esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
## 3 Results and Discussion
### Results under Low-Pass Filtering  
Figure 4 shows the results for the DESED Public evaluation set after applying low-pass filtering with \(f_{c}=2000\) Hz, \(f_{c}=1000\) Hz and \(f_{c}=500\) Hz. The performances decrease when lowering the cutoff frequency of the filter, which is the expected behavior. When using a cutoff frequency \(f_{c}=2000\) Hz, the best overall performance is obtained by the multiresolution system _5 res_, whereas for \(f_{c}=1000\) Hz both _3 res_ and \(T_{++}\) achieve the highest macro \(F_{1}\). When the cutoff frequency is set to \(f_{c}=500\) Hz, the best macro \(F_{1}\) scores are obtained with the single-resolution systems \(T_{+}\) and \(T_{++}\).  
Figure 3: \(F_{1}\) scores of single-resolution systems (\(F_{++},F_{+},BS,T_{+},T_{++}\)) and multiresolution systems (_3 res_, _5 res_) over DESED Public Evaluation set applying a high-pass filter with cutoff frequencies of 500 Hz (**top**), 1000 Hz (**center**), and 2000 Hz (**bottom**). “Macro F1” shows average score over 10 target categories. Best viewed in color.
# Effect of Wing Corrugation on the Aerodynamic Efficiency of Two-Dimensional Flapping Wings
## 3 Effect of Wing Corrugation on the Two-Dimensional Flapping Wing
### Effect of Corrugation at \(r=0.375R\)
This can explain the reduction in lift coefficient of all wings. However, the changes in lift coefficient of the four corrugated wings are significant at \(t=(0.5\) and \(1.0)T\). The difference in lift coefficient can be explained by the pressures acting on the surface in the leading edge of each wing, which are discontinuous due to the triangular shape of the corrugation. At \(t=(0.5\) and \(1.0)T\), the pressures acting on the surfaces of the S and Z wing are almost horizontal or slightly downward. However, due to the larger corrugation in the leading edge of the LE1, LE2, and LE3 wings, most of the pressures acting on the surfaces in the leading edges of these wings are oriented upward. Furthermore, the formation of the local vortex causes suction pressure on the upper surfaces of each edge in the corrugations. These explain the increase in lift coefficient of the three leading-edge corrugated wings, especially the LE3 wing. As can be seen at \(t=0.6T\), the formation of the LEVs can explain the high lift coefficient of all wings. The differences in lift and drag coefficients are due to the flow fields and swirling strength in the leading edges of each wing. The LEV formation of the Z wings is quite similar to that of the S wing. However, the LEVs of the three leading-edge wings are stronger, with higher velocities at the leading edge of those wings compared to that of the S wing, which may explain the increase in lift coefficient of the three corrugated wings at \(0.6T\). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & & \multicolumn{2}{c}{**Z**} & \multicolumn{3}{c}{**LE1**} & \multicolumn{3}{c}{**LE2**} & \multicolumn{3}{c}{**LE3**} \\ \cline{2-13}  & \(\mathbf{C_{L}}\) & \(\mathbf{C_{D}}\) & \(\mathbf{C_{L}/C_{D}}\) & \(\mathbf{C_{L}}\) & \(\mathbf{C_{D}}\) & \(\mathbf{C_{L}/C_{D}}\) & \(\mathbf{C_{L}}\) & \(\mathbf{C_{D}}\) & \(\mathbf{C_{L}/C_{D}}\) & \(\mathbf{C_{L}}\) & \(\mathbf{C_{D}}\) & \(\mathbf{C_{L}/C_{D}}\) \\ \hline Downstroke (\%) & \(-0.2\) & \(0.0\) & \(-0.1\) & \(2.0\) & \(-0.7\) & \(2.7\) & \(2.8\) & \(-1.6\) & \(4.5\) & \(1.8\) & \(-4.1\) & \(6.1\) \\ Upstroke (\%) & \(-5.1\) & \(-3.3\) & \(-1.9\) & \(1.0\) & \(-1.6\) & \(2.7\) & \(1.5\) & \(-2.6\) & \(4.3\) & \(5.6\) & \(-4.1\) & \(10\) \\
1 cycle (\%) & \(-2.6\) & \(-1.6\) & \(-0.9\) & \(1.5\) & \(-1.1\) & \(2.7\) & \(2.1\) & \(-2.1\) & \(4.3\) & \(3.6\) & \(-4.1\) & \(7.9\) \\ \hline \end{tabular}
\end{table} 
Table 1: Changes in the average lift coefficients (\(\mathbf{C_{L}}\)), drag coefficients (\(\mathbf{C_{D}}\)), and gliding ratios (\(\mathbf{C_{L}/C_{D}}\)) of the Z, LE1, LE2, and LE3 wings, compared to those of the S wing, at \(r=0.375R\).
