esa-sceva/satcom-chunk-collection huggingface dataset:
# Runway Pavement Structural Analysis Using Remote Laser Doppler Vibrometers
## 3 Compensation Methods
### Vibration Compensation
#### 3.2.2 Experimental Data Processing and Analysis
It demonstrates that the primary vibration frequency range of the fixed-end beam spans from 0 to 40 Hz. Figure 8: RLDV data and Fourier transform (100 kN). (**a**) Vibration measurement data and Fourier transform of the experimental group; (**b**) Vibration measurement data and Fourier transform of the control group. Figure 7: Flowchart for vibration compensation of RLDV test data. Step 3. Extract the pavement vibration data of the same time-domain from the experimental group RLDV, as depicted in Figure 9, and utilize a Butterworth high-pass filter to eliminate low-frequency vibration signals. Figure 9(a),b, respectively, illustrate the vibration data before and after the filtering process. Comparing the vibration curves before and after filtering, Figure 9(b) eliminates some low-frequency vibration. Step 4. The time-domain vibration curves from the experimental and control groups are integrated to derive the vibration displacements of the concrete slab, as demonstrated in Figure 11. The waveforms, the magnitudes, and the frequency oscillations of the displacement curves were very similar in the experimental and the control groups. Figure 10: Filtering of vibration data (100 kN). (**a**) Vibration data before filtering from RLDV; (**b**) Vibration data after filtering via the Butterworth filter. Figure 9: Accelerometer data and Fourier transform (100 kN). (**a**) Vibration measurement data and Fourier transform of the accelerometer; (**b**) Intercept vibration measurement data and Fourier transform of the accelerometer. For the remaining sets of experimental data, Steps 1-4 were iteratively performed to derive the displacement curve of the concrete slab. The peak values of the pavement displacement curve, or the pavement deflection, were extracted for the experimental and control groups under five different load levels. Table 2 presents the average pavement deflection values for the experimental and control groups under the five loading levels. As per Table 2, the pavement deflection in the experimental group is marginally smaller than that in the control group due to the removal of effective vibration information during the filtering process. The maximum measurement errors across all five sets of experiments were 6.48%, a value less than 12.76%, signifying that the preliminary compensation method for the vibration of the fixed-end beam is effective. Future work should focus on optimizing the compensation algorithm using the currently obtained experimental data.
# Improving the Sensory Quality of Black Tea by Blending Varieties During Processing
## 3 Results and Discussion
### Analysis of Volatile Components by GC-MS
#### 3.3.2 Key Aroma-Active Volatiles in Different Blending Treatments
Linalool is not only sourced from glycosides but can also form from the degradation of carotenoids [52], with its content being influenced by the composition, distribution, and manufacture of carotenoids. The volatile 2-methylbutanal is a volatile compound and a typical Strecker aldehyde derived from isoleucine, commonly found as an aroma compound in black teas. Three single-variety cooked black teas (Fud, JGY, and JX) contained a higher concentration of 2-methylbutanal, but its content was lower in FJG and FJX (Supplementary Materials Table S2). In contrast, linalool oxide (pyranoid), benzyl alcohol, and phenylacetaldehyde presented higher contents in JGY and JX, with an increasing trend in both FJG and FJX compared to Fud. The blending of fresh tea leaves during processing can enhance the content of floral-like aroma compounds when blended with tea of the oolong variety. Except for linalool and linalool oxide (pyranoid), blending finished tea (AFJG and AFJX) led to an increase in the content of methyl salicate and geraniol compared with Fud (Supplementary Materials Table S4), both of which were also present at a higher level at JGY and JX (Supplementary Materials Table S3). Therefore, both blending fresh leaves and blending finished tea can improve the aroma quality of black tea by increasing the content of sweet and floral aroma compounds. However, blending fresh leaves has a greater impact on enhancing the flavor of black tea, while blending finished tea has a more noticeable effect on the aroma. The differences in aroma compounds result in their aroma characteristics not being entirely consistent.
