esa-sceva/satcom-chunk-collection huggingface dataset:
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 7 Conclusions  
A number of information criteria have been proposed and investigated for estimating the amount of signal-related SSA components in the noisy signal model, including applications to real-world time series. The approach that considers the matrix residuals and the weights resulting from the Hankel structure of the trajectory matrices enabled the construction of appropriate criteria, particularly in the case of white noise. The whitening technique was employed in the processing of the red noise case. As might be expected, the red noise case is more complex and offers less accurate results. Consequently, it is necessary to implement additional verification procedures within the information criteria for signals exhibiting trends. We have studied the possibility of testing the stationarity of the residuals. In general, the proposed approach can be extended to the case of AR(\(p\)) noise.  
Since the model order is typically used for a specific purpose, it is necessary to further investigate the methods discussed in this paper from the perspective of addressing a particular problem, such as trend extraction, parameter estimation, and forecasting.  
Conceptualization, N.G. and N.Z.; methodology, N.G. and N.Z.; software, N.G. and N.Z.; validation, N.G. and N.Z.; formal analysis, N.G. and N.Z.; investigation, N.G. and N.Z.; resources, N.G.; writing--original draft preparation, N.G. and N.Z.; writing--review and editing,N.G. and N.Z.; visualization, N.G. and N.Z.; supervision, N.G.; project administration, N.G.; funding acquisition, N.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the RSF, project number 23-21-00222.  
The scripts in R can be found in [[https://doi.org/10.5281/zenodo.1329](https://doi.org/10.5281/zenodo.1329)]([https://doi.org/10.5281/zenodo.1329](https://doi.org/10.5281/zenodo.1329)) 8984 (accessed on 1 September 2024).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SSA & Singular spectrum analysis \\ LRR & Linear recurrent relation \\ IC & Information criterion \\ AIC & Akaike information criterion \\ BIC & Bayesian information criterion \\ MDL & Minimum description length \\ AR(\(p\)) & Autoregressive process of order \(p\) \\ \end{tabular}
## 2 Materials and Methods
### Identification and Quantification of Individual Phytochemicals in LRE  
The LRE was further analyzed using UPLC/ESI-QTOF-MS to elucidate its phytochemical composition. The chromatographic separation was performed on an ACQUITY Ultra Performance LC system (Waters Corporation, Milford, MA, United States) equipped with a vacuum degasser, an autosampler, a binary pump, and a DAD detector. A volume of 2 \(\upmu\)L of diluted extract was loaded onto an RP18 column (1.7 \(\upmu\)m, 2.1 mm \(\times\) 100 mm; ACQUITY UPLC BEH Shield RP18) in the present chromatographic separation. The mobile phase was composed of acetic acid (1%, _v/v_) (Phase A) and acetonitrile (Phase B) and was pumped through the analytical column at a rate of 0.6 mL min\({}^{-1}\). The gradient elution program was set as follows: 0 min, 1% B; 2.3 min, 1% B; 4.4 min, 7% B; 8.1 min, 14% B; 12.2 min, 24% B; 16 min, 40% B; 18.3 min, 100% B, 21 min, 100% B; 22.4 min, 1% B; and finally, a conditioning cycle of 3 min with the initial conditions.  
MS analyses were performed with the employment of a Q/TOF micro mass spectrometer (Waters Corporation, Milford, MA, USA). The negative ionization mode was used, and the conditions were as follows: cone gas flow, 40 L/h; drying gas flow (N\({}_{2}\)), 11,000 L/h; nebulizer pressure, 50 psi; gas drying temperature, 360 \({}^{\circ}\)C; capillary voltage, 2500 V; fragmentor voltage and scan range, 3500 V and \(m/z\) 50-1500, respectively. The compounds were monitored at 280 nm. Integration and data elaboration were performed using MassLynx 4.1 software (Waters Corporation, Milford, MA, USA). The quantification was performed using the calibration curves that were prepared from the limit of quantification (LOQ) to 100 \(\upmu\)g mL\({}^{-1}\) obtained by MS. All calibration curves showed good linearity among different concentrations (r \(>\) 0.999).
