, whereas greater amounts of steam yielded improved surface area results for spruce biochar. Raising the steam proportion to 40% did not induce higher surface area for pine bark biochar despite the larger activation burn-off. Mesoporosity development was greater for both bark biochars using the higher gas flow rate. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \multirow{2}{*}{**Starting**} & **Steam** & \multirow{2}{*}{**Particle Size**} & \multirow{2}{*}{**N\({}_{2}\) Gas Flow**} & \multirow{2}{*}{**Burn-off**} & \multirow{2}{*}{**Surface Area**} & \multicolumn{2}{c}{**Total Pore Volume**} & \multicolumn{2}{c}{**Micropores \textless{}2 nm**} & \multicolumn{2}{c}{**Mesopores \textgreater{}50 nm**} \\ \cline{3-11}  & & \% & & L/h & \% & m\({}^{2}\)/g & cm\({}^{2}\)/g & \% & \% & \% \\ \hline Fine bark & - & large & 300 & 61.1 & 2.2 & 0.005 & 8.9 & 68.9 & 22.2 \\ Spruce bark & - & large & 300 & 63.1 & 12 & 0.016 & 18.8 & 81.3 & 0.0 \\ \hline \multicolumn{11}{c}{**Activated Biochar**} \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Steam** \\ **Amount** \\ \end{tabular} } & \multirow{2}{*}{**Particle Size**} & \multirow{2}{*}{**N\({}_{2}\) Gas Flow**} & \multirow{2}{*}{**Activation Burn-off**} & \multirow{2}{*}{**Surface Area**} & \multicolumn{2}{c}{**Total Pore Volume**} & \multicolumn{2}{c}{**Micropores \textless{}2 nm**} & \multicolumn{2}{c}{**Mesopores \textgreater{}50 nm**} \\ \cline{3-11}  & & & L/h & \% & m\({}^{2}\)/g & cm\({}^{2}\)/g & \% & \% & \% \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Pine bark \\ biochar \\ \end{tabular} } & 30 & small & 66 & 25 & 454 & 0.165 & 92.7 & 6.7 & 0.6 \\  & small & 300 & 28.7 & 603 & 0.240 & 79.6 & 20.4 & 0.0 \\  & large & 300 & 24.5 & 615 & 0.230 & 86.1 & 13.9 & 0.0 \\  & small & 300 & 31.6 & 539 & 0.200 & 86.5 & 13.5 & 0.0 \\  & large & 300 & 27.4 & 556 & 0.206 & 86.9 & 13.1 & 0.0 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Spruce bark \\ biochar \\ \end{tabular} } & 30 & small & 66 & 21.5 & 272 & 0.098 & 91.3 & 5.8 & 2.9 \\  & large & 66 & 22.3 & 233 & 0.084 & 89.3 & 8.9 & 2.4 \\  & small & 300 & 22 & 187 & 0.071 & 85.4 & 10.4 & 4.2 \\  & large & 300 & 20 & 185 & 0.072 & 84.5 & 9.9 & 5.6 \\  & small & 300 & 23.3 & 369 & 0.132 & 90.0 & 7.7 & 2.3 \\  & large & 300 & 21.6 & 222 & 0.084 & 86.5 & 9.9 & 3.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The surface areas, porosities and activation burn-offs of the produced activated biochars.
## 2 Materials and Methods
### Residual Diesel Oil Concentration  
The residual diesel oil concentration was measured in sample extracts achieved from each microcosm. The extraction was done via the EPA method 3546 (moisture 15%-30% by weight), based on microwave heating.  
From each microcosm, 2 g of wet soil was mixed with 30 mL of solvent (acetone and \(n\)-hexane with ratio 1:1 by volume) and 2 g of anhydrous sodium sulfate. The mixture was then put in a microwave oven, where the thermal cycle was:  
* heating at 110 \({}^{\circ}\)C and power 1100 W for 15 min;
* constant temperature at 110 \({}^{\circ}\)C and power 1100 W for 10 min;
* cooling for 20 min.  
When the extraction was concluded, the sample was filtered through 0.45 \(\upmu\)m filter.  
From each microcosm, two soil samples were taken in order to replicate the extraction. As for fluorescein production, the results have been reported without error bars and standard deviations, since two replicates cannot support a reliable statistical analysis.  
The diesel oil concentration was evaluated using EPA method 8015. The sample extract was analyzed using a gas chromatograph equipped with a flame ionization detector and DB-5 fused silica capillary column, operated with helium as carrier and with injector and detector maintained at 220 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively. The thermal cycle was:  
* keeping at 50 \({}^{\circ}\)C for 1 min;
* heating at 320 \({}^{\circ}\)C by rate 8 \({}^{\circ}\)C-min\({}^{-1}\);
* keeping at 320 \({}^{\circ}\)C for 40 min;
* cooling at 50 \({}^{\circ}\)C.  
