esa-sceva/satcom-chunk-collection huggingface dataset:
# mDARAL: A Multi-Radio Version for the DARAL Routing Algorithm
## 4 Simulation Testbed
### Metrics Overview
#### 4.1.3 Energy Consumption during the Set-Up Phase  
The energy consumption has been measured during the set-up phase in order to measure the impact of multi-radio approaches during the network forming phase. It is necessary to point out the importance of intelligent energy management methods in cooperative transmissions such as the multi-radio systems presented in [32] by [PERSON] et al. They demonstrated that, through the use of power-allocation methods, it is possible to reduce the total power consumption while maintaining the required QoS, which is highly necessary for these systems. For this study, the processing time for the channel selection has been excluded, focusing exclusively on connection time of the network, regardless of the multi-radio configuration.  
The different configuration parameters related to energy, such as battery capacity and consumption configuration, have been identically defined, obtaining directly comparable results in \(mWs\). The energy consumption has been measured through a variable that has stored the remaining energy in its battery, allowing for calculating the energy used during the experiment. The result is presented as the mean of the energy consumption for all nodes in the net.
## 2 Measuring Systems, Materials and Methods
### Measuring System and Samples  
#### 2.2.1 Samples  
Figure 8 displays a schematic representation of the sample along with its parameters. RC structures are defined by three key parameters: concrete cover thickness (\(h\)), rebar diameter (\(D\)), and class of reinforced steel. The class parameter indicates the ductility and strength of the alloy. Class A-I has the highest strength and lowest ductility, while A-IIIN has the lowest strength and highest ductility [1].  
Figure 7: The algorithm for extracting association rules from NDT data.  
The main tests are conducted on samples P1-P4 (Figure 8c). Each measurement is repeated a minimum of ten times. To further explore and validate the results from the main tests, supplementary measurements are performed using the eddy current (EC) method. The additional tests are mostly used to explore how the size of the EC transducer affects the outcomes and more accurately investigate association rules connected to the diameter (\(D\)) and thickness of the concrete cover (\(h\)) identification. The rebars used during supplementary measurements are presented in Figure 9.
## 4 Conclusions  
In this study, a centrifugal pump radiation noise measurement system was established in a soundproof room, then the changing rules of radiation noise in a wide range of frequencies and the proportion of noise in different frequency ranges to overall noise, as well as the noise at certain frequencies were studied under various rotational speeds. The main conclusions are drawn as follows:  
1. Sound pressure levels (SPLs) at different monitoring points show a fluctuating ascending trend in the low frequency range from 31.5 to 1000 Hz, then reach a maximum value between 1000 and 2000 Hz and this is also the main contribution range to overall noise. When the frequency is higher than 2000 Hz, SPLs decrease gradually. Additionally, SPLs at most frequencies of the monitoring points in the outlet direction are generally higher than those of other monitoring points.
2. The acoustic energy between 1000 and 2000 Hz accounts for more than half of the total acoustic energy and the proportion also increases by 22.61% when rotational speed increases from 1700 to 2900 rpm. While the acoustic energy between 0 and 1000 Hz accounts for about 0.30, the proportion in this range shows a decreasing trend and decreases by 38.55%. When frequency is higher than 2000 Hz, however, the radiation noise contributes very little to overall noise, the acoustic energy between 2000 and 8000 Hz accounts for about 0.12, but the proportion shows little change with the change of rotational speed.
3. With increasing rotational speed, total sound pressure levels (TSPLs) at various frequencies increase gradually and the increase of radiation noise at high frequency is higher than that at low frequency, specifically, when rotational speed increases from 1700 to 2900 rpm, TSPLs at 500, 1000, 2000, 4000 and 8000 Hz increase 8.72%, 13.46%, 10.77%, 14.01% and 17.66%, respectively.  
[PERSON] designed the study, conducted the experiment and collected the experimental data; [PERSON] analyzed the experimental data; [PERSON] wrote the manuscript; [PERSON] reviewed and edited the manuscript.  
