esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions and Future Perspectives
For instance, biodegradable polymers should be engineered to minimize toxic degradation products, while recycling processes must incorporate advanced decontamination steps to eliminate legacy substances. Finally, existing regulations, such as Regulation (EU) No 10/2011, focus on intentional additives and predefined SMLs, leaving NIAS oversight fragmented. Harmonized guidelines mandating non-targeted screening for novel materials, coupled with transparency in reporting unidentified peaks, will enhance consumer protection. Regulatory acceptance of non-targeted data, including provision for semi-quantitative thresholds and bioassay evidence, must be fostered through stakeholder engagement and scientific consensus. In conclusion, the intersection of analytical chemistry, toxicology, and materials science will define the next era of NIAS research. Emerging technologies, such as IMS, machine learning, or artificial intelligence-driven spectral prediction, promise to deepen our understanding of NIAS migration, transformation, and biological impact. However,translating these advancements into actionable policies requires sustained collaboration among academia, industry, and regulators. Moving forward, operational roadmaps should prioritize the harmonization of analytical workflows, the coordinated generation of reference standards, the integration of mixture toxicity into risk models, and regulatory frameworks that incorporate non-targeted evidence. Ultimately, ensuring the safety of plastic FCMs demands a proactive, precautionary approach that balances innovation with rigorous hazard characterization, fostering public trust in sustainable packaging solutions while safeguarding global health. Conceptualization, P.M., E.F.-F., C.S.-H., and C.C. ; data curation, P.M., E.F.-F., and C.S.-H.; formal analysis, P.M.; investigation, P.M.; resources, P.M., E.F.-F., C.S.-H., and C.C. ; supervision, P.M. and C.C. ; visualization, P.M., E.F.-F., and C.S.-H.; writing--original draft, P.M.; writing--review and editing, P.M., E.F.-F., C.S.-H., and C.C. ; project administration, C.C. All authors have read and agreed to the published version of the manuscript. This research was funded by Project CPP2022-009753, which is funded by MICIU/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTRTR. [PERSON] would like to thank the \"Instituto de Salud Carlos III\" (ISCIII) for his \"[PERSON]\" (CD22/00070) postdoctoral fellowship (funded by the European Union--Next Generation EU). [PERSON] would like to thank the \"Cabildo de Tenerife\" and UTE Nivaria for her contract under the \"Eliminacion de contaminantes en liviviaulos mediante el uso de microplasticicos y/o residuos plasticos uphanos\" (reference number 24100080) project. No new data were created or analyzed in this study. Data sharing is not applicable to this article. The authors declare no conflicts of interest.
## 1 Introduction
Nodes dynamically maintain their neighbor tables through data packets, eliminating the need for additional control packets, which not only reduces energy consumption but also improves the efficiency of network resource utilization. The remainder of this paper is organized as follows. Section 2 reviews the literature related to existing routing protocols for underwater acoustic sensor networks. Section 3 describes the system model, including the network architecture and underwater acoustic channel modeling. Section 4 presents the proposed routing protocol in detail, covering the packet structure, routing and neighbor table design, route initialization, relay node selection and update, and data reception procedures. Section 5 evaluates the performance of the proposed protocol through simulations and analyzes the results. Section 6 concludes the article, highlights its limitations, and discusses directions for future work.
# Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction
## 3 Proposed Method  
### Problem Preliminaries  
A traffic network can be regarded as a weighted directed graph \(\mathcal{G}(\mathcal{V},\mathcal{E},\mathbf{A})\). Here, \(\mathcal{V}\) is the set of \(N\) traffic nodes (i.e., sensors or road segments), \(\mathcal{E}\) is the set of edges (i.e., directed connections between traffic nodes), \(\mathbf{A}\in\mathbb{R}^{N\times N}\) is a certain type of weighted adjacency matrix representing the proximity between each nodes pair, e.g., road distance graph.  
The traffic features observed at time slot \(t\) are represented as the graph signals \(\mathbf{X}_{t}\in\mathbb{R}^{N\times F}\), where \(F\) is the number of traffic features of each node. For example, the traffic features can be the traffic speed, traffic volume and congestion index of the road segments or other traffic related parameters. Given a traffic graph \(\mathcal{G}(\mathcal{V},\mathcal{E},\mathbf{A})\), the purpose of traffic prediction is learning a model \(f\) to map the historical graph signals observations of \(P\) time steps to the signals of the next \(Q\) time steps, which is denoted as:  
\[[\mathbf{X}_{t-P+1},\cdots,\mathbf{X}_{t}]\overset{f}{\rightarrow}[\mathbf{X}_ {t+1},\cdots,\mathbf{X}_{t+Q}]. \tag{1}\]
# Multiple Factor Analysis Based on NIPALS Algorithm to Solve Missing Data Problems
## 2 Methods
### Regularized Iterative Multiple Factorial Analysis  
RIMFA is based on iterative PCA and iterative MCA, both regularized [30; 31]. If the group of variables is qualitative or quantitative, an imputation method could serve as possible solution to missing data. Thus, the steps of RIMFA are given in Algorithm 1.  
