esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison between Model Simulations and HF Radar Data
This confirms that the errors in area C (Figure 7b) were mainly from the Shangchuan station. Figure 6: Comparisons of monthly averaged velocities (cm/s, vector) from radar measurements and model. Figure 9 shows the scatter plots of the radial velocities derived from the model and the two radar stations. Compared to the model results, the data quality of the Wanshan station was systematically better than that of the Shangchuan station for all three areas. Especially in area C, the radar velocities were underestimated by half compared to the model (Figure 9a). In area A, the tidal currents were dynamically affected by coastlines, topography, and river discharges. We know that the HF radar data were derived from electromagnetic echoes, which were apparently unable to resolve these physical and dynamical factors. In area C, the currents were mainly determined by tides and winds, which can be adequately captured by Doppler spectra; therefore, the HF radar should have been able to resolve the currents in this region. Thus, on the basis of Figure 9a, we infer that the errors of the Shangchuan station were likely caused by inversion algorithms. Figure 8: Radial error distribution of the M\({}_{2}\) ellipse amplitude (cm/s, shaded) attributed to (**a**) the Shangchuan radar station and (**b**) the Wanshan radar station. Isobaths (m) are marked with color-changing solid lines. Figure 7: **(a)** M\({}_{2}\) tidal ellipses for HF radar (blue line) and model (red line) in the radar coverage area. The data are plotted at 0.05\({}^{\circ}\) intervals. (**b**) Error distribution of the M\({}_{2}\) ellipse amplitude (cm/s, shaded) and current anomaly (cm/s, vector). Isobaths (m) are marked with color-changing solid lines. The current anomaly was derived as \(\mathrm{V_{radar}}-\mathrm{V_{model}}\).
# Smart Roads for Autonomous Accident Detection and Warnings
## 2 Related Works  
A large amount of research is being carried out in the domain of accident avoidance and accident alarms by a large number of researchers and practitioners. In order to avoid accidents, many approaches are utilized to enhance safety. For ease of reference, the literature on accident detection and avoidance is separated into three approaches: stand-alone, cooperative, and hybrid. Stand-alone approaches use sensors, such as radar and light detection and ranging (LiDAR), for accident avoidance and detection, whereas cooperative approaches rely on V2X technology, and hybrid approaches combine the two methods.
# Biosynthesis of Edible Terpenoids: Hosts and Applications
## 3 Application of Terpenoid Microbial Cell Factories in the Food Industry
### Acting as Medicinal Health Food (MHF)
#### 3.4.1 Germacrene A and \(\beta\)-Elemene  
Germacrene A with spicy and peppery scents can be used as flavor and fragrance in the food industry [11]. Moreover, it is one of the most critical intermediates in the biosynthesis of terpenoid compounds and can serve as a precursor of \(\beta\)-elemene and patchoulol. \(\beta\)-Elemene, extracted from the traditional Chinese herb _R Rhizoma zedooriae_ ([PERSON]) and _Curcuna longa_ L. ([PERSON]), is a volatile sesquiterpene and can be synthesized through intramolecular Cope rearrangement via germacrene A in vitro. It is an important flavor in Chinese Baijiu, not only giving a unique flavor to the liquor but also stimulating the consumer's taste buds [1]. Additionally, \(\beta\)-elemene has also been widely used as a clinical anti-cancer drug due to its anti-tumor activity and immune protective properties [1]. Owing to their great application value, scientists attempt to heterologously synthesize germacrene A and \(\beta\)-elemene in microorganisms. Currently, they have been synthesized in various microorganisms, including _E. coli_[28], _S. cerevisiae_[128], _Y. lipolytica_[99], and _P. pastoris_[62].  
