esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Adaptive Time-Frequency Analysis
### Empirical Wavelet Transform and Optimization Models
#### 3.3.1 Empirical Wavelet Transform
Results indicate that this method can effectively and accurately identify the instantaneous frequency of time-varying systems in noisy environments [120]. Furthermore, several emerging models have been proposed and applied to fault diagnosis. A new vibration model and EWT method have been used for compound fault diagnosis and analysis of wind turbine generator gearboxes. By decomposing signals using EWT and combining it with the vibration model for fault diagnosis, multiple faults can be effectively detected and diagnosed [121]. A weak feature enhancement method for mechanical fault diagnosis based on EWT and improved adaptive bistable stochastic resonance (IABSR) has been proposed. By combining EWT and IABSR for signal separation and fault feature enhancement, numerical simulations and case studies have verified the effectiveness and superiority of this method [122]. Strength and Weakness
* Adaptive frequency band partitioning and captures local features
* Sensitive to noise and may have endpoint effect
* May cause modal aliasing or modal loss issues  
Its adaptive frequency band partitioning capability effectively captures local features based on the spectral characteristics of the signal, making it particularly suitable for processing nonlinear and non-stationary signals. Furthermore, through high-resolution time-frequency analysis, EWT can simultaneously provide both the temporal and frequency information of the signal, accurately capturing transient features in mechanical fault diagnosis. Additionally, EWT boasts high computational efficiency, enabling rapid processing of large-scale data, as exemplified by its integration with LSTM neural networks for efficient prediction in the processing of electromechanical oscillation signals in power systems, which was mentioned previously. However, EWT also exhibits certain limitations. First, it is relatively sensitive to noise, which may lead to inaccurate frequency band partitioning in high-noise environments, though numerous studies have been conducted to optimize this limitation [109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122]. Second, parameter selection is complex, and improper settings can trigger modal aliasing or modal loss. Finally, the endpoint effect problem may cause decomposition distortion at the signal boundaries.
## 3 Results
### Food Intake and Feed Efficiency  
Control animals consumed, on average, 2.68 \(\pm\) 0.21 g per LFD animal per day and 2.84 \(\pm\) 0.29 g per HFD animal per day for the duration of the study (Figure 7a). Although daily food intakes were similar between LFD and HFD animals, the caloric densities of these diets differed significantly (3.85 kcal/g versus 5.24 kcal/g). Therefore, HFD mice experienced larger energy intakes in the range of 14.88 kcal/animal/day versus 10.32 kcal/animal/day for the LFD counterparts. Nearly all additional calories came in the form of dietary fats due to the nature of the HFD.  
Both SR mice and NR mice showed average food intakes similar to those of HFD controls, suggesting that differences in energy intakes were not responsible for the observed changes in body weight gain and body composition (Figure 7a,b). If anything, NR mice showed increased food intake that nearly reached statistical significance (3.18 \(\pm\) 0.28 g per animal per day, \(p\) = 0.066). Their feed efficiency--or the ability to convert consumed food into a body weight gain--was reduced by 39.6% (0.033 \(\pm\) 0.002 g/kcal versus 0.053 \(\pm\) 0.002 g/kcal for HFD controls, \(p\) = 0.043). SR-LFD mice showed negative feed efficiency due to progressive body weight loss after switching from an HFD to the LFD (Figure 7c).  
Figure 6: Exercise capacity of lean controls (LFD), obese controls (HFD), obesity-resistant non-responders (NR), obesity-prone super-responders (SR), and super-responders fed an LFD at the end of the study. Results are expressed as means \(\pm\) SEM (n = 8). Data were analyzed using one-way ANOVA followed by [PERSON]’s multiple comparisons; \({}^{*}p<\) 0.05 versus the HFD controls.