# Anti-Inflammatory Mechanisms of Novel Synthetic
## 5 Anti-Inflammatory Mechanisms of Ruthenium Compounds
### Targeting NF-kB and Nrf in Anti-Inflammatory Ruthenium Compounds  
Nuclear factor kappa B (NF-kB) transcription factor is an upstream regulator of inflammatory mediator NO, and a major target for treating inflammatory diseases [46]. Evidence has proved that NF-kB activation in microglia stimulates the expression of proinflammatory cytokines and activators [47]. Inhibition of NF-kB signaling in microglia was proposed as the major target of drugs to exhibit the protective effect against the neuropathology of ischemic stroke [48]. As shown in Figures 2 and 3, LPS induces NF-kB  
Figure 4: In vivo anti-inflammatory mechanism of TQ-4: TQ-4 effectively reduced LPS-induced serum alanine transaminase (ALT), aspartate transaminase (AST), TNF-\(\alpha\), IL-1\(\beta\), iNOS, NF-\(\kappa\)B p65 and JNK phosphorylation in LPS-induced mice liver tissues.  
activation and its nuclear translocation and \(\mathrm{IkB\alpha}\) degradation in LPS treated macrophages was found to be recovered by TQ-4 [39] and TQ-6 [42].  
Nuclear factor erythroid 2-related factor 2 (Nrf2), a key transcription factor, plays a vibrant role in controlling oxidative stress-mediated inflammatory responses. Normally, Nrf2 is stored in the cytoplasm with its inhibitor Keap1. In an activated state, like NF-kB, this transcription factor translocates into the nucleus and regulates the transcription of antioxidant enzyme HO-1 [49]. An investigation stated that Nrf2 deficient mice are more vulnerable to LPS-induced neuroinflammation [50]. Additionally, Nrf2 activation in microglia attenuates neuroinflammation stimulation via inhibiting NF-kB-associated inflammatory mediators [14]. For instance, thymoquinone, a major component of _Nigella sativa_ inhibits neuroinflammation by activating Nrf2 and inhibiting NF-kB [51]. Thus, we also explored the anti-inflammatory effects of TQ-6 through NF-kB and Nrf2 signaling; the results determined that TQ-6 inhibited NF-kB expression and its nuclear translocation, and also increased the expression of Nrf2 and HO-1 [42]. Nrf2 protects brain cells from ischemic stroke injury, as evidenced by increasing cerebral infarction ratio and neurological deficits score in Nrf2 knockout ischemic reperfusion induced rats [52]. The novel ruthenium compound TQ-6 potentially reduced brain infarct volume and edema in middle cerebral artery occlusion (MCAO) induced mice (Figure 5) [42]. Consequently, the protective effect of TQ-6 in microglia by regulating Nrf2/HO-1 could be regarded as an ideal therapeutic target for the treatment of stroke associated inflammatory events.
# Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach
## 3 Energy-Efficiency Model of the Electric Drive
### Fitting Process of the Motor and Converter Characteristics  
The polynomial fitting is essential to capture the complex relationship between speed, torque, and losses so that we can create highly accurate efficiency maps. The loss data for the motor and the converter were fitted to a polynomial surface model in Matlab [50]. The model has the form  
\[P_{loss}(n,M)=\sum_{i=0}^{K}\sum_{j=0}^{L}a_{ij}n^{i}M^{j}\text{ for }i+j\leq\max(K,L), \tag{5}\]  
where the number of terms is determined by the degree of the speed and torque inputs. If \(K\) is the degree of the speed and \(L\) is the degree of the torque, the total degree of each polynomial term must not exceed the maximum of \(K\) and \(L\).  
