esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Nowadays, evolutionary algorithms are applied in solving problems of optimization, classification, clustering, forecasting, and regression in various practical fields [1, 2, 3, 4].  
In addition, evolutionary algorithms are an efficient tool for solving identification problems [5, 6]. System identification is the process of constructing mathematical models of dynamic systems based on measurements that describe input and output variables of the process under consideration. One of the most informative ways to represent models of dynamical systems are differential equations and their systems [7, 8]. This way of describing the processes makes it possible to present the relationship between variables in an interpretable form as well as to reflect the dynamics.  
The problem of parametric identification is mainly solved with the help of evolutionary algorithms. Thus, evolutionary approaches to parametric identification are considered in the following papers [9, 10].  
Parametric identification determines the parameter values of a model with an already known structure. This class of models makes it possible to describe a lot of real processes. For example, there exist processes described by a homogeneous differential equation in economics, biology and chemistry [6].  
The presented papers consider a genetic programming algorithm applied to construct a model in the form of an ordinary differential equation (ODE). However, they are all from a class of homogeneous equations. Consequently, the method is not universal due to its applicability to only one class of ODEs [11,12].  
However, there is a need to identify processes with a previously unknown structure with the increasing complexity of technological processes. In this case, there is a need for automated methods to construct models of dynamic systems of arbitrary structure. The need to search for such models is faced by specialists in such fields as metallurgy, chemical industry and thermal power plants.  
Various methods have been developed due to the current significance of the problem being solved. Nonparametric methods and artificial neural networks are of the greatest importance [13, 14, 15, 16, 17].  
However, these methods are often unable to construct a model that reflects the true nature of the process in an understandable and interpretable form. The paper presents approaches to the identification of dynamical systems in the form of ODEs and their systems.
# Fit for What Purpose? NER Certification of Automatic Captions in English and Spanish
## 7 Conclusions  
The results obtained in this study are the first ones to report an industry-standard average accuracy rate for automatic captions in English and Spanish. Until now, random instances of automatic live captions in these languages had been noted to reach the 98% NER-based quality threshold [7,22], but the average accuracy rates of the full samples analysed always fell below. Lexi's automatic captions reported in this article generally surpass the quality threshold, with a few exceptions that reveal what the main challenges are in this area, namely speaker identification and colloquial conversations with fast and overlapping speaker exchanges. Once these issues are addressed (for instance, in the case of speaker ID, by feeding the SR software (v.3.5.10) with individual tracks per speaker), there is no reason to believe that the accuracy of automatic live captions cannot rival that of human live captions.  
However, ultimately, when discussing or choosing between human and automatic live captions, it essential to consider not only their accuracy (as has been extensively addressed in this article), but also their overall quality and the purpose that they are expected to serve. For the time being, they seem to serve two different purposes. As noted in the previous section, automatic live captioning, and perhaps automated access in general, provides access in bulk. It is still subject to improvement, but it is also increasingly accurate and  
Figure 4: Classification of live captions along the Caption to Transcription Continuum.  
available in contexts that were until now inaccessible and for a rate that cannot be matched by human captions. In contrast, human live captioning, and perhaps human access in general, provides curated access. It is more costly and time-consuming, but it is the only one that puts the user at the centre. It acknowledges that live captioning is not only about making captions available, but about considering different types of users and prioritising those who may not be served by access in bulk.  
The hope is that, going forward, human and automatic captions (and in general access professionals and technology) can coexist to ensure that captions are provided in bulk for a majority of users, as well as in a more curated manner for those who need access to be not only a service, but a form of human connection [30].  
Conceptualization, P.R.-F.; Methodology, P.R.-F.; Formal analysis, Y.V.G.; Investigation, P.R.-F.; Data curation, Y.V.G.; Writing--original draft, P.R.-F.; Writing--review & editing, Y.V.G.; Funding acquisition, P.R.-F. All authors have read and agreed to the published version of the manuscript.  
This research has been partially supported by the Spanish government-funded project NewsMAS (Human-Machine Interaction in the New Media Accessibility Spectrum, PID2023-148829 NB-I00).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines
## 5 Discussion
### Use of Edge Architecture vs. Cloud Computing in the Proposed Framework  
In our proposal, we integrated an edge layer to address the primary goal of having a device layer data with minimal latency, treating them as immediate data, and considering only minimal framework layer latency for bottleneck prediction. This layer operates near the manufacturing environment, providing the advantage of rapid and accurate availability. Dedicated secure edge gateways, as mentioned in [66], are offered by some vendors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Register\_Date** & **Qa\_Time\_Engine\_Machine** & **Qa\_Time\_Thermal\_Machine** \\  & **(Sg)** & **(Sg)** \\ \hline
13 November 2023, 16:42:10 & 1.89 & 0.847 \\
13 November 2023,  
In contrast to cloud computing, sending data to the cloud can introduce delays, and there is the challenge of network unavailability, as highlighted by various authors [67; 68]. Therefore, ensuring high data availability and, depending on criticality, employing a different network to mitigate network failures would be essential.  