[PERSON] et al. identified a germacrene A synthase (_D_IGAS) with the highest activity in _Y. lipolytica_ using a bioinformatics strategy. They further enhanced the germacrene A synthetic flux by substrate-channel engineering, MVA, and acetyl-CoA supplying. The final titer of germacrene A was achieved at 39 g/L by using glucose as a carbon source [99]. [PERSON] et al. identified another germacrene synthase, LTC2, which originated from _Lactuca sativa_, that can be used in _Y. lipolytica_, and then they integrated the LTC2-expressing cassette into the genome of Po1f-\(\Delta\)ku70. They promoted the synthesis of germacrene A through a \"push-pull-restrain\" strategy successively. Finally, \(\beta\)-elemene was produced by shifting the temperature to 250 \({}^{\circ}\)C, and the titer reached 5.08 g/L. This work is the first combination of engineering _Y. lipolytica_ with chemical transformation [100].
# Cooperative Admission Control with Network Coding in 5G Underlying D2D-Satellite Communication
## 1 Introduction
Network codes are designed so that good channel conditions transmit the significant coded packets and increase the network's performance. So effective joint admission control designed with network coding for multiple interfaces is essential for cooperative communication. Instantly Decodable Network Coding (IDNC) technology leads to instant decodability. It withstands with adverse channel conditions [21], so a novel admission control method for IDNC-assisted distribution of the data algorithm is proposed but does not consider multiple interfaces. Previous literature techniques considered cooperative relay selection, resource allocation, and spatially diversity in network coding but did not account for effective admission control under joint channel conditions with random linear network codes underlying multiple interfaces. Different admission control techniques have been formulated for terrestrial-satellite networks with optimized power allocation. The purpose is to increase the throughput by using the resources efficiently [22; 23] but did not consider the strength of network coding. The cooperative admission control (mode assignment, power allocation, and user selection) with opportunistic network coding for multiple access interfaces, i.e., cellular with D2D-satellite supported network jointly optimized adaptive to channel condition and packets available, has not been taken in literature due to its infeasible nature and complexity to best of our knowledge.
## 5 Numerical Experiments  
In this section, we shall check the effectiveness of the newly proposed methods. We employ the present methods Equation (23) (for \(\beta=1,\ \gamma=12\) ), Equation (24) (for \(\beta=1,\ \mu=12\)) and Equation (25) (for \(\beta=1,\ \eta=12\) ), denoted by \(MM_{8}^{1}\), \(MM_{8}^{2}\) and \(MM_{8}^{3}\), respectively to solve nonlinear equations. We compare them with [PERSON] and [PERSON] method Equation (26) (\(KTM_{8}\)), [PERSON] method Equation (27) (\(SM_{8}^{1}\)), [PERSON] _et al._ method Equation (28) (\(ZM_{8}\)), [PERSON] _et al._ method Equation (29) (\(SM_{8}^{2}\)), respectively. The test functions and their roots are displayed in Table 1. Comparison of different eighth-order derivative-free iterative methods with respect to the same number of functional evaluations (TNE = 12) are provided in Tables 2-4. All computations have been performed using the programming package \(Mathematica\ 9\) with multiple precision arithmetic. We use \(\epsilon=10^{-35}\) as a tolerance error. The following stopping criteria are used for computer programs: \((i)\ |x_{n+1}-x_{n}|<\epsilon\), \((ii)\ |f(x_{n+1})|<\epsilon\). These methods are employed to solve some nonlinear equations of two classes: **smooth functions** and **non-smooth functions:**  
