esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Experimental Results from Comparative Study on Different Decomposition Algorithms  
To verify the effectiveness of the VMD-EWT algorithm in decomposing Raman spectral sequences for spectral denoising, comparative experiments are conducted using the EMD [29], VMD [30], and EBiLSTM network only, and the VMD-EWT-EBiLSTM network structure. The Raman spectral curves from the test set are randomly selected for validation, with the results shown in Figures 7 and 8.  
Gaussian noise at 20 dB and 30 dB is added to the collected Raman spectral sequences. Under low noise interference, all denoising methods achieve the Raman spectral denoising task relatively well. However, with increasing noise, the denoising performance by the EMD and VMD methods decreases markedly. Using only the EBiLSTM method leads  
Figure 8: Comparative study on different decomposition denoising algorithms for 30 dB noisy signals.  
Figure 7: Comparative study on different decomposition denoising algorithms for 20 dB noisy signals.  
to larger fluctuations in denoising and some errors compared to the ideal spectral curve around the peaks. The VMD-EWT-EBiLSTM network structure used in this paper provides denoising results closer to the true values, and better denoising performance.  
Objective metrics on the signal-to-noise ratio (SNR) and mean squared error (MSE) are further utilized to evaluate the performance of different modal decomposition methods. The specific results are shown in Table 2 below. As shown in Table 2, the modal decomposition method proposed in this paper achieves a lower MSE and a higher SNR, with the MSE reduced by 23-56% and the SNR improved by 10-66% during denoising. This further verifies that under interference from different noise levels, using the VMD-EWT decomposition method to extract feature components helps improve the denoising accuracy for the Raman model.
# Data Hiding of Multicompressed Images Based on Shamir Threshold Sharing
## 3 Methodology
### Secret Message Extraction Stage  
In the subsecret message extraction phase, the user first downloads at least \(m\) = 3 triple representations of covert images containing subsecrets from different servers. First, judge whether the difference between the H and L of the triplet is less than the preset threshold T = 10. If H - L < T, take out the binary subsecret message in the bitmap in this triplet.  
Figure 5 shows 4 bitmaps of triples containing subsecrets, obtained from 4 shadow images stored on different servers. All bits are extracted from these 4 bitmaps, and then every 4 bits are converted into decimal, for example. As shown in Figure 5a, the extracted 16 bits is [0000 0101 0001 0100], which is [\(X_{1}\)\({}^{1}\), \(Y_{1}\)\({}^{1}\), \(X_{2}\)\({}^{1}\), \(Y_{2}\)\({}^{1}\)] = [0, 5, 1, 4] after conversion to decimal; the decimal numbers extracted from the remaining 3 bitmaps are [\(X_{1}\)\({}^{2}\), \(Y_{1}\)\({}^{2}\), \(X_{2}\)\({}^{2}\), \(Y_{2}\)\({}^{2}\)] = [2, 0, 3, 8], [\(X_{1}\)\({}^{3}\), \(Y_{1}\)\({}^{3}\), \(X_{2}\)\({}^{3}\), \(Y_{2}\)\({}^{3}\)] = [4, 3, 5, 1], [\(X_{1}\)\({}^{4}\), \(Y_{1}\)\({}^{4}\), \(X_{2}\)\({}^{4}\), \(Y_{2}\)\({}^{4}\)] = [6, 14, 7, 0]. After 1 is added to all elements, the four points {(1, 6), (3, 1), (5, 4), (7, 15)} on the \(f_{1}(x)\) are obtained, while the four points {(2, 5), (4, 9), (6, 2), (8, 1)} on the \(f_{2}(x)\) are obtained. Take any three points on the two functions and bring them into the Equation (1) to restore the coefficients; that is, the embedded secret numbers are [1, 2, 3] and [5, 6, 7], respectively.  
Figure 4: Example for subsecret embedding stage. (**a**) Cover image. (**b**) Bitmap before embedding. (**c**) Embedded bitmap.  
Figure 5: The bitmaps from four shadow images with embedded secret messages. (**a**) Bitmap 1. (**b**) Bitmap 2. (**c**) Bitmap 3. (**d**) Bitmap 4.  
