esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
We considered that the deviation was resolved when it was larger than the system detection sensitivity. As the signal of each oxygen concentration was measured 15 times, the detection error was obtained as the error bars shown in Figure 5a. The average delta absorbance was 0.0045 for different oxygen concentrations. According to the linear relationship, this detection error denoted a detection sensitivity of 0.6%, which is shown as the dotted line in Figure 5c. Obviously, the deviation was smaller than the error bar value when the oxygen concentration was lower than 21%. The deviation was larger than the error bar value and could be resolved when the oxygen concentration was higher than 21%. Hence, we considered that the transition point was an oxygen concentration of 21%. The absorbance value at the transition point was 0.17. Thus, a linear relationship between the absorbance and gas concentration could be ensured when the absorbance was lower than 0.17 for this system. This result was universal for different gases in  
Figure 5: (**a**) Relationship between the OP and oxygen concentration; the inset shows an enlarged view of the low concentration range. (**b**) Residual error between the experimental data and the non-linear fitting result. (**c**) Deviation between the linear and non-linear fitting results. such a system. For other detection systems, the transition point is related to the detection error of the system. We have provided an experimental method to determine the transition point of linear and non-linear measurements for a diffused integrating cavity.
* We emphasize that the proposed algorithms provides a higher robustness not only to the security and reliability factors but also to the power consumption by reducing the SNR at the legitimate receiver for decoding the message, which suits short-packet FD IoT transmissions and green communications. The remainder of this paper is organized as follows. Section 2 briefly describes the general system model of the FD transceiver in the passive/active eavesdropper scenarios. The conventional schemes without feedback and security gaps are also mentioned in this section. Section 3 studies the application of the joint iterative blind channel estimation and decoding algorithm at the legitimate receiver in the case of passive eavesdroppers, with numerical results and comparisons with the conventional blind algorithm without feedback. Section 4 introduces the semi-blind algorithm for SI channel estimation and equalization processes in the legitimate receiver in case of active eavesdropper and simulation results. Finally, some highlights and conclusions will be discussed in Section 5. The notations in this paper are summarized in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Meaning** \\ \hline \(K\), \(N\), \(R\) & Information length, code word length, and code rate \\ \(\mathbf{x}_{X}\), \(\mathbf{y}_{X}\) & Transmitted signal vector and received signal vector at user X \\ \(\mathbf{h}_{XY}\) & Channel gain vector between X and Y \\ \(\mathbf{h}_{XX}\) & Self-interference channel gain vector at user X \\ \(\mathbf{y}_{XX}\) & Self-interference signal vector at user X \\ \(\mathbf{y}_{XY}\) & Receiving signal vector that transmitted from user X to user Y \\ \(\mathbf{x}\) & Estimated signal vector \\ \(\mathbf{\hat{y}}\) & Residual signal vector \\ \(\mathbf{x}_{pilot}\) & Pilot symbols vector \\ \(SNR_{Y}\) & Signal-to-noise ratio at user Y \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of Notations.
## 3 Coordinate Control Strategy of Distributed Parallel-Series
### Active Voltage Injection Control for Onshore AC Fault  
The active power from wind turbines flowing to the onshore converter can be seen as a waterfall, and the onshore converter can be seen as a large water pool with a drainage outlet. If an AC grounding fault occurs at the grid, the drainage outlet is blocked. The onshore converter operates in the DC current mode, meaning that the water still flows from offshore, and the energy keeps accumulating, as shown in Figure 6. The capacitor voltage will increase until the protection of the onshore converter is triggered, and the converter is blocked. However, the DC voltage will remain unchanged in the first stage of the fault.  
If the DC current in the onshore converter is reduced during an onshore fault, the power flow from the MMC-HVDR will be blocked. The capacitor voltage in the MMC  
Figure 5: The response with the control parameter.  
Figure 6: The mechanism in the fault ride-through control strategy.  
HVDR will rise, increasing the DC voltage in the DC collection network. Then, the active power controller in the grid-side converter will saturate, no more active power will be transmitted, and the whole system will stop automatically.  