# The Usage of Virtual and Augmented Reality in Underwater Archeology
## 5 Use of Augmented and Virtual Reality in Underwater Archeology--Example Solutions
### ARROWS Project  
The ARROWS project (\"ARchaeological RObot systems for the World's Seas\") was implemented in the years 2012-2015 under the 7 th Framework Programme. The project was financed by the European Commission [114]. The project was implemented by an international consortium consisting of 10 organizations from 6 countries--Italy, Estonia, Scotland, Turkey, Spain, and England ([[http://www.arrowsproject.eu/](http://www.arrowsproject.eu/)]([http://www.arrowsproject.eu/](http://www.arrowsproject.eu/)), accessed on 1 August 2024). The main objectives of the ARROWS project were related to the development and integration of advanced technologies for use in underwater and coastal archaeological sites, including tools for mapping, diagnosis, and securing these sites [114].  
Research conducted as part of the project focused primarily on automated vehicles dedicated to underwater archaeological work, but also developed auxiliary technologies [115, 116]. One of the technologies developed was a virtual environment enabling the presentation of all available data collected in various formats, in an interactive form and in three-dimensional space. The system was dedicated both to experts, for research pur poses, and to the general public, including for educational and information dissemination purposes in the field of underwater archeology [114].  
The Unity platform was used for visualization, and the \"Oculus Rift\" goggles and a gesture-responsive interface, \"Leap Motion\", were used as peripheral devices [114]. As part of the developed system, the user could interact with various objects and obtain additional information about them, i.e., 3D mesh reconstruction for a given object which was possible to be viewed from various perspectives, videos recorded from places where diving took place, raw data from measuring devices and other supporting information [114]. Tests of these technologies were carried out in the Mediterranean Sea at the archaeological site of Cala Minnola and in conditions similar to those in the Baltic Sea--in the Rummu quarry, Estonia [116].
## 6 Data-Driven DES
### Conclusion Regarding Data-Driven DES  
Overall, the data-driven DES shows how one can use a DTC based on construction equipment data. The simulation results support the observation that, as the amount of information increases, the results improve and approach the real construction time. Since the data comes from a real construction project, each pile's actual processing time duration and production steps vary. Statistics help to find dependencies, e.g., pile production time depends on pile length. Due to uncertainty and variability in the production data it is important to update the DES with as-built data in order to make reliable decisions.  
However, the prediction overestimates the as-built duration. A reason may be the increased throughput due to the learning curve. [PERSON] et al. (2016) found out that it takes  
Figure 12: Results from the simulation runs: the forecasts of the construction end time get closer to the as-built duration as more piles are produced.  
Figure 13: Cumulative duration of pile production during the construction project: as-built (blue) vs. as-predicted by the simulation runs (red) based on (**a**) 25%, (**b**) 50%, and (**c**) 75% as-built data (CI: confidence interval).  
at least one week to create a team, and the more well-rehearsed the team is, the greater their performance will be, e.g., in reducing the setup of the rig. Another reason for this may be the distribution of the individual pile lengths over the construction project. In contrast to the use case (Figure 14a), if piles were sorted by the ascending pile length (Figure 14b), then the time required to construct the first 25% of them would be significantly less than that required to construct the last 25%. Therefore, the simulation would underestimate the as-built duration as the input data would constantly represent shorter piles.  
The DES model presented here is limited in regard to capturing specifics of the construction process itself; it is a simple Petri net. A more detailed model is required to question the current production flow, including material, equipment, or work shifts, and investigate different impacts to optimize the process.
## 5 Performance Analysis  
WSNs are widely used in IoT systems, and the energy efficiency of sensor nodes affects the network performance of IoT systems. D-LEACH aims to improve the energy efficiency by transferring data in consideration of the direction of data transmission.  
In this section, the performance of D-LEACH is compared with that of LEACH and MH-LEACH. We considered a fixed number of sensors and a square field. To verify D-LEACH performance using MATLAB 2014a, 100 sensor nodes was randomly distributed on networks of various sizes (100 \(\times\) 100 m\({}^{2}\), 200 \(\times\) 200 m\({}^{2}\), and 300 \(\times\) 300 m\({}^{2}\)). The initial energy of the sensor node is 0.5 J, and the sensor node is the same under various conditions. The BS is placed at the bottom left (0, 0). The simulation parameters are listed in Table 1. Each network was simulated 20 times in 3500 rounds. The energy consumption model in Section 2.2 was used to evaluate the energy consumption of the sensor nodes.  