Finally, the \(n\) shadow images embedded with the subsecret messages are stored on different servers, respectively. In our method, only \(m\) shadow images are needful to extract the original secret message. The remaining (\(n-m\)) shadow images do not affect the extraction of the secret message.
## 3 Results and Discussion
### Effect of Bond Young's Modulus  
The effect of the bond Young's modulus (\(E_{b}\)) was investigated. The relationship between the bond Young's modulus and simulated Young's modulus (\(E_{s}\)) is examined in Figure 6. The bond strength (\(\sigma_{b}\)) was 1.0 MPa and the relative particle size (\(h/d\)) was 7.06. The simulations were performed at three bond Young's modulus. Figure 6a presents the stress-deflection curve. The slope of the stress-deflection curve increased as the bond Young's modulus increased, while the maximum stress was almost consistent. Figure 6b shows the stress-strain curve. The slope of the stress-strain curve increased as the bond Young's modulus increased, while the maximum stress was almost consistent. The material stiffness was proportional to the bond Young's modulus, but the flexural strength (\(\sigma_{f}\)) and the compressive strength (\(\sigma_{c}\)) were not related to the bond Young's modulus. Figure 6c shows the ratio of the simulated Young's modulus to the bond Young's modulus (\(E_{s}/E_{b}\)). The normal bond stiffness (\(k_{b,n}\)) was proportional to the bond Young's when the bond length (\(l_{b}\)) was constant. Therefore, a uniform ratio (\(E_{s}/E_{b}\)) was obtained under the given simulation conditions. The ratio (\(E_{s}/E_{b}\)) greater than 1 meant that the ice fracture occurred at a lower deflection than the targeted ice condition. However, the uniaxial compressive tests showed a lower result of the ratio (\(E_{s}/E_{b}\)) than that of the three-point bending tests. This was due to the assumption of equal contact and bond modulus. When the contact and bond modulus were equal, the contact stiffness at a small overlap was much lower than  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Present** & **[PERSON] et al. [24]** & **[PERSON] et al. [26]** & **[PERSON] et al. [33]** & **[PERSON] et al. [35]** \\ \hline Bond Young’s modulus (\(E_{b}\)) (GPa) & 1.0 & 1.0 & 1.0 & 1.0 & - \\ Bond strength (\(\sigma_{b}\)) (MPa) & 0.75 & 0.74 & 0.73 & 0.7 & - \\ Flexural strength (\(\sigma_{f}\)) (MPa) & 1.15 & 1.59 & 1.25 & 1.27 & 1.08 \\ Compressive strength (\(\sigma_{c}\)) (MPa) & 3.03 & 2.55 & 5.64 & 3.73 & 2.56 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulated flexural strength in different DEM models.  
Figure 5: Typical simulation results: (**a**) stress–deflection curve in three-point bending test; (**b**) stress–strain curve in uniaxial compressive test. Points A and B indicate two arbitrary selected points to derive the simulated Young’s modulus (\(E_{s}\)). Point C corresponds to the maximum load (\(P_{max}\)) for calculating the compressive (\(\sigma_{c}\)) and the flexural strength (\(\sigma_{f}\)) in Equations (19) and (20), respectively.  
the bond stiffness. Therefore, the simulated Young's modulus was lower in the uniaxial compressive tests where contact was a dominant part of the load-resisting mechanism [25].
## 5 Conclusions  
The balance efficiency of the motor-driven mechanical balancing device before optimization was the largest when the rotation speed was 1000 r/min and the unbalance amount was 16.5 g.mm, with a maximum balance efficiency of 74%, and an average balance efficiency of 60.25%. After optimization, the amplitude of the spindle decreased by an average of 30.39%, and the maximum amplitude decreased by 50.18% compared to the unoptimized balance. The average balance time decreased by 31.72%, and the maximum balance time decreased by 43.86%. It can be seen from the experiments that the mass compensation model of the built-in mechanical on-line dynamic balancing system greatly improves the balancing efficiency of the single-plane balancing method. This model can calculate the optimal phase of the counterweight balance spindle through a genetic algorithm to balance the unbalanced force of the spindle and effectively reduce the vibration amplitude of the spindle. When the balance time is long and the working conditions greatly change, the moving mode of the balance head mass block can be changed by the mass compensation optimization model to improve balance time and efficiency. The accurate phase of the counterweight can quickly and effectively balance the high-precision rotating machinery of the spindle, and it can also reduce the spindle's balance time. The optimization model of mass compensation can improve the balance accuracy and efficiency of rotating machinery, lay the foundation for further reducing the vibration and noise of equipment, reduce loss, prolong the service life, and provide the basis for the on-line dynamic balance control strategy of the spindle, which has certain engineering application values. In the future, we will do further research on the situation of unbalanced force and unbalanced moment for long spindles, and we will achieve more accurate dynamic balancing through the double-plane correction method.  
