esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Frequency Range and Jump Behavior of Horizontal Quasi-Zero Stiffness Isolator
## 3 Isolator Installed on Linear Structure
### Parametric Study for Effective Frequency Range  
The variation of the effective frequency range of the isolator as a function of the excitation amplitude is shown in Figure 16. The effective frequency range is shown as the white region of this figure. It is shown that the lowest boundary curve for effective frequency varies slightly with the excitation amplitude. However, the amplitude of the isolator exceeds that of the primary structure after the excitation amplitude reaches 1.5 N in the resonance band of the primary structure, and the frequency range in which the isolator loses efficacy becomes wider with further increases in the force. The higher branches of response [30; 31] occur after the excitation amplitude reaches 2.5 N, and a new frequency range in which the isolator loses efficacy arises between the two resonance frequencies of the system.  
Figure 14: Jump behavior of the vibration isolation system when the primary structure is subject to an impact. Showing the response of the **(a)** primary structure and **(b)** isolator. Results are for the parameters \(p=3\) N, \(\Omega=15\) rad/s, and \(f=50\) N.  
Figure 15: Jump phenomenon of the vibration isolation system when the isolator is subject to an impact. Showing the response of the **(a)** primary structure and **(b)** isolator. Results shown are for the parameters \(p=3\) N, \(\Omega=15\) rad/s, and \(f=50\) N.  
The influence of damping on the effective frequency range for \(p=2\) N is shown in Figure 17a. When the damping is relatively weak, the system produces higher branches of response, which leads to the narrowing of the effective frequency range. Interestingly, the higher branches can be eliminated by increasing the damping; for this reason, the effective frequency range increases significantly for damping greater than 8 N\(\cdot\)s/m. However, the frequency ranges around zero and the resonance frequency of the primary structure remains unaffected and changes only slightly with variations in the damping. The influence of the elongation of the linear springs is shown in Figure 17b. The linear natural frequency of the isolator increases with increasing elongation, which leads to an increase in the lowest boundary of the effective frequency. The boundaries around the resonance frequency of the primary structure remain almost constant despite variations in the damping and the elongation of the linear springs.
# Real-Time Vehicle Energy Management System Based on Optimized Distribution of Electrical Load Power
## 4 Real-Time Energy Management Strategy
### Acquisition of the Co-State \(\gamma(t)\)  
According to Equation (8), \(\gamma(t)\) is the penalty factor of the SoC change rate, and the changes of \(\gamma(t)\) affect the SoC changes. The constrained conditions of Equation (7) indicate that the acquisition of \(\gamma(t)\) is a problem of two-point boundary values. Generally, the shooting method is used to solve the problem by converting it into an initial value problem [18]. In this method, the initial co-state of \(\gamma(t)\), i.e., \(\gamma_{0}\), can be determined by repeatedly trying until the final states satisfy the boundary condition, as shown in Figure 8. If the initial state \(SoC_{0}\) is known, and \(\gamma_{0}\) is set as a value, then the final state \(SoC_{f}\) is obtained by solving differential Equation (5). If \(SoC_{0}\) is not equal to \(SoC_{0}\), then \(\gamma_{0}\) is updated and the process is repeated until \(SoC_{f}\) = \(SoC_{0}\).  
For a specific driving cycle, if the load power change is known, \(\gamma_{0}\) can be calculated offline by using the given shooting method. An algorithm to calculate \(\gamma_{0}\) offline corresponding to a particular load power is presented in Figure 9. The algorithm inputs are \(SoC_{0}\), load power \(P_{L}\), fuel consumption rate set in the time [\(t_{0}\), \(t_{f}\)] of the given driving cycle, and calculation constants \(\delta\) and \(\pounds\). The main process of the algorithm is detailed as follows:  
1. The upper and lower limits of \(u\) are set according to \(P_{L}\): \(u_{\max}=\min[P_{L},P_{B\_max}]\) and \(u_{\min}=\max[P_{L}-P_{C\_max},P_{B\_min}]\).  
Figure 8: Trajectory of the shooting method.  
Set \(\Delta u=[u_{\max}-u_{\min}]/n\) and discretize \(u\in[u_{\min},u_{\max}]\) as \(u_{i}=u_{\min}+i\cdot\Delta u\), \(i=0,\,1,\,2,\,\ldots,\,n\).
3. For a given \(\gamma_{0}\), calculate the Hamiltonian function \(H_{i}\) for each \(u_{i}\) at \(t_{k}\) by Equation (8).
