esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The Internet of Things (IoT) has revolutionized various sectors, including automated transportation, smart cities, surveillance systems, healthcare, agriculture, urban security, water management, environmental monitoring, and more, by enabling seamless connectivity and data exchange between a multitude of devices [1,2]. Central to the functioning of IoT ecosystems are Sensor Nodes (SNs), which are responsible for the continuous collection and transmission of data. These SNs, typically deployed in large numbers, form the backbone of IoT networks, making their efficient management and optimization critical to the overall performance and sustainability of these systems [3]. To further enhance the efficiency and functionality of IoT networks, data fusion plays a pivotal role [4]. Data fusion refers to the process of integrating data from multiple SNs to produce more consistent, accurate, and useful information than could be achieved by individual SNs alone [5]. By aggregating and processing data at various levels within the network, such as at individual SNs, clusters, or gateways, data fusion techniques reduce redundancy, mitigate noise, and improve decision-making capabilities [6]. These methods also significantly reduce communication overhead and energy consumption by minimizing the transmission of raw data, thus extending the overall network lifetime. Additionally, data fusion groups (DFGs), where SNs are organized into groups, each managed by a designated leader known as a Data Fusion Head (DFH) and supported by other nodes termed DFG Members (DFGMs), enhance the efficiency of data fusion processes. The hierarchical structure created by data fusion grouping optimizes resource utilization, conserves energy, and extends the network's lifespan by facilitating localized data aggregation, minimizing redundant data transmission, and balancing the computational and communication load among SNs [7]. Integrating data fusion techniques within clustered IoT architectures ensures scalable, energy-efficient, and robust operations, which are essential for the continued evolution of IoT applications across diverse domains [8,9].
# Assessment of AC Corrosion Probability in Buried Pipelines with a FEM-Assisted Stochastic Approach
## 3 Probabilistic Evaluation of the Leakage Current Density through a Holiday in the Coating
### Probability of Exceeding the Limit Value \(J_{lim}\)  
By considering Formulas (7)-(9), it is possible to write the following relationship expressing the probability that a leakage current \(J\), flowing through a holiday located in a point at potential \(V\), exceeds the limit value \(J_{lim}\). One has:  
\[P(J(V)\geq J_{\text{lim}})=P(A\leq A_{\text{max}}(V))=\left\{\begin{array}{cc} \int\limits_{0}^{A_{\text{max}}(V)}\frac{1}{A\
u\sqrt{2\pi}}e^{-\frac{1}{2} \frac{(\ln A-\mu)^{2}}{\sigma}}dA&if\ V>V_{\text{min}}\\ 0&otherwise\end{array}\right. \tag{10}\]  
Therefore, if the induced voltage profile is known along all the pipeline routes, the above formula calculates the probability that the leakage current \(J\) exceeds \(J_{lim}\) at any point of the pipeline provided that a holiday is present at the same point. In such a way, it is possible to identify along the pipeline route the sections that are more exposed to the induced AC corrosion risk.
## 3 Filtering and Combining the Light
### Interferometry  
In certain astronomical studies, such as measuring the stellar diameters and observing exoplanets, it is crucial to achieve high-resolution imaging and/or suppress light from a bright star next to a faint planet. For ground-based observations, the necessity of high spatial resolution requires combining light from two or more telescopes. In the past, the technique of interferometry has been used to measure stellar radii using two or more telescopes separated by a distance \(D\), to allow \(\sim\)\(\lambda/D\) angular resolution [48]. Another way, called aperture masking interferometry, achieves the diffraction limit through artificially created sub-apertures formed by segmenting the telescope pupil [49; 50]. The image (i.e., brightness distribution) is reconstructed using Van Cittert-Zernike theorem, which states that the spatial coherence function of light is the Fourier transform of the angular brightness distribution of the light source [51]. Integrated photonic reformatters and interferometers have recently been developed with compact designs, better phase control, and mechanical/thermal stability [52].  
A nulling interferometer is an offshoot of the technique described above, where an extra phase delay of \(\pi\) is added to one of the two combining beams to create destructive interference of the central source (e.g., a bright star), making any off-center source visible (e.g., a planet). A photonic nulling interferometer called GLINT has recently been demonstrated which can be used for observing exoplanets, possibly able to measure their spectra in the future [53]. A current analogue which offers these functionalities (R \(\sim\) 20, 500, 4000 and angular resolution of tens of milliarcseconds in the K-band) is the GRAVITY instrument--a VLT interferometer [54; 55; 56]. A photonic counterpart of GRAVITY will be shoebox-sized, with improved mechanical stability, thus allowing deeper observations of faint, background-limited sources. A sample schematic of such a future device is shown in Figure 3.  
Integrated photonic interferometers also show promise in the mid-IR wavelengths, with ongoing developments in mid-IR fabrication technologies [57].  