The residual diesel oil concentration was calculated using a calibration line made with the commercial diesel oil used in the tests. In addition, it was possible to identify the alkanes from C-8 to C-24 and the polycyclic aromatic hydrocarbons using reference standards.  
Just the peaks between 6 and 33 min were evaluated, since this time range contains the characteristic peaks of diesel oil.  
The monitoring was done for 138 days for the microcosms with C/N = 120 and C/N = 180. The focus on these microcosms was due to their better efficiency in terms of fluorescein production.  
For the microcosms with C/N = 60 and C/N = 300, the diesel oil removal was monitored only from t = 0 to t = 35 days.
## 3 Results and Discussion
### Twenty-DOF Chain Model with Many Groups of Closely Spaced Modes  
To further examine the effectiveness of the present method for a structural system with more DOFs, we conduct output-only modal estimation using a 20-DOF chain model rigidly fixed at both ends containing the mechanical properties of each \(1\)kg mass and all 600 N/m spring constants. The damping matrix is assumed to be proportional to a combination of the mass and the stiffness matrices as given by \(C=0.05M+0.001K\)\(N\)\(s\)\(/m\). This structure is subjected to a vibration practically recorded at Sun-Moon Lake on September 21, 1999 when the Chi-Chi Earthquake with a moment magnitude of 7.6 occurred in central Taiwan. The sampling interval and period of this seismic record are \(\Delta\)t = 0.005 sec and T = 59.995 sec, respectively. A sample of the seismic record, which serves as the excitation input acting on the sixth mass of the model, is shown in Figure 14. Figure 15 is the stabilization diagram of the system, roughly showing the first 12 natural frequencies of the identified structural modes. The system considered has many groups of closely spaced modes, as listed in Table 5. The results of identification are also summarized in Table 5, which shows that the errors in natural frequencies are less than 5% and the errors in damping are larger. From Table 5, 12 out of the 20 vibration modes are identified more accurately with MAC \(\geq\) 0.9 as the threshold. Because the structural response generally has lower sensitivity to damping ratios and mode shapes than to the natural frequencies, the errors of identified damping ratios and mode shapes are somewhat larger. Since the contribution of the high-order vibration modes to the structural response is somewhat less than that of the low-order  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{3}{c}{**Natural Frequency (rad/s)**} & \multicolumn{3}{c}{**Damping Ratio (\%)**} & \multirow{2}{*}{**MAC**} \\ \cline{2-2} \cline{5-8}  & **Exact** & & & & & & \\ \hline
1 & 5.03 & 5.01 & 0.36 & 1.25 & 1.19 & 4.13 & 0.99 \\
2 & 7.82 & 7.78 & 0.44 & 1.03 & 1.06 & 2.77 & 1.00 \\
3 & 18.47 & 18.43 & 0.22 & 1.19 & 1.42 & 19.07 & 1.00 \\
4 & 73.79 & 70.56 & 4.38 & 3.76 & 4.51 & 19.99 & 0.81 \\
5 & 73.87 & 74.03 & 0.22 & 3.76 & 4.44 & 18.08 & 0.85 \\
6 & 87.93 & 83.78 & 4.72 & 4.45 & 3.12 & 29.94 & 0.80 \\
7 & 88.07 & 85.73 & 2.66 & 4.46 & 3.72 & 16.52 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Modal parameter identification result of seven-DOF car model.  
Figure 12: Stabilization diagram of seven-DOF car model.  
vibration modes, the high-order vibration modes are not identified as accurately as the low-order vibration modes in general.  
Figure 14: A recorded sample of the Chi-Chi earthquake.  
Figure 13: Mode shapes of seven-DOF car model (the solid line is an analytic solution, the dotted line is ERA/DC identification result).
# Technological Advances on Fault Diagnosis in Wind Turbines: A Patent Analysis
###### Abstract  
Given the urgency of mitigating the effects of global warming and the depletion of fossil energy sources, renewable sources of energy, such as wind power, are the focus of the future. However, due to the rapid growth of this technology, concerns about the security and reliability of wind turbines are increasing, especially because of associated hazards and financial costs. Hence, health monitoring and fault identification for wind turbine blades have become an important focus of research. Thus, the objective of this study was to generate data on the current scenario of the techniques used to identify failures and defects in wind turbines and their components. Through the results found, companies can find ways to make decisions and identify potential new technologies. In this way, a technology prospection was conducted that focused on patents to investigate the use of vibration analysis, thermography, and machine learning. A total of 635 patent documents were found, and the evolution in the number of patents over the years has demonstrated the current interest in developing new technologies in this research area. China, the world's leading country in the area of wind energy, was the country with the highest number of filings, followed by the United States. In the patent documents analyzed, it was possible to identify that these innovative technologies for predicting and detecting failures are a topic of interest for the world's largest economies. Additionally, it was clear from the results that the application of artificial intelligence to traditional techniques is a current trend and will continue in the future. Technological prospection studies can foster the development of new methods and devices, providing economic and environmental gains for the wind energy industry.  