## 2 Materials and Methods  
### Plant Material, Sequencing, and Genome Size Estimation  
_A. remotiiflora_ was collected from Yeongyang County, Gyeongsangbukdo, Republic of Korea (1085 m, N 36\({}^{\circ}\)48\({}^{\prime}\)23.87\({}^{\prime\prime}\), E 129\({}^{\circ}\)05\({}^{\prime}\)22.78\({}^{\prime\prime}\)). Genomic DNA was isolated from fresh leaves. DNA integrity, purity, and concentration were confirmed using a NanoDrop 2000 (Thermo, Waltham, MA, USA) with 1% agarose gel. Illumina paired-end (PE) libraries with two 125 bp read lengths were constructed, and sequenced on an Illumina HiSeq X platform (Illumina, San Diego, CA, USA) according to the Illumina manual protocol. Among the sequenced raw reads, poor-quality reads (PHRED score < 20) were filtered via the trimming check of the CLC Assembly Cell (CLC, Aarhus, Denmark). The estimation of genome size was performed using two genome estimation tools (Jellyfish, GenomeScope) [11; 12]. The optimal _k_-mer value was calculated by changing the _k_-mer distribution (17, 19, 21, 23, 25, 27, 29, and 31). According to the optimal _k_-mer value, the genome size was estimated using two genome estimation tools.
## 7 Challenges and Limitations of Pavement Prediction Model  
Pavement prediction model studies for road construction design have been an ongoing challenge in the past decade. Some studies have resulted in predictions of the extreme asphalt temperatures, which triggered the requirements to enhancing pavement stability, strength, and durability. Based on this literature review, the essential factors and challenges in developing temperature prediction models are as follows.  
* Current models for predicting the asphalt pavement's temperature consider environmental factors' impact on the asphalt pavement. However, these models do not consider the reverse impact that occurs from the heated pavement and thus contributing to heat accumulation in the surrounding environment, such as that found in urban heat island (UHI) effects. To summarise, these models cannot fully reflect the two-way interactions that become apparent between the pavement and the environment, thus making them incapable of evaluating the effect of pavement on UHI effects. Future studies are recommended and advised to be taken with this factor into account.
* The quantitative evaluation for prediction development of pavement temperature model allows for mechanistic-empirical modelling of pavement deterioration caused by climate change. However, qualitative or quantitative techniques to assess indirect impacts from climate variations, specifically to demography and traffic demand changes, should be considered in future studies [105].
* As mentioned in the current literature, asphalt pavement materials' thermal properties can be specified [119]. However, this range is unnecessarily too broad to ensure that the model would make an accurate prediction. There is a lack of practical guidance for selecting material thermal properties based on material category, gradation, age, water content, and other information [120]. Moreover, current studies also do not take into account critical thermal properties and the resistance to thermal contact between pavement layers. Most studies even assumed a perfect thermal contact between pavement layers that still remain questionable.
# Vibration Energy Harvester Based on Bilateral Periodic One-Dimensional Acoustic Black Hole
## 4 Experimental Test
### Results and Discussion  
#### 4.2.1 Output Voltage of VEH with ABH  
The output voltage of VEHs with an ABH in the frequency domain is shown in Figure 18, where the external load resistance is 10 k\(\Omega\). The peak output voltage of the VEH with a traditional ABH is 8.5 V at 153 Hz, and the peak output voltage of the VEH with the bilateral ABH Model 1 is 10.5 V at 146 Hz separately. The peak output voltage of the VEH with ABH Model 2 is 22.1 V at 140 Hz, and the peak output voltage of the VEH with ABH Model 3 is 29.2 V at 130 Hz. The peak output voltage of the VEH with the ABH increased with the number of ABH arrays. The peak output voltage of VEH Model 3 is 3.4 times that of the traditional counterpart. The specific natural frequency and output voltage are shown in Table A2 in Appendix A.  
A comparison of the experimental and simulated natural frequencies of the VEHs is shown in Table 3. The deviation between the simulation and experiment is mainly due to the insufficient precision of actual machining and the vibration interference of the vibrator itself cannot be completely eliminated.
## 7 Conclusions  
In this study, we used a physical model to establish a multiparameter inversion framework for crop phenotypes based on optimal estimation inversion theory. Firstly, we unified PROSAIL and UNL-VRTM models to achieve synchronous atmospheric correction. Secondly, multiple phenotypic parameters inversion framework was established based on the optimized estimation method with prior constraints. Then, the contribution of parameters to the model was obtained by parameter sensitivity analysis, and the parameters to be retrieved were determined according to the information content analysis method. Finally, the optimized estimation inversion framework self-consistency test was performed, and the model inversion values were validated against the true values under different observation geometries and aerosol loadings. The results showed that the coefficients of determination (R\({}^{2}\)) between the inversion values and the true values were above 0.99, indicating that UAV multispectral observations can support the inversion of LAI and Cab. It also indicated that the inversion framework can make full use of the available radiation spectral information and has good stability. The findings from this study suggest that our proposed forward model has a strong coupling between vegetation reflectance and atmosphere. Moreover, there were good convergence and consistency for the inversion framework in obtaining inversion LAI and Cab. The optimal inversion framework for the synchronous retrieval of crop phenotypic parameters is expected to be performed on actual observation data in the future and can be applied to the monitoring of crop parameters in the field environment.  
