esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Active Power Model Predictive Control of Wind-Turbine Cluster
### Predictive Model  
The cluster active power tracking state-space model in (12) can be further discretized through the zero-order holder method into the following discrete state space model:  
\[\left\{\begin{array}{l}x(k+1)=A_{d}x(k)+B_{d}u(k),\\ y=C_{d}x(k),\end{array}\right. \tag{13}\]  
where the matrix parameters \((A_{d},B_{d},C_{d})\) are the discretized version from \((A,B,C)\) system. Set the initial time as \(k\) and the prediction horizon as \(N\); then, the predictive model can be reorganized as follows:  
\[X(k)=F_{x}x(k)+G_{x}U(k), \tag{14}\]  
in which  
\[X(k)=\left[\begin{array}{c}x(k+1)\\ \vdots\\ x(k+N)\end{array}\right],\ U(k)=\left[\begin{array}{c}u(k)\\ \vdots\\ u(k+N-1)\end{array}\right],\]  
\[F_{x}=\left[\begin{array}{c}A_{d}\\ \vdots\\ A_{d}^{N}\end{array}\right],\ G_{x}=\left[\begin{array}{ccc}B_{d}&\cdots&0 \\ \vdots&\ddots&0\\ A_{d}^{N-1}B_{d}&\cdots&B_{d}\end{array}\right].\]  
Since the state variables \(x\) are the active power output for each unit at different predict time step, the cluster power output can be calculated directly without any further prediction.
## 4 Interference Detection Based on the Reassigned Spectrogram  
From Section 3, the Heisenberg's inequality prohibits the existence of the window function with an arbitrarily short time duration and small frequency bandwidth at the same time. Therefore, the conventional spectrogram approach presents the TF resolution trade-off problem and provides poor localization properties in the TF plane, which is not suitable to be directly used in interference detection for GNSS receivers. To deal with the TF resolution trade-off problem with the spectrogram, WVD can be usually used for detecting interference present in the received GNSS signal. Although the WVD has many good properties and provides nearly the optimal TF resolution that allows one to perfectly concentrate the signal components in the TF plane, due to its intrinsic quadratic nature, its use in practical applications is limited by the presence of serious oscillating cross-terms coexisting with the signal auto-terms, when it is applied to multi-component signals. These cross-terms may lead to an erroneous interpretation of the TF structure of the interfered GNSS signal, since they may overlap with the searched TF characteristics of the interfering signal. This drawback severely hinders the usefulness of WVD for identifying interference characteristics in the TF plane for GNSS receivers. In order to obtain a suitable trade-off between good cross-term attenuation and good TF concentration with the TF analysis, a reassigned spectrogram method has been proposed in interference detection for GNSS receivers.
## 2 Methodology  
### Algorithm Overview  
The network structure of YOLOv5 is mainly composed of the input, backbone, neck, and head parts. The input side includes the input image as well as preprocessing the image. The backbone part mainly utilizes the CSP structure to extract features from the input image, which allows the model to learn more features. The neck part uses the FPN (feature pyramid network) [15] plus PAN (path aggregation network) [16] structure, which can extract image features more fully and retain richer feature information. Finally, the head layer predicts the target features by the loss function and non-maximal value suppression, and makes position regression and judgments of the presence or absence of targets and classification.  
The YOLOv5 algorithm, as a commonly used target detection algorithm, has been realized in many fields, but there are still some shortcomings in the scenario of marine vessel target detection.  
Our algorithm firstly integrates the weight-sharing-based cavity convolution scale-aware RFE model (RFE) module into the SPPF module in the backbone after forming the SPPF-REF module, which enlarges the receptive field, improves the information extraction ability for multi-scale targets, and reduces the risk of overfitting. Secondly, the lightweight convolution (GSconv) replaces the neck part of the convolution module, which can serve to reduce the complexity of the model. Subsequently, the one-time aggregation module VoV-GSCSP is used instead of the C3 module, and the slim-neck module is formed with GSConv to improve the accuracy. A coordinate attention module is incorporated into the end of the neck as a way to improve the algorithm's detection ability. Finally, SIoU is used instead of CioU as the loss function to improve the accuracy again and accelerate the convergence speed as a way to better adapt to the sea surface environment. The improved network structure is shown in Figure 1.