However, the above speed of the blocking process will be affected by the control parameters. To speed up the blocking process, an active injection control strategy is proposed for the MMC-HVDR, as shown in Equation (16). Once the capacitor voltage exceeds its upper limit, such as 1.1 p.u., the active injection control strategy will operate. As the \(V_{\text{T}}\) is smaller than the \(V_{out}\), the active power transmission will be blocked. The active injection control strategy can also be applied directly to the wind turbines by detecting the braking resistor state.  
\[V_{dapref}=\begin{cases}K_{pv}(i_{dapref}-i_{dq})+\frac{K_{pv}}{8}(i_{dapref}- i_{dq})v_{c-avg}<1.1p.u.\\ 1-K(v_{c-avgref}-v_{c-avg})v_{c-avg}\geq 1.1p.u\end{cases} \tag{16}\]
## 2 Preparation for the \({}^{87}\)Sr Optical Lattice Clock  
### Experimental Setup  
The experimental setup for the preparation of cold atoms and detecting the clock transition of the \({}^{87}\)Sr optical lattice clock is shown in Figure 1. For cooling atoms to several microkelvin, two-stages of cooling were conducted. In the first stage of cooling, the blue magnetic optical trap (MOT) formed by three pairs of counterpropagating laser beams at \(\lambda=461\) nm was used to obtain about \(10^{7}\) atoms with a temperature of 5 mK. In the second cooling stage, the red MOT at \(\lambda=689\) nm was carried and about \(10^{6}\) atoms with a temperature of 3.9 \(\mu\)K were then trapped [28]. The lattice laser operated at the wavelength of 813.42 nm, the \"magic wavelength\", and the one-way optical power was 280 mW. The incident lattice laser beam, along the horizontal direction, was focused on the center of the MOT by a convex lens (CL) with a focus of 300 mm and a Glan-Taylor polarizer (GP) was used to make its polarization linear. The retroreflected beam superposed with the incident beam to form a one-dimensional lattice by using a concave mirror (CM) with a radius of 250 mm. The beam waist of the lattice laser was 100 \(\mu\)m and the lifetime of the trapped atoms was more than 7 s. The clock laser, corresponding to the transition of 5s\({}^{21}\)S\({}_{0}\)\(\rightarrow\)5s5p \({}^{3}\)P\({}_{0}\) at \(\lambda=698\) nm, was generated by an extended-cavity diode laser. To suppress frequency noise, the clock laser was locked to an ultralow expansion (ULE) cavity with a finesse of 200,000 using the technology of PDH (Pound-Drever-Hall) stabilization; eventually, the linewidth of the clock laser was about 1 Hz [29]. The clock laser was collimated by CL and CM with a beam waist of 2 mm at the center of the MOT and overlapped with the lattice laser beam. The polarization of the clock laser was also linear and parallel to the lattice laser.
## 4 Performance Analysis
### The Performance Metrics  
The authors of [31] summarized the performance metrics to aid developing realistic sensor network models. Especially, the energy efficiency, fault-tolerance, and latency should be considered to evaluate sensor network protocol. However, we intended to mainly concentrate on fault-tolerance and latency according to the increment of instability in WSNs. These metrics can provide evidence for the robustness of a routing protocol in lossy WSNs. A fault-tolerance can be represented by means of a data delivery rate and protocol overhead. An average transmission delay indicates the one-way latency between transmitting data and receiving it at a sink.  
We measured the data delivery rate, the protocol overhead, and the average transmission delay to compare One Phase Pull (OPP) with the proposed algorithm (RA) in lossy WSN model with various topologies:  
\[R_{delivery}\left(\%\right)=\frac{N_{sink}}{N_{source}}\times 100 \tag{3}\]  
Data delivery rate (\(R_{delivery}\)) is the percentage of successfully delivered packets at a sink, where \(N_{source}\) is the number of packets sent from the source node and \(N_{sink}\) is the number of successfully received data at a sink node in Equation (3):  
\[O_{protocol}=\frac{N_{control}}{N_{data}} \tag{4}\]  
\begin{table}
\begin{tabular}{|p{113.8 pt}|p{113.8 pt}|} \hline Network size & 200 m \(\times\) 200 m \\ Topology & Grid, Random \\ The number of sink & 1 \\ The number of source & 1, 3, 4 \\ Number of nodes & 100 (Grid), 200 (Random) \\ Transmission range & 30 m \\ Initial node energy & 30 J \\ Location of sink node & (10, 10 ) \\ Location of source node & (190, 190) \\ Error rate & 5\%, 10\%, 15\%, 20\% \\ Simulation time & 1,000 s \\ Interest duration & 60 s \\ \hline \end{tabular}
\end{table}
Table 2: The simulation parameters.  
