esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, a preamble signal, a superimposed-LFM-based preamble signal, was designed for real-time Doppler estimation. Based on the designed preamble signal, a Doppler factor estimation algorithm and an enhanced channel estimation algorithm were proposed. In the proposed Doppler estimation algorithm, the relationship between the Doppler factor and the peak position offset of the matched filter was utilized to estimate the real-time Doppler factor. For the CPS-IOMP algorithm, the excellent autocorrelation characteristic of the designed preamble signal was used to estimate the channel sparsity, and a simplified dictionary matrix was constructed to reduce the computational complexity. The CIR was estimated using the OMP algorithm with the simplified dictionary matrix. The designed preamble signal, the proposed Doppler estimation algorithm, and the proposed channel estimation algorithm were validated by simulations and sea experiments. Numerical results show that the proposed Doppler estimation algorithm achieves a more accurate estimation performance. Furthermore, the proposed CPS-IOMP algorithm performs well in terms of good channel estimation performance and less CPU running time.  
Conceptualization, C.L., Q.S. and H.C.; methodology, Q.S., C.L. and H.C.; software, C.L.; validation, Q.S. and C.L.; formal analysis, C.L., Q.S. and H.C.; investigation, C.L. and Q.S.; resources, H.C.; data curation, C.L., Q.S. and L.X.; writing--original draft preparation, C.L. and H.C.; writing--review and editing, C.L., H.C. and L.X.; supervision, H.C. and L.X.; project administration, H.C. and L.X.; funding acquisition, H.C. and L.X. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Key Research and Development Program of China, grant number 2021 YFC2801201; the National Natural Science Foundation of China, grant number 62271442 and grant number 42227901; the Science and Technology Department of Zhejiang Province, grant number LGG22F010007; and the Natural Science Foundation of Zhejiang Province, grant number LZ23F010006.  
Not applicable.  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.
# Wave Polarization Control in Anisotropic Locally Resonant Materials
## 4 Transmission Analyses  
To validate the polarization band properties investigated through asymptotic homogenization, we perform several numerical transmission analyses employing the real geometry of the locally resonant metamaterial. For this purpose, we consider two different geometries of the unit cell, characterized by the same geometrical dimensions (\(R_{c}/\ell=0.36\), \(R_{f}/\ell=0.17\) and \(e/\ell=0.15\)) but with two different angles of eccentricity of the fibres: \(\theta=0\) and \(\theta=\pi/4\).  
Figure 5: Polar diagrams of effective wave phase velocity (in black), expressed in m/s, as a function of the propagation direction for different values of frequency. Green segments give the polarization directions; (**a**) \(f^{*}=2\) m/s; (**b**) \(f^{*}=8\) m/s; (**c**) \(f^{*}=8.8\) m/s; (**d**) \(f^{*}=11.6\) m/s.  
The adopted geometric dimensions of the metamaterial cell allow to completely separate the intervals in which the principal values of \(\rho^{0}(\omega)\) are negative as shown in Figure 6b such that no complete band gaps are present.
# Robust Detection of Background Acoustic Scene in the Presence of Foreground Speech
###### Abstract  
The characterising sound required for the Acoustic Scene Classification (ASC) system is contained in the ambient signal. However, in practice, this is often distorted by e.g., foreground speech of the speakers in the surroundings. Previously, based on the iVector framework, we proposed different strategies to improve the classification accuracy when foreground speech is present. In this paper, we extend these methods to deep-learning (DL)-based ASC systems, for improving foreground speech robustness. ResNet models are proposed as the baseline, in combination with multi-condition training at different signal-to-background ratios (SBRs). For further robustness, we first investigate the noise-floor-based Mel-FilterBank Energies (NF-MFBE) as the input feature of the ResNet model. Next, speech presence information is incorporated within the ASC framework obtained from a speech enhancement (SE) system. As the speech presence information is time-frequency specific, it allows the network to learn to distinguish better between background signal regions and foreground speech. While the proposed modifications improve the performance of ASC systems when foreground speech is dominant, in scenarios with low-level or absent foreground speech, performance is slightly worse. Therefore, as a last consideration, ensemble methods are introduced, to integrate classification scores from different models in a weighted manner. The experimental study systematically validates the contribution of each proposed modification and, for the final system, it is shown that with the proposed input features and meta-learner, the classification accuracy is improved in all tested SBRs. Especially for SBRs of 20 dB, absolute improvements of up to 9% can be obtained.  
