esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
To address the challenges of low accuracy and difficulty in identifying acoustic emission signals, we propose a multi-classification method for mine acoustic emission signal waveforms based on the improved CELMD method and Swin Transformer neural network. This method aims to achieve intelligent and high-accuracy identification and classification of acoustic emission signals in mines.  
The improved CELMD method first utilizes the empirical optimal envelope method instead of the traditional sliding average method to extract the main features of the signal. Furthermore, by incorporating methods such as mirror extension and endpoint correction, it further enhances the decomposition effect and reduces the occurrence of mode mixing and endpoint effects commonly encountered during signal decomposition.  
Compared to convolutional neural networks and ViT neural networks, Swin Transformer neural networks demonstrate higher accuracy and convergence speed. Furthermore, utilizing data preprocessed with the improved CELMD method enhances the performance of Swin Transformer neural networks.  
With an increase in dataset size, the accuracy, stability, and convergence speed of Swin Transformer neural networks continue to improve. Training with data preprocessed using the improved CELMD method yields superior results compared to training without preprocessing.  
Data curation, X.X.; Formal analysis, X.X.; Methodology, Y.Y.; Resources, X.X. and Y.Y.; Software, Y.Y.; Writing--original draft, Y.Y. 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. If any researcher is in need of the data and codes, email: [EMAIL_ADDRESS]. The data are not publicly available due to Laboratory's private data.  
The authors declare no conflicts of interest.
###### Abstract  
Traffic state estimation (TSE) is a critical component of the efficient intelligent transportation systems (ITS) operations. In the literature, TSE methods are divided into model-driven methods and data-driven methods. Each approach has its limitations. The physics information-based neural network (PINN) framework emerges to mitigate the limitations of the traditional TSE methods, while the state-of-art of such a framework has focused on single road segments but can hardly deal with traffic networks. This paper introduces a PINN framework that can effectively make use of a small amount of observational speed data to obtain high-quality TSEs for a traffic network. Both model-driven and data-driven components are incorporated into PINNs to combine the advantages of both approaches and to overcome their disadvantages. Simulation data of simple traffic networks are used for studying the highway network TSE. This paper demonstrates how to solve the popular LWR physical traffic flow model with a PINN for a traffic network. Experimental results confirm that the proposed approach is promising for estimating network traffic accurately.  
traffic state estimation (TSE); PINNs; deep learning; traffic flow models; LWR
###### Abstract  
In this paper, we explore the impact of the COVID-19 lockdown in Serbia on the air pollution levels of CO, NO\({}_{2}\) and PM\({}_{10}\) alongside the possibility for low-cost sensor usage during this period. In the study, a device with low-cost sensors collocated with a reference public monitoring station in the city of Belgrade is used for the same period of 52 days in 2019 (pre-COVID-19 period), 2020 (COVID-19 lockdown) and 2021 (post-COVID-19 period). Low-cost sensors' measurements are improved by using a convolutional neural network that applies corrections of the influence of temperature and relative humidity on the low-cost sensors. As a result of this study we have noticed a remarkable decrease in NO\({}_{2}\) (primarily related to traffic density), while on the other hand CO and PM\({}_{10}\), related to domestic heating sources and heating plants, showed constant or slightly higher levels. The obtained results are in accordance with other published work in this area. The low-cost sensors have shown a satisfactory correlation with the reference CO measurements during the lockdown, while the NO\({}_{2}\) and PM\({}_{10}\) measurements of 2020 were corrected using a convolutional neural network trained on meteorological and pollutant data from 2019. The results include an improvement of 0.35 for the R2 of NO\({}_{2}\) and an improvement of 0.13 for the R2 of PM\({}_{10}\), proving that our neural network model trained on data from 2019 can improve the performance of the sensor in the lockdown period in 2020. This means that our neural network model is very robust, as it exhibits good performance even in the case where training data from the prior year (2019) are used in the following year (2020) in very different environment circumstances--a lockdown.  