Another aspect to consider is the security level. According to the authors of [68], one of the biggest problems of the Cloud is the data stored with different providers and in different locations. In addition, industrial data transmitted to the cloud are sensitive, as they contain relevant manufacturing information that can be subject to interception and lead to losses [67].  
Conversely, in edge computing, we handle and process a minor set of data compared to the extensive processing capability of the cloud. In this context, the authors of [67] proposed diverse architectures to address the limitations of both edge and cloud computing. They also introduced the concept of Fog computing, emphasizing the advantage of establishing a network with lower latency and reduced data load toward the cloud.  
Ultimately, the integration of technologies, such as edge, fog, and cloud, aims to foster collaboration among them, with the choice depending on the specific application and its requirements. For our proposed architecture, incorporating a cloud layer at the enterprise level is conceivable. Cloud services could handle analysis, observation, and monitoring, leveraging historical data, while also accommodating newly integrated real-time backend information for relevant comparisons. The front-end component of the Andon system would also necessitate consideration, as it relies on real-time data responses.
# State of the Art in Metal Matrix Composites Research: A Bibliometric Analysis
## 2 Publication Distribution along Various Verticals
### Research Directions  
After the previous subsection on the distribution of the publication sources, the present sub section throws light on the various research directions focused by investigators in the field of MMCs. Figure 5 provides these research directions with their specific article counts filtered by the WoS corpus search. The analysis results indicate that the maximum publications (7580, 75.66%) were categorized under the general topic of'materials science', indicating the materials specific focus of most articles. The topic of'metallurgy/metallurgical engineering' emerged as the next most investigated area (3242, 32.36%). This indicates the number of articles dedicated to the various processing methods of the metal matrix composites. The next most significant chunk of the publications (2703, 26.98%) belonged to the general area of 'engineering', indicating a more general engineering based approach to the problems associated with MMC fabrication, machining and testing. Similarly,'science technology and other topics' included 1082 (10.80%) articles, showing the percentage of investigations into novel scientific and technological aspects of metal matrix composites. The articles dealing with the physical and chemical properties of the various particulate materials and corresponding reinforced metal matrices numbered 839 (8.38%) and 738 (7.37%), respectively. About 436 (4.35%) articles reported investigations upon the various mechanics and mechanisms involved in processing and machining these advanced materials. Researchers also published 151 (1.51%) papers aimed at the integration of MMC machining aspects in industrial control systems for automated manufacturing. In addition, 83 articles focused on the mining, extraction and treatment of micro and nano particles for imparting desirable properties to the reinforced base metal alloys. Investigators also worked on applications of metal matrix composites in the field of and fibre optics and optical instruments (72 articles). Figure 6 depicts the publication trends of each of the above-mentioned research directions during 2001-2020. These results clearly indicate the prevalence of'materials science' as the highest trending general research area in the field of metal matrix composites. It is readily evident that the majority of all published MMC articles have'materials science' as their central theme. The next most important trending topics  
Figure 4: The top 10 publication sources.  
are'metallurgy metallurgical engineering' and 'engineering', implying the ever increasing general interest in processing, machining and engineering applications of various MMCs. The related topics of'science technology', 'chemistry' and 'physics' witnessed moderately incremental interest over the past two decades. Research areas such as 'automation control systems','mining mineral processing' and 'optics' do not indicate any upward publication trend. Hence, these research directions offer maximum opportunities for carrying out novel MMC investigations.
## 4 Experiments
### TfEn-based feature extraction  
To more fully take advantage of the useful information, in the proposed TfEn, each decomposed Imf is first transformed into the time-frequency matrix, and then each 2D matrix is further divided and calculated to the energy density matrix using the normalization operator. The energy density matrix exhibits the irregularity and dynamic change from both the time and frequency domain, and it can be finally quantified as a TfEn feature.  
Specifically, the related hyper-parameters of STFT are set as follows: the window length (window) is set as 512, the cover length (novelap) is set as 510, the Fourier transform length (nfft) is set as 512 that is equal to the window length. And the hyper-parameters of normalization operator are defined: the span of frequency-axis (F) is set as 64, the span of time-axis (T) is set as 4, the translation span of frequency-axis (L\({}_{\ell}\)) is set as 32 and the translation span of time-axis (L\({}_{\ell}\)) is set as 2.  
Figure 6: Raw signal and decomposed intrinsic mode functions (Imfs) under the normal mode.  
Figure 7: Raw signal and decomposed Imfs under the outer-race fault.  