## 4 Proposed Protocol
### Evaluation and Voting  
During the voting phase, as shown in Figure 6, voters request model update information from the smart contract for evaluation. In the end, the voters submit their votes to the smart contract. Details of the process are listed below.  
Figure 5: The sequence diagram for each trainer’s training process.  
1. The voter requests the URL for model updates from the \(SC\).
2. The \(SC\) sends \(H_{\beta}\) and \(\beta_{ipfs}\) to the voter.
3. The voter requests \(Y_{\beta}\) from the IPFS URL provided in \(\beta_{ipfs}\).
4. IPFS returns the trainer's \(\beta\) to the voter.
5. IPFS computes \(H_{\beta}\)', the hash of \(\beta\), and compares the value with the downloaded \(H_{\beta}\) from the \(SC\) to verify the integrity of \(\beta\).
6. The voter decrypts \(Y_{\alpha}\) (as shown in Figure 4) and performs training on the decrypted \(M_{0}\). They claim \(\delta^{\prime}\) and compare their scores with \(\delta\), producing \(\gamma\) (the decision of the voter). If the values are sufficiently close, the voter must choose **'accept'**; otherwise, the voter may choose **'reject'**. This step tests the honesty of voters, as dishonest voters may choose to _accept_ even when accuracy fluctuates while rejecting the model updates that should have been accepted.
7. The voter sends \(\gamma\) to the \(SC\).
8. The \(SC\) records \(\gamma\).
9. The \(SC\) calculates the score of the trainer based on the current voter's reputation. For example, if a trainer has a score of \(x\) and a voter has a reputation of \(y\), the current score is \(x^{\prime}=x+y\). Note that if the vote is a _'reject'_ decision, the value of \(y\) is negative.
10. The \(SC\) relays a successful transaction event to the voter.
## 5 Issues Relating to the Fundamental Design Aspects
### Velocity Control  
The UAV's velocity can be dynamically controlled when needed to meet the target deadlines of data-gathering applications. At the same time, altitude control is fundamental, especially since the UAV should be able to effectively avoid obstacles while establishing reliable LOS communication with ground sensor nodes and IoT devices. The aim should be to effectively reduce the energy expended due to mobility, transmission, and reception. To minimize energy waste, the UAV can be made to remain in a waiting mode when no horizontal flight takes place [92]. Similarly, in an EH setup, an energy threshold can be set such that when the UAV's energy is lower than the set threshold, the UAV takes no action and rather harvests energy. Only after harvesting sufficient energy does it switch to working mode to continue collecting data.
# Research on Multi-Scale Fusion Method for Ancient Bronze Ware X-ray Images in NSST Domain
## 3 Results  
To evaluate the effectiveness of the proposed fusion method, this study used four sets of eight registered Han Dynasty bronze mirror X-ray images for fusion performance testing. To ensure consistency, both the high- and low-energy X-ray images of the same bronze mirror must have the same size. The resolution of the images is set at 300 \(\times\) 300. The image data provided for this study were obtained from the Shaanxi Provincial Institute of Cultural Relics Protection. The X-ray exposure of bronze mirrors was conducted using an ART-GIL350/6 fixed flaw detector manufactured by GILARDONI, Milan, Italy. This detector operates within a voltage range of 95 to 350 kV and has a maximum current of 5 mA. Figure 3 illustrates the ART-GIL350/6 fixed flaw detector.  
In this study, the proposed method is compared to eight other multi-scale image fusion techniques. This comparison is carried out using both subjective visual evaluation and the evaluation of six sets of objective metrics. These evaluations aim to authenticate the efficacy of the proposed method. The eight multi-scale fusion methods are LDR [21], MDLatLRR [22], F-PCNN [23], NMP [24], IVF Fusion [25], PL-NSCT [9], MMIF [26], and IFE [27], with the same parameters as in the papers on each of these eight methods. The  
Figure 3: Bronze mirror x-light source images (**a**). Clear X-ray image of rim area and (**b**) clear X-ray image of the decorative area. (1)–(4) Bronze mirror images for the first to fourth groups, respectively.  
objective evaluation metrics are selected as entropy (EN) [28], average gradient (AG) [28], mutual information (MI) [29], gradient-based fusion performance (Q\({}^{\text{AB/F}}\)) [30], peak signal-to-noise ratio (PSNR) [31], and visual information fidelity (VIF) [32], totaling 6 metrics. The method uses the following parameters: the NSST decomposition consists of four layers, with 16, 16, 8, and 8 decomposition directions respectively, the MSMG operator has three scales, and \(t_{max}=110,\tau_{0}=0.3,r=7,p=1\) in the CNP system. The fusion performance tests were implemented on the Intel(R) Core(TM) i7-6700 CPU @ 3.40 GHz and 16.00 GB of RAM produced in Shanghai, China in the MATLAB 2018B environment, and the other compared fusion methods were implemented on the same platform using open-source code.