This paper is supported by National Natural Science Foundation of China (No. 51776111) and Shandong Province Natural Science Foundation (No. ZR2016 EEM35).  
The authors declare no conflict of interest.  
Figure 7: Changes in available net positive suction head (NPSHa) and required net positive suction head (NPSHr) under various rotational speeds.
## 3 System Description
### Performance Evaluation Metrics
#### 3.5.2 Bit Error Rate  
In this study, since we have considered OOK-NRZ modulation such that light is transmitted to encode bit '1' and no light transmission occurs for bit '0', therefore, assuming the rectangular bit shape whose pulse duration equals the bit period, the BER is given by [10; 11]:  
\[BER=Q(\sqrt{SNR}) \tag{24}\]  
where Q(.) is the Q-function which defines the error probability and it is given by [5]:  
\[Q(x)=\frac{1}{2\pi}\int_{x}^{\infty}e^{\frac{-x^{2}}{2}}du \tag{25}\]  
For instance, the SNR that is required to achieve our targeted BER of \(10^{-9}\) is 15.6 dB.
## 2 Materials and Methods
### Train Test Split and Value of k in the Cross Fold Validation  
In this study, the entire dataset was split into training and testing subsets using an 80:20 ratio. To ensure balanced representation of the target variable--namely, the occurrence of cardiovascular disease (CVD)--stratified sampling was employed during the split. This method preserved the overall event rate across both sets, thereby maintaining consistency in class distributions. The event rate of CVD in the dataset was 24.93%, consisting of 2899 positive cases and 8728 negative cases. This results in a class imbalance ratio of approximately 1:3 (positive to negative), which presents a challenge for predictive modeling.  
To mitigate the effects of this imbalance and enhance model robustness, we implemented k-fold cross-validation with k set to 5, in conjunction with grid search for hyperparameter tuning. This approach allowed the model to be trained and validated on different subsets of the training data, reducing overfitting and improving generalizability. Within the training data, we further evaluated model performance under two resampling strategies to address class imbalance:  
Synthetic Minority Over-sampling Technique (SMOTE) was applied to synthetically increase the number of minority class samples.  
Random under-sampling was performed to reduce the number of majority class instances.  
Each strategy was tested independently to compare its effect on model performance. In addition to AUC-ROC, which remains the primary evaluation metric, we also calculated the Area Under the Precision-Recall Curve (AUPRC). AUPRC is particularly suited for imbalanced datasets, as it provides a more informative measure of model performance with respect to the minority class. This dual-metric evaluation ensures a comprehensive assessment of the classifiers' discriminatory ability and practical utility.
# Vibration Reduction on Circular Disks with Vibroacoustic Metamaterials
## 3 Experimental Characterization
### Non-Rotating State  
The circular disks as shown in Figure 9 are firstly investigated in the non-rotating state using the experimental setup described in Figure 10 to validate the functionality of the VAMM. For this purpose, the circular disks with and without the VAMM are excited with the automatic impact hammer at a radial distance of 137 mm from the center of the disk. The surface velocities are measured at 116 points each according to Figure 11. The measurement points are positioned in such a way that they are situated in between the resonators.  
Figure 10: Experimental setup for characterization of the circular disk.  
The measurement results can be seen in Figure 12. Here, the transfer function of the accelerometer is shown for three groups of measurement points located at the outer edge of the circular disk, in between the resonators, and in the inner area of the circular disk. As expected, the averaged transfer function of the points at the circumference does not exhibit a distinct stop band, as the vibrations excited at the excitation point can propagate freely along the circumference. When considering the middle and inner measurement points, a stop band in the frequency range of approx. 2000 Hz to 3000 Hz can be observed. The location of the stop band matches well with the numerical predicted stop band of the finite disk in between 2045 Hz and 2967 Hz.  