\[g_{1}(x) =|x^{2}-2|,\ \ \alpha\approx 1.414213562373 \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Methods**} & \(|g_{2}(x_{1})|\) & \(|g_{2}(x_{2})|\) & \(|g_{2}(x_{3})|\) \\ \hline Method \(\ KTM_{8}\) Equation (26) & D & D & D \\ Method \(\ SM_{8}^{1}\) Equation (27) & D & D & D \\ Method \(\ SM_{8}^{2}\) Equation (28) & 2.63e\(-\)1 & 6.16e\(-\)7 & 1.87e\(-\)40 \\ Method \(\ SM_{8}^{2}\) Equation (29) & 0.786e\(+\)3 & 0.12e\(+\)1 & 1.03e\(-\)4 \\ Our Method \(\ MM_{8}^{1}\) Equation (23) & 1.09e\(-\)1 & 7.44e\(-\)7 & 5.53e\(-\)13 \\ Our Method \(\ MM_{8}^{2}\) Equation (24) & 2.61e\(-\)3 & 4.05e\(-\)25 & 1.40e\(-\)199 \\ Our Method \(\ MM_{8}^{3}\) Equation (25) & 2.52e\(-\)3 & 3.94e\(-\)25 & 1.43e\(-\)199 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different methods for non-smooth function \(g_{2}(x)\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Methods**} & \(|g_{2}(x_{1})|\) & \(|g_{2}(x_{2})|\) & \(|g_{2}(x_{3})|\) \\ \hline Method \(\ KTM_{8}\) Equation (26) & D & D & D \\ Method \(\ SM_{8}^{1}\) Equation (27) & D & D & D \\ Method \(\ SM_{8}^{1}\) Equation (27) & D & D & D \\ Method \(\ SM_{8}^{2}\) Equation (28) & 2.63e\(-\)1 & 6.16e\(-\)7 & 1.87e\(-\)40 \\ Method \(\ SM_{8}^{2}\) Equation (29) & 0.786e\(+\)3 & 0.12e\(+\)1 & 1.03e\(-\)4 \\ Our Method \(\ MM_{8}^{1}\) Equation (23) & 1.09e\(-\)1 & 7.44e\(-\)7 & 5.53e\(-\)13 \\ Our Method \(\ MM_{8}^{2}\) Equation (24) & 2.61e\(-\)3 & 4.05e\(-\)25 & 1.40e\(-\)199 \\ Our Method \(\ MM_{8}^{3}\) Equation (25) & 2.52e\(-\)3 & 3.94e\(-\)25 & 1.43e\(-\)199 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different methods for non-smooth function \(g_{2}(x)\).
As the maximum step-size that ensures convergence with a sequential PU algorithm is \(N\) times larger than the maximum step-size for a full updates adaptive algorithm, one can introduce a parameter called gain in step-size, that determines the factor by which the step-size \(\mu\) can be multiplied to improve the convergence rate of the sequential PU adaptive algorithm. Note that the theoretical analysis of the strategy developed in [29] excludes the use of certain frequencies corresponding to notches appearing in the gain in step-size whose width and exact location depend on the system parameters, namely the decimation factor, the sampling frequency and the length of the adaptive filter. In this paper, our purpose is hence to study the relevance of the combination of the Mod FxLMS, the HLMS and the sequential PU LMS with gain in step size. The resulting ANC approach is called the modified filtered-x hierarchical sequential PU LMS algorithm with gain in step-size (G\({}_{\mu}\)--Mod Fx H Seq LMS). The other contributions of this paper consist in:(1) deriving the theoretical gain in step-size of the control strategy. It is defined as the ratio between the upper bounds on the step-sizes evaluated in the two following cases: when only a subset of the weights of the hierarchical filter proposed by [PERSON] [25] is updated at each iteration and when every tap--regardless the position of the weight in the hierarchy of subfilters--is updated at every cycle. We will see that the frequency response of this gain in the step-size exhibits notches. Their width and location depend on the length of the slowest subfilter of the hierarchy, the decimation factor, and the sampling frequency. Therefore, this phenomenon has to be taken into account when the input signal contains harmonics at frequencies corresponding to the location of the notches;  
(2) carrying out computer-based experiments to confirm that the predicted theoretical gain in step of the G\({}_{\mu}\)--Mod Fx H Seq LMS algorithm is well suited to the maximum affordable increase in step-size obtained by simulations;  
(3) completing additional computer-based simulations to test the performance of the G\({}_{\mu}\)--Mod Fx H Seq LMS algorithm for active attenuation of periodic disturbances. The paper is organized as follows. In Section 2, we propose the modified filtered-x hierarchical sequential PU LMS algorithm with gain in step-size. Section 3 deals with the convergence analysis of the proposed algorithm. The approach consists in applying to the hierarchical filter used in our proposal the results provided in the Appendix A for a conventional adaptive FIR filter. Results of computer-based simulations are provided in Section 4. We carry out a comparison between the theoretical prediction and the experimental behavior of the proposed algorithm. The experiments also include a comparative study of various ANC strategies in terms of convergence rate and computational complexity. Section 5 is devoted to discussion.