acoustic scene classification; speech enhancement; noise floor; deep learning; foreground speech robust ASC; ResNet +
Footnote †: journal: _Applied Sciences_  
14609
## 3 Results and Discussion
### Fourier-Transform Infrared Spectroscopy (FTIR) of Algal Pigment Microparticles  
FTIR analysis confirmed the encapsulation of algae pigments in PEG microparticles. Figure 3 shows the FTIR spectra of PEG 4000, and algae pigment microparticles of raw algal materials and extracts obtained with different extraction techniques. In the comparison of PEG with microparticles, the spectrum in the absorption bands at 430-520 and 1100-1400 cm\({}^{-1}\) showed a considerable contribution of algae extract in the microparticles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Microparticles**} & **Encapsulation** & **EE Chlorophyll a** & **EE Chlorophyll b** & **EE Carotenoids** \\  & **Yield (\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline RAM+PEG & 61.60 & 56.98 & 65.48 & 43.02 \\ \hline SCO\({}_{2}\)E+PEG & 66.03 & 95.00 & 75.91 & 62.31 \\ \hline CON+PEG & 64.24 & 70.73 & 83.40 & 57.33 \\ \hline WAT+PEG & 73.73 & 57.01 & 70.95 & 55.37 \\ \hline \hline \end{tabular}  
* EE—encapsulation efficiency, RAM+PEG—raw algal material, SCO\({}_{2}\)E+PEG—extract obtained with supercritical extraction, CON+PEG—dry extract obtained with conventional extraction, WAT+PEG—polyethylene glycol.  
\end{table}
Table 1: Encapsulation yield and pigment encapsulation efficiency for microparticles obtained with the PGSS process.  
Figure 3: FTIR spectra of the algae pigment microparticles obtained by the PGSS process (RAM+PEG—raw algal material, SCO\({}_{2}\)E+PEG—extract obtained with supercritical extraction, CON+PEG—dry extract obtained with conventional extraction, WAT+PEG—dry extract obtained with ethanol/water extraction, PEG—polyethylene glycol).  
The absorption characteristics of PEG in the algae pigment microparticles processed by PGSS were represented from 4000 to 520 cm\({}^{-1}\) (Figure 3). The carrier's (PEG 4000) spectrum bands were visible at 2881.65 cm\({}^{-1}\) due to -C-H stretching vibrations, and at 1465 and 1340 cm\({}^{-1}\) due to -C-H vibrations. Two bands at 1278-1058 cm\({}^{-1}\) were due to the stretching vibrations of the alcoholic -O-H and -C-O-C ether linkages [22]. In the comparison of PEG and microparticles, the band at 950 cm\({}^{-1}\) of the microparticle showed a considerable contribution of algae extract in the microparticles. The band is assignable to the C=C, a trans-substituted functional group that is characteristic of brown macroalgae [23]. These findings allowed us to confirm that SCO\({}_{2}\) extract was well encapsulated in PEG 4000. However, FTIR spectra of PEG and microparticles prepared by the PGSS technique were very similar, demonstrating that the PEG's chemical structure was not changed by the PGSS processing.
# Matching-Based Resource Allocation for Satellite-Ground Network
## 6 Performance Results and Analysis
As shown in Figure 6, the algorithm proposed by us has an obvious growth trend, and it serves more users than the two-sided matching. The greedy algorithm and the random algorithm are far lower than the three-sided matching algorithm. The number of users served by these four algorithms increases with the increase in time slots. Figure 4: Revenue of satellites vs. users. Figure 5: Total revenue of satellites vs. time slots.
# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 5 Experiment and Results Analysis
### Results Analysis  
The steganalysis experiments are performed on five kinds of speech segments: CM, CW, EM, EW and Hybrid:  
1. Through the true positive rate (TPR), the false positive rate (FPR) and the probability (Pr), ACD and SCD are also performed on the five types of speech segments. The experimental results are compared with the steganalysis results of the detection based on Mel frequency cepstrum coefficient (MFCC) method in the literature [8].
2. The paper analyzes the ACD and SCD of speech segments which includes different durations and different steganographic embedding rates. At the same time, the paper also analyzes the advantages and disadvantages of ACD and SCD to detect these two situations.
## 3 Results and Discussions
### The Course Keeping Simulation Based on S Surface Controller
#### 3.3.2 The Course Keeping Simulation
In other words, the aim of improving the course keeping ability of the model is to enhance the roll stability through the way of limiting the growth of drift angle. This further proves that, from the perspective of reverse verification, excessive yaw deviation is the root cause of roll instability for emergency rising at medium incidence angle. To sum up, in the small longitudinal blown position of 362 mm forward from the model center of gravity, the yaw angle can be controlled within the deviation range of 0.2\({}^{\circ}\) by manipulating the rudder, which is helpful to limit the growth of the model's underwater drift angle, and then greatly reduce the maximum roll angle after surfacing. Under these conditions, the model can not only obtain good course keeping ability, but also avoid excessive pitch and excessive roll simultaneously. Consequently, the model will achieve the best emergency rising stability and safety.
## 4 Wind System Control at Variable Wind Speeds from \(V_{max}\) [m/s] up to 21 [m/s]
### Linearized System Analysis and Control  
We bring the dynamical system (18) again to a controllable object form where  
\[\begin{split}\frac{dn(t)}{dt}=g(n(t),V(t),\beta(t)),\\ y(t)=h(n(t),V(t),\beta(t))=P_{TV}(n,V,\beta),\end{split} \tag{19}\]  
and now the control objective is to regulate \(P_{TV}\) at the setpoint \(P_{TV}^{*}=1.5\) [MW] using the blade pitch angle \(\beta(t)\) as the control input variable. Let us consider again a linearization point \(\mathcal{P}(V_{0}=V_{MAX},n_{0}=3.8269\times V_{0},\beta_{0}=0)\) to reflect operation near the MPP. Using the first-order linearization around \(\mathcal{P}\), we can write  
\[\begin{split}\frac{d\Delta n(t)}{dt}=\frac{dg}{dt}\Big{|}_{V_{0} n_{0},\beta_{0}}\Delta n+\left.\frac{dg}{df}\right|_{V_{0}n_{0},\beta_{0}} \Delta\beta+\left.\frac{dg}{dV}\right|_{V_{0},n_{0},\beta_{0}}\Delta V,\\ \Delta y(t)=\left.\frac{dh}{dn}\right|_{V_{0},n_{0},\beta_{0}} \Delta n+\left.\frac{dh}{df}\right|_{V_{0},n_{0},\beta_{0}}\Delta\beta+\left. \frac{dh}{dV}\right|_{V_{0},n_{0},\beta_{0}}\Delta V=\Delta n(t),\end{split} \tag{20}\]  
where we obtain the Jacobian of the functions \(g,h\) with respect to their arguments at the operating point \(\mathcal{P}(V_{0},n_{0},\beta_{0})\) and \(\Delta\) means the small variation of the argument about the linearization value. The transfer functions that result are \(H_{\beta\to P_{TV}}(s)=-1.603\times 10^{5}\)\((1.70716s+1)/(1.70719s+1)\) and \(H_{V\to P_{TV}}(s)=8.527\times 10^{5}(1.70716s+1)/(1.70719s+1)\). Notice that the numerator and denominator time constants are only slightly different in both cases; therefore, pole-zero cancellation is safely employed, which means that in the linearized model, we write \(P_{TV}(s)=k_{\beta}\beta(s)+k_{V}V(s)\), which renders the process a static one. Of course, the underlying system is nonlinear time-varying and the values \(k_{\beta},k_{V}\) will change with the operating point.  