# A Reconfigurable SRAM CRP PUF with High Reliability and Randomness
## 3 Proposed SRAM CRP PUF
### Simulation Results Comparison  
To evaluate the effectiveness of the proposed approach when compared to the conventional SRAM CRP PUF, we conducted simulations to analyze the impact of switching delay differences on the PUF cell output. The randomness of the PUF output is achieved using Monte Carlo simulation on the Cadence Virtuoso tool. The characteristics of each transistor, including parameters such as channel length, channel width, oxide layer thickness, etc., were modeled with random variations reflective of the manufacturing process, generating 1000 data points. Output responses were then gathered by applying a predefined set of 10 specific challenge inputs to a PUF cell.  
Figure 4 presents the simulation results. In a conventional SRAM CRP PUF, the '1' ratio at the PUF cell output has a mean of 65.32% and a standard deviation of 0.4962% for post-simulation with the presence of delay mismatches in the layout design. For the proposed SRAM CRP PUF cell, the average value is 46.06% and the standard deviation is 1.0113%. The largest deviation from the ideal value is 4.8%, presenting a decrease of approximately 3.4 times when compared to the simulation result of the conventional structure of 16.4%. This simulation outcome clearly demonstrates that the proposed approach improves the randomness of the PUF output.  
The output randomness can be considered further based on predicting the output according to the threshold voltage mismatch from [28], which can be expressed as follows:  
\[MF=WF.\Delta P-(1-WF).\Delta N \tag{1}\]  
where \(MF\) is the mismatch metric used to predict the output value _OUT_; \(WF\) is the weight factor applied to each mismatch component, accounting for the intrinsic differences between PMOS and NMOS transistors; \(\Delta P\) is the threshold voltage mismatch between PMOS transistors of the left and right inverter arrays; \(\Delta N\) is the threshold voltage mismatch between NMOS transistors of the left and right inverter arrays in Figure 1a.  
In the absence of a delay mismatch, the predicted output is a random value ('1' or '0') due to its dependence only on the threshold voltage mismatches \(\Delta P\) and \(\Delta N\). If \(\Delta P\gg\Delta N\), then \(MF>0\), indicating the predicted output is '1'. Conversely, if \(\Delta P\ll\Delta N\), thus \(MF<0\), hence the predicted output is '0'. However, the presence of delay mismatch in the layout design, as presented in Section 2.2, results in the output depending not only on the threshold  
Figure 4: Simulation results of the conventional and proposed SRAM CRP PUF.  
voltage mismatch but also on the delay mismatch of the control signal lines. Consequently, the PUF output will be biased more towards '1' or '0' and the ratio of '1' or '0' at the output will deviate from the ideal value of 50%. By using the proposed method, the dependence of the output on the control signal lines is eliminated, and the output randomness can be maintained in Equation (1).
# Study of Low-Frequency Hydroacoustic Waves' Behavior at the Shelf of Decreasing Depth
## 6 Conclusions  
As a result of the experimental and model studies, the general patterns of the low-frequency hydroacoustic waves' propagation at the shelf of decreasing depth and their transformation into surface-type seismoacoustic waves at the water-bottom interface were identified. It was established that at sea depths of more than half of the hydroacoustic wave by 9%-10%, about 4%-7% of the energy of the hydroacoustic waves was transformed into seismoacoustic energy of the Rayleigh type. With decreasing depth, the percentage of the transformed energy abruptly increased.  
The obtained experimental results allowed us to determine the critical depth at which the shelf \"locks\" the passage of hydroacoustic energy in water at a frequency of 22 Hz. This depth coincides very well with theoretical calculations and is equal to 17.8 m. We can expect that with a decrease in the frequency of the emitted hydroacoustic signal, this effect will manifest itself at greater depths. Thus, for example, when a hydroacoustic signal is emitted at a frequency of 1 Hz at the shelf of decreasing depth and with similar elastic parameters of the seabed, this signal will not be in the water and will start from depths of about 390 m.  
Conceptualization G.I.D., data curation S.G.D., V.A.C., S.S.B. and S.V.Y., format analysis G.I.D., S.S.B., S.G.D. and V.A.C., funding acquisition S.P., investigation G.I.D. and S.S.B., methodology G.I.D., project administration S.G.D., software Y.S., X.W., Y.D. and S.S.B., supervision S.G.D. and S.P., visualization Y.S., X.W., Y.D., S.S.B., V.A.C. and S.G.D., writing original draft G.I.D., S.S.B. and S.P., writing review and editing S.G.D. All authors have read and agreed to the published version of the manuscript. This work was carried out with partial financial support of the topic AAAA-A20-120021990003-3 \"Research of fundamental foundations of the origin, development, transformation and interaction of hydroacoustic, hydrophvised and geophysical fields of the World Ocean\". The authors declare no conflict of interest.