```
0: NAs are replaced by the average by column in the group of quantitative variables. For the qualitative groups, a complete disjunctive table is formed and the NAs are replaced with the proportion of ones by column. while\(l\leq L\)do\(l=1,2,3,\ldots,L\)  PCA or MCA depending on the kind of variables group.  Taking into account weighing \(1/\lambda_{1}^{(l)}\), build a juxtaposed table \(\mathbf{Z}\).  Global PCA for \(\mathbf{Z}\).  Consider \(q\) (\(q\leq p\)) dimensions for the estimation of NAs.  Use the matrix reconstitution of the matrix to impute the NAs using \(q\) dimensions. The values with NAs are imputed and the non-NAs are left in their original form. The convergence is found when: \[\|\mathbf{Z}^{\prime}-(\mathbf{\psi}\mathbf{u})^{l+1}\|^{2}<\epsilon\approx 0.0001.\] (10) Increment: \(l=l+1\). endwhile
```  
**Algorithm 1** RIMFA  
Selection of the Number of Dimensions \(q\)  
This procedure can be performed through cross-validation (leave one out), where the number of dimensions is fixed and the change of mean square error (MSE) is observed when an \(i\)th observation is removed from the dataset. The cross-validation for the RIMFA could be the same as PCA, as suggested by [PERSON] and [PERSON] [13; 32]. The MSE with \(q\) dimensions is:  
\[MSE(q)=\frac{1}{np}\sum_{i=1}^{n}\sum_{j=1}^{p}(Z_{ij}-\widehat{Z}_{ij}^{q})^{2}, \tag{11}\]  
where \(\widehat{Z}_{ij}^{q}\) is the estimation of the \((ij)\)th element of \(\mathbf{Z}\) using \(q\) dimensions.
## 1 Introduction
Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa. In contrast, a digital twin (DT) is a high-fidelity virtual model that mirrors a physical entity. A DT simulates the physical entity's behaviour in a virtual environment by utilising real-time data, allowing system performance monitoring, prediction, and optimisation. To underscore this distinction, consider a traffic monitoring and control system. In the CPS context, this system involves surveillance cameras, traffic sensors, traffic lights, and embedded computers communicating with each other to adjust the traffic flow based on real-time road conditions. On the other hand, a DT for the same system would be a detailed virtual model of the traffic network, replicating the real-world situation in real-time based on data collected from sensors and cameras. While CPS and DT aim to integrate the physical and digital worlds, their functions and applications differ. CPS focuses on the real-time monitoring and control of physical systems using computational processes, while DT centres around virtual modelling and simulation. Even though CPS and DT share the goal  
Figure 1: Conceptual model of the digital twin. of bridging the gap between the physical and digital domains, their primary distinction lies in their categorisation. As a scientific category, CPS focuses on integrating computational and physical processes. Conversely, as an engineering category, DT emphasises using a digital copy of a physical system for real-time optimisation. This difference influences their respective roles and applications in industrial practices, with DTs being more prevalently used for enhancing precision and management. Let us consider another example--a wind turbine system to illustrate these differences. In the CPS context, integrated sensors and controllers within the wind turbine system continuously measure variables such as wind speed, blade position, and power output. These data are then processed to adjust the turbine's blade position and other operational parameters to optimise the real-time power output. On the other hand, a DT of the same wind turbine would be a virtual model that uses real-time input data from the CPS to simulate the turbine's operation in a virtual environment. Engineers can use this model to anticipate and plan maintenance, assess the impact of different weather scenarios on turbine performance, and test new control strategies or equipment configurations in a safe and controlled environment. These examples illustrate how CPS and DT complement each other to maximise the efficiency and reliability of modern industrial systems. While both technologies allow for greater integration between the physical and digital worlds, they approach this task from different perspectives and with various tools, proving their immense value in the era of Industry 4.0. manufacturing; their symbiosis promises a leap forward in the efficiency, flexibility, and intelligence of production systems.