4. Select \(u^{*}(t_{k})=\arg\min\{H_{1},\,\ldots,\,H_{i},\,\ldots,\,H_{n}\}\) and calculate the current \(SoC(t_{k})\).
5. Repeat steps (1)-(4) until the driving cycle is over, i.e., \(t_{k}=t_{f}\).
6. Check whether \(\mid SoC_{f}-SoC_{0}\mid>\xi\). If it is false, return the current \(\gamma_{0}\); otherwise, update \(\gamma_{0}\) and repeat steps (1)-(6) until a valid \(\gamma_{0}\) is obtained.
# Prediction of Air Quality Combining Wavelet Transform, DCCA Correlation Analysis and LSTM Model
###### Abstract
It is widely used in natural language processing [15], speech signal recognition [16], text translation [17], robot control [18], weather analysis and prediction [19], and healthcare [20]. [PERSON] et al. suggested in 2022 a novel hybrid CNN-LSTM model for multistep wind prediction to handle the problem of unmanned sailboats at sea that are significantly affected by wind speed [21]. [PERSON] et al. combined a new self-attentive temporal convolution structure (SATCN) with LSTM to predict wind power generation, with a 17.56% improvement in accuracy compared to LSTM. The model was validated on meteorological data and wind power data from two different wind farms in the United States [22]. [PERSON] et al. examined the time series and correlation of temperature and precipitation variability in the Indian Himalayan states and designed and optimized a climate deep long and short-term memory model (CDLSTM) to predict temperature and precipitation values in the Himalayan states [23]. [PERSON] et al. proposed an automatic spatiotemporal seizure detection framework based on deep learning to address the issue that EEG-based epilepsy detection efforts do not fully utilize the spatiotemporal information of EEG signals, employing BiLSTM on the back-end to mine temporal relationships and make final diagnostic decisions based on the pre- and post-state of the current moment [24]. [PERSON] et al. addressed the limitations of conventional decreasing curve analysis and time series models to capture changes in oil productivity by utilizing LSTM models to predict oil, gas, and water productivity and validating them using SACROC oil field data [25]. [PERSON] et al. utilized long short-term memory (LSTM) networks to monitor and forecast terrestrial water storage change (TWSC) and ground water storage change (GWSC) based on gravity recovery and climate experiment (GRACE) datasets from 2003 to 2025 for five basins in Saudi Arabia [26]. The subjects of study in this paper were a total of 10-dimensional data, including air quality data and meteorological data. The coefficient of detrended inter-relationships under three scales of short range, medium range, and long range were obtained to summarize the overall characteristics between time series. Finally, the DCCA-LSTM model, which performs feature screening at small scales and determines weighting factors at medium and long scales, was created, and its prediction effect was compared to the prediction effect of conventional models.
# Automated Personalized Loudness Control for Multi-Track Recordings
## 2 Materials and Methods
### Dataset  
For this paper, a dataset of 20 songs was meticulously curated, combining tracks from the Cambridge Music Technology database [20] and recordings from local Romanian bands sourced by the participating engineers. These selections encompass a wide range of genres and styles, providing a robust foundation for examining the nuances of audio mixing. The multi-track collection spans a deliberately broad spectrum of musical genres and styles, including Soul, Indie Pop, Alternative Rock, Country, Hip-Hop, Latin Pop, Funk, Rock 'n' Roll, Blues Rock, Indie-Psych Rock, R&B, Soft Rock, Pop Rock, Funk/Soul, Art Rock, Power Pop, Metal and more. This selection is critical for a comprehensive analysis of the diverse mixing techniques preferred by different sound engineers. The decision to include such a varied range of genres serves multiple purposes. Firstly, it mirrors the wide-ranging musical landscape that sound engineers encounter in professional settings, ensuring our findings have broad applicability. Secondly, it allows us to examine how different musical styles influence the mixing process, particularly in terms of dynamic range, tonal balance and spatialization, which can vary significantly from genre to genre. This diversity not only enriches our dataset but also strengthens our investigation into the adaptability and effectiveness of the personalized multi-track leveling algorithm in mimicking the nuanced approach that engineers bring to projects across the musical spectrum.  