## 3 Results and Discussion
### Effect of Thermal Processing on Photosynthetic Pigments in Broccoli Extracts  
The concentration of chlorophyll \(a\), chlorophyll \(b\), porphyrins, and \(\beta\)-carotene did not differ significantly between the five thermal processing techniques investigated (Table 2). Previously, boiling and stir-frying were shown to significantly decrease the chlorophyll content of broccoli [12; 73]. On the other hand, these authors reported an opposite effect of steaming on chlorophyll content, [PERSON] et al. [74] reported a decrease, while it remained unchanged in the work of [PERSON] et al. [12]. In agreement with our results, chlorophyll content did not differ significantly among boiled, steamed (only in [PERSON] et al. [71]), and stir-fried broccoli. However, it should be noted that the values obtained in this study were lower than those reported in their work [12; 74]. [PERSON] et al. [74] also showed that the concentration of \(\beta\)-carotene was the same in raw, boiled, and steamed broccoli. Again, the levels of \(\beta\)-carotene obtained in this study were much lower than those reported by [PERSON] et al. [74]. Air-fried broccoli had the highest concentration of carotenoids (6.73 \(\pm\) 2.89 mg/kg fw) and lycopene (0.91 \(\pm\) 0.19 mg/kg fw), while pan-fried broccoli had the lowest content of carotenoids (1.77 \(\pm\) 0.43 mg/kg fw). This might be due to the lipophilicity of the carotenoids, which were absorbed from the tissue by the oil. Carotenoids are also sensitive to heat and the lower content of carotenoids in pan-fried broccoli could also be due to the higher temperature achieved because of the added oil. High temperature can also cause oil to produce hydroperoxide radicals and accelerate the degradation of carotenoids [75]. The stability of carotenoids in vegetables is variable due to both intrinsic and extrinsic factors, such as characteristics of the food matrices (chemical composition, oxygen dissolved, size, physical state of the carotenoid) and type and severity of heat treatment [76]. For broccoli, contrary results were obtained. [PERSON] et al. [77] found a higher content of carotenoids in boiled than in steamed broccoli, while [PERSON] et al. [12] found a lower content in boiled broccoli than in steamed and stir-fried. In cauliflower, which also belongs to the _Brassicaceae_ family, it was observed that raw vegetables had the highest carotenoid content, followed by blanched, while boiled and stir-fried cauliflower had the lowest carotenoid content [13]. Boiled broccoli had the lowest concentration of lycopene (0.47 \(\pm\) 0.22 mg/kg fw). As a polyene, lycopene contains unsaturated double bonds and is isomerized and degraded during processing at high temperatures [77]. Interestingly, isomerization contributes to the antioxidant activity of lycopene [78], and the highest concentration of lycopene found in air-fried broccoli was indeed consistent with the highest antioxidant potential (DPPH) of air-fried broccoli (Table 3).
# A Combined Dynamic-Kinematic Extended Kalman Filter for Estimating Vehicle Sideslip Angle
## 1 Introduction
In addition to the approach adopted for modeling the system, the KF scheme should also be carefully selected. The extended Kalman filter (EKF) is widely employed in the literature, since its formulation is suitable for nonlinear dynamic models [13]. However, the main drawbacks of the EKF are represented by the computational burden of computing the Jacobian matrices of the system and its sensitivity to sampling time, which should be kept as small as possible to mitigate the intrinsic linearization error of the method [14]. While having a similar computational cost, an unscented Kalman filter (UKF) is often preferred, since it does not require the computation of Jacobian matrices and it can be used with larger sampling time [15; 16]. However, the UKF requires a very fine tuning of its parameters, increasing system complexity. This paper proposes a combined dynamic-kinematic extended Kalman filter (DK-EKF), that integrates both approaches with an innovative strategy. The proposed DK-EKF tries to solve the observability issue of kinematic-based Kalman filters and to increase the performance of dynamic-based ones. Differently from [11], that proposes an adaptive state observer that combines a kinematic model and a feedback part based on a dynamic model, this work exploits the Kalman filter approach, for both the kinematic and the dynamic part. Since the kinematic model is linear, a conventional Kalman filter is used, as performed in [12], while the dynamic part is an extended Kalman filter. Moreover, the kinematic-dynamic combination is different from the one proposed in [12], which weights the results of the kinematic part and of the dynamic part, and forces the estimated lateral velocity of the kinematic part to zero below a certain threshold of yaw rate. Instead, in the following, the lateral velocity output of the dynamic part is used as a measurement for the kinematic one, solving the related observability issue, as demonstrated in Section 2.3. The paper is organised as follows: Section 2 presents dynamic and kinematic models and the proposed combined structure; Section 3 describes the experimental test campaign performed to validate the DK-EKF; results are discussed in Section 4.