Conceptualization, Z.W. and K.Z.; software, B.Z.; validation, B.Z., K.Z., and G.Y.; formal analysis, Z.W., B.Z., and Z.W.; resources, Z.W. and G.Y.; data curation, B.Z.; writing--original draft preparation, B.Z.; writing--review and editing, Z.W. and B.Z.; supervision, Z.W. and B.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51805337, No.51675353, No.51705340), Natural Science Foundation of Liaoning Province (No. 2019-ZD-0653).  
The authors declare no conflict of interest.  
Figure 12: Comparison of balance time.
## 4 Development of Machine Learning (ML) Models
### Data Preprocessing  
The TBM operation parameters were recorded at a much higher resolution, about a fraction of a meter, as compared to the rock mass data, which were collected every 4 to10 m. The rock mass data was taken at a coarse resolution because the rock properties in this section of the tunnel were not changing much within a short interval. Where a change in rock mass characteristics was observed, a finer rock mass data collection resolution was used in order to capture all the variations in the rock mass. Another possible reason for coarser resolution in the rock mass data is that taking the rock mass data involves shutting down the operations to allow for geologist to be able to access the tunnel walls. On the other hand, the machine operating parameters are easier to collect and does not require any downtime. For this study, the resolution of the machine data and the rock mass data had to be matched to enable usage of the machine data to predict the rock mass conditions. The chainage interval in the two datasets was used as a key to match the two datasets. That is, the rock mass record for a particular chainage interval is adjoined to all the TBM records in that chainage interval. This was done for all the data points in the rock mass dataset, creating the aggregate dataset used for this study.  
The variables in the dataset consist of a wide range of scales, tens to thousand. Consequently, the data was normalized so that the input variables are in the same scale. According to [PERSON] and [PERSON] (1993), normalization helps minimize bias caused by different scales of the input variables. Computational speed is also improved by data normalization since the features are put on the same scale. As a result, that dataset in this study was normalized using the min-max normalization which preserves the relationship between the input and output variables. The input variables were scaled to a range between a minimum of zero and a maximum of one. The preProcess() function in R software was used to normalize the data in this study. The normalization is achieved using Equation (1) ([PERSON] and [PERSON], 1993).  
\[x^{\prime}=(x_{max}-x_{min})\times\frac{(x_{i}-x_{min})}{(x_{max}-x_{min})}\ +\ x_{min} \tag{1}\]  
where \(x^{\prime}\) is the rescaled feature x, \(x_{max}\) is the maximum value of feature x, \(x_{min}\) is the minimum value of feature x, and \(x_{i}\) is the ith value of feature x.
## 2 Peculiarities of Implemented ML Pipelines for IR and THz Absorption Spectra Analysis
### Classification  
In the case of qualitative IR gas spectra analysis, the authors of the studied articles report successful ML methods application for a target component presence or gas mixture constituent identification. For instance, there is research devoted to mixture composition estimation relying on 15 functional molecular groups spectra [33]. According to these spectra, it is possible to define whether a molecule belongs to one of the studied functional groups. It can be highlighted that in the set of articles containing ML methods, the comparative analysis as well as data model architecture variations investigation in order to determine an optimal resulting data classification model is not large [59; 60; 61]. The characterization of the most often used ML methods presented in Tables S3 and S5 regarding the interpretability of their results is presented in Table 2. The main outline derived from Table 2 is the fact that, generally, only linear and simple models are interpretable. The complex models require the external tools mentioned above for their prediction results explanation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Idea** & **Advantages** & **Disadvantages** & **Ref. ** \\ \hline PCA & Data are transformed into a new system of coordinates with the first axis (the first principal component) oriented along a direction of the data maximal dispersion. If necessary, the second and other axes (the second principal component, etc. ), are oriented perpendicularly to the previous axis. & PCA is not suitable for the case of data spatial distribution similar to a nonlinear curve (nonlinear manifold).