# Elite Multi-Criteria Decision Making--Pareto Front Optimization in Multi-Objective Optimization
## Appendix A
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Test Function** & **Method** & **General Distance \(GD\)** & **Spread (\(\Delta\))** & **Spacing (\(Sp\))** \\ \hline Schaffer Function N2 & NSGA-II & 0.001906 & 0.742149 & 0.006122 \\  & NSGA-III & 0.130632 & 1.514319 & 0.097936 \\  & M-PF & 0.010325 & 1.319566 & 0.023796 \\  & eMPF & 0.096201 & **1.621567** & 0.070892 \\ \hline Poloni’s Two Objectives & NSGA-II & 0.0127 & 1.47718 & 0.737613 \\  & NSGA-III & 0.102661 & 1.789741 & 0.693373 \\  & M-PF & 0.031244 & 1.791253 & 0.664818 \\  & eMPF & 0.186827 & 1.55847 & 0.984685 \\ \hline Zitzler-Deb–Thiele’s N1 & NSGA-II & 0.042448 & 0.7066 & 0.074755 \\  & NSGA-III & 0.052309 & 0.712719 & 0.07806 \\  & M-PF & 0.045381 & 0.721581 & 0.07787 \\  & eMPF & 0.048533 & 0.685075 & 0.147347 \\ \hline Zitzler-Deb–Thiele’s N2 & NSGA-II & 0.073596 & 0.697774 & 0.19127 \\  & NSGA-III & 0.041085 & 0.710485 & 0.113637 \\  & M-PF & 0.068744 & 0.738163 & 0.120021 \\  & eMPF & 0.070442 & 0.712368 & 0.16111 \\ \hline Zitzler-Deb–Thiele’s N3 & NSGA-II & 0.032912 & 0.714989 & 0.098628 \\  & NSGA-III & 0.030987 & 0.660256 & 0.102081 \\  & M-PF & 0.044047 & 0.662726 & 0.111724 \\  & eMPF & 0.040325 & 0.72205 & 0.084448 \\ \hline Zitzler-Deb–Thiele’s N4 & NSGA-II & 0.810677 & 0.744636 & 9.043884 \\  & NSGA-III & 2.272926 & 0.757631 & 7.516822 \\  & M-PF & 2.018945 & 0.786935 & 8.574137 \\  & eMPF & 2.279626 & 0.738201 & 9.78726 \\ \hline Zitzler–Deb–Thiele’s N6 & NSGA-II & 0.191495 & 0.685276 & 0.115021 \\  & NSGA-III & 0.099431 & 0.59864 & 0.140287 \\  & M-PF & 0.065991 & 0.638246 & 0.173285 \\  & eMPF & 0.092465 & 0.69825 & 0.260617 \\ \hline Osycak and Kundu Function & NSGA-II & 1.504086 & 1.178373 & 3.283527 \\  & NSGA-III & 1.294484 & 1.127386 & 2.84922 \\  & M-PF & 1.316762 & 1.132501 & 3.077236 \\  & eMPF & 1.388888 & 1.2232 & 3.686033 \\ \hline Constr-Ex Problem & NSGA-II & 0.00495 & 0.714432 & 0.012929 \\  & NSGA-III & 0.043087 & 1.313374 & 0.067725 \\  & M-PF & 0.02453 & 1.179857 & 0.042256 \\  & eMPF & 0.07408 & 1.683737 & 0.090675 \\ \hline MSGA Test 1 & NSGA-II & 0.000538 & 0.972065 & 0.002284 \\  & NSGA-III & 0.013212 & 1.038185 & 0.015305 \\  & M-PF & 0.012998 & 1.492854 & 0.006631 \\  & eMPF & 0.003187 & 1.242157 & 0.004642 \\ \hline VR-UC Test 1 & NSGA-II & 0.01332 & 0.786952 & 0.05018 \\  & NSGA-III & 0.070166 & 1.427575 & 0.176857 \\  & M-PF & 0.142823 & 1.316431 & 0.190408 \\  & eMPF & 0.38772 & 1.695775 & 0.402943 \\ \hline VR-UC Test 2 & NSGA-II & \(9.4\times 10^{-5}\) & 0.717724 & 0.00000 \\  & NSGA-III & \(9.4\times 10^{-5}\) & 0.636498 & 0.00000 \\  & M-PF & 0.000324 & 0.577738 & 0.00000 \\  & eMPF & 0.000273 & 0.712867 & 0.00000 \\ \hline Vinnet Function & NSGA-II & 0.00169 & 0.782624 & 0.015777 \\  & NSGA-III & 0.627138 & 0.989541 & 0.087255 \\  & M-PF & 1.615187 & 1.21211 & 0.169447 \\  & eMPF & 0.648107 & 1.557092 & 0.095202 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._
## 6 Conclusions  
In this paper, we reviewed various existing methods for the assessment of probabilistic forecast models and discussed their advantages and disadvantages. While CRPS is only applicable to univariate models and ES suffers from the curse of dimensionality, CRPS-Sum was introduced to help us with assessing multivariate probabilistic forecast models. Unlike CRPS and ES, the properties of CRPS-Sum have not been studied in the past. Our sensitivity study illustrates that the CRPS-Sum behavior is not symmetric concerning the covariance of data distribution. CRPS-Sum is more sensitive to changes in the covariance of the model when the covariance of the data is negative. This is an undesirable behavior and makes result interpretation difficult.  