As observed in the numerical investigation, distinct peaks occur in the stop band region. The three numerically investigated modes are also present in the measured transfer function at 2452 Hz, 2710 Hz and 2794 Hz within the stop band frequency range. These peaks align well with the frequencies of numerically predicted modes and can be attributed to modes with high amplitudes in the circumferential region of the circular disk (see  
Figure 11: Measurement points (red) on the circular disk without (**a**) and with VAMM (**b**).  
Figure 12: Averaged transfer functions (accelerate) for the outer (**top**), middle (**middle**) and inner measurement points (**bottom**), both for a disk with VAMM and without. Sketch of the disks: Red: Evaluated measurement points. Blue: Excitation point.  
Figure 13). Since there are no resonators placed close to the circumference, these modes cannot be influenced.
# Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging
## 3 Results
### Parameter Analysis for \(p\)  
The parameter \(p\) describes an initial estimation of the amount of noise corruption in the data cube. It is the only real-valued parameter of LRMR's initial parameters. Hence, nonlinear optimization can be applied to determine the optimal initial value for \(p\). We present results for the nonlinear optimization method: the direct search method (i.e., the Nelder-Mead simplex algorithm [15]). We applied the method on the different test sets and varied the starting value for \(p\). The other parameters were set as \(r=7\), \(b=20\), and \(s=8\). We computed the optimization results of this section in MATLAB. In Figure 13, the negative PSNR values of the iteration steps of the nonlinear minimization method, the Nelder-Mead simplex algorithm, computed with respect to \(p\) are presented. We present the results for the Indian Pines data set. The starting value \(p=0.15\) was chosen.  
Figure 13: The function values of the iteration steps of the nonlinear optimization Nelder–Mead simplex algorithm with respect to \(p\) for the Indian Pines data set and starting value \(p=0.15\).  
\begin{table}
\begin{tabular}{c c c c} \hline \(b\) & **PSNR** & \(t_{Matlab}\) & \(t_{Pythun}\) \\ \hline
15 & 50.14 & 11.01 & 48.13 \\
16 & 49.96 & 11.58 & 83.56 \\
17 & 49.87 & 26.39 & 102.33 \\
18 & 49.73 & 26.41 & 124.62 \\
19 & 49.62 & 26.23 & 122.86 \\
20 & 49.42 & 24.71 & 127.33 \\
21 & 49.36 & 24.29 & 97.29 \\
22 & 49.23 & 23.37 & 92.63 \\
23 & 49.16 & 23.04 & 102.33 \\
24 & 49.07 & 21.16 & 179.84 \\
25 & 48.88 & 127.72 & 257.69 \\ \hline \end{tabular}
\end{table}
Table 3: The CPU times in seconds for computing the denoised cubes for different values of \(b\) in MATLAB \(t_{Matlab}\) and Python \(t_{Pythun}\).
# Mapping with Pleiades--End-to-End Workflow
## 6 Discussion  
Researchers and operators require robust and automatic workflows that yield highly accurate 3D mapping products from VHR multi-view stereo satellite data. Thus, this manuscript proposed an end-to-end workflow that yields the desired 3D mapping on the example of the Pleiades satellite constellation. The contributions w.r.t. the methodological development are as follows:  
1. Introduction of the complete end-to-end 3D mapping workflow.
2. RPC-based sensor model adjustment in object-space, employing [PERSON]'s method to solve the non-linear equation systems.
3. Epipolar rectification, where the method in Reference [54] was modified and the first time applied to optical satellite images.
4. Interpolative approach in the non-linear epipolar rectification for speed-up.
5. Combined Census transform with SAD matching cost for increasing the accuracy of image matching.
6. Explicit modeling of the function which is used to vary the penalty \(P_{2}\) in SGM.
7. Extension of classical SGM to a truncated SGM by limiting the local disparity range, resulting in significant speed-up, while yielding comparable accuracy.