## 4 Performance Evaluation and Discussion
### Overall Evaluation  
We assessed the performance of our proposed model on a publicly available database and the subjective test. In this way, we could validate the predicted cumulative QoE in both quantitative and qualitative manners. Typically, the model can precisely provide cumulative QoE prediction in  
Figure 11: Performance of our predicted cumulative QoE in comparison with the subjective cumulative QoE.  
Figure 10: Scatter plot of predicted cumulative QoE and subjective cumulative QoE.  
different scenarios. Therefore, our proposal promisingly provides an alternative and reliable approach in modeling QoE towards QoE based control and management.
## 2 Materials and Methods
### Characterization Methods  
#### 2.4.1 Thermal Analysis  
Differential scanning calorimetry (DSC) was used to study the thermal properties and phase transitions of the electrospun PLLA fibers by using a DSC Q200 (TA instrument). The glass transition temperature (\(T_{g}\)), cold crystallization temperature (\(T_{c}\)) and melting temperature (\(T_{m}\)) of the samples were measured. As-prepared and annealed electrospun fibers deposited on aluminum sheets were first peeled off from the metallic substrate and a sufficient number of samples were put inside an aluminum pan (5-7 mg). The samples were then heated from 20 to 200 \({}^{\circ}\)C with a heating rate of 10 \({}^{\circ}\)C min\({}^{-1}\). The crystalline content (\(X_{c}\)) of the sample can be estimated quantitatively, as calculated by the following equation:  
\[X_{c}=\frac{\Delta H_{m}-\Delta H_{c}}{\Delta H_{m}^{0}} \tag{1}\]  
where \(\Delta H_{m}\) and \(\Delta H_{c}\) are the enthalpies of melting and cold crystallization determined from the DSC thermogram, respectively. \(\Delta H_{m}\) is the enthalpy of melting for a PLA single crystal which is 93.7 J/g [35].
## 6 Simulation Results
### Transmission Using Proposed Algorithm
#### 6.4.1 QoS at \(D\)  
The SNR achieved at \(D\) for specific x and y locations in the warehouse plane is shown in Figure 10. The algorithm operates by maintaining a balance between the received signal conditions to achieve a better data rate as well as to ensure that the energy consumption through the system is maintained appropriately. We consider that an initially fully charged battery is used in the system; thus, the algorithm does not have any conditions for the initial selections, therefore providing a better opportunity to maintain higher performance.  
As can be seen from the results, the SWIPT scheme was selected in most cases, and ABCS was selected only in scenarios where the SNR was considerably low. Since \(S\) moves across the warehouse floor, it is evident that specific locations that are further away from \(R\) result in considerably lower SNR, and in such instances, the additional requirement of the ability to harvest sufficient energy for transmission plays a major role in determining the ideal transmission scheme.  
Figure 9: SNR at \(D\) if the relay used ABCS for transmission at specific S locations of x and y coordinates in the warehouse plane given in Figure 4.  
In instances where the SNR of the signal received at \(R\) is considerably low, the ability to harvest the required amount of energy for transmission is hindered; hence, the algorithm opts for ABCS to support energy consumption.  
The highest performance is shown when \(S\) moves along the \(y=3\) and \(y=4\) axes, and this is due to the placement of \(R\) at (3.5, 3.5). Maintaining a closer distance between the \(S-R\) link undoubtedly supports the transmission in the \(R-D\) link; however, only the furthermost points of the warehouse show considerably lower SNR values. Better performances can be seen at the \(y=1\) and \(y=2\) axes; however, the lowest performance is seen at the \(y=6\) axis.  
The drastic decrease in performance, which would be inevitable if the system were to adopt either ABCS or SWIPT, as well as the massive battery power consumption if the system were to rely on another relay model as shown, is significantly mitigated while providing better performance through the selection between the provided transmission schemes.