Furthermore, CRPS-Sum cannot reflect the performance of a model on each dimension due to the loss of information caused by summation during its calculation. We demonstrated this problem with simple examples and experiments on the exchange-rate dataset, where a dummy model based on random noise achieved better CRPS-Sum than the state-of-the-art model. Additionally, with the experiment on the taxi dataset, we portrayed that the study of the CRPS-Sum discrimination ability in higher dimensionalities is not feasible.  
To conclude, CRPS-Sum cannot provide an unbiased and accurate assessment for multivariate probabilistic forecasters. Thus, we suggest avoiding CRPS-Sum if possible. For data with low dimensionality, we can use ES. In higher dimensions, the assessment of the probabilistic forecast model is still an open problem. In the current state, it is difficult to rely solely on any existing metric and manual qualitative analysis should be used to evaluate the performance as well.
# A Hardware-Oriented Algorithm for Real-Time Music Key Signature Recognition
## 1 Introduction
In essence, the algorithmic tonality detection process boils down to the evaluation of the correlations existing between a given input vector, representing the analyzed fragment of the musical piece, and the major and minor key profiles. The key profile for which the value of the correlation coefficient is greatest indicates the overall tonality of the analyzed sample. It is worth noting that the process of tonality detection has recently gained much support from applications implementing various machine learning techniques, including artificial neural networks [18; 26]. Unfortunately, all these methods are characterized by a relatively high computational complexity, which constitutes a major limiting factor as far as the real-time generation of musical notation is concerned. The goal of this paper is to present the concept of an algorithm enabling the real-time recognition of the key signature corresponding to the analyzed fragment of a given piece of music. The essence of the method proposed herein comes down to the analysis and proper quantification of a music signature [28]. The discussed algorithm is well suited for hardware implementation in electronic musical instruments enabling generation of musical notation. It is a computationally simple technique, featuring a very low-latency generation of music notation. The cumulative nature of the method enables continuation of the detection process even after finding the matching key signature, and correcting it as soon as the algorithm finds a better match (using more notes). The paper is comprised of four main sections. Following this introductory section, in Section 2, the basics of the music theory related to the matter discussed herein and the proposed algorithm for the real-time recognition of the music key signature are presented. The results of the conducted experiments, as well as the analysis confirming the effectiveness of the proposed approach, are covered in Section 3. The paper ends with a discussion (in Section 4), in which conclusions from the study are drawn. In this section the authors of the paper also point out directions of their further endeavors.