Repeating the above operations for all 10 Imfs decomposed by CEEMD, we will get the TfEn feature vector of size \(10\times 1\). Limited to the paper space, we only present the corresponding results of the \(lmf_{1}\) under the modes of normal and outer-race fault, which are shown in Figures 8 and 9.  
As shown in Figures 8 and 9, 2D time-frequency matrices exhibit the difference of energy distribution among the Imf\({}_{1}\)s under the modes of normal and outer-race fault. Through the calculation of normalization operators block by block, these 2D matrices are further extracted and compressed into the energy density matrices of smaller size. The normalization operator is similar to the convolution filters in convolutional neural networks, which condenses each low-level local information into the high-level features. As shown by these energy density matrices, the irregularity of time-frequency distribution under each mode are further highlighted and depicted.  
As described in Section 3.3, each density matrix is finally quantified as a TfEn feature, it measures the complexity and dynamic change of each Imf sub-signal from both time and frequency domains. Repeating the above operations for all the other Imfs, we obtain the TfEn feature vector including 10 elements.
## 3 Methodology  
### Numerical Models  
Various computational techniques have been employed to simulate the evolutions of turbidity currents and undersea debris flows. These techniques include one- and two-dimensional shallow water equations [34], the finite element method [35], the finite volume method [36], the smoothed particle hydrodynamics (SPH) method [37], the finite difference method [38], the material point method [39], the CFD method and the arbitrary Lagrangian-Eulerian method [40; 41]. Compared with the other methods (e.g., SPH and MPM), the CFD describes a fluid by describing the density and energy of the material passing through the coordinate points, which is more in line with the characteristics of the water-soil energy exchange during the sliding process. Therefore, in this study, the CFD method, together with the multiphase flow theory, is used to simulate the transformation process using a commercial package, ANSYS fluent.  
In previous studies using the CFD, debris flows, treated as bed loads, were frequently simulated using a multi-phase model of the volume of fluid (VOF) or the mixture, while turbidity currents, considered as suspended loads, were modelled using single-phase fluids with specific solid concentrations. Note that the VOF model uses a technique for tracking interfaces on a Eulerian mesh [42], being used for interactions between two or more fluids when the locations of the interfaces between two fluids are crucial. Due to this explicit link, the VOF model can effectively capture the impact of shearing during the transformation from debris flows into turbidity currents [43]. In addition, the volume fraction and turbulence formation can be used to represent the outcome of the turbidity current. However, the mixture between the diluted debris flow and the ambient water cannot be represented. In comparison, the interacting phases can undergo interpenetration in the mixture model [44]. As a result, the mixture model can be used to characterize the mixing process of turbidity currents after the impact of shearing between the debris front and the ambient water is analyzed. Herein, the mixture model is chosen for the later analyses.
## 5 Evaluations
### Performance Analysis of the Object Detection Function
#### 5.2.1 Box and Class Loss Validation of the Object Detection Function  
We validated the model by applying the _Box Loss_ and _Class Loss_ validation methods. These methods compare detection results over training and validation datasets. This is intended to measure the performance of the model for different datasets to avoid overfitting.  
The _Box Loss_ method measures the difference between the detected bounding box and the ground truth bounding box coordinates. A lower value of the _Box Loss_ indicates better performance in terms of accurately detecting the location of objects in images. As shown in Figure 5(a), the trained model accurately predicts the object's bounding box. It achieved final _Validation Box Loss_ and _Train Box Loss_ values of 0.311 and 0.256, respectively. These results represent the ability of the _YOLOv8_ model to detect the bounding box over a varied dataset.  
The _Class Loss_ method measures the difference between the detected class and the ground truth class. A lower value of the measure indicates that the _YOLOv8_ model accurately predicts the class of the objects. Figure 5(b) shows the validation of the model in predicting the class of the objects. It achieved accurate classification results, with final _Validation Class Loss_ and _Train Class Loss_ values of 0.182 and 0.164, respectively. These results represent the ability of the trained model to detect the class in the datasets. The validation results indicate that the trained model successfully predicted the class of the object and avoided overfitting.