wind turbine; maintenance; fault detection; patents +
Footnote †: journal: Applied Sciences
# Distributed Triggered Access for BSM Dissemination in 802.11 bd V2V Networks
## 5 Conclusions  
In this paper, we have proposed a novel channel access scheme, Distributed Triggered Access (DTA), for distributed V2V BSM dissemination in future platooning environment.  
The proposed scheme is designed based on the 802.11p DSRC standard. Envisioning the ongoing Next Generation V2X WLAN standard, 802.11 bd, DTA takes advantage of advanced MAC features of the IEEE 802.11 ax standard, such as TUA and MU-RTS, with modified PHY layer frame structure proposed for 802.11 bd.  
Applying TUA to distributed V2V networks, the proposed scheme allows a VSTA to trigger simultaneous broadcast BSM transmissions from neighboring VSTAs whenever the VSTA grants a TXOP. Since triggered VSTAs can transmit the serving BSM before they finish their backoff procedure, the channel contention intensity can be relieved effectively. By introducing the concept of active/non-active BSM, DTA not only allows a triggering VSTA to maximize the channel use of its TXOP without complicated scheduling procedures, but also allows triggered VSTAs to improve the successful delivery rate of ack-less broadcast BSM frames. In order to protect the long airtime of broadcast multiplexed data, DTA suggests exploiting MU-RTS with reduced overhead.  
To analyze the delay characteristics, we have presented the mathematical model of the proposed scheme using Markov chain analysis and M/G/1 queuing model, taking backoff resumption and unsaturated data traffic into account.  
Figure 12: Performance impact of the payload size and the arrival rate.  
The simulation results have shown that DTA outperforms the conventional EDCA in the overall measurement environments. The MU-RTS feature effectively improves the transmission success probability and the triggers access procedure and (non-)active BSM scheduling greatly reduces the average BSM collecting delay especially with highly congested channel condition.  
Conceptualization, W.A.; Data curation, W.A.; Formal analysis, W.A.; Funding acquisition, R.Y.K.; Investigation, W.A. and R.Y.K.; Methodology, W.A.; Project administration, R.Y.K.; Software, W.A.; Supervision, R.Y.K.; Validation, R.Y.K.; Writing--original draft, W.A.; Writing--review & editing, R.Y.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2017R1A2B4003987).  
The authors declare no conflicts of interest.
## 4 Extraction of the Word-Based SISO Semisystolic Processor
### Word-Based SISO Scheduling Function  
Based on the scheduling methodology discussed in [30], we can partition the DG into \(k\times k\) equitemporal zones using the following scheduling function:  
\[t(\mathbf{p}) = \left\lceil\frac{n}{k}\right\rceil\,\left\lfloor\frac{i-1}{k} \right\rfloor\,+\,\left\lfloor\frac{n-1-j}{k}\right\rfloor+1 \tag{14}\]  
where \(t(\mathbf{p})\) represents the time allocated to each node \(\mathbf{p}\) of the DG, \(1\leq i\leq n+\omega\), \(-\zeta\leq j<n-1\), \(\omega=k\left\lceil\frac{d}{k}\right\rceil-d\), \(\zeta=k\left\lceil\frac{n}{k}\right\rceil-n\).  
By applying the chosen scheduling function to the DG, we obtain the node timing (scheduling time) allocated to each zone, as indicated in Figure 2. This figure is developed for the case when \(n=7\) and \(k=3\). The green zones have \(k\times k\) nodes that are executed at the time index (blue numerals) allocated to each zone. The yellow zones have \(1\times k\) postprocessing nodes that are executed also at the time index assigned to each of the yellow zones.  
Figure 2: Scheduling time for the case when \(n=7\) and \(k=3\).  
For the upper \(d\) rows of the DG, it is important to make the number of rows and columns multiple integers of \(k\). Therefore, \(\omega\) more rows and \(\zeta\) more columns should be added to the DG. For our case, when \(n=7\) and \(k=3\), both \(\zeta\) and \(\omega\) will equal two, and hence, we should add two more rows and two more columns to the DG as indicated in Figure 2. This leads to the least significant two bits of the input variables \(E\), \(G\), and \(G^{\prime}\), and the initial values of the intermediate variables \(X\), \(Y\), \(P\), and \(S\) should be assigned zero values, as shown at the rightmost edge of the DG in Figure 2. Furthermore, the most significant two bits of input variables \(E\) and \(F\) should be assigned zero values at the two rows before the last row of the DG, as shown in Figure 2. It is worth noticing that the \(e_{n-1}^{i-1}\) and \(e_{n-2}^{i-1}\) signals should be updated at the nodes of the leftmost column, as displayed in Figure 2, and then broadcast horizontally and collectively with the signals \(e_{2i-2}\), \(e_{2i-1}\), \(f_{2i-2}\), and \(f_{2i-1}\) to the nodes of row \(i\).  