# Data-Aided Frequency Offset Estimation for CE-OFDM Broadband Satellite Systems
## 5 Pilot Symbol-Based CFO Estimator and the CRB
From Figure 11 and Equation (11), we can get the conclusion that the pilot symbols should be placed symmetrically concerning the DC subcarrier to minimize the CRB. This conclusion proves that the proposed pilot symbol structure has optimal estimation performance.
## 2 Materials and Methods
### HS-SPME-GC-MS Analysis  
Headspace solid-phase microextraction (HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was employed to analyze the volatile compounds in tea samples [11]. For analysis, 2 g of the powdered sample was placed in a 20 mL headspace vial with 5 \(\upmu\)L of 50 \(\upmu\)g/mL ethyl decanoate (\(\geq\)99%, Sigma-Aldrich, Shanghai, China). The vial was sealed and heated to 50 \({}^{\circ}\)C for 30 min to allow the aroma components to volatilize. A solid-phase microextraction fiber (50/30 \(\upmu\)m DVB/CAR/PDMS, Supelco, Bellefonte, PA, USA) was inserted to adsorb volatile compounds for 30 min, after which the fiber was immediately injected into the GC-MS port and held for 5 min. The SPME fiber was aged as per the manufacturer's instructions prior to use.  
The analysis was conducted using an Agilent HP-5 MS column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m). The column temperature was programmed as follows: an initial temperature of 50 \({}^{\circ}\)C, held for 2 min; followed by heating to 80 \({}^{\circ}\)C at 2 \({}^{\circ}\)C/min, held for 4 min; then heated to 180 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, held for 5 min; and finally heated to 220 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, held for 10 min. High-purity helium was used as the carrier gas at a flow rate of 1.0 mL/min with splitless injection. The electron ionization was operated in electron impact mode, scanning ions in the range of 40-600 m/z. The ion source, quadrupole, and transmission line temperatures were set at 230 \({}^{\circ}\)C, 150 \({}^{\circ}\)C, and 280 \({}^{\circ}\)C, respectively.
# Advances in the Biosynthesis and Molecular Evolution of Steroidal Saponins in Plants
## 4 Functions of Steroidal Saponins in Plants  
Steroidal saponins distribute and accumulate in tissue or organ-specific ways at different stages of plant growth and development, indicating that steroidal saponins may also play a role in plant growth and development [3; 14; 57; 58]. The ability of plants to synthesize specific metabolites is essential for their survival. As far as steroidal saponins are concerned, the content and composition of steroidal saponins in plants are affected by the environment and growth stage [14].  
In plants, steroidal saponins, like other plant antitoxins, act as a line of defense against infection by pathogens. The difference is that steroidal saponins are mainly accumulated and stored in plants and the biosynthesis and accumulation of steroidal saponins require a lot of energy, but plant antitoxins are more often produced by stress, so energy consumption is small [58]. However, there is another possibility: when plants are infected by pathogens, the content of precursor substances for steroidal saponin synthesis will increase due to stimulation, which will also lead to an increase in the steroidal saponin content, thus reducing energy consumption, to a certain extent [59]. Take the research on the relationship between saponins and pathogens in oat (_Avena sativa_) as an example: both triterpen saponins and steroidal saponins are present in oat, and the triterpen saponins (avenacins) are accumulated in root tip epidermal cells through active glycosylation, while avencias have strong antifungal activity and can be released into the soil rhizosphere [60; 61; 62]. Compared with triterpen saponins, steroidal saponins in oats are stored in the vacuoles of plants as inactive disaccharides. When pathogenic bacteria infect plant tissues and destroy cell membranes, steroidal saponins will be activated so that \(\beta\)-Glucosidase hydrolyzes the D-glucose unit to form toxic single anthocyanin, which destroys the fungal plasma membrane by membrane pores, thus leading to fungal cell death [63; 64].  