## 4 Proposed Routing Protocol
### Relay Node Selection and Routing Update
Since N1 is within the communication range when N2 transmits to N4, N1's reception of the packet can be treated as an implicit ACK from N2. At the same time, N3 and N5, although not the intended recipients are within N2's transmission range and also receive the packet. These nodes, upon verifying that the destination ID does not match their own, simply update their neighbor tables and discard the packet. After receiving the data packet, N4 selects the optimal next-hop node N6 from its neighbors N6, N7, and N8 based on the forwarding probability factors and attempts to forward the data packet. However, because N2 is not within the transmission range from N4 to N6, it does not receive an implicit acknowledgment. To ensure reliable delivery, N4 adjusts its transmission power and explicitly sends an ACK to N2 after sending a data packet to N6. Additionally, during N4 the transmission to N6, node N8, although not the target recipient, also receives the data packet. It updates its neighbor table with the received information and then discards the packets. Figure 5: Routing reply process.
# MAC-Based Compression Ratio Improvement for CAN Security
## 4 Simulation
Figure 8: Peak load simulation using CANoe system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**No. of Original Bytes**} & \multicolumn{2}{c}{**Compressed Bytes**} & \multirow{2}{*}{**Difference**} \\ \cline{3-3}  & & **Triple ID** & **Proposed** & \\ \hline CF00400 & 164,704 & 66,346 (59.71\%) & 28,872 (82.47\%) & 22.76\% \\ \hline CFE4523 & 32,904 & 4163 (87.34\%) & 4 (99.99\%) & 12.65\% \\ \hline
18 FE5600 & 3288 & 421 (87.19\%) & 0 (100.00\%) & 12.81\% \\ \hline
18 FF2100 & 65,872 & 8238 (87.49\%) & 0 (100.00\%) & 12.51\% \\ \hline
18 FF6121 & 65,448 & 8181 (87.50\%) & 0 (100.00\%) & 12.50\% \\ \hline
18 FF200 & 32,936 & 10,781 (67.26\%) & 4321 (86.88\%) & 18.02\% \\ \hline
18 FF9E21 & 65,456 & 8497 (87.02\%) & 13 (99.98\%) & 12.96\% \\ \hline
19 FFA010 & 65,648 & 14,191 (78.38\%) & 32 (99.95\%) & 21.57\% \\ \hline
19 FFA030 & 32,736 & 5153 (84.26\%) & 530 (98.38\%) & 14.12\% \\ \hline Average & 58,777 & 13,997 (76.18\%) & 3752 (93.62\%) & 17.44\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of compression ratios for an LS MTRON tractor. The transmission part of the proposed algorithm inverts 4 bits in 4 bytes according to the 12-bit value after performing the Triple ID algorithm, and the receiving part performs the opposite process. Therefore, the calculation cost of the proposed algorithm is slightly higher than that of the Triple ID algorithm. However, compared to the entire process of the Triple ID algorithm, the cost of the additional computational process required by the proposed algorithm is almost negligible, so the computational cost of the proposed algorithm is almost the same as that of the Triple ID algorithm.