The chosen scheduling function reduces the total number of iterations required to execute the adopted bipartite multiplication/squaring algorithm to be \(\left(\left\lceil\frac{d}{k}\right\rceil+1\right)\left\lceil\frac{n}{k}\right\rceil\) instead of \((d+1)n\), and hence, this significantly reduces its time complexity.
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 2 Materials and Methods  
### Materials  
In this study, we formulate the SE signal model in the TF domain using the STDCT and further define the predicting targets for the proposed SE network.  
Given the noisy speech \(x(n)\), it can be formulated as follows:  
\[x(n)=s(n)+z(n) \tag{1}\]  
where \(s(n)\) and \(z(n)\) denote the clean speech and additive noise, respectively. \(n\) represents the discrete time index.  
In order to obtain the STDCT spectrum of signal, we need to perform framing and windowing operations on the time-domain waveform and then apply the discrete cosine transform (DCT) to each frame. Correspondingly, we can adopt inverse windowing, inverse discrete cosine transform (IDCT), and overlap-and-add operations to transform the STDCT spectrum back to the time-domain signal. The DCT and IDCT, respectively, are defined as follows:  
\[X(\mu)=c(\mu)\sqrt{\frac{2}{N}}\sum_{n=0}^{N-1}x(n)\cos\left[\frac{\pi\mu(2n+ 1)}{2N}\right] \tag{2}\]  
\[x(n)=\sqrt{\frac{2}{N}}\sum_{\mu=0}^{N-1}c(\mu)X(\mu)\cos\left[\frac{\pi\mu(2n +1)}{2N}\right] \tag{3}\]  
where \(\mu\in[0,1,\ldots,N-1]\) denotes \(N\) frequency bins from low to high. The parameter \(c(\mu)\) is defined as follows:  
\[c(\mu)=\left\{\begin{array}{cc}\frac{1}{\sqrt{2}},&\mu=0\\ 1,&\mu=1,2,\ldots,N-1\end{array}\right. \tag{4}\]  
According to Equation (2), it can be found that DCT operates in the real-valued domain. Therefore, the STDCT spectrum is a kind of real-valued spectrum. Furthermore, with STDCT, Equation (1) can be transformed as follows:  
\[X_{t,\mu}=S_{t,\mu}+Z_{t,\mu} \tag{5}\]  
where \(X_{t,\mu}\in\mathbb{R}\), \(S_{t,\mu}\in\mathbb{R}\), and \(Z_{t,\mu}\in\mathbb{R}\) denote the STDCT spectrum of noisy, clean, and noise signals, respectively. \(t\) and \(\mu\) index the time frame and the frequency bin. In the following, we omit the time and frequency indices for brevity.  
We adopt the masking method [35; 36] to recover both the clean speech and pure noise from the noisy speech. Therefore, the predicting targets of speech and noise branches in UniInterNet are:  
* Speech branch: \[M_{s}=\frac{S}{X}\] (6)
* Noise branch: \[M_{z}=\frac{Z}{X}\] (7)  
where \(M_{s}\) and \(M_{z}\), respectively, are the DCT ideal ratio mask (DCTIRM) for speech and noise estimations.
# The Potential Role of Timosaponin-AIII in Cancer Prevention and Treatment
## 3 Pharmacokine Profiles of TSAIII
It has been reported that hydrophobicity and low bioavailability limit the antitumor efficacy and application of TSAIII. Indeed, new delivery systems for TSAIII have been developed. The anti-CD44 antibody, as a tumor-target ligand, has been used to improve liposome accumulation in tumors by specifically interacting with CD44 receptors. It has been reported that anti-CD44 antibody-modified TSAIII-loaded liposomes (CD44-TSAIII-LP) could increase the circulation time, the bioavailability, the tumor-target accumulation, and the antitumor activity of TSAIII, demonstrating a potential agent for the therapeutic management of CD44-positive cancers [33]. A grapheme oxide-based nanocomposite hydrogel (GPP) has been developed to improve the bioavailability of TSAIII, enhance the efficacy of photothermal therapy, and elevate its protective activity against tumor development. Specifically, the encapsulation rate of GPP-TSAIII is 66.36%, and this delivery system was found to have a slower release and higher uptake of TSAIII in mouse melanoma B16F10 cells in vitro [34].