Protocol overhead (\(O_{protocol}\)) is a ratio between the number control packet and data packet, where \(N_{control}\) is the number of control packets and \(N_{data}\) is the number of data packets in Equation (4). A large \(O_{protocol}\) indicates that many control packets is needed to deliver data to a sink successfully.  
\[T_{avg}=\frac{\sum\left(T_{sink}-T_{source}\right)}{N_{delivery}} \tag{5}\]  
Average transmission delay (\(T_{avg}\)) represents the average time difference on successfully received data at a sink from the source node, where \(T_{source}\) is the sending time of data at source and \(T_{sink}\) is the receiving time of the same data at a sink and \(N_{delivery}\) is the number of delivered data in Equation (5).
# A GPU-Accelerated Method for 3D Nonlinear Kelvin Ship Wake Patterns Simulation
###### Abstract  
The study of ship waves is important for ship detection, coastal erosion and wave drag. This paper proposed a highly paralleled numerical computation method for efficiently simulating three-dimensional nonlinear kelvin waves. First, a numerical model for nonlinear ship waves is established based on potential flow theory, the Jacobian-free Newton-Krylov (JFNK) method and the boundary integral method. To reduce the amount of data stored in the JFNK method and improve the computational efficiency, a banded preconditioner method is then developed by formulating the optimal bandwidth selection rule. After that, a Graphics Process Unit (GPU)-based parallel computing framework is designed, and we used the Compute Unified Device Architecture (CUDA) language to develop a GPU solution. Finally, numerical simulations of 3D nonlinear ship waves under multiple scales are performed by using the GPU and CPU solvers. Simulation results show that the proposed GPU solver is more efficient than the CPU solver with the same accuracy. More than 66% GPU memory can be saved, and the computational speed can be accelerated up to 20 times. Hence, the computation time for Kelvin ship waves simulation can be significantly reduced by applying the GPU parallel numerical scheme, which lays a solid foundation for practical ocean engineering.  
kelvin wake pattern; GPU acceleration; boundary integral method; JFNK method; banded preconditioner method +
Footnote †: journal: Applied  
Article
# Discriminative Local Feature for Hyperspectral Hand Biometrics by Adjusting Image Acutance
###### Abstract  
Image acutance or edge contrast in an image plays a crucial role in hyperspectral hand biometrics, especially in the local feature representation phase. However, the study of acutance in this application has not received a lot of attention. Therefore, in this paper we propose that there is an optimal range of image acutance in hyperspectral hand biometrics. To locate this optimal range, a thresholded pixel-wise acutance value (TPAV) is firstly proposed to assess image acutance. Then, through convolving with Gaussian filters, a hyperspectral hand image was preprocessed to obtain different TPAVs. Afterwards, based on local feature representation, the nearest neighbor method was used for matching. The experiments were conducted on hyperspectral dorsal hand vein (HDHV) and hyperspectral palm vein (HPV) databases containing 53 bands. The results that achieved the best performance were those where image acutance was adjusted to the optimal range. On average, the samples with adjusted acutance compared to the original improved by a recognition rate (RR) of 29.5% and 45.7% for the HDHV and HPV datasets, respectively. Furthermore, our method was validated on the PolyU multispectral palm print database producing similar results to that of the hyperspectral. From this we can conclude that image acutance plays an important role in hyperspectral hand biometrics.  
discriminative local feature; hyperspectral hand biometrics; image acutance +
Footnote †: journal: Applied Sciences
# Anti-Jamming Power Control Algorithm for Wireless Communication Systems Based on MPC
## 4 Algorithm Steps and Flowchart  
Considering the aforementioned analysis, the particular stages of the proposed Algorithm 1 can be outlined as follows:  
The algorithm flowchart is shown in Figure 5.  
```
1:Model the system as a linear control system and discretize it.
2:Set the initial conditions, including the prediction horizon, state and input weights, target bit error rate, and establish the system's equilibrium point. Additionally, specify the start and end times for the simulation and the calculation step size.
3:for each \(k=1,2,\ldots,\frac{t}{T}\) do
4: Use the system state \(x(k)\) and substitute it into Equation (7) to obtain the system output bit error rate \(\mathrm{y}_{1}(k)\) at the current time step.
5: Use Equations (9) and (10) to calculate the state error and steady-state input error, then substitute these into the quadratic performance index to derive Equation (12).