Polynomial surface fitting has its limitations, especially when applied to sparse data and complex nonlinear relationships. With sparse data, the model tends to extrapolate into regions where there are not enough data points, leading to inaccurate predictions and poor generalisation. Polynomials as smooth functions have difficulty capturing sharp transitions or complex non-linear behaviour, which can lead to underfitting for low-degree polynomials and overfitting for higher-degree polynomials. Careful selection of the polyno  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Combined with** & **Output Power** & **Apparent Power** & **Max. Current** & **Efficiency Level** \\
**Motor** & **(kW)** & **(kVA)** & **(A)** & **Efficiency Level** \\ \hline
15 kW IE3 & 15 & 22 & 32 & IE2 \\
15 kW IE5 & 15 & 24 & 35 & IE2 \\
18.5 kW IE3 & 18.5 & 26 & 38 & IE2 \\
18.5 kW IE4 & 18.5 & 30 & 43 & IE2 \\
22 kW IE5 & 18.5 & 30 & 43 & IE2 \\
30 kW IE1 & 30 & 42 & 61 & IE2 \\ \hline \end{tabular}
\end{table}
Table 3: Selected converters.  
mial degree and alternative techniques such as regularisation or machine-learning models may be necessary to mitigate these problems. In our case, however, the input data are not sparse and the relationships between the variables are not particularly complex. The system behaviour could be approximated to some extent by known physical relationships, making the polynomial surface-fitting method suitable for this application.  
To find the solution, a robust least squares method called Least Absolute Residual (LAR) is used, which minimises the absolute difference of the residuals and is therefore less sensitive to outliers. The data were normalised and centred before fitting.  
Figure 5 shows the RMSE for the different degrees of speed and torque. Fifth-degree polynomials offer no advantage in terms of RMSE (in the case of the converter, their performance is worse). However, examination of the residuals has shown that the worst performance (relatively speaking) occurs at high speeds and torques, which could be mitigated by a higher-degree polynomial. As the operating points of the pump drive fall within this range, it was decided to use a fourth-degree polynomial for speed and a fifth-degree polynomial for torque for all motors and converters.
# Present and Future Marine Microwave Satellite Missions in China
## 3 Progress History of China's Marine Microwave Satellite Remote Sensing Technology
### Calibration Technology for Marine Dynamic Environmental Satellites  
The calibrations of satellite remote sensors are key to the quantitative applications of data. In the research regarding calibration methods, [PERSON] and [PERSON] [58] summarized the distributions of calibration fields, as well as calibration methods for the backscattering coefficient and sea surface heights of satellite radar altimeters. In the Yellow Sea, the measured tide gauge data were extrapolated into sub-satellite points to calibrate the sea surface heights, thereby providing a new method for the on-orbit calibrations of radar altimeters. [PERSON] et al. [59] analysed the factors affecting the accuracy of GPS buoy height measurements, and accumulated experience for the use of GPS buoys for the absolute calibrations of sea surface heights. [PERSON] et al. [60] summarized the main technologies and developmental trends of satellite altimeter calibrations in China and internationally and put forward suggestions on the construction and planning of China's calibration fields. After summarizing the methods and content of satellite radar altimeter calibrations and tracking and investigating the configurations of international altimeter calibration fields, [PERSON] et al. [14] put forward a basic idea for the construction of China's marine satellite altimeter calibration field. In addition, the geophysical parameters and hydro meteorological parameters affecting altimeter calibrations in sea areas around the calibration fields were preliminarily analysed, and a basic scheme for the configuration of China's calibration field was successfully determined. As a result, the foundation for the construction of China's satellite radar altimeter calibration field was laid out.
## 3 Results  
### Comparison of Etched and Au Gratings  
A set of transmission spectra through samples from the wafer with two QWs is shown in Figure 4. In unprocessed sample with no periodic structure on the surface, we observe signatures of the CR transition, indicated with vertical bars. Plotting positions (in \(B\)) of minima of the CR as a function of the energy of photons allowed us to estimate an effective electron mass to be equal to 0.103 \(m_{e}\), which is the same as that determined on other CdTe/CdMgTe QWs in previous studies [23; 24].  