The effectiveness of our tailored multi-track leveling process relies heavily on the expertise and subjective assessments of sound engineers. In order to assess the efficacy and versatility of our algorithm, we enlisted the participation of 10 sound engineers with diverse levels of expertise, ranging from beginners to professionals in the music industry. The selection process aimed to include individuals with a range of mixing techniques and preferences, in order to thoroughly evaluate the performance of the algorithm across various artistic approaches. Before starting the experiment, the sound engineers undertook a training session to become acquainted with the details of the algorithm. This encompassed a comprehensive examination of its design concepts and operational mechanics.
## 4 Simulation Results
### PCA Application  
Continuing with the general scheme shown in Figure 1, first, we normalized all features in the dataset to ensure they were all on the same scale. Then, we conducted the PCA analysis considering all the input variables to understand the individual contribution of each characteristic in terms of variance. Figure 2a shows this representation, and from characteristic eight, in terms of variance, we note less relevance.  
Therefore, this graph indicates that we must consider reducing the dimensions from 13 to approximately 9. Likewise, we know the cumulative sum of the importance of the variances of each component individually since the permits visualization of which main component to consider. Figure 2b shows the importance of each component until reaching 100% of the variance.  
Figure 2b shows that from the ninth principal component onwards, the graph maintains an almost linear shape and does not represent more than 5% of the main variance of the data. We calculated the cumulative sum to determine the exact percentage of variance in each component. Table 3 shows the results of this analysis.  
Observing the results of Table 3, we note, with nine principal components guaranteed, approximately 95% of the original variance of the data. With this, it is possible to reduce the dimensions of 13 training variables to 9, which is considered sufficient to fulfill the purpose of this article.  
Therefore, we created a new dataset (PCA dataset) of dimension (165, 10); the 165 rows of data are maintained, the nine columns correspond to the nine main components, and the last column corresponds to the labels of the 5G services for classification. In Appendix A, Table A1 shows a fragment of the PCA dataset containing the first ten rows.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Principal Component** & **Variance (\%)** & **Accumulative Variance (\%)** \\ \hline
1 & 27.81 & 27.81 \\
2 & 18.3 & 46.11 \\
3 & 14.35 & 60.46 \\
4 & 11.73 & 72.19 \\
5 & 7.39 & 79.58 \\
6 & 5 & 84.58 \\
7 & 4.4 & 88.98 \\
8 & 3.1 & 92.08 \\
9 & 2.76 & 94.84 \\
10 & 2.29 & 97.13 \\
11 & 1.43 & 98.56 \\
12 & 1.02 & 99.58 \\
13 & 0.42 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accumulative variance of principal component.  
Figure 2: (a) Explained variance importance in the dataset. (b) Importance of the principal component in the dataset.
## 1 Introduction
(2) Based on the previous study of the signal loss formula, quantization models of the multipath effect (ME), non-line-of-sight interference (NLOSI), and atmospheric interference (AI) error factors are proposed. (3) To adapt to various environments, a variable-weighted dynamic error-propagation model (DEPM) is presented based on the quantization models. (4) A cable UWB network in an intelligent manufacturing lab is designed to validate the proposed model. The rest of this paper is organized as follows: In Section 2, we review related research concerning UWB signal propagation. In Section 3, we establish a UWB signal model to study the signal transmission mode and performance. In Section 4, we present a variable-weighted DEPM based on the quantization models of ME, NLOSI, and AI error factors. Section 5 describes static and dynamic positioning experiments conducted in an intelligent manufacturing lab, and the error weight is analyzed. In Section 6, we summarize the study and introduce future work.