air pollution monitoring; COVID-19; emergency lockdown; low-cost PM and gas sensors (CO and NO\({}_{2}\)); neural network; sensor calibration +
Footnote †: journal: Applied Sciences  
1
Footnote †: journal: Applied Sciences
## 2 Preliminaries
### Notation  
In this paper, vectors are denoted by lower case letters, e.g., \(x\); matrices are represented by upper case letters, e.g., \(X\); and the set of matrices are represented by \(\mathbb{X}=\{X_{1},\cdots,X_{N}\}\). \(R^{D}\) is the Euclidean space. \(Sym_{++}^{D}\) is the SPD manifold, which will be formally defined later. \(T_{X}\big{(}Sym_{++}^{D}\big{)}\) is the tangent space to the SPD manifold at \(X\in Sym_{++}^{D}\). \(Gr(d,D)\) is the Grassmannian manifold, i.e., the set of \(d\)-dimensional subspaces of \(R^{D}\). \(GL(D)\) is the general linear group, i.e., the set of all invertible \(D\times D\) matrices. \(Sym_{+}^{D}\) is a positive semidefinite cone, i.e., the set of all \(D\times D\) positive semidefinite matrices. \(\mathcal{H}\) is the reproducing kernel Hilbert space.
## 3 Water Consumption
### Water Consumption in the Power Industry  
The production of electricity and heat involves the consumption of gigantic amounts of fresh water. Water for the commercial power industry is a necessary raw material. Poland's water resources for entities from the energy sector are critical because the Polish energy mix is based mainly on coal. Coal energy consumes around 70% of the water used in Poland and around 7% of the water used for industrial purposes. Water in this sector is needed at several stages, starting with coal mining [12]. For this purpose, groundwater is pumped out, and the suspension formed during coal washing often enters water reservoirs, causing their contamination. However, the largest amounts of water are used for the production of electricity in power plants based on the steam cycle, where water is necessary to replenish losses in boiler feed and heating installations, as well as for slag and dust removal processes, and above all for cooling energy devices, hence the frequent location of conventional power plants with large water reservoirs. Water is also used in the extraction of raw materials such as crude oil and natural gas. Water is forced under pressure into gas or oil deposits to \"squeeze\" them to the surface. The most water-consuming process is extraction from unconventional deposits--the so-called shale--where the amount of one-time injected water is from 10,000 to 14,000 m\({}^{3}\)[13]. Countries such as the United States, experiencing the \" shale revolution\", experience the impoverishment of water resources.  
Figure 3 shows water consumption with different energy production methods (water consumption in cubic meters per megawatt-hour of energy produced in different technologies) [12].  
Figure 2: Changes in water consumption in the world [11].  
The amount of water used differs depending on the energy carrier and the type of cooling used, so it is important to choose technologies that minimise water consumption, primarily using the most water-consuming fuels for energy production. Renewable sources for energy production use the smallest amounts of water. Most of this raw material is used to generate energy from coal and nuclear power plants (Figure 3) [12,14].
# A Novel Approach to Enhance Crude Oil Recovery Ratio Using Selected Bacterial Species
## 4 Discussions
### Transformation of Hydrocarbon
cyiacigeorgica_ was reported to degrade aliphatic and branched chain alkanes as well as alkylbenzene of oil in contaminated soils [32], whereas in the current study this isolate showed the ability to biotransformation heavy oil with an API gravity value of 26\({}^{\circ}\), reducing its viscosity and increasing the oil recovery to 7.9%. In a bioaugmentation treatment to remove hydrocarbons, _Pseudomonas putida_ CB-100 enhanced TPH decontamination of contaminated aged soil; 40.6% of total petroleum hydrocarbons (TPH) was removed and 1.54 mg/kg rhamn ups reported produced [33]. Our _P. putida_ utilized the heavy fraction of heavy crude oil and gave 61% of biotransformation, a reduction in viscosity from 1.565 Pa.s at 0.7622 s\({}^{-1}\) to 0.5415 Pa.s at 98.67 s, and, interestingly, increased the oil recovery by 10.66%. Considerable oil recovery has been obtained by the biotransformation of heavy crude oil compared to the control experiment. The extra recovery measurements were based on the residual oil present in the core. The highest recovery of residual oil among different bacterial species was approximately 11.38% at 55 \({}^{\circ}\)C by _Bacillus_ species. Similar results were reported by [34], where an additional recovery of 9.6% at 37 \({}^{\circ}\)C and 7.2% at 55 \({}^{\circ}\)C of crude oil 26 API occurred due to the combined effect of biosurfactants and the biotransforming ability of _Bacillus_ spp. In addition, 14.3% oil recovery for unfratured core models was recorded by [35] using a different indigenous microorganism isolated from oil samples from the Payedar oilfield. Oil recovery can generally be improved by 5% using microbial flooding solely [36]. This approach has a significant effect of the fracture angle on performance of the process and has indicated that oil recovery was increased by 6-10% of initial oil in place using microbial mediated oil, depending on oil saturation at the start of the flooding [37].