## 2 The Classical Exact Methods  
In this section, modal superposition method [18] and [PERSON]'s method [19] are briefly reviewed for calculating the eigenvector sensitivity. Assuming structural stiffness and mass matrices with \(n\) degrees of freedom (DOFs) are \(K\) and \(M\), the eigenvalue and eigenvector of the system can be obtained by solving the following generalized eigenvalue problem as:  
\[(K-\lambda_{j}M)\phi_{j}=0 \tag{1}\]  
\[\phi_{j}^{T}M\phi_{j}=1 \tag{2}\]  
where \(\lambda_{j}\) and \(\phi_{j}\) are the \(j\)th eigenvalue and eigenvector, \(j=1,2,\cdots,n\). Apparently, \(K\),\(M\),\(\lambda_{j}\) and \(\phi_{j}\) are the functions of the design parameters \(p_{i}\) such as elastic modulus, cross sectional area and density. By taking partial derivative of Equation (1) with respect to \(p_{i}\), one has:  
\[(K-\lambda_{j}M)\frac{\partial\phi_{j}}{\partial p_{i}}=(\frac{\partial\lambda _{j}}{\partial p_{i}}M+\lambda_{j}\frac{\partial M}{\partial p_{i}}-\frac{ \partial K}{\partial p_{i}})\phi_{j} \tag{3}\]  
where \(\frac{\partial\lambda_{j}}{\partial p_{i}}\) is defined as the first-order eigenvalue sensitivity and \(\frac{\partial\phi_{j}}{\partial p_{i}}\) is defined as the first-order eigenvector sensitivity. By transposing Equation (1) and using the symmetry of \(K\) and \(M\), one has:  
\[\phi_{j}^{T}(K-\lambda_{j}M)=0 \tag{4}\]  
Multiplying Equation (3) by \(\phi_{j}^{T}\) and using Equations (2) and (4), one can obtain the calculation formula of the eigenvalue sensitivity as:  
\[\frac{\partial\lambda_{j}}{\partial p_{i}}=\phi_{j}^{T}\frac{\partial K}{ \partial p_{i}}\phi_{j}-\lambda_{j}\phi_{j}^{T}\frac{\partial M}{\partial p_{ i}}\phi_{j} \tag{5}\]Apparently, Equation (5) is simple and easy to program. Next, it focuses on the calculation method of eigenvector sensitivity. As mentioned before, the calculation formula of eigenvector sensitivity is relatively complex. The modal superposition method and [PERSON]'s method are briefly described as follows:
# Traffic-Predictive Routing Strategy for Satellite Networks
## 6 Simulation Analysis
### Simulation Scenario  
The literature [25] proposes a simple and efficient Enhanced_AODV routing scheme (E-AODV), designed to enhance the performance of the AODV routing protocol by establishing the most stable and reliable route from the source node to the destination node. The literature [26] introduces a reactive-based DSR (DS-DSR) approach, confirming its capability to consistently provide packet transmission. The literature [27] suggests a Neighbor Coverage Multi-path DSDV (NCMDSDV) protocol as a measure to recover from link failures. This protocol can create multiple paths to the destination node, ensuring data integrity.  
In scenarios with varying packet transmission rates and numbers of communication connections, we conducted comparative experiments on four strategies: G-AODV, E-AODV, DS-DSR, and NCMDSDV. This aims to provide more references and evaluations for the performance of the G-AODV protocol. Following the parameter settings required by the STK satellite network simulation tool, the operational speed of the satellite nodes is set to 7.9 km/s, equivalent to the first cosmic velocity. To simulate the motion status of satellite nodes in low orbit, a pause time of 0 s is set.