## 3 Methods
### Wire Spacing Detection Method
#### 3.3.3 Coordinate Extraction
The contour lines of ROI images usually do not start and end at a certain peak or trough, but mostly start somewhere between the peak and the trough, so that two false peak and trough coordinates will be obtained in the previous calculation step, such as (\(x_{2}\), \(y_{2}\)) and (\(x_{n}\), \(y_{n}\)) points in Figure 18a, and the existence of these two pseudo points will affect the calculation results at the end. The method to eliminate these two points is by judging the size of \(x_{1}\) and \(x_{2}\), if \(x_{1}>x_{2}\), the image is considered to start from the wave peak and delete (\(x_{2}\), \(y_{2}\)); if \(x_{1}<x_{2}\), the image is considered to start from the wave trough and delete (\(x_{1}\), \(y_{1}\)); the same for the tail processing, so that the combination of two will form four types, which are defined as \"W-type\" and \"M-type\", \"N-type\", and \"inv-N-type\", and according to the distribution form of these points, the subsequent calculation requires the corresponding type of separate calculation results. Figure 16: Schematic diagram of pixel expansion. Figure 17: Pole simplification model. Determine the coordinates of the intersection point. Connect the neighboring peak and valley coordinate points, and find the mid-slope of each line, each adjacent two mid-slope lines will intersect to form the intersection, such as a certain valley-to-peak line of the mid-slope can be expressed by the point-slope of the position of the coordinate point \((k_{1},x_{1},y_{1})\), k\({}_{1}\) is the slope, and the adjacent peak to valley line of the mid-slope of the point-slope expressed as \((k_{2},x_{2},y_{2})\), calculate the two mid-slope line of intersection point formula is:  
\[ \begin{array}{l}x=(k_{1}\times x_{1}-y_{1}-k_{2}\times x_{2}+y_{2})/(k_{1}-k_ {2})\\ y=k_{1}\times(x-x_{1})+y_{1}\end{array}  \tag{8}\]  
where \(x\) and \(y\) are the intersection coordinates of two lines. Figure 18: Establish four models of traverse profile: (**a**) W-type; (**b**) N-type; (**c**) inv-N-type; (**d**) M-type. 6. Obtain spacing information. After judging the image, start the order from step 4, as shown in Figure 19; this is an ideal M-type contour, and every adjacent mid-pipeline will intersect to form several blue coordinate points. A set of data is thus obtained, which represents the pixel location coordinates of all the wires in the ROI image. As shown in Figure 20, the location of the white pixel is the shape center of the wire contour. Using the location coordinates, combined with the pixel conversion scale relationship, the Euclidean distance of the neighboring wires can be determined, and on this basis, subtracting a wire width, the true distance of the neighboring wires can be obtained, and based on this distance information, it is possible to determine whether there is large spacing in the ROI area.
# An Improved Algorithm of Drift Compensation for Olfactory Sensors
## 4 Results
### Sensor Drift Algorithm Comparison Experiment
#### 4.2.2 Experiment 2  
Table 3 below is the recognition effect of eight different algorithms in Experiment 2. Bold data also represents the highest recognition effect of each Batch. In order to better display the comparison results between different algorithms, the data in Table 3 are also converted into a histogram in Figure 5.  
Looking at Table 3 and Figure 5, similar conclusions can be drawn as in Experiment 1. Compared with the seven commonly used sensor drift compensation algorithms, DTSWKELM performs better overall, with an average accuracy of 88.30%, which is 6.82% higher than TDACNN. Compared with the SWKELM algorithm, it has a better recognition effect, and the average accuracy is 7.25% higher, which reflects the effectiveness of the domain conversion process. In addition, it can be seen that the recognition effect of each  
Figure 4: The histogram of the recognition effect of each algorithm in Experiment 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Task** & **1->2** & **2->3** & **3->4** & **4->5** & **5->6** & **6->7** & **7->8** & **8->9** & **9->10** & **AVG** \\ \hline SVM-rbf & 74.36 & 87.83 & 90.06 & 56.35 & 42.52 & 83.53 & 91.84 & 62.98 & 22.64 & 68.01 \\ SVM-comgfk & 74.47 & 73.75 & 78.51 & 64.26 & 69.97 & 77.69 & 82.69 & 85.53 & 17.76 & 69.40 \\ ML-comgfk & 80.25 & **98.55** & 84.89 & 89.85 & 75.53 & 91.17 & 61.22 & 95.53 & 39.56 & 79.62 \\ ELM-rbf & 70.63 & 40.44 & 64.16 & 64.37 & 72.7 & 80.75 & 88.2 & 67 & 22 & 63.36 \\ DAELM-S(5) & 72.66 & 69.99 & 72.61 & 79.54 & 52.93 & 87.18 & 91.36 & 56.66 & 29.05 & 68 \\ DTBLS & 78.67 & 97.65 & 79.88 & 67.01 & 75.34 & 90.44 & **95.1** & 68.09 & 54.47 & 78.52 \\ TDACNN & **89.56** & 97.46 & 87.58 & 94.68 & 73.9 & 80.18 & 78.43 & 83.19 & 47.64 & 81.48 \\ SWKELM & 76.13 & 90.73 & 90.68 & 93.4 & 73.43 & 83.73 & 87.07 & 92.55 & 41.69 & 81.05 \\ DTSWKELM & 88.26 & 97.23 & **93.17** & **98.98** & **78.43** & **93.99** & 93.19 & **96.38** & **55.08** & **88.30** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The recognition effect of each algorithm in Experiment 2 (%).  
algorithm in different tasks in Experiment 2 is generally higher than that in Experiment 1. This is mainly because the data of adjacent batches are relatively less affected by sensor drift, and the distribution difference between the data is relatively small.  
In summary, through two different sets of experiments verify the effect of the DTSWKELM algorithm, the same conclusion is obtained, and DTSWKELM shows the best recognition effect.