All of these form the set of next-generation photonic devices that will illuminate the next-generation photonic spectrographs and perhaps be integrated with them on a monolithic platform.  
Figure 2: A schematic of the full setup of an integrated photonic spectrograph with OH-emission suppression, reprinted with permission from [36].
## 3 Materials and Methods
### Data Symbolization  
The first step for data symbolization was segmentation, which changed the total duration of the signals into \(N\)-second segments for each recording--Each recording has a different duration in hours, hence, a different \(N\) value. Once the 21 recordings were split into one-second segments, they were processed through one-dimensional symbolic aggregate approximation (1d-SAX); see Figure 2 2.  
Figure 4: EEG electrode placement, according to the 10–20 system. The highlighted electrodes (red and blue) record the data required to obtain the selected derivations or channels: Fp2-F4, F4-C4, Fp1-F3, and F3-C3.  
In order to better understand 1d-SAX symbolic transformation, it is necessary to understand symbolic aggregate approximation (SAX) and piecewise aggregate approximation (PAA):  
* **PAA** is a time series downsampling in which the mean value of each fixed-sized segment is retained [36];
* **SAX** is based on PAA but performs an additional quantization of the mean value. Under the assumption that the time series follows a standard normal distribution, the quantization boundaries are computed to ensure that the symbols are assigned to each quantized mean value with equal probability [37];
* **1d-SAX** is a symbolic representation of a time series based on SAX. This representation contains information about each segment's slope and a symbol that is associated with the segment's mean value. In other words, each segment is represented by an affine function of two quantized parameters: the slope and mean value [38].  
In this particular case, the mean value is represented with 1 of the 26 letters in the lowercase English alphabet \(a,b,c,...,y,z\), whereas the slope is represented with an integer between 1 and 10. Hence, the number of 1d-SAX symbols created by combining a letter and an integer is 260. Examples of the final symbols are \(a1,b8,c4,d3,e7,f2\), and so on, with all of these having a specific _meaning_ of the segment's features. The 1d-SAX symbols will be referred to as \"words\" from now on.  
Thus, \"phrases\" will refer to the sequences of \"words\" corresponding to the symbolic one-second segments obtained from each channel--Each channel was analyzed separately. These unlabeled symbolic \"phrases\" were used to train the model described in Section 3.3 using unsupervised learning.  
On the other hand, a structured database was created with _longer_ \"phrases\" corresponding to the CAP labeled segments; for reference, the CAP A-phase duration is between 2 and 60 s. The classification experiments (see Section 3.4) were carried out using this structured database with supervised learning.
# Cornering Stiffness Prediction Based on Geometric Method
## 2 Method Description  
For a given tire, the cornering stiffness is dictated by the contact patch geometric characteristics and the contact pressure distribution. Here, the assumption is made that the change in contact pressure distribution due to camber can be simplified as a load shift between the left and right half of the tire, and this load shift will be enough to approximately model the corresponding change in cornering stiffness. In this way, the half tire cornering stiffness could be obtained based on the whole tire cornering stiffness, since the left and right half of the tire are ideally the same in physics. An error may be imported because of the lack of load change description within the half-tire. According to the validation results, this error is acceptable. In this section, we will introduce a method for estimating the load shift due to the camber and use this load to calculate the cornering stiffness at different camber angles.  
As is shown in Figure 1, if the tire is divided into two parts along its width's central plane, loads acting on both parts are essentially equal under free rolling conditions. A camber angle will induce a load shift from one part to the other. Since cornering stiffness is strongly dependent on load, the cornering stiffness contribution from each part will also change. Hence, by treating the camber as a load shift, a model of how cornering stiffness changes with camber angle can be obtained as the sum of the cornering stiffness contributions of each part, so that we can write  
\[\left\{\begin{aligned} & F_{z}=F_{z1}+F_{z2}\\ & K_{y}=K_{y1}+K_{y2}\end{aligned}\right. \tag{1}\]  
where \(F_{z}\) and \(K_{y}\) are the load and cornering stiffness of the whole tire, respectively, while \(F_{zi},\ i=1,2\ \text{ and }K_{yi},\ i=1,2\ \text{ are the corresponding half-tire results.}\)
## 6 Challenges and Future Development
### Edge Computing  
Intelligent fault diagnosis requires high real-time performance and hardware resources. Practical issues such as network unavailability, full bandwidth, or insufficient sensor computing and storage resources are frequently encountered in agricultural scenarios, making it difficult for centralized and distributed fault diagnosis strategies to meet sensor fault diagnosis requirements. Edge computing is a remedy for the aforementioned issues. Edge computing allows faster response times and more secure data transmission and processing than other sensor fault diagnosis methods. At the same time, edge computing can be combined with deep learning algorithms to reduce model training time and make sensor fault diagnosis more efficient [148]. Edge computing transfers a portion of the central server's computing and storage resources to edge nodes and collects sensor data on the edge