For the proposed CMUDPD, a single feedback path is utilized, which is sufficient to capture the nonlinear correlation of all PAs. Notably, PAs operate in different modes while at the same compression point, where the supply voltages, bias voltages, and input power levels are different. In Table 1, the proposed CMUDPD is compared with several representative state-of-the-art DPD methods. Several DPD technologies studied in [5; 10; 12; 24] consider uniform arrays. Although parametric variations in \(P_{\text{in}}\) are involved in [10; 12], their amplitudes are relatively small compared to those in low-sidelobe arrays. In [10], the resultant average DPD coefficients were obtained by training a set of random amplitude and phase data following the normal distribution. For the array DPD method studied in [20] for low-sidelobe arrays, it is mandatory to synthesize all PAs' outputs by two steps, i.e., linearizing the higher driven PAs, followed by cancelling compression by the lower driven PAs, to form a single feedback signal. Consequently, its linearization performance is highly dependent on the consistency of the PAs, and a large amount of couplers is required. Compared with [20], the proposed CMUDPD does not require the synthesis of all PAs' outputs, and an arbitrary PA's output can be used as the feedback signal. Moreover, different operation modes with variations in PA supply voltage \(V_{\text{DD}}\), bias voltage \(V_{\text{GG}}\), and input power \(P_{\text{in}}\) can be fully linearized using the proposed CMUDPD. Compared to our conference publication [29], this paper presents the theoretical foundation explaining why the proposed CMUDPD can linearize different PA operation modes, as explained in Section 2. Moreover, experimental results are presented to show that the proposed CMUDPD can achieve an averaged adjacent channel power ratio (ACPR) suppression of 13 dB. This paper is organized into five sections. Section 2 introduces the principle of the proposed CMUDPD scheme. The simulation setup and results are illustrated in Section 3. Section 4 presents the experimental results and discussion. Finally, the conclusion of this work is provided in Section 5.
## 5 Implementation  
After logically defining three reference architectures, this section will show the technologies used to implement them, mapping them for each component. Only then, before proceeding with simulations, will different applications be developed to demonstrate the correct design for each of the three architectures. Each application will be a concrete example of use, showing how each architecture behaves in real-life usage contexts.
## 4 Results of Measurements
Analyzing Figure 15a, it should be noted that the highest peak-to-peak value of the input current I\({}_{\text{inpp}}\) = 0.82 A was obtained for output voltage V\({}_{\text{out}}\) = 20 V, and the lowest value I\({}_{\text{inpp}}\) = 0.15 A for V\({}_{\text{out}}\) = 5 V.  
In Figure 15b, it can be seen that the largest normalized values of harmonics of the spectrum of current drawn from the power network were obtained for output voltage V\({}_{\text{out}}\) = 20 V. In turn, for output voltage V\({}_{\text{out}}\) = 5 V, only the first and second harmonics with the maximum normalized value could be observed. The subsequent harmonics had increasingly smaller amplitudes. The results presented in Figures 14 and 15 show that the input current of the charging system is much less distorted when operating at low output voltage and high load resistance. Unfortunately, these are not typical operating conditions for such a system and can be observed when the load is a low-voltage battery in a state close to full charge. The investigations results presented in this section refer only to room temperature. As demonstrated in the paper [27], temperature significantly affects the characteristics of charging and discharging of batteries and their electric capacity. Similarly, in the paper [42], it was found that the properties of switch-mode power supply systems can significantly change when the junction temperature of their components changes. However, thanks to the use of specialized control systems and compensatory properties of the feedback loop, it is possible to ensure the desired parameters of the battery charging current.
# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 5 Challenges and Future Directions
### Future Directions
#### 5.2.3 Advanced Simulation Methods  
Numerical simulation techniques, including FEM, SPH, FDM, DEM, FDEM, and CEL, offer advanced tools for studying soil-structure interactions. These methods enable precise analysis of stress, displacement, and failure mechanisms in both homogeneous and heterogeneous soil conditions as discussed in the computational section. However, the coupling between different simulation methods in scenario of open caisson remains unexplored. Understanding the relationships between soil behavior and structural responses during excavation operations requires the integration of mechanical and hydraulic analysis into numerical simulation methods for open caisson excavation shafts.  