Conceptualization, F.Z., J.J. and H.M.; methodology, F.Z. and X.W.; validation, F.Z. and X.W.; formal analysis, F.Z. and J.J.; investigation, F.Z. and X.W.; resources, F.Z., X.W. and S.Z.; data curation, F.Z. and X.W.; writing--original draft preparation, F.Z.; writing--review and editing, F.Z., X.W. and H.C.; supervision, Y.S.; project administration, H.C., Y.S. and J.J.; funding acquisition, J.J. and H.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Key specialized research and development breakthrough in Henan province (No. 222102110105; No. 222102110215); Major science and technology project of Henan Province (No. 221100110800); the Key Scientific Research Projects of Colleges and Universities in Henan Province (No. 21A416003).  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We are very grateful to [PERSON] and [PERSON] for the UNL-VRTM radiative transfer code. The data and Fortran codes used for the forward simulations are available on the UNL-VRTM website ([[http://www.unl-vrtm.org](http://www.unl-vrtm.org)]([http://www.unl-vrtm.org](http://www.unl-vrtm.org)), accessed on 23 March 2021). We acknowledge the L-BFGS-B software package developed by [PERSON], [PERSON], and [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Database Creation and Structure  
Creating an organized and effective database to handle the extracted entities was a crucial part of the development of an approach to linked corpus creation over the literary heritage of the A.S. [PERSON]. In this section, we talk about the database we created that utilized the NER model we trained to store the extracted entities from the textual data. The database we built contains the following tables, where Figure 3 shows a scheme for the database:  
* **Main Table:** This table contains all the entities extracted from the dataset by utilizing the NER model we trained. Each entity within the table has a unique ID as well as information that describes the entity type, entity span text, and text from which it was extracted. * **Separate Tables for Entity Types**: To optimize efficiency and the structure, we created distinct tables for every kind of entity: Persons (PER), Organizations (ORG), Locations \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Comments** & **Entity** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{Model \#1} & \multirow{4}{*}{Tok2 vec model} & Ents & 0.6202 & 0.6466 & 0.6331 \\  & & PER & 0.7804 & 0.8276 & 0.8033 \\  & & before adjusting & LOC & 0.5337 & 0.5471 & 0.5403 \\  & & the work-of-art & WOA & 0.1103 & 0.0947 & 0.1019 \\  & & DATE & 0.7280 & 0.9032 & 0.8062 \\  & & ORG & 0.3272 & 0.3333 & 0.3302 \\ \hline \multirow{4}{*}{Model \#2} & \multirow{4}{*}{Transformer-based} & Ents & 0.7718 & 0.7806 & 0.7761 \\  & & PER & 0.7994 & 0.8214 & 0.8102 \\  & & model after & LOC & 0.5361 & 0.5597 & 0.5476 \\  & & adjusting the & WOA & 0.8637 & 0.6987 & 0.7725 \\  & & work-of-art entities & DATE & 0.7306 & 0.8930 & 0.8037 \\  & & ORG & 0.3142 & 0.2868 & 0.2962 \\ \hline \multirow{4}{*}{Model \#3} & \multirow{4}{*}{Tok2 vec model} & Ents & 0.8742 & 0.8367 & 0.8550 \\  & & PER & 0.8850 & 0.8674 & 0.8761 \\  & & LOC & 0.6412 & 0.5283 & 0.5793 \\  & & treated on the & WOA & 0.9030 & 0.9114 & 0.9114 \\  & & DATE & 0.8764 & 0.7880 & 0.8299 \\  & & ORG & 0.7352 & 0.4629 & 0.5681 \\ \hline \multirow{4}{*}{Model \#4} & \multirow{4}{*}{Transformer-based} & Ents & 0.9020 & 0.8885 & 0.8952 \\  & & PER & 0.9357 & 0.9004 & 0.9177 \\ \cline{1-1}  & & model trained on & LOC & 0.8478 & 0.7358 & 0.7878 \\ \cline{1-1}  & & the checked & WOA & 0.8577 & 0.9083 & 0.8823 \\ \cline{1-1}  & & dataset & DATE & 0.9077 & 0.8909 & 0.8992 \\ \cline{1-1}  & & ORG & 0.6949 & 0.7592 & 0.7256 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of the NER models for processing [PERSON]. Pushkin’s heritage.