## 3 Results and Discussion
### Sensitivity Analysis  
The sensitivity analysis was performed with the purpose of screening the variance of moisture content attributed to drying conditions (\(T,RH,v\)). The general procedure involves the inclusion of Equations (2), (6) and (25) and using Monte Carlo simulation to generate randomized combination of values of drying conditions in order to assess the relation between the inputs and outputs. Figure 7 presents the tornado plot in which parameters are ranked by influence. According to the sensitivity analysis, temperature \(T\) is the most significant variable which governs the moisture transport in all cases. A value of \(-0.799\) was observed for the standardized regression coefficient. This means that the increase in temperature \(T\) of drying air has a negative effect in moisture content by reducing it. In analogy, values of \(0.337\) and \(-0.051\) were observed for relative humidity \(RH\) and velocity \(v\). Noticeably, drying of wheat can be enhanced by the increase of \(T\), reduction of \(RH\), and increase of \(v\).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Drying Conditions** & \(D\)**, \(\mathbf{m^{2}s^{-1}}\) & \(R^{2}\)**, \(-\)** & \(RMSE\)**, \(-\)** & \(MAPE\)**, \%** \\ \hline T10/RH40/V015 & \(2.474\times 10^{-12}\) & \(0.941\) & \(3.281\times 10^{-2}\) & \(4.5\) \\ T20/RH40/V015 & \(5.811\times 10^{-12}\) & \(0.957\) & \(4.403\times 10^{-2}\) & \(9.4\) \\ T30/RH40/V015 & \(8.963\times 10^{-12}\) & \(0.956\) & \(4.501\times 10^{-2}\) & \(8.2\) \\ T40/RH40/V015 & \(1.917\times 10^{-11}\) & \(0.946\) & \(5.213\times 10^{-2}\) & \(10.7\) \\ T50/RH40/V015 & \(3.921\times 10^{-11}\) & \(0.947\) & \(5.595\times 10^{-2}\) & \(10.8\) \\ T30/RH20/V015 & \(9.822\times 10^{-12}\) & \(0.956\) & \(4.570\times 10^{-2}\) & \(8.6\) \\ T30/RH60/V015 & \(7.843\times 10^{-12}\) & \(0.964\) & \(4.005\times 10^{-2}\) & \(7.0\) \\ T30/RH40/V05 & \(9.494\times 10^{-12}\) & \(0.962\) & \(4.186\times 10^{-2}\) & \(7.4\) \\ T30/RH40/V1 & \(1.061\times 10^{-11}\) & \(0.959\) & \(4.451\times 10^{-2}\) & \(7.8\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effective moisture diffusion coefficients \(D\) for short times (\(1\leq X^{*}\leq 0.2\)) depending on the drying conditions (\(T,RH,v\)) and statistical results (\(R^{2}\), \(RMSE\), \(MAPE\)).
## 2 Materials and Methods
### FIB Corrosion Depth Measurement Data Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Aloy/Environment/Time** & **6** & **12** & **18** \\ \hline \multirow{3}{*}{NiTi-1} & air & 32.75 \(\pm\) 4.48 & 39.00 \(\pm\) 43.42 & 53.11 \(\pm\) 6.96 \\  & tide & 31.11 \(\pm\) 6.08 & 34.41 \(\pm\) 8.25 & 54.50 \(\pm\) 7.71 \\  & sea & 48.75 \(\pm\) 8.94 & 316.39 \(\pm\) 200.88 & 476.17 \(\pm\) 151.33 \\ \hline \multirow{3}{*}{NiTi-2} & air & 35.80 \(\pm\) 4.20 & 36.17 \(\pm\) 4.41 & 58.58 \(\pm\) 9.74 \\  & tide & 47.93 \(\pm\) 10.52 & 158.02 \(\pm\) 116.09 & 286.59 \(\pm\) 190.89 \\ \cline{1-1}  & sea & 144.27 \(\pm\) 73.66 & 239.28 \(\pm\) 97.55 & 548.02 \(\pm\) 227.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean values and Standard Deviations (\(\mu\pm\sigma\)) of measured corrosion wear on the NiTi-1 and NiTi-2 alloys exposed to the influence of air, tide and sea, measured after 6, 12 and 18 months.
# Reconditioning of Diamond Coated Tools and Its Impact on Cutting Performance for CFRP Laminates
## 4 Conclusions  
In this study, one-shot diamond-coated drills have been reconditioned two times without the drill regrinding between reconditioning processes. Then, the cutting performance of the reconditioned diamond-coated drills in drilling CFRP laminates was experimentally investigated and compared with the new diamond-coated drills. The main conclusions obtained were as follows:  
(1) The RIBE decoating has demonstrated an insignificant influence on the WC-Co while completely removing a B-MCD film.  
(2) The repeated substrate WCE pretreatment process resulted in increased crater formation in the WC-Co and should be tailored for the tool reconditioning in order to maintain the adhesion force of the B-MCD and maintain the WC-Co surface region mechanical robustness.  
(3) The cutting performance of the reconditioned drills was confirmed through the drilling tests. All produced CFRP holes kept the required hole size and showed the absence of critical delamination and burrs. Moreover, even the two-time reconditioned drills showed the cutting performance results regarding the flank wear and the hole quality comparable with the new drills.  
(4) The discussed RIBE-based reconditioning approach to the B-MCD-coated cutting tools may give significant advantages in CFRP machining in terms of the cost and the material savings and may facilitate a transition towards an eco-friendly circular economy.  