Comparing results presented in this figure one can notice that in the case of the Au grating, the plasmonic shift is very small and in some cases its presence is not evident. On the other hand, it is quite well visible in the etched sample. Another general observation visible in Figure 4 concerns the shape of the lines which apparently carries features of a dispersive profile. We attribute this fact to interferences in the structure, an effect which was described long ago [34]. On the other hand, we cannot exclude a possible influence of diffraction of THz radiation on gratings on the lineshape since diffraction changes conditions of propagation of the radiation and thus contributes to the overall interference pattern. In the case of Au gratings, one could fabricate structures with different thickness of gratings. In the case of etched gratings, one could think about dry etching which should lead to more sharp profiles of etched grooves. Then, details of the diffractive structures could be modified and the impact of modifications on the lineshape could be studied.  
Figure 4: Transmission spectra of samples from the wafer B. In the case of both etched and Au gratings, the period of the grating was equal to 8 μm and \(\alpha\approx 0.5\) (see Appendix A for a definition of \(\alpha\)). Vertical bars indicate the position of the CR. The wavelenght of radiation used is indicated in the figure.  
Figure 3: A dependence of the depth of etched grooves on their width at a constant time of etching equal to 60 s. Error bars show uncertainity given by measurements with the AFM.  
A clear plasmonic shift in Au-grating samples is visible in spectra with a grating of a shorter period, as it is presented in Figure 5. This is in accordance with Equation (A1) with \(k\) inversly proportional to the period of grating. Also, plasmonic resonances visible in the spectra of etched samples show the amplitude much smaller than that for other two types of samples. Below we show that the amplitude of the resonance is strongly dependent on the depths of grooves in the grating.
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 2 Materials and Methods
### Methods
#### 2.2.3 Unidirectional Interaction Module  
A previous study [29] has demonstrated that the feature information interaction between the speech and noise branches can effectively enhance the modeling capabilities for speech and noise, leading to better recovery of the target speech and pure noise signals. Nevertheless, the bidirectional information interaction mechanism implies that during the practical deployment, the SE model must conduct forward propagation calculations in both the speech and noise branches. This results in a substantial growth in model size and computational complexity compared to conventional single-branch DNN-based methods. In this work, we introduce a unidirectional interaction module, as illustrated in Figure 4. This module exclusively facilitates unidirectional feature transfer from the speech branch to the noise branch.  
Figure 4: Structure of the unidirectional interaction module.  
Figure 3: The diagram of time-frequency sequence modeling (TFSM) block. During temporal sequence modeling, a causal gated recurrent unit (GRU) layer is employed in the speech branch, while a non-causal bidirectional GRU (BiGRU) layer is utilized in the noise branch.  
In the proposed unidirectional interaction module, the intermediate feature representation \(\mathcal{F}_{s}\) from the speech branch is fed into a 2D convolutional gated linear unit (ConvGLU) [15], which extracts the interactive feature information \(\mathcal{F}^{inter}\) as follows:  
\[\mathcal{F}^{inter}=(W_{1}*\mathcal{F}_{s}+b_{1})\otimes\sigma(W_{2}*\mathcal{ F}_{s}+b_{2}) \tag{10}\]  
where \(*\) represents convolution operations with kernels \(W_{1}\in\mathbb{R}^{5\times 2}\) and \(W_{2}\in\mathbb{R}^{5\times 2}\), as well as biases \(b_{1}\in\mathbb{R}^{5\times 2}\) and \(b_{2}\in\mathbb{R}^{5\times 2}\). \(\otimes\) and \(\sigma\) denote element-wise multiplication and the sigmoid function, respectively.  
Afterwards, \(\mathcal{F}^{inter}\) is concatenated along the channel dimension with the intermediate feature representation \(\mathcal{F}_{z}\) from the noise branch to obtain the integrated feature \(F_{z}^{intreg}\) as follows:  
\[\mathcal{F}_{z}^{intreg}=[\mathcal{F}_{z};\mathcal{F}^{inter}] \tag{11}\]  
Finally, the integrated feature \(F_{z}^{intreg}\) is sent to subsequent noise modeling network.  
Therefore, in the model training stage, the gradient information related to noise modeling results can be backpropagated into the speech branch through the unidirectional interaction modules, thereby updating the speech branch's parameters. This further promotes the optimization of the speech branch.