# EvoSplit: An Evolutionary Approach to Split a Multi-Label Data Set into Disjoint Subsets
## 6 Application to Large Image Data Sets  
Given the recent relevance of multi-label computer vision applications using deep learning techniques, EvoSplit has also been validated using some large multi-label data sets widely employed by the research community: Microsoft COCO, ImageNet, and OpenImages.
## 3 Our Proposed Method
### Case Study 1: Artificial Dataset  
We generated twenty groups of artificial datasets with network sizes = 10, 20,..., 190, and 200. In each group, twenty different networks were randomly generated, for a total of 400 tested networks. The state of each gene (node) was randomly initialized among 0, 1, and 2 and updated over 29 time steps using an update function selected uniformly and randomly from a set of update functions. The set of update functions contained a huge number of possible update functions for each target gene regulated by \(k\) other genes, and no discretization method was required for this dataset. See Table 1 for details about the settings of the generated dataset system. MIDNI inferred a network structure and a set of predicted regulatory rules for all genes. Finally, we analyzed the performance of MIDNI in terms of both structural and dynamic accuracy.  
The experimental results of MIDNI are shown in Figure 2. As shown in the figure, the dynamics accuracy, precision, and recall decrease as the size of the network increases. Contrary to these three metrics, the structural accuracy gradually rises as the size of the network increases. This is because the number of true positives declines as the network size increases, whereas the number of true negatives increases significantly. Furthermore, we examined the effect of the number of incoming links on performance (Figure 3). The number of incoming links specifies the difficulty of the inference problem. When there is only one incoming link for a target gene (the easiest case), our method always predicts correctly. However, the performance metrics gradually decrease as the number of incoming links increases.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Artificial Dataset** & _Escherichia coli_ **Dataset**[25] \\ \hline Size of networks (\(N\)) & 10, 20,..., 190, 200 & 10, 50, 100, 200 \\ Noise rate & without noise & 0, 5, 10\% \\ Time lag & 1 & 1 \\ Number of time points (\(T\)) & 30 & 30 \\ Include at least regulators & 0.4 \(\times\)\(N\) & 0.4 \(\times\)\(N\) \\ \hline \end{tabular}
\end{table}
Table 1: The setup parameters used to generate the dataset.  
Figure 2: Performance of MIDNI on the artificial dataset. The y-axis denotes the performance values for precision, recall, dynamic accuracy, and structural accuracy (see Sections 2.3 and 2.4), while the x-axis represents the network size (the number of genes), which varies from 10 to 200.
# Energy Efficiency of a Decode-and-Forward Multiple-Relay Network with Rate Adaptive LDPC Codes +
## 3 Power Allocation in Dual-Hop Networks
### Optimized Power Allocation (OPA)  
When equal transmit power is provided to all the transmitting nodes in network, a certain performance margin can be obtained but EPA scenarios do not maximize the system efficiency. However, this performance gap can be bridged by providing the relay nodes with an optimized power. Following that idea, we consider distributing the power in a different manner. We consider that the source \(S\) and all the relays \(R_{i}\) share a certain pool of power, where the source always transmits with half of the total power whereas, each relay gets the fair share of transmit power on need basis. The optimized power allocation ensures the system efficiency by fair allocation of transmit power at multiple relays.  
We treat this type of power allocation as an optimization problem. The transmit power is allocated in a way, such that it maximizes the energy efficiency of the system. We consider that the source always emits with a fixed transmit power \(P_{s}\). The optimization problem is formulated as:  
\[\begin{split}\underset{P_{i}}{\text{max}}&\eta, \quad i=1,...,N\\ \text{s.t}&\sum_{i=1}^{N}P_{i}\leq P_{i}^{max},\end{split} \tag{11}\]  
where \(P_{i}^{max}=P_{s}\).  