8. Memory optimization in SGM by discretizing the disparity space image to 16 bits.
9. Explicit modeling of nodata values in the epipolar images during matching for speed-up.
10. Disparity prediction for increasing the robustness and efficiency in top-level image matching.
11. Subpixel normalization of the resulting disparity maps.
12. Extension of the local DSM fusion method to allow downsampling within the fusion step.
13. Novel multi-directional slope dependent DTM generation method, which extends the approach in Reference [93].  
Additionally, all steps of the workflow were evaluated by in-depth accuracy assessments based on a multitude of satellite and the related metadata. The assessment itself represents a main contribution of this work. Since the presented methods were implemented within a commercially available software interested audience can directly build upon our findings. Note that the presented workflow was already successfully applied by other research groups, for instance, in References [134; 135; 136; 4; 110]. Especially, the DTM generation was applied in References [137; 138; 139; 140; 141; 142; 143].
## 3 Proposed Audio Watermarking Method
### Feedback Process  
A feedback process was added to the watermark embedding procedure (Figure 1) to ensure that the watermark is embedded with enough strength. As illustrated in Figure 7, after embedding the watermark, the generated watermarked signal \(\overline{x}(t)\) is immediately input into the watermark extractor to calculate the \(\overline{D}_{i}\) values. By comparing the extracted \(\overline{D}_{i}\) and \(TH_{i}\), we can know whether the desired watermarking strength is achieved. If the strength is not enough, the gain g is increased by a small quantity \(\alpha\), and then it is put back into the watermark embedder to generate a stronger watermark signal. The feedback process works in an iterative manner.
## 5 Fermentation Processes  
### Fed-Batch Fermentation  
The microbial synthesis of 3-HP was mainly developed in laboratory-scale bioreactors using fed-batch fermentation. In these processes, the concentration of substrate is usually kept low, which prevents high substrate concentrations from inducing the production of by-products and inhibiting microbial growth [99]. Among the key enzymes commonly selected for the construction of the glycerol pathway in engineered strains, the activity of GDHt is higher than that of ALDH, and the accumulation of toxic intermediate 3-HPA can be prevented by limiting the glycerol concentration. _K. pneumoniae_ was found to be subject to significant substrate inhibition when the glycerol concentration was increased to above 40 g/L under anaerobic conditions [100]. Acetate must be maintained at a low concentration when used as substrate for 3-HP synthesis because a high concentration of acetate is toxic to cells. According to [PERSON] et al. [47], the initial concentration of acetate was only 1.4 g/L in fed-batch fermentation for the synthesis of 3-HP from acetate by engineered _E. coli_. A sugar-limited fed-batch fermentation that maintains basal levels of glucose and xylose in the medium was designed for 3-HP synthesis by engineered _E. coli_ via the glycerol pathway, because high concentrations of glucose and xylose would prevent the efficient conversion of glycerol to 3-HP [69].  
In addition to feeding strategies based on the substrate concentration, researchers have also developed alternative feeding strategies for 3-HP production. [PERSON] et al. [31] selected the pH-stat feeding strategy guided by pH in the production of 3-HP, which employs automated feeding when the pH rises above 7.02. To mitigate the metabolic stress response, a recirculating feeding method was developed for 3-HP production, in which the fermentation broth was centrifuged, mixed with fresh medium, and finally, returned to the original bioreactor [28].
## 2 2 DOF IMC for SRM
### Stability Analysis and Parameter Tuning  
For the closed-loop system, the necessary and sufficient condition for stabilization is:  
\[|F(j\omega)G_{n}(j\omega)|\bar{l}_{m}(j\omega)<1\qquad\forall\omega \tag{16}\]  
where \(\bar{l}_{m}(j\omega)\) is the upper limit of the model error and \(F(j\omega)G(j\omega)=\frac{1}{\lambda_{2}(j\omega)}\). Then,  
\[\bar{l}_{m}(j\omega)<|\lambda_{2}(j\omega)\mid\qquad\forall\omega \tag{17}\]  
For a certain modeling error upper bound \(\bar{l}_{m}\), if the inequality condition (17) can be satisfied [23], the stability of the system can be guaranteed. Furthermore, the larger \(\lambda_{2}\), the greater the allowed range of error between the model and process model. As a result, the selection of the parameter \(\lambda_{2}\) not only determines system stability but also affects the control performance.  