The role of \(V(t)\) will be important, since it will be modeled as a ramp-like disturbance. This time, different from OR1, the control goal will not be MPP tracking (which adapts \(n_{OPTIM}\) based on \(V\)) but the WT operation imposed by regulating \(P_{TV}\) at the setpoint of \(P_{TV}^{*}(t)=1.5\) [MW]. In this context, the control must be designed so as to deliver both reference tracking and disturbance rejection. With the underlying analyzed process transfer function, a straightforward option is to use a double integrator feedback controller on the feedback error \(e(t)=P_{TV}^{*}(t)-P_{TV}(t)\), namely \(C(s)=(p_{1}s+p_{0})/s^{2}\).
## 3 The Optimization Model
### The Optimized Objective  
Utilizing the sum of the bending energies of the main beam and the pylon [13] as the optimization objective function, this function provides a comprehensive representation of the overall structural state, leading to satisfactory results for both the displacements and member forces. The objective function can be expressed as  
\[U=\int\limits_{\text{g}}\frac{M^{2}}{2 EI}\text{d}s+\int\limits_{\text{P}} \frac{M^{2}}{2 EI}\text{d}s \tag{2}\]  
where g represents the main beam, and p represents the pylon.  
For discrete beam structures, assuming a constant material elastic modulus \(E_{\text{i}}\) and sectional moment of inertia \(I_{\text{i}}\) along each element length, the combined bending energy of the steel box girder and pylons can be expressed as  
\[U=\sum\limits_{\text{i=1}}^{\text{m}}\frac{L_{\text{i}}}{4E_{\text{i}}I_{ \text{i}}}\left(M_{\text{Li}}^{2}+M_{\text{Ri}}^{2}\right) \tag{3}\]  
where \(m\) represents the total number of main girder and pylon units; \(L_{\text{i}}\) represents the length of element i; and \(M_{\text{Li}}\) and \(M_{\text{Ri}}\) represent the bending moments of the left and right sides of element i, respectively, under a dead load.  
Formula (3) can be expressed in matrix form  
\[U=M_{\rm L}^{\rm T}BM_{\rm L}+M_{\rm R}^{\rm T}BM_{\rm R} \tag{4}\]  
where \(B={\rm diag}(L_{\rm i}/4E_{\rm i}I_{\rm i})(i=1,2,\cdots,m)\); \(M_{\rm L}\) and \(M_{\rm R}\) represent the bending moment vectors of the left and right sides of the elements, respectively, under a dead load.  
The concept of the influence matrix [14] yields  
\[\begin{cases}M_{\rm L}=M_{\rm LD}+C_{\rm ML}T\\ M_{\rm R}=M_{\rm RD}+C_{\rm MR}T\end{cases} \tag{5}\]  
where \(M_{\rm LD}\) and \(M_{\rm RD}\) represents the bending moment vectors of the left and right sides of the elements, respectively, under self-weight and a secondary dead load; \(C_{\rm ML}\) and \(C_{\rm MR}\) represents the influence matrix of the bending moments of the left and right sides of the elements, respectively, under a unit cable force; \(T\) represents the cable force vector.  