There are five assumptions about the routing algorithm being evaluated.  
1. Sensor nodes are randomly placed in the network. The position of the deployed sensor node does not change.
2. All sensor nodes in the network have the same sensing technology, computing technology, and communication technology.
3. All sensor nodes have limited energy and are not charged.
4. In the event of a failed packet transmission resulting from a link error, retransmissions of packets are not considered.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of Sensor nodes & 100 nodes \\ Initial Energy & 0.5 J \\ Data Size & 2000 bit \\ Transfer Energy (\(E_{cice}\)) & 50 ml/bit \\ Transmitter Amplifier Energy(\(E_{fs}\)) & 10 pj/bit/m\({}^{2}\) \\ Transmitter Amplifier Energy(\(E_{inp}\)) & 0.0013 pj/bit/m\({}^{4}\) \\ Merge Energy(\(E_{dd}\)) & 5 nJ/bit \\ Number of Layer & 4 Layer \\ Time of Simulation & 3500 rounds \\ Network Size & 100 \(\times\) 100 m\({}^{2}\), 200 \(\times\) 200 m\({}^{2}\) and 300 \(\times\) 300 m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the simulation.  
Figure 12: Steady-state of D-LEACH.  
5. The BS periodically transmits the starting message of the round for data collection, and the sensor node transmits the data according to the routing algorithm. In this study, the lifetime of the network, the energy efficiency and the packet reception rate of the BS were used as indicators of algorithm performance for evaluation.  
1. The lifetime of the network: the duration of the operation of WSNs from the beginning to the death of the first node (FND). This Indicates the stable state of the network.
2. Energy efficiency: amount of energy used in a stable network, including the total amount of energy used in a stable network and the amount of energy used in one round.
3. Packet-received ratio: The number of packets received by the BS while the network is stable.
## 2 Materials and Methods  
### Microdosimetric Calculations of Lineal Energy (y)  
In classical microdosimetry, radiation quality is not represented by LET, but rather by its stochastic analogue, the lineal energy, \(y\)[1]. Contrary to LET, lineal energy represents the actual energy imparted (\(\epsilon\)) in a volume by a single primary particle (along with all its secondary particles) divided by the mean chord length of that volume (\(\overline{l}\)). The energy imparted to the matter in a volume, \(\epsilon\), is a stochastic quantity that equals the summation of all individual energy transfers (\(\epsilon_{K}\)) which result from the interactions of the primary charged particle with the atomic electrons:  
\[\epsilon=\sum_{k}\epsilon_{k} \tag{1}\]  
The quantity lineal energy, \(y\), is the quotient of \(\epsilon\) divided by the mean chord length, \(\overline{l}\), of the volume of interest:  
\[y=\frac{\epsilon}{\overline{l}}. \tag{2}\]  
For a convex body, \(\overline{l}=\frac{4V}{A}\), with \(A\) being the area and \(V\) its volume [1; 13; 14]. It follows that for a sphere, \(\overline{l}=\frac{2}{3}d\), where \(d\) is the sphere's diameter. Lineal energy refers to a single event, that is, one primary particle that randomly traverses the volume of interest. It is important to recognize that, contrary to LET, lineal energy depends not only on radiation quality, i.e., the type and energy of the radiation, but also on the size (and shape) of the target volume (via \(\overline{l}\)) [13; 14]. Furthermore, since \(y\) is a stochastic quantity, each radiation quality is represented by a distribution of \(y\) values (which is again contrary to LET). Therefore, we may define two mean values of \(y\), namely, its frequency-mean lineal energy defined by [1]:  
\[y_{F}=\int yf(y)\mathrm{d}y \tag{3}\]  
with \(f(y)\) being the probability density function, and its dose-mean lineal energy defined by:  
\[y_{D}=\frac{1}{y_{F}}\int y^{2}f(y)\mathrm{d}y \tag{4}\]  
The physical quantities defined above (\(y\), \(y_{F}\), \(y_{D}\)) are used in various definitions of \(\mathrm{Q}\) as discussed in subsequent sections (Section 2.3).