###### Abstract  
One approach employed in brain-computer interfaces (BCIs) involves the use of steady-state visual evoked potentials (SSVEPs). This article examines the capability of artificial intelligence, specifically convolutional neural networks (CNNs), to improve SSVEP detection in BCIs. Implementing CNNs for this task does not require specialized knowledge. The subsequent layers of the CNN extract valuable features and perform classification. Nevertheless, a significant number of training examples are typically required, which can pose challenges in the practical application of BCI. This article examines the possibility of using a CNN in combination with data augmentation to address the issue of a limited training dataset. The data augmentation method that we applied is based on the spectral analysis of the electroencephalographic signals (EEG). Initially, we constructed the spectral representation of the EEG signals. Subsequently, we generated new signals by applying random amplitude and phase variations, along with the addition of noise characterized by specific parameters. The method was tested on a set of real EEG signals containing SSVEPs, which were recorded during stimulation by light-emitting diodes (LEDs) at frequencies of 5, 6, 7, and 8 Hz. We compared the classification accuracy and information transfer rate (ITR) across various machine learning approaches using both real training data and data generated with our augmentation method. Our proposed augmentation method combined with a convolutional neural network achieved a high classification accuracy of 0.72. In contrast, the linear discriminant analysis (LDA) method resulted in an accuracy of 0.59, while the canonical correlation analysis (CCA) method yielded 0.57. Additionally, the proposed approach facilitates the training of CNNs to perform more effectively in the presence of various EEG artifacts.  
BCI; SSVEP; CNN; EEG; data augmentation; transfer-learning Article
## 2 Related Work
Although such DNN-based methods are efficient and accurate, their accuracy depends on generating training data with the same distribution as real-world data, which is an arduous task. Furthermore, DRL is the third method to tackle dynamic channel quality. [PERSON] et al. first introduced DRL to the satellite resource allocation and proposed a dynamic channel allocation method for GEO satellites to decrease long-term blocking probability and improve spectrum efficiency [17; 18]. Then, a multi-agent reinforcement learning-based bandwidth allocation of each beam was presented to satisfy the varying traffic demand [19]. [PERSON] et al. studied power allocation and drew support from DRL to adjust each beam's transmit power according to the varying traffic demands in cache queue and channel conditions [20]. In summary, the success of the above DRL-based spectrum and power allocation methods can be attributed to the interference management and dynamic environmental perception ability of DRL. DRL-based methods [21] allocate resources according to the current specific channel quality and traffic demand, which is similar to optimization or DL-based methods. Furthermore, DRL-based methods can continuously adjust the allocation strategy intuitively to promote long-term reward according to the environment's feedback. Such characteristics provide them with the following two advantages over DL or optimization-based ones. The first is that DRL-based methods can perform real-time adjustments with dynamic channel quality, which is changing continuously. Although the channel quality is also regarded as the observation of the environment, the DRL method uses DNN to perceive the environmental information, which shows strong robustness to noisy and error data. Therefore, it does not completely rely on the accuracy of CSI to ensure performance. The second is their long-term reward promotion of online multiple decision problems, which is precisely needed in large-scale S-IoT terminal access. However, the DL approach focuses more on current benefits, which may lead to a local optimum when solving online problems.
# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
## 5 Optimal Offloading Decision for Collaborative Task Execution
### Dynamic Programming Algorithm
The backward iteration equation with minimum task completion time is expressed by  
\[H_{k-1}(r_{k-1})=\min_{s_{k}}[t_{k}(r_{k-1},s_{k})+H_{k}(r_{k})] \tag{20}\] \[H_{n+1}(r_{n+1})=0\]  
where \(t_{k}(r_{k-1},s_{k})\) refers to the completion time of task \(k\) being offloaded to \(s_{k}\) after task \(k-1\) at position \(r_{k-1}\) is executed. Both \(H_{k}(r_{k})\) and \(t_{k}(r_{k-1},s_{k})\) are known. The latter one can be obtained using (5), (7), (9) and (11) when the task is offloaded, that is, \(t_{k}(r_{k-1},s_{k})=t_{x,y}^{k}+t_{k}^{y}\) and \(y=s_{k}\). Third, let \(J_{k-1}(r_{k-1})\) be the minimum aggregated cost from task \(k-1\) to task \(n+1\). \(J_{0}(r_{0})\) is the minimum aggregated cost for all tasks of the application, given \(J_{n+1}(r_{n+1})=0\). Knowing \(H_{k}(r_{k})\) at state \(k\) for location \(r_{k}\), we obtain every decision at state \(k-1\) so that the aggregated cost from state \(k-1\) to state \(n+1\) is minimized. The backward iteration equation with minimum task aggregated cost is represented by  
\[J_{k-1}(r_{k-1})=\min_{s_{k}}[b_{k}(r_{k-1},s_{k})+\lambda t_{k}(r_{k-1},s_{k} )+J_{k}(r_{k})] \tag{21}\] \[J_{n+1}(r_{n+1})=0\]  
We use iterative Equations (19)-(21) to implement the processes of _PathAlgorithm(S, D, b)_, _PathAlgorithm(S, D, d)_ and _PathAlgorithm(S, D, b)_ to find the minimum cost, time delay and aggregated cost, respectively. Finally, the minimum-cost offloading decision strategy with time delay constraints is obtained in Algorithm 1. The proposed approach is designed for the applications considered. This is not the only scenario that can be used. Other scenarios, such as an individual vehicle or a cluster of vehicles in the cellular network, also can use the proposed method.