Conceptualization, A.S. and A.R.; methodology, A.S. and A.R.; investigation, A.S. and A.R.; writing of the original draft preparation, A.S. and A.R.; writing of review and editing, A.S., A.R., and A.O.; supervision, A.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.
## 3 Methods
### Accuracy Assessment  
To assess the accuracy of the forest foliage profile estimation, the root mean squared errors (_RMSEs_) of the simulated foliage profile and the retrieved foliage profiles under all data acquisition conditions were calculated according to Equation (5). When the _RMSE_ was lower, the estimation accuracy was higher. The absolute difference between the simulated leaf area index and the retrieved total leaf area index (_Diff_) could also be used to assess the foliage profile retrieval accuracy, and the calculation method is shown in Equation (6). The estimation accuracy was higher when the absolute difference was lower.  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}{{{({{\hat{y}}_{i}}-{y_{i}})}^{2}}}}{n}}, \tag{5}\]  
\[Diff=|LAI_{simulated}-LAI_{retrieved}|, \tag{6}\]  
where \({{{\hat{y}}_{i}}}\) represents the retrieved foliage profile value in the \(i\)th layer, \(y_{i}\) represents the simulated foliage profile value in the \(i\)th layer, and \(n\) represents the number of layers. The \(LAI_{simulated}\) represents the simulated total \(LAI\), \(LAI_{retrieved}\) represents the retrieved total \(LAI\), and _Diff_ represents the absolute difference between \(LAI_{simulated}\) and \(LAI_{retrieved}\).
# Future Perspectives for Gamma-ray Burst Detection from Space
## 11 Svom
### Expected Performance  
The understanding of the GRB physics requires observations in the largest spectral domain and in the largest temporal interval, from the possible precursor up to the transition between the prompt and afterglow emissions. Simultaneous observation of the prompt GRB event in the gamma-ray, X-ray, and visible bands, combined with narrow field observations of the afterglow in the X-ray, visible, and near-infrared bands immediately after the beginning of the event will enable a better understanding of the mechanisms at work in such events. New measurements (gravitational wave, gamma-ray polarization, neutrinos) may also become possible in the future, and their impact on the physical understanding of the GRB phenomenon will be maximized if these measurements are made for bursts whose \"standard\" properties, including the distance, are well measured. The SVOM mission is well adapted to these objectives. Compared to previous missions, it offers simultaneously (i) the capacity to trigger on all types of GRBs (especially on X-ray-rich and ultra-long ones); (ii) an excellent efficiency of the follow-up and the redshift measurement; (iii) a good spectral coverage of the prompt emission by ECLAIRs+GRM, allowing a detailed modeling (for a significant fraction of SVOM GRBs, the GWAC will provide, in addition, a measurement or an upper limit on the prompt optical emission); and (iv) a good temporal and spectral coverage of the prompt and afterglow emission thanks to the MXT, VT, and GFTs.  
Concerning multi-messenger astrophysics, SVOM with its ground and space instruments will offer a large and complementary follow-up capability through ToOs. The GWAC with its 5000 sq. deg. coverage can start the observation from the alert reception. The GFTs with their small FoV will confirm GWAC candidates and will be able to perform follow-up for well-localized events. To activate the satellite instruments, we will rely on a specific ToO program to send the observation program using S-band stations. This program guarantees less than 12 h between the alert and the start of space observations (less can be expected for most cases) and can be activated around 20-times per year. From space, the MXT and its 1 sq. deg. FoV will have the possibility to cover a larger sky portion using a specific tiling procedure.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **ECLAIRs** & **GRM** & **MXT** & **VT** \\ \hline Energy/Wavelength & 4–150 keV & 15–5000 keV & 0.1–10 keV & 650–1000 nm \\ Field of View & 2 sr & 2.6 sr (combined) & 58\({}^{\prime}\)\(\times\) 58\({}^{\prime}\) & 26\({}^{\prime}\)\(\times\) 26\({}^{\prime}\) \\ Localization Accuracy & \(<\)12\({}^{\prime}\) & \(<\)20\({}^{\circ}\) & \(<\)2\({}^{\prime}\) & \(<\)1\({}^{\prime\prime}\) \\ Expected GRBs Year\({}^{-1}\) & 60 & 90 & 50 & 40 \\ \hline \end{tabular}
\end{table}
Table 3: Summary of the characteristics of the SVOM space instruments.