## 4 Methodology
### Proposed EESR-LMNN Protocol  
In this section, the proposed EESR protocol with the LMNN approach is introduced. The flow-chart of the proposed protocol is shown in Figure 7. Firstly, the protocol setup requires the initialization of the diverse parameters. Then, the BS generates the query for data from the network. If the network is executing for the first time, it is assumed that the network energy is at the highest rate, and thus, the step for calculation sensor energy is skipped. Then the sensor nodes start sensing data for the specified time span considering TDMA. Secondly, when the data are gathered at the sensor nodes, all the sensor nodes start transmitting the data to the BS via intermediate nodes or nearer manager  
Figure 6: A flow chart for the step-wise execution of the proposed sub-cluster LEACH-LMNN protocol.  
nodes. A traditional EESR is followed. However, to make this protocol more efficient, the LMNN approach is used, founded on a neural network. The LMNN approach helps in the selection of the most feasible path towards the manager nodes. In all four quadrants, the manager nodes are strongly connected to other manager nodes. Henceforth, the LMNN approach stabilizes the connectivity between manager nodes and smooths the manager-manger communication. The sensor nodes to manager nodes communication is totally dependent on the distances among them. Sensor-to-sensor connectivity is usually followed by transmission of data towards the nearest manager nodes. The nearest situated sensor nodes to manager nodes are strongly connected, and transmission of data tasks is accomplished, and nearest sensor-to-manager communication is also triggered in a smooth way by the LMNN approach. Thirdly, when the data are aggregated in the nearest manager nodes, the data are transferred to BS via manager-to-manager communication. This phase is also called the steady phase. Fourthly, after the whole data transmission is completed, the sensor node energy is checked. Obviously, due to the execution of all involved processes, the energy depletes to some extent. Therefore, the energy of each sensor node is compared to the threshold energy (threshold energy is the minimum energy required to take part in communication for next round). If the energy of the sensor node is greater than the threshold value, its ID is saved as an alive node, and it is able to take part in next round. Otherwise, the node is declared as a dead node. Finally, the list of nodes is updated. Based on this information, the next round starts until the last node is dead. Results of this proposed protocol are also presented and discussed in Section 4 as follows.
# A Survey of Graphene-to-Phoneme Conversion Methods
## 3 Strategies of G2P Conversion
### Transfer Learning for G2P Conversion  
For low-resource languages, a single language dataset cannot meet the requirements of the Transformer model. The G2P system supports multiple languages and can be trained on several languages datasets to improve data availability in low-resource languages [41].  
Ref. [42] utilized transfer learning to construct an end-to-end multilingual grapheme-to-phoneme model to improve the conversion performance of foreign words in different languages. The author combines the model with corresponding input parameters, embeds language distribution (Bidage ID) and language labels (System ID) (such as <en US>,<en UK>,<de DE>,<fr FR>, etc.) as input embeddings, connects them with attention context vectors, and feeds them forward to the decoder's fully connected layer before softmax. The language distribution vector represents the degree to which a specific word belongs to each language, and the calculation formula is as follows:  
\[p(id\mid I)=\frac{C(w)}{\log|N|},\quad\sum_{l}p(id\mid I)=1 \tag{4}\]  
where \(N\) refers to the size of the dictionary, \(C(w)\) refers to the count of occurrences of words found in a given language dictionary. The language ID vector helps the model distinguish words with multiple pronunciations in different languages and model their phoneme distribution accordingly. The performance of this work shows that for low-resource languages, the average phoneme error rate decreased by 7.2%.  
Transformer-based models require a large amount of training data and are not suitable for dialects with limited data. [PERSON]. [43] adopts a transfer learning-based approach to cross-learn English dialects and establishes a G2P model based on Transformer, achieving high accuracy in dialect conversion with small available data. The model structure is illustrated in Figure 11.  
Figure 10: Multimodal G2P.  
[PERSON]. [44] combined the global character vector GCV and bidirectional recurrent neural network BRNN to establish a high-performance multilingual G2P model, which enables the model to process multiple languages simultaneously. And further explored multilingual G2P and monolingual G2P in multiple languages.  