Substituting Formula (5) into Formula (4) yields Formula (6):  
\[U=T^{\rm T}H\mathbf{T}+2\mathbf{c}^{\rm T}\mathbf{T}+\mathbf{F} \tag{6}\]  
where \(\mathbf{H}=\mathbf{C}_{\rm ML}^{\rm T}\mathbf{B}\mathbf{C}_{\rm ML}+\mathbf{C}_{\rm MR}^{\rm T}\bm {B}\mathbf{C}_{\rm MR}\) is an \(n\)-order symmetric positive definite matrix, which represents the influence of the unit cable force on the sum of the bending energy of the girder and the pylon; \(\mathbf{c}^{\rm T}=M_{\rm LD}^{\rm T}\mathbf{B}\mathbf{C}_{\rm ML}+M_{\rm RD}^{\rm T}\mathbf{B }\mathbf{C}_{\rm MR}\) is an \(n\)-dimensional column vector, which represents the influence of the self-weight, secondary dead load, and unit cable force on the sum of the bending energy of the girder and the pylon; and \(\mathbf{F}=M_{\rm LD}^{\rm T}BM_{\rm LD}+M_{\rm RD}^{\rm T}BM_{\rm RD}\) is a constant which represents the influence of self-weight and the secondary dead load on the sum of the bending energy of the girder and the pylon.
###### Abstract
[PERSON] [33] uses Granger causality and transfer entropy to analyze the vibration of the site and finds that transfer entropy can determine the cause and effect of the vibration more accurately. [PERSON] [34] and [PERSON] [35] have also attempted to investigate the structural response by the transfer entropy based on data acquired from field tests rather than numerical simulations. [PERSON] [36] applies the transfer entropy to the vibration-response analysis of the powerhouse structure to describe the vibration-transfer energy characteristics of different variables and the same variable in different directions. In this research, transfer entropy is introduced and verified as an innovative method that can analyze vibration-transmission paths based on structural vibration response data. However, using vibration monitoring data directly from field tests, the analysis process may be affected by strong background noise, resulting in inaccurate analysis results. Considering the above problems and the advantage of CEEMDAN, SVD and TE in signal-analysis work, this paper proposes a new vibration-transmission path identification method for hydropower houses based on CEEMDAN-SVD-TE. Firstly, the accuracy and superiority of CEEMDAN-SVD-TE, which is higher than TE in information-transmission-direction identification, is verified by simulation-signal analysis. A large hydropower house is taken as the research object, and the CEEMDAN-SVD method is used to extract the tailwater-fluctuation signal as the characteristic signal for the transmission-path analysis; finally, the vibration-transmission path of tailwater fluctuation is analyzed using the transfer-entropy theory, and the information-transmission rate between different measurement points is calculated. This research can offer a dependable theoretical basis and technical support for the identification of the vibration mechanism and optimization of structural vibration reduction in hydropower houses. This article is structured as follows. Section 2 introduces the basic theory used in the following sections. Section 3 presents the simulation analysis of the method proposed in the paper. Section 4 shows the results for the analysis of the vibration-transmission path from the measured point signals in the hydropower house. Finally, this article is concluded in Section 5.
# Privacy-Preserving Authentication Based on PUF for VANETs
## 3 Preliminaries
### Fuzzy Extractor  
Fuzzy extractor (FE) [18] is a cryptographic tool designed to convert imperfect, noisy data (such as fingerprints or PUFs) into secure keys. In cryptographic mechanisms, secret values are typically required to be evenly distributed and precisely regenerable when needed. However, in real-world applications, such as fingerprints or PUF values, these requirements are challenging to fulfill due to physiological and environmental factors that introduce variations in the detected data across different measurements. FE can address this issue by correcting certain differences in the input data. It allows for a certain level of noise in the input, and as long as the input is sufficiently similar, it can output an identical, uniformly random string. As depicted in Figure 2, the FE consists of two main parts:  
**Generator:**\((P_{F},R_{F})\gets Gen(W)\). This function takes a string \(W\) (a one-time sampling of a random noise source) as input and outputs two strings: \(R_{F}\), a random string, and \(P_{F}\), an exposed auxiliary string.  