## 5 Summary
For short-term stability over 960 s, the GPS, Galileo, and Galileo/GPS PPP systems achieve approximate stability levels of (5.29 \(\times\) 10\({}^{-13}\), 3.34 \(\times\) 10\({}^{-13}\), and 1.60 \(\times\) 10\({}^{-13}\)), respectively. When considering long-term stability over 15,360 s, the respective figures are (1.49 \(\times\) 10\({}^{-13}\), 1.02 \(\times\) 10\({}^{-13}\), and 4.06 \(\times\) 10\({}^{-14}\)). [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments, analyzed the data, and wrote the paper; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] helped in the discussion and revision. All authors have read and agreed to the published version of the manuscript. This study is sponsored by the Science and Technology Planning Project of the Jiangsu Province Market Supervision Administration (No. KJ2024062), the Science and Technology Planning Project of Nanjing Institute of Metrological Supervision and Testing (No. JLY2024003), National Natural Science Foundation of China (Program No. 12403080), China Postdoctoral Science Foundation Funded Project (Project No. 2024M764304), Postdoctoral Fellowship Program of CPSF (Program No. GZC20233565), and Natural Science Basic Research Program in Shaanxi Province of China (Program No. 2023-JC-QN-0027). Not applicable. The datasets analyzed in this study are managed by IGS. The authors gratefully acknowledge IGS for providing broadcast ephemeris, data, and satellite information. Author [PERSON] was employed by the company China Satellite Network System Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 6 Frequency Tracking
### Least Square Method  
To make use of the subcarrier without identical signals to estimate the frequency offset, one efficient way is to calculate the CFRs to eliminate the effect of multi-path channels. A two-step scheme is presented in [31]. Firstly, the CFR of each pilot symbol is obtained by least square estimation, which is carried out by  
\[\hat{H}_{p,f_{f}}^{(m)}(k)=R_{p,f_{f}}^{(m)}(k)\left(S_{p,f_{f}}^{(m)}(k) \right)^{-1}. \tag{36}\]  
Then, under the assumption that the CFRs are constant within the correlation distance \(D\), we divide the CFRs into two vectors. They are \(\mathbf{H}_{1}=[\hat{H}_{p,f_{f}}^{(m_{1})}(k)\cdots\hat{H}_{p,f_{f}}^{(m_{j}) }(k)\cdots\hat{H}_{p,f_{f}}^{(m_{l})}(k)]^{T}\) and \(\mathbf{H}_{2}=[\hat{H}_{p,f_{f}}^{(m_{1}+D)}(k)\cdots\hat{H}_{p,f_{f}}^{(m_{ l}+D)}(k)\cdots\hat{H}_{p,f_{f}}^{(m_{l}+D)}(k)]^{T}\). The RFO is estimated by  
\[\hat{\epsilon}_{RFO}=\frac{N}{2\pi D(N+N_{g})}\angle(\mathbf{H}_{1}^{H} \mathbf{H}_{2}), \tag{37}\]  
where \(I\) represents the number of pilot correlation pairs and \((\cdot)^{H}\) is Hermitian transposition.
# An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction
## 4 Experimental Results
### Effect of Design of Experiment for Training Sample Generation  
The training points for surrogate modeling are typically generated by design of experiments (DOE). A commonly used DOE method is Latin hypercube sampling (LHS) which has proved conducive to improving the quality of approximation for its uniform sampling performance and scalability for high-dimensional problems [31]. However, a major disadvantage of LHS is randomness in sampling, which has great effect on sample quality. In this SCTV prediction problem with only two-dimensional geographical input, full-factorial design (FFD) [31] can be applied to ensure the uniform design of experiment. To demonstrate the effect of DOE on the proposed PWTA method and the candidate surrogates, 60 training points are sampled in each sub-domain using LHS and FFD respectively, and PRS, OK, RBF as well as PWTA are used to construct the surrogates for each sub-domain based on those training sets. Due to the randomness of LHS, the mean R\({}^{2}\) values obtained by 100 independent runs are used for comparison. The results are presented in Table 3. The best value in each column is shown in bold for ease of comparison. It can be observed that OK, RBF, and PWTA using FFD generally  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Surrogate Model** & **Details** \\ \hline PRS & The second-order polynomials are used \\  & A constant regression function and a Gaussian correlation model are employed \\ OK & in the mode. In all cases, \(\mathbf{\theta}_{0}=\mathbf{1}_{m\times 1}\), and \(1\leq\theta_{i}\leq 5\), for \(i=1,2,\ldots,m\) where \(m\) is the number of variables and \(\mathbf{1}_{m\times 1}\) is the vector whose entries are all equal to 1 \\ RBF & The form of basis function is the multiquadric function and we set \(c=0.9\)[20] \\ PWTA & The above three models are used as the candidate surrogates. The control \\  & parameter \(\delta=3\) \\ WTA & The same as PWTA \\ BestGMSE surrogate & The same as PWTA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for different surrogate models.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Sub-Domains** & **No. of Test Sample Points** \\ \hline North America & 1680 \\ Western Europe & 1370 \\ Caribbean Sea & 695 \\ South America & 1045 \\ Russia & 1790 \\ Middle East & 930 \\ East Asia & 1060 \\ Oceania & 1025 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numerical setup for the sub-domains.  