These studies suggest that transfer learning is an effective approach to building powerful G2P models for dialects with limited data. For low-resource languages, using pre-trained models for transfer learning can significantly improve PER and quality, while increasing dictionary size can improve model performance. Using dictionaries and generated phonemes to fine-tune the model can also improve its accuracy. The drawback is that pre-trained models require a large amount of training data for transfer learning between different dialects or languages.
# Active Thermography for the Detection of Sub-Surface Defects on a Curved and Coated GFRP-Structure
## 2 Materials and Methods  
### Long-Pulse Thermography  
For the examination of the coated and curved test specimen, long-pulse thermography in reflected mode is used, where the excitation unit and the infrared camera are located on the same side of the test specimen. The schematic representation is shown in Figure 1.  
The sample is excited optically for several seconds, which induces a heat flow in the sample. In homogeneous materials the heat propagation is uniform. If there are inhomogeneities such as air inclusions in the material, the heat flow is influenced by the different thermodynamic material properties. Thus, the reflected heat of the sample changes as well. When the heating phase and thus the excitation period is over, the cooling phase with the recording of the measurement data begins [13]. The local temperature differences on the surface of the test specimen during the cooling phase, which are caused by the material-dependent heat flow, are detected by an infrared camera and displayed as a thermogram. The measured raw data are therefore the recorded radiation intensity images in the infrared range over time.  
In contrast to pulse thermography, long-pulse thermography is more suitable for low thermal response materials, since the long excitation time allows sufficient heat to penetrate into deeper layers of the load-bearing material. Furthermore, the heat sources are inexpensive and available in a large variety. In contrast to lock-in thermography, long-pulse thermography is faster because only one heating impulse is used, while lock-in thermography uses multiple short to long impulses. [PERSON] et al. did comparative investigations between pulse thermography, step-heating thermography and long-pulse thermography combined with different classical evaluation methods such as PCA, phase Fourier analysis and thermographic signal reconstruction which showed that the best results for the investigation of fiber-reinforced materials were achieved with long-pulse thermography [18]. A disadvantage compared to pulse thermography or step-heating thermography, however, is the comparatively slow generation of data due to the long excitation recording time, which both are determined by the material's conductivity.
## 3 Results and Discussions
### Determination of Total Phenols, Flavonoids, Ascorbic Acid Content and Antioxidant Capacity  
The amount of total phenols and flavonoids, ascorbic acid and antioxidant capacity, of _M. nigra_ fruits, depends on a variety of factors such as the extraction method, the fruit cultivar, the stage of maturity of the fruit, as well as the climatic conditions [1, 10, 11, 54, 55].  
The content of total phenols, total flavonoids, ascorbic acid and antioxidant capacity determined by the FRAP method of MnE are presented in Table 2.  
The results obtained in this study are similar to those reported in the literature [10, 11]. _M. nigra_ has the highest total phenolic content (368.2 mgGAE/100 g FW) and total flavonoid content (247.9 mg CTE/100 g FW), when compared to fruits of _M. alba_[56].  
In the study of [PERSON] and [PERSON], 2007, the content of total polyphenols, total flavonoids, as well as ascorbic acid, were quantified from the fruits of _M. nigra_, _rubra_ and _alba_, and the highest value of TPc and TFc was recorded for _M. nigra_ (1422 mg GAE/100 g dw and 276 mg QE/100 g fw, respectively), followed by _M. rubra_ and _M. alba_. The highest value of ascorbic acid, was recorded in _M. alba_ (22.4 mg/100 mL), followed by _M. nigra_ and _M. rubra_ with 21.8 and 19.4 mg/100 mL respectively [11]. Also, [PERSON] and [PERSON], 2015 determined the content of ascorbic acid in the three types of mulberry fruits, grown in China where _M nigra_ had the highest content of (48.4 mg/100 g fw), compared to the fruits of _M. rubra_ and _alba_ (5.64 mg/100 g fw and 6.01 mg/100 g fw, respectively) [10]. Ascorbic acid a powerful water-soluble antioxidant compound has the ability to neutralize and scavenge the reactive oxygen species and it has been shown that has an important role in protecting the skin against oxidative damage such as pollutants and UV radiation [10, 57, 58].  
In a study by [PERSON] and [PERSON], 2020, the antioxidant capacity of 12 mulberry genotypes by the FRAP method revealed values ranging from 1.33 to 82.87 mg TE/g dw [28].