**Regenerator:**\(R^{\prime}_{F}\gets Rep(W^{\prime},P_{F})\). The regeneration algorithm takes another sampling of the noise source, \(W^{\prime}\), and the exposed auxiliary string \(P_{F}\) as inputs, and outputs a string \(R^{\prime}_{F}\).  
**Correctness:** The correctness requirement for the FE is that if the distance between the two samples \(W\) and \(W^{\prime}\) is close enough, then \(R^{\prime}_{F}=R_{F}\), ensuring that \(R_{F}\) can be accurately reproduced.  
**Security:** The security requirement is that if the random source has sufficient entropy, then \(R_{F}\) will be uniformly random, providing a high level of security for the generated keys.  
vehicle is detected as malicious, its real identity, pseudonym, and the current timestamp are added to the revocation list. Finally, CA selects the hash function \(H(\cdot)\).
## 2 Materials and Methods
### RNA Isolation and qRT-PCR  
Quantitative real-time PCR (qRT-PCR) was applied to determine the expression levels of defense-related genes of wheat seedlings in different treatment groups. The wheat seedlings were collected at 0, 12, 24, 48, 72, and 96 h after inoculation with _F. graminearum_. The total RNA was extracted from the wheat tissues (50 mg) using a Quick RNA Isolation kit (Huayueyang Biotech, Beijing, China). The total RNA quality was evaluated with an Agilent 2100 bioanalyzer system (Agilent Technologies, Santa Clara, CA, USA) and a Nanodrop spectrophotometer 2000(tm) (Thermo Fisher Scientific, Shanghai, China). The first-strand cDNA was obtained with the FastKing cDNA Kit (Takara Biotech, Beijing, China). Then, qRT-PCR reactions were conducted by LightCycler apparatus (Roche Diagnostics GmbH, Switzerland) with the SYBR Green detection system. The relative _F. graminearum_ biomass in each treatment was quantified by qPCR with the \(\beta\)-tubulin gene (FGSG_09530) as a reference to _F. graminearum_ and wheat _Ta54227_ primers (Table S1) [23]. The expression of SA- (_PR1.1_, _PR2_, _PR5_, and _PAL1_) and JA/ET-related genes (_PR3_, _PR4a_, _PR9_, and plant defensin 1.2 _PDF1.2_) in wheat seedlings was assessed, and the _Ta54227_ gene was applied as the internal reference. The relative expression levels of genes were analyzed with the \(2^{-\Delta\Delta\text{Ct}}\) approach [8]. Each treatment had three replicates, and three independent experiments were conducted.
# MRI Diffusion Connectomics-Based Characterization of Progression in Alzheimer's Disease
#### 1.1.3 Implications
DWI was designed to study white matter (WM) structure [14], a tissue to which AD has been associated [15; 16; 17]. This modality is particularly useful, given that AD exhibits degeneration of cellular barriers of neurons and fiber tracts as a result of the buildup of Tau proteins [18]. In recent years, a large body of research has focused on leveraging DWI to classify AD stages and predict disease progression [10; 13; 19; 20; 21; 22; 23; 24]. Despite growing evidence that diffusion-weighted imaging correlates with disease severity [25], we have been unable to find a comprehensive analysis of whether diffusion-weighted imaging can be used to predict established biomarkers used for staging AD. In this study, we investigate the potential of tractography metrics to predict neuropsychological and neurobiological test results in the context of Alzheimer's disease progression while also aiming to identify early prognostic biomarkers of AD. Our analysis addresses four key questions: (Q1) Do phenotypic characteristics predict cognitive decline within our current dataset? (Q2) Are WM tract features predictive of neuropsychological and neurobiological indicators? (Q3) Does baseline tract volume change with cognitive decline? (Q4) To what extent can tractography metrics predict cognitive decline? We present our methodology for data acquisition and analysis, followed by results corresponding to each research question. Finally, we discuss the implications of our findings for both clinical practice and future research directions in neuroimaging and Alzheimer's disease.