## 6 Numerical Simulation, and Experiment
### Resolution Ability Comparison  
It is assumed that two sources are situated at \((\theta,\varphi)=(40^{\circ},60^{\circ})\) and \((40^{\circ}+\theta,60^{\circ}+\theta)\), and the angle of separation \(\theta\) ranges from \(1^{\circ}\) to \(30^{\circ}\). The on-sphere super resolution approach presented by [PERSON] et al. [58] as S-R-Sphere is referred to and compared with the average error of the DoA estimates of the SH-ANM-ESPRIT approach and S-R-Sphere approach under an ideal (without noise) single snapshot scenario. The average estimate error of 250 trials against the angle of separation is as depicted in Figure 2. It can be observed that both approaches share identical error levels in the estimation of the DoA, i.e., they probably share a similar resolution. Evaluating the complexity in computation, the average run time for each trial was computed for the two approaches. The proposed approach took about 0.5499 s and the S-R-Sphere approach took 1.3737 s per trial. This implies that the proposed method is more computationally efficient.
## 6 Speech Quality and Intelligibility Evaluation
### Results and Discussion
Higher values present better results (A) White noise enhancement; (B) pink noise enhancement; (C) train noise enhancement; (D) bubble noise enhancement; and (E) restaurant noise enhancement. **  
higher speech quality results than the Wiener filter and the individual DDAE for the two sensorineural hearing loss audiograms under most listening conditions of different noises in every SNR levels, while the DDAE alone presents better results for SNR 5 dB for the white and restaurant noises. (Figure 13), shows the HASPI scores for five types of noises from experiment (1) in the four different SNR levels (i.e., 0, 5, 10, 15 dB). The results indicate that HC-DDAEs provided significantly higher HASPI scores than the other methods (i.e., Wiener filter and single DDAE separately) under most of the tested conditions. In contrast, the individual DDAE presents better results for SNR 10 of pink noise.
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 7 Simulations and Results
### Number of VUEs and Reliability  
In order to ensure the reliability of the VUEs, we define the term SINR probability as the ratio of VUEs with SINR more than 5 dBs divided by the total number of VUEs.  
SINR Probability = Amount of UE with SINR more than 5 db/total amount of UE  
Figure 8 shows that higher number of VUEs results in better SINR probability.  
Figure 8: Number of VUE vs. Reliability.  
Figure 7: Vehicular Speed vs. V2P throughput.
# A New Effective Narrowband Active Noise Control System for Accommodating Frequency Mismatch
## 2 Materials and Methods
### NANC System with MLMS  
In order to improve the lag of the tracking curve, a method based on the momentum minimum least mean square (MLMS) error is proposed; that is, a simple predictor is added when updating the coefficients of the AR model [31]:  
\[c_{i}(n+1)=c_{i}(n)-\mu_{c_{i}}e(n)[\overset{\wedge}{a_{i}}(n)\overset{\wedge }{x}_{a_{i}}(n-1)+\overset{\wedge}{b_{i}}(n)\overset{\wedge}{x}_{b_{i}}(n-1) ]+\lambda_{c_{i}}[c_{i}(n)-c_{i}(n-1)] \tag{13}\]  
The formula for updating the AR coefficients based on the MLMS algorithm is shown above. The principle of the MLMS algorithm is to make use of the correlation of coefficients in the adaptive process, and then bring in a momentum term \(\lambda_{c_{i}}(c_{i}(n)-c_{i}(n-1))\) in the iteration process. However, its convergence is relatively slowwith 10% FM and may not meet the requirements of some applications.