In future research, these methods should be coupled, for example, using FEM for structural mechanics and FDM for fluid dynamics. Engineers can more accurately forecast how caissions will behave in various scenarios by using these coupled numerical techniques, which will improve safety and construction approaches.
# Approaches for Dealing with Seasonality in Clinical Prediction Models for Infections
## 4 Experiments and Results
### Analysis of the Effects of Different Approaches in Combination
#### 4.4.1 Synthetic Dataset--Condensed
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Filter** & **Balancing** & **Seasonal Drift Approach** & **Mean AUC** & **Mean Features per Model** \\ \hline None & None & 3-month window & 0.992 (0.991, 0.992) & 7.907 (7.665, 8.150) \\ \(p\)-Value & None & 3-month window & 0.991 (0.990, 0.993) & 6.112 (5.934, 6.291) \\ \(p\)-Value & Undersample 2:1 & 5-month window & 0.989 (0.988, 0.989) & 8.742 (8.574, 8.909) \\ None & Undersample 2:1 & 5-month window & 0.988 (0.987, 0.988) & 9.991 (9.777, 10.204) \\ \(p\)-Value & Undersample 1:1 & 5-month window & 0.988 (0.987, 0.988) & 8.287 (8.122, 8.452) \\ \(p\)-Value & None & 5-month window & 0.988 (0.987, 0.988) & 9.057 (8.874, 9.241) \\ \(p\)-Value & Undersample 2:1 & 3-month window & 0.987 (0.985, 0.990) & 6.197 (6.023, 6.370) \\ None & None & 5-month window & 0.987 (0.987, 0.988) & 10.280 (10.051, 10.509) \\ None & Undersample 1:1 & 5-month window & 0.987 (0.986, 0.988) & 9.542 (9.333, 9.750) \\ None & Undersample 2:1 & 3-month window & 0.987 (0.984, 0.989) & 7.533 (7.278, 7.789) \\... &... &... &... \\ None & None & None & 0.919 (0.918, 0.919) & 9.600 (9.114, 10.086) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Top ten results obtained on the _sinusoidal_ synthetic dataset with logistic regression models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Filter** & **Balancing** & **Seasonal Drift Approach** & **Mean AUC** & **Mean Features per Model** \\ \hline None & None & 7-month window & 0.671 (0.664, 0.678) & 10.532 (10.034, 11.029) \\ None & None & 5-month window & 0.670 (0.664, 0.677) & 10.793 (10.357, 11.229) \\ \(p\)-Value & None & 7-month window & 0.667 (0.660, 0.675) & 9.562 (9.284, 9.839) \\ \(p\)-Value & None & 5-month window & 0.667 (0.660, 0.674) & 9.411 (9.156, 9.666) \\ None & None & Season as feature & 0.665 (0.659, 0.670) & 12.990 (12.107, 13.873) \\ None & None & 9-month window & 0.664 (0.657, 0.671) & 10.344 (9.780, 10.909) \\ \(p\)-Value & None & 9-month window & 0.663 (0.657, 0.670) & 9.272 (9.003, 9.540) \\ \(p\)-Value & None & Season as feature & 0.663 (0.658, 0.668) & 10.400 (9.957, 10.843) \\ \(p\)-Value & Undersample 2:1 & Season as feature & 0.662 (0.656, 0.668) & 8.740 (8.309, 9.171) \\ None & Undersample 2:1 & Season as feature & 0.661 (0.654, 0.669) & 10.350 (9.475, 11.225) \\... &... &... &... \\ None & None & None & 0.660 (0.654, 0.666) & 11.050 (10.193, 11.907) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Top ten results obtained with logistic regression models on the _Acinetobacter_ spp. dataset extracted from MIMIC-III.