# Underwater Drone Architecture for Marine Digital Twin: Lessons Learned from SUSHI DROP Project +
## 2 Materials and Methods  
The approach followed for the development of the UUV was to produce a complete and modular prototype system, called Blucy, fully customized in hardware and software for scientific surveys, instead of using a commercial drone with black-box operation.  
Numerous marine robots have been developed in recent decades [36]. In general, UUVs can be divided into the following categories: Remotely Operated Vehicles (ROV) [37; 38], Autonomous Underwater Vehicles (AUV) [39; 40] or hybrids [41]. The new Blucy prototype (Figure 1) falls into the latter category, according to an approach that has made it possible to develop a drone at a considerably lower cost than comparable commercial vehicles.  
Figure 1: UUV prototype, Blucy and fiber optical cable during first tests in open sea.  
This choice is dictated by the desire to maintain a dual option of autonomous navigation, but at the same time the ability to transmit high-resolution images, video stream, acoustic measurements and any other data of interest in real time. This need is particularly useful for the study of living organisms. The ROV mode is particularly useful for missions in complex areas, while the AUV mode carried out in complete autonomy allows to obtain information on large area such as the morphology of the seabed to be investigated in further detail.
# Parameter Optimization Based BPNN of Atmosphere Continuous-Variable Quantum Key Distribution
## 4 Performance Analysis  
In this part, we analyze the key rate that can be realized according to the consequences of Sections 2 and 3. The secret key rate is evaluated by the Monte Carlo method. The excess noise  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **L** & \(\sigma_{R}^{2}\) & \(\langle T\rangle\) & \(C_{n}^{2}\) & \(V_{A}\) & **K** \\ \hline Optimized & 2 km & 2.3269 & 0.8597 & \(2\times 10^{-15}\) & 0.70 & 0.2550 \\ BPNN & 2 km & 2.3269 & 0.8597 & \(2\times 10^{-15}\) & 0.6975 & 0.2556 \\ Optimized & 4 km & 8.2922 & 0.3859 & \(2\times 10^{-15}\) & 0.1160 & 0.0388 \\ BPNN & 4 km & 8.2922 & 0.3859 & \(2\times 10^{-15}\) & 0.1161 & 0.0391 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimal vs. back-propagation neural network (BPNN)-predicted parameters. The modulation variance \(V_{A}\) is the BPNN-predicted parameter, and key rate \(K\) is generated by applying the BPNN predicted \(V_{A}\). The parameters \(L\), \(\sigma_{R}^{2}\), \(\langle T\rangle\) and \(C_{n}^{2}\) are the systems conditions. Moreover, \(C_{n}^{2}\) and \(\sigma_{R}^{2}\) can be converted by the formula \(\sigma_{R}^{2}=1.23C_{n}^{2}k^{(7/6)}L^{(11/9)}\), hence the input dimension is 3 instead of 4.  
Figure 5: (**a**) comparison of BPNN-predicted and optimized modulation variance \(V_{A}\); (**b**) comparison of the key rate generated using the BPNN predicted parameter and optimized key rate. The excess noise \(\varepsilon\) is set as 0.01, detector efficiency \(\eta=0.6\), and electrical noise \(v_{d}=0.01\).  
induced by the phase fluctuation is incapable of compensating precisely yet, and after adopting diverse valid compensation approaches, it is tough to evaluate the actual excess noise [50]. Therefore, we ignore the change of phase excess noise free space situations temporarily. However, we still study the key rates that can be achieved at different fixed excess noise levels: \(\varepsilon=0.01\) and \(\varepsilon=0.03\).  
The secret key rate with excess noise \(\varepsilon=0.01\) is illustrated in Figure 6a. As Figure 6a shows, the secret key rate of the system employing BPNN is higher than LSA, a little higher but not much. Then, the excess noise is set as \(\varepsilon=0.03\), and the performance is as portrayed in Figure 6b. In comparison with Figure 6a, the realized transmission distance is significantly shortened.  
The performance profiling in this section points out a few of pivotal cores. Above all, the BPNN based CVQKD system furnishes a higher achievable key rate and more efficient parameter optimization. Secondly, we find that transmittance fluctuations are negative to key rates. Therefore, the effect of beam wandering, broadening and deformation should be emphasized in practical experiments. Third, due to the considerable influence of excess noise, the phase excess noise will be much larger than 0.03; valid approaches to possess the phase excess noise are required to improve the key rate.