# Lignocellulosic Biomass as a Substrate for Oleaginous Microorganisms: A Review
## 3 Conversion of Lignocellulosic Biomass into Assimilable Sugars in Nature  
In nature, degradation of lignocellulosic material can be mediated mainly by degrading animals and fungi. Insects such as wasps, ants and termites, other animals, such as beavers, rats and woodpeckers and molluss from aquatic environments perform mechanical degradation, while decaying fungi perform enzymatic degradation [170; 173]. Wood decay fungi have been extensively studied. They are a diverse group of species belonging mainly to the Basidiomycota and Ascomycota divisions [71]. They are saprophyytes, capable of penetrating and colonizing the interior of plants with their hyphae, entering through the pores or through the ruptures of cell walls caused by enzymes that they secrete [166; 170].  
Decomposition of wood by fungi is typically classified into three types: brown rot white rot and soft rot. Brown rot is commonly brown and crumbly and is produced by non-enzymatic and enzymatic systems. Brown rot fungi produce cellulases but not enzymes that degrade lignin. However, they possess a non-enzymatic mechanism that results in the modification of lignin and a slow depletion of lignin from wood in the process of decomposition known as the chelation-mediated Fenton system (CMF) [173; 174]. White rot fungi, possessing lignin-degrading enzymes, are the only organisms capable of degrading all wood components. The capacity of these fungi to secrete non-specific ligninolytic and cellulolytic enzymes allows the transformation of a great variety of recalcitrant compounds into assimilable carbon forms, as the extracellular nature of the above enzymes allows fungi to access non-polar and insoluble compounds [175; 176; 177]. Soft rot fungi produce cavities and erosion in the cell walls of wood. Under aerobic conditions, these fungi can completely mineralize lignin and polysaccharides (cellulose, hemicelluloses) into CO\({}_{2}\) and H\({}_{2}\)O, in addition to causing wood rot [178].  
The natural mechanisms usually cause degradation at low rates. However, some technologies based on natural processes, such as biopulping and biodegradation (e.g., used in the degradation of resins and dyes), have been applied to degrade lignocellulosic biomass [177; 179; 180].
# Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
## 4 Backpropagation Neural Network
### BPNN Architecture  
The network structure, together with the input dimension, can greatly influence the prediction accuracy of a BPNN model [45,46]. Therefore, the influences of the number of hidden layers and nodes, as well as the dimension of features, are discussed to obtain the optimal BPNN architecture.  
The R\({}^{2}\) and mean absolute error (MAE) are used as evaluation indexes to select the optimal BPNN structure. The random initial weights are used for training, therefore, the results of each training are different. In this section, each BPNN structure was trained four times, respectively, the results are shown in Figures 7 and 8.  
For the cases with 40 feature values as input (Figure 7), with the increase of the number of nodes in the hidden layers, the prediction accuracy increases and then decreases. When the number of nodes reaching a certain number, the increase of nodes in the hidden layer can no longer improve the BPNN model.  
The BPNN with a single hidden layer achieves the best prediction accuracy when the number of nodes is 3, of which the average R\({}^{2}\) equals 0.5771, and the average MAE equals 0.1537. The BPNN with two hidden layers has the best prediction effect when the number of nodes in each layer is 3, with the average R\({}^{2}\) equals 0.5910, and the average MAE equals 0.1448.  
Figure 6: Ten feature values of ground motions.  
For the cases with 10 feature values as input, the result in Figure 8 indicates that with the increase of the number of nodes in the hidden layer, the accuracy of the model increases and then decreases. Moreover, if the number of nodes in the hidden layers further increases, the R\({}^{2}\), and MAE indexes become unstable.  
The BPNN with a single hidden layer achieves the best prediction accuracy when the number of nodes is 7, of which the average R\({}^{2}\) equals 0.6740, and the average MAE equals 0.1539. The BPNN with two hidden layers has the best prediction effect when the number of nodes in each layer is 4, with the average R\({}^{2}\) equals 0.6848, and the average MAE equals 0.1488.  
Figure 7: Optimization of the BPNN structure (40 feature values): (**a**) R\({}^{2}\) of validation set (one hidden layer); (**b**) mean absolute error (MAE) of validation set (one hidden layer); (**c**) R\({}^{2}\) of validation set (two hidden layers); (**d**) MAE of validation set (two hidden layers).