# Spatial Variations in Microfiber Transport in a Transnational River Basin
## 4 Discussion
### Influencing Factors of Microplastic Contamination
#### 4.2.3 Impoundment and Microplastic Transport  
The flow of the Tisza River is regulated by three dams at Tiszalok, Kiskore and Novi Becej (Figure 1). Upstream of the dams the flow velocity drops, allowing the accumulation of the suspended MP particles, whereas downstream of them the clear water erosion can mobilize the deposits on the bottom of the channel [76]. The SSC in all reservoirs gradually declined downstream, referring to sedimentation, whereas the clear water erosion downstream of the Kiskore Dam is clearly indicated by the increased SSC. On the other hand, the transportation of suspended MPs has dissimilar trends in the reservoirs of the Middle Tisza compared to the reservoir of the Lower Tisza. In the Middle Tisza, behind the Tiszalok and Kiskore Dams, the SSC gradually decreased, simultaneously with SSC, due to the decreasing gradient and flow (Figures 3 and 5). This suggests that the MP transport is influenced by the same factors as the transport of natural fluvial sediments. In contrast, in the reservoir of the Novi Becej Dam, though the SSC is declining downstream, the amount of transported MP has an increasing downstream trend. This can be explained by the increased MP input via untreated wastewater discharge, as in Serbia the WWTPs are poorly operating [77].  
The increased stream power downstream of the Kiskore Dam mobilized the sediments with MPs on the channel bottom, thus high MP pollution was measured (at Sites \"p-q\") at the sites downstream of the dam. Similar observations were made by [PERSON] et al. [78].  
Thus, dams and reservoirs can break the longitudinal, downstream MP transport trend. A similar spatial trend in MP content was measured along the Tisza in the freshly deposited sediments [35], as downstream of the most polluted tributaries the amount of MP increased, and in the reservoirs towards downstream it decreased due to the gradual deposition of natural and plastic particles.
# Energy Aware and Quality of Service Routing Mechanism for Hybrid Internet of Things Network
## 6 Conclusions  
This paper proposed an energy-aware and QoS routing algorithm for wireless mesh-connected visual sensor nodes in a hybrid IoT network. The routing algorithm allows routing a set of video streams with guaranteed bandwidth and limited delay with the assumption that the end-to-end packet transmission delay is proportional to the number of hops in a path, while involving the minimum number of active visual sensor nodes in the network. We formulated the problem as an integer linear program (ILP), and for finding the minimum number of visual sensor nodes to be used (activated), we imposed bandwidth and delay constraints. We used a branch-and-bound algorithm to solve the integer linear programming optimization model. The proposed approach was evaluated via simulation and compared with previous approaches from the literature, such as an aggregation algorithm and a shortest path algorithm. A clear tradeoff was observed between energy saving and QoS. The simulation results show that the proposed approach achieves considerable overall energy savings while guaranteeing QoS in terms of bandwidth and delay. The proposed approach allows as many visual sensor nodes as possible to be completely turned off to reduce the global energy consumption of the network. Putting nodes in a complete showdown mode has the add-on benefit of reducing wireless interference between wireless nodes. The work considered mesh-connected visual sensor nodes with a single gateway, but it could be extended to consider a different number of gateways to assess the variation of the different performance metrics. The study can also be extended to develop feasible heuristic solutions for networks involving a large number of nodes.  
Conceptualization, E.D.D. and K.A.A.; methodology, E.D.D. and K.A.A.; software, E.D.D. and K.A.A.; writing--original draft preparation, E.D.D.; writing--review and editing, E.D.D., D.P.S., and K.A.A.; supervision, D.P.S. and K.A.A. All authors have read and agreed to the published version of the manuscript.  
This research was part of a Ph.D. under a program supported by the French Embassy in Ethiopia and the Ethiopian Ministry of Science and Higher Education.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflict of interest.
# An Anomaly Detection Method for Wireless Sensor Networks Based on the Improved Isolation Forest
## 4 Discussion
### Experimental Parameter Setting  
The experimental platform used in this paper is a Windows host with a 3.40 GHz CPU and 24.00 GB memory. The experimental program is written in Java. BS-iForest has two input parameters: the subsample size, \(\psi\), and the number of items, \(T\). The subsample size controls the size of the training data because when \(\psi\) increases infinitely, it will increase the processing time and require more memory. This paper uses \(\psi=256\) and \(T=100\) as the experimental values.