6: Substituting Equation (21) into Equation (12) simplifies the quadratic performance metrics into a standard quadratic programming form. The optimal transmit power sequence is then computed using MATLAB, with the first term being substituted into Equation (29) as the transmit power for the next time step.
7: The system BER is calculated by estimating the system SJNR through Equation (7).
8:\(k=k+1\)
9:end for
```  
**Algorithm 1** Steps
# Properties of Green Tea Waste as Cosmetics Ingredients and Rheology Enhancers
## 3 Result and Discussion
### Chemical Composition
#### 3.1.2 Carbohydrate Compositional Analysis of Green Tea Residual  
Based on Monosaccharide Composition  
The monosaccharide components in the GTW after hot water extraction were characterized using 1H NMR and are shown in Figure 1. It has glucuronic acid 6.08%, rhamn 3.34%, glucose 16.26%, xylose 3.7%, galactose 4.98%, arabinose 3.08%, and mannose 2.3%. Monosaccharide composition was measured and calculated by 1H-NMR spectroscopy with different chemical shifts in amomeric hydrogen at 4.4-5.5 ppm region with an integrated peak area. However, green tea residue from Damin Company, Fujian Province, China, has galacturonic acid 7.6%, rhamne 0.8%, glucose 13.4%, xylose 2.1%, galactose 3.4%, arabinose 2.8%, mannose 1.1%, and fucose 0.4% [17]. Green tea waste from Spain contains glucose 18.57%, xylose 5.08%, galactose 6.96%, mannose 3.43%, rhamne 1.98%, arabinose 2.7%, fucose 0.4% [39].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Extraves**} & \multicolumn{6}{c}{**Polysaccharide**} & \multirow{2}{*}{**Polysaccharide**} & \multirow{2}{*}{**Polysaccharide**} \\ \cline{2-3} \cline{5-12}  & **Acetone** & & & & & & **Calcronic** & & & **Aclod** & & & **Rhamnose** & **Glucose** & **xylose** & **Galacose** & **Arabinese** & **Mannose** & **Total** \\ \hline Green & 0.73 \(\pm\) & 11.6 \(\pm\) & 21.40 \(\pm\) & 27.37 \(\pm\) & 6.08 \(\pm\) & 0.01 & 3.34 \(\pm\) & 0.03 & 16.26 \(\pm\) & 3.70 \(\pm\) & 4.98 \(\pm\) & 0.38 \(\pm\) & 0.03 & 2.30 \(\pm\) & 38.90 \\ tea & 0.20 & 0.4 & 0.7 & 0.2 & & 0.09 & & 0.09 & & & & 0.04 & & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of green tea waste biomass (%).  
Figure 1: \({}^{1}\)H NMR green tea leave residue after hot water extraction. Signal: A, \(\alpha\)-glucoronic acid; B, \(\alpha\)-galactose; C, \(\alpha\)-arabinose; D, \(\alpha\)-glucose; E, \(\alpha\)-xylose; F, \(\alpha\)-mannose; G, \(\alpha\)-rhammose; H, \(\beta\)-mannose; I, \(\beta\)-rhammose; J, \(\beta\)-glucoronic acid; K, \(\beta\)-glucose; L, \(\beta\)-galactose; M, \(\beta\)-xylose; N, \(\beta\)-arabinose.  
Based on Polysaccharide Contents  
GTW after hot water extraction as polysaccharide relative compositional analysis has glucuronic acid 15.63%, rhamnose 8.37%, glucose 40.76%, xylose 9.29%, galactose 12.48%, arabinose 7.73%, and mannose 5.75%. However, the results obtained by [PERSON] et al., green tea residue after microwave heating at 110 \({}^{\circ}\)C was rhamnose 3.4%, glucose 49.9%, xylose 16.1%, galactose 14.2%, arabinose 13.6%, mannose 2.8% [37]. Tea residue from Jiangxi Jinggangshan Co., Ltd. (Jiangxi, China), has glucuronic acid 35.02%, galactose 19.63%, arabinose 16.83%, glucose 9.93%, fucose 5.16%, fructose 4.13%, rhamnose 3.28%, galacturonic acid 1.96%, xylose 1.6%, and mannose 0.75% [40].