## 3 Results and Discussion
### Effects of the Operating Conditions on the SCWG of Cellulose
#### 3.1.1 Effects of Temperature on the SCWG of Cellulose
Figure 4: Volatile compounds obtained in the liquid effluent from the SCWG of cellulose at 560 \({}^{\circ}\)C.  
Figure 3: Gas chromatography–mass spectrometry (GC-MS) chromatogram of the effluent obtained from an SCWG process carried out at 560 \({}^{\circ}\)C, 250 bar, with an initial concentration of cellulose = 0.3 g/L and n = 0.4. On the other hand, although the best results in terms of gasification efficiency have been obtained at 560 \({}^{\circ}\)C, the influence of other operating variables has also been determined at 535 \({}^{\circ}\)C, where yields are smaller and any improvements in the process may be more evident.
## 6 Numerical Examples
### Uniformly Distributed Points  
We consider the MOT\({}_{\eta}\) problem for uniform measures \(\mu^{k}\) of uniformly distributed points on \(\Omega=[-1/2,1/2]\). We chose the entropy regularization parameter \(\eta=0.1\), so that a boundary regularization (13) for the fast summation method is necessary. For the tree-structured cost function, we set the boundary regularization \(\varepsilon_{\text{B}}=1/16\), the Fourier expansion degree \(M=156\), and the smoothness parameters \(p=3\) (see Section 5). In Figure 2 left, we see the linear dependence of the computational time on the number \(K\) of marginals. For a growing number \(N\) of points, the NEFT-Sinkhorn algorithm, which requires \(\mathcal{O}(KN)\) steps, clearly outperforms the standard method, which requires \(\mathcal{O}(KN^{2})\) steps (see Figure 2 right).  
In Figure 3, we show the computation times for the circle-structured cost function, with the parameters \(\eta=0.1\), \(\varepsilon_{\text{B}}=3/32\), \(M=2000\), and \(p=3\). As the Sinkhorn iteration requires matrix-matrix products, it is more costly than for the tree-structured cost function, and so we used a lower number of points \(N\). The advantage of the NEFT-Sinkhorn is smaller than for the tree, but still considerable. We point out here that the fast summation method is applied column by column to the matrix-matrix product, and the Fourier expansion degree \(M\) is larger.  
Finally, we investigate how the approximation error between the Sinkhorn and NEFT-Sinkhorn algorithm, \(|\mathcal{S}^{(r)}-\mathcal{S}^{(r)}|\), depends on the entropy regularization parameter \(\eta\) and the Fourier expansion degree \(M\) at a fixed iteration \(r=10\), where \(\mathcal{S}^{(r)}\) denotes the evaluation with the Sinkhorn algorithm and \(\mathcal{\tilde{S}}^{(r)}\) its evaluation with the NEFT-Sinkhorn algorithm. Since the time differences for different \(M\) in the one-dimensional case are very small, we consider the two-dimensional uniform marginal measures for the MOT\({}_{\eta}\) problems with tree- or circle-structured cost functions. In Figures 4 and 5, we see that for smaller \(\eta\), we need a larger expansion degree \(M\) to achieve a good accuracy. The error stagnates at a certain level and does not decrease anymore for increasing \(M\). This could be improved by increasing the approximation parameter \(p\) and the cutoff parameter of the NEFT, cf. [43] (Section 7). The parameter choice methods for the NEFT-based summation were discussed in [47]. For an appropriately chosen \(M\), the NEFT-Sinkhorn is usually much faster than the Sinkhorn algorithm. However, for very small \(\eta\), the kernel function (12) is concentrated over a small interval, and therefore, a simple truncation of the sum (11) might be beneficial to the NEFT approximation.  
Figure 2: Computation time in seconds of MOT\({}_{\eta}\) with tree-structured cost function with regularization parameter \(\eta=0.1\). **Left**: fixed \(N=10^{4}\). **Right**: fixed \(K=10\).
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 3 Methodology
### Particle Filter
#### 3.4.5 Position Estimation  
The weighted sum of particles is used to estimate the final position, which can be expressed as:  
\[\mathbf{X}_{k+1}=\sum_{i=1}^{m}\tilde{w}_{k+1}^{(i)}\mathbf{X}_{k+1}^{(i)}. \tag{21}\]