In the above equation \(\eta\) is given by Equation (8). An \(i^{th}\) relay can transmit with the maximum transmit power \(P_{i}=P_{s}\) or a power less than \(P_{s}\). However, the sum of transmit powers at all the relays should not exceed the \(P_{s}\). This constraint in Equation (11) limits the maximum transmit power to maximize the EE. In case of OPA, supposedly, a relay sends with a higher code rate when the channel conditions are good. Different code rates \(C_{R}\) will be selected on the basis of CSI feedback from the receiver node. We presume that a good channel condition means a high channel gain. On the basis of the waterfilling algorithm notion, we allocate more power to the channels with higher gain. Consequently, every relay gets a fair share of transmit power according to its needs. Power is distributed among multiple relays in this regard and as mentioned earlier, \(S\) transmits with half of the total transmit power \(P_{tot}\). OPA works under the assumption that high channel gain deserves more transmit power allocation. It is different from the EPA-II methodology because there is not only the code rate adaptation on the basis of CSI feedback as in EPA-II but also that the power will be allocated on need basis. The numerical analysis of all the PA protocols we have discussed so far is presented in the next section along with simulation results.
## 3 Results and Discussion
### Impact of Drilling Fluid
#### 3.2.3 Impact of Kinematic Viscosity
This phenomenon occurs because higher kinematic viscosity generates greater dynamic pressure, providing enhanced support and lubrication for the drill string and minimizing direct contact with the wellbore wall. The variation trends of SFED and TREE shown in Figure 30 indicate that increasing kinematic viscosity effectively reduces frictional energy dissipation. Computational results demonstrate that at a kinematic viscosity of \(1\times 10^{-6}\) m\({}^{2}\)/s, SFED and TREE decrease by 24.28% and 27.41%, respectively. However, when the kinematic viscosity further increases, its improvement on the SFED tends to level off, only showing a slight decrease. Meanwhile, the TREE also gradually increases. The critical value is approximately \(2.5\times 10^{-5}\) m\({}^{2}\)/s, and at this time, the TREE is approximately 262.14. This suggests that moderately increasing kinematic viscosity enhances lubrication efficiency, whereas an excessively high viscosity amplifies internal shear stress within the fluid, necessitating additional energy input to sustain drill string motion. It can also be seen from Figure 30 that when an excessively high kinematic viscosity is selected, the frictional contact between the drill string and the wellbore wall will not be significantly improved. Figure 28: Axial velocity under different kinematic viscosities: (**a**) \(1\times 10^{-6}\) m\({}^{2}\)/s; (**b**) \(2.5\times 10^{-5}\) m\({}^{2}\)/s; (**c**) \(4.5\times 10^{-5}\) m\({}^{2}\)/s; (**d**) \(9\times 10^{-5}\) m\({}^{2}\)/s. Figure 29: The effect of kinematic viscosity of drilling fluid on friction torque.
## 4 Experiments
### Quantitative Comparison with State-of-the-Art Methods
#### 4.2.2 Result on Flare7K++ Dataset  
As shown in Table 2, On the Flare7K++ dataset, our proposed method also demonstrates outstanding performance, achieving a PSNR of 31.4508 dB, a SSIM of 0.9822, and reducing MSE to 87.9677, significantly outperforming all competing methods. These results indicate that our approach ensures high-fidelity image reconstruction, excellent structural consistency, and reduced pixel-level errors even under challenging nighttime glare scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **LPIPS \(\downarrow\)** & **MSE \(\downarrow\)** \\ \hline EnlightenGAN [13] & 9.5874 & 0.5346 & 0.4660 & 7762.5161 \\ Zero-DCE [50] & 10.8350 & 0.5562 & 0.4461 & 5900.8370 \\ RUAS [18] & 6.1093 & 0.3674 & 0.6472 & 17,553.7916 \\ URteinex [17] & 10.4410 & 0.5630 & 0.4521 & 6338.3320 \\ PSENet [51] & 10.5074 & 0.5341 & 0.4459 & 6105.1058 \\ IAT [21] & 16.4169 & 0.6588 & 0.3671 & 1802.1397 \\ Ours & **26.4192** & **0.9602** & **0.0792** & **400.5131** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparison of state-of-the-art methods on the NRGD, where bold indicates the best results.