According to (15), the set-point tracking of the 2 DOF IMC is affected by \(\lambda_{1}\) and \(\lambda_{2}\), while only \(\lambda_{2}\) determines disturbance rejection. As a result, in this paper, the \(\lambda_{2}\) is adjusted firstly, and then the \(\lambda_{1}\) is determined.  
With reference to [25], the \(\lambda_{2}\) is set to be less than \(\lambda_{1}\), and the relationship of both parameters is given:  
\[\lambda_{2}=\gamma\lambda_{1} \tag{18}\]  
where \(0\leq\gamma\leq 1\), and then the controller \(T(s)\) can be written as:  
\[T(s)=\frac{1+\gamma\lambda_{1}}{1+\lambda_{1}} \tag{19}\]  
It should be noted that, on the one hand, due to adopting an average model, the uncertainties of the model with rotor position are neglected in the 2 DOF IMC controller. On the other hand, the parameter variations, inductance harmonics, and other uncertainties caused by the magnetic saturation are also not considered. These time-variable uncertainties can not be perfectly eliminated by constant filter parameters, which will affect the control performance. Hence, a disturbance observer controller is introduced to estimate and eliminate these uncertainties in real time.
## 3 Materials and Methods
### Data Preprocessing
#### 3.3.3 Temporal Indexing  
Dates were converted into a standardized time index to ensure uniform time series input formats across all models, especially for ARIMA and Prophet, which require a consistent datetime structure.
# Bias and Variance Analysis of Contemporary Symbolic Regression Methods
## 4 Results
### Bias and Variance  
Given that there is no irreducible error on the target variable in SRBench's test partitions [8], the error can be decomposed into bias and variance. The distributions of bias and variance values as defined in Equations (3) and (4) for each data set over all noise levels are shown in Figure 5. As expected, algorithms with more accurate models tend to be low both in bias and variance. However, both properties do not behave the same across all algorithms. The bias of AIFeynman increases from no bias at all to the highest bias of all algorithms with increasing noise level, as Table 4 shows. For other algorithms, the median bias as well as the median variance increase across all algorithms with the median error. Exceptions regarding variance are ITEA and to some extent FFX, which suffer primarily from high bias and not from high variance. Considering the restricted search space of both algorithms, this appears plausible.  
Figure 5: Distribution of bias and variance values as defined in Equations (3) and (4) over all problems. The blue line shows the median, the boxes show the interquartile range.  
Figure 6: Distribution of problem-wise ranks for bias and variance. We compare the bias and variance per problem and algorithm from Figure 5 and assign ranks from one to ten for each problem. The box plots show the median as blue lines and the interquartile range as boxes over all rank values. (**a**) Rank distribution of bias. Rank one is assigned to the model with the smallest bias. (**b**) Rank distribution of variance. Rank one is assigned to the model with the smallest variance.  
Figure 6 and Tables 4 and 5 show a rank-wise comparison of bias and variance. Operon and GP-GOMEA provide the smallest bias without significant differences according to the pairwise \(p\)-values and significance values in Table 6. Table 4 shows that the distribution of bias values for AIFeynman is distored by its very high bias at high noise levels. Multiple algorithms are on the same level regarding variance. Tables 6 and 7 show that not only Operon and GP-GOMEA but also the rank-wise less accurate AIFeynman and ITEA have similar variance rank distributions without statistically significant differences. This shows that ITEA, FFX and AIFeynman suffer primarily from high bias and comparably low variance.