Another role of steroidal saponins in plants is to protect them from phytophagous animals or insects. The main mechanism of action is to inhibit feeding by herbivores and insects as toxins and digestive inhibitors [65; 66; 67]. However, the specific mechanism may be that steroidal saponins have cytotoxicity and can destroy the cell membranes of blood cells and many animal cells [68]. Another possible explanation is that steroidal saponins lead to the penetration of insect cell membranes and the loss of cell integrity [58]. It has also been reported that steroidal saponins isolated from _Allium porrun_ have a strong antifeedant effect on Lepidoptera insects, such as _Peridroma saucia_ and _Mamestra configurata_[69].
## 1 Introduction
The RLS-DCD algorithm generates performance similar to classical RLS versions, such as the one employing the matrix inversion lemma [1]. The RLS-DCD replaces the classical matrix inversion problem with an auxiliary system of equations and exploits the time-shift nature of the input data in order to generate a solution using only additions and bit shifts. The overall complexity corresponding to the RLS-DCD algorithm is proportional to the adaptive filter's length multiplied by a small factor (in terms of multiplications and additions). In this paper, we improve the tracking capabilities of the RLS-DCD algorithm working within the WL framework by employing the recently introduced data-reuse (DR) approach for adaptive algorithms from the RLS family [23; 24]. The DR methodology is based on performing multiple filter updates using the same input data (i.e., in the same adaptive filter iteration). In the recent literature, it proved to generate superior performance for other RLS versions, with the cost of some extra arithmetic effort when used for real valued adaptive filters in system identification scenarios [23]. Simulation results will demonstrate that the namely WL-DR-RLS-DCD has increased tracking capabilities even when identifying long impulse responses and using highly correlated input signals, such as speech sequences. The complexity analysis will also reveal that the proposed method requires acceptable hardware resources, and it is a suitable candidate for practical applications in SAEC scenarios with long acoustic paths. The remainder of this paper is organized as follows. In Section 2, the SAEC system configuration based on the WL model is introduced. Section 3 describes the complex valued WL-RLS-DCD algorithm for SAEC scenarios, and Section 4 introduces a new version of the presented adaptive method based on the DR approach with improved tracking capabilities. Simulation results are discussed in Section 5. Several relevant aspects are discussed in Section 6, and a few conclusions are drawn in Section 7 based on the theoretical contributions in the presented simulations.
# Application of a Local Dynamic Model of Large Eddy Simulation to a Marine Propeller Wake
## 4 Results and Discussion
### Instantaneous  
The instantaneous contours of normalized velocity components \(U_{x}/U\), \(U_{y}/U\) and \(U_{z}/U\) in the \(xy\) plane are shown in Figure 12a. The near field wake of the propeller is mainly composed of coherent blade tip vortices and blade trailing edge vortices, which gradually become unstable and break in the far field. The axial velocity cloud maps at 15 locations (\(x/D=-0.1\), \(-0.08\), \(-0.06\), \(0.06\), \(0.08\), \(0.1\), \(0.5\), \(1\), \(2\), \(3\), \(4\), \(5\), \(6\), \(7\), \(7.8\)) in the flow direction are presented on the transverse planes, as shown in Figure 12b. The mutual induction of blade tip vortices and rolled-up blade trailing edge vortices dominates the  
Figure 10: PSD considering aliasing and leakage effects of time history of thrust coefficient at \(J=0.889\) for LES-LDKSGS. (in zero padding: black line for the entire time series length \(t\) at once, blue line for \(2t\), red line for \(4t\); in block size: black line for block size \(t\), blue line for \(t/8\), red line for \(t/32\); in overlap: black line for zero overlap, blue line for \(0.05t/8\) overlap, red line for \(0.5t/8\) overlap, all with block size \(t/8\)).  
evolution process of propeller wake from near field to far field, and the actual evolution mechanism depends on the geometry and operating conditions of the propeller.