# Acoustic Emission Analysis of the Cracking Behavior in ECC-LWSCC Composites
## 7 Results and Discussion
### Failure Mode Characterization Using Rise Time/Maximum Amplitude (RA) Analysis  
The rise time/maximum amplitude (RA) versus average frequency (AF) are crucial acoustic emission (AE) parameters that effectively distinguish between flexural and shear failure modes [25; 53; 54; 55]. The flexural failure mode is typically characterized by tensile cracking, whereas the shear failure mode involves shear cracking. Studies have shown that, initially, AF values are significantly higher compared to RA values, whereas towards the end of the test, RA values become higher than AF values. This disparity arises because tensile cracks predominantly occur during the early stages of flexural testing, while shear cracks tend to appear later in the process [56; 57; 58]. RA analysis has been successfully employed in previous studies to identify fracture modes in concrete structures [25; 53; 54; 55]. RA and AF values are determined using Equations (3) and (4) [55]:  
\[\text{RA}=\text{ Rise time/maximum amplitude} \tag{3}\]  
\[\text{AF}=\text{counts/duration time} \tag{4}\]  
According to the JSCM-111 [59] code, RA analysis was implemented by obtaining the average of RA and AF values based on 50 hits. In this study, the average values of rise time, maximum amplitude, and average frequency were acquired from the three sensors for each beam. Furthermore, the average of RA and AF of the 50 hits was obtained to construct the charts in Figure 5 for all the tested beams. The diagonal line was constructed by drawing a horizontal line at the farthest AF point and a vertical line at the farthest RA point (Figure 5). The intersection point is then determined by constructing the two lines. The slope of each diagonal point was then calculated and presented in Figure 5. This method has been used in previous papers with similar research investigations [60; 61; 62].  
Figure 4: Signal amplitude variations at the first crack for the three sensors of all the tested beams.  
By observing the charts in Figure 5, it is evident that in all the beams, the majority of the points dominate the left side of the diagonal line, although some points lie on the right side of the line. Consequently, the majority of the cracks are tensile cracks, and therefore, all the beams are depicted as failed in flexural mode. Based on the aforementioned results, RA vs. AF analysis proved to be an effective tool in identifying the failure mode of the tested beams.  
Figure 5: RA vs. AF analysis for all six beams: (**a**) ECCS; (**b**) ECCP; (**c**) ECCP-C; (**d**) ECCS-C; (**e**) ECCP-T; (**f**) ECCS-T.
## 6 Conclusions  
A new retaining system named HRS was introduced. Three numerical models were constructed and analyzed to study the action mechanism and effectiveness of the new retaining system in reducing lateral pile deflection and bending moment. The following conclusions can be drawn from the analyses presented in this paper:  
1. When HRS is used, the vertical piles rotate around the wale during excavation. This causes the tops of the vertical piles to rebound outwards while their lower parts (between the wale and their toes) move slightly inwards because of the restraining effect of the batter piles.
2. During HRS-supported excavation, the bending moments acting on the vertical piles vary in a complex manner along the length of the piles. Three obvious extreme values may develop and the largest bending moment develops at the point where the wale is installed.
3. During HRS-supported excavation, the axial and lateral resistance forces acting on the batter piles increase as excavation proceeds and provide adequate support for the vertical piles. The batter piles are subjected to a certain bending moment and the position at which the maximum bending moment occurs does not change significantly as excavation proceeds.
4. Our results show that HRS is a viable alternative measure to use to protect excavations. The lateral deformation of the vertical piles and bending moments acting on them are significantly reduced compared to the CRS method. The level of support is as good as that provided using the HSRS method.  
Figure 26: Photographs of the excavation and strutting system showing: (a) the west side of the excavation; (b) the east side of the excavation.  
It should be mentioned that the study in this paper is based on an excavation with idealized conditions. Certain parameters, including soil conditions, geometric configurations of the HRS, the stiffness of the retaining walls, and strutting systems are not taken into consideration. Thus, readers should be cautious of any extrapolation from the results above. Further investigations are needed to study the mechanism and the construction technology of the HRS through numerical methods, a physical model experiment, and field tests in the future.  
Conceptualization and methodology, C.C. and M.L.; Data curation, H.Z.; Software, Y.S.; Writing--original draft, Y.Z. ([PERSON]); Writing--review and editing, Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The research was funded by National Key R&D Program of China (Grant No. 2017 YFC0805307) and Natural Science Foundation of Hubei province, China (Grant No. 2018 CFB450).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.