perform better than those using LHS. It suggests that FFD can improve the prediction accuracy of the models in this two-dimensional SCTV prediction problem. Furthermore, for these two different DOE methods, PWTA performs well both in sub-domains and globally. The accuracy issue will be further discussed in Section 4.5.
## 2 Bullet Low-Overlap Point Cloud Registration Based on Line Feature Detection
### Extraction of Linear Features by Discrete Hoff Transform
#### 2.3.2 Line Feature Detection  
1. After identifying subsets \(G_{A}\)' \(\subset G_{B}\) and \(G_{B}\)' \(\subset G_{B}\) that contain sharp feature points, we continue to look for linear features in these subsets.
2. The line features are found by discretizing the Hough parameter space into a regular icosahedron. This step uses the method in [28].
3. Use an improved version of the Hough transform algorithm, which is applied iteratively and corrected with least squares error line fitting to improve the accuracy.
## 3 Structural Design of Plasmon-Mediated Photocatalysts
### Generating Efficiency Improvement of HCs
#### 3.1.2 Plasmonic Cavity for Efficient Photons Trapping  
To solve the limit of insufficient light absorption, another type of broadband absorption system, plasmonic absorbers-spacer-mirror (ASM), is also taken into consideration [72; 73]. [PERSON] et al. [74] proposed Au NPs-TiO\({}_{2}\)-Au mirrors structures by successively depositing thick Au films, TiO\({}_{2}\) layers and packed Au NPs with a certain range of sizes (Figure 5a). Additionally, they explained that the improved photon harvesting ability was rooted in plasmonic Fabry-Perrot (FP) cavity modes, in which incident photons were reflected and scattered between the Au NPs and Au mirror (see inset in Figure 5a). Therefore, the generating efficiency of photons was improved (near 40-fold compared to TiO\({}_{2}\)/Au NPs) and showed a broad absorption band in the spectrum from 400 nm to 1200 nm (Figure 5b). On the other hand, [PERSON] et al. [75] reported a plasmonic particles-on-film nanocavity by assembling Au nanorods-TiO\({}_{2}\) dumbbells on the Au films (Figure 5c). In the process of visible catalytic reactions, the enhancement of the degradation of MB via the integration with Au film, when compared to that of ITO, was attributed to the synergistic effect of d-band electrons transitioning from Au films and SPs induced sp-band electrons transitioning from Au rods, in which SPs induced HCs are dominant at longer wavelengths (\(>\)550 nm) and the effects of d-band electrons are emphasized at shorter light excitations (\(<\)550 nm) (Figure 5d).  
The plasmonic absorbers described above were concentrated on the plasmonic metals with a small size based on localized surface plasmon resonance (LSPR). In recent works, it has been reported that surface plasmon polaritons (SPPs), which travel along the surface of the metallic waveguide structure, are also an efficient medium for the efficient photons conversion by constructing patterned metal structures [76; 77]. [PERSON] et al. [78] proposed 3D tandem structures consisting of period Ag gratings, a TiO\({}_{2}\) layer and Al films (from top to bottom) (Figure 5e). In this way, the Ag gratings on the top layer acted as optical antennas for focusing incident light and as sources for generating HCs when in contact with the TiO\({}_{2}\) layer, based on the LSPR and SPPs effects, respectively. To acquire optimal light absorptive structures, they investigated two types of Ag gratings by changing W\({}_{1}\) and W\({}_{2}\), the rectangular gratings (RG) and trapezoidal gratings (TG). Furthermore, they found that TG based structures had a greater capacity to trap photons compared to other shapes, which was attributed to a lower reflection of TG resulting from the generating gradient in the effective refractive index (Figure 5f). Such SPPs induced structures are much more steerable in their optical character by simply adjusting the light absorption layer, and they are useful for designing angles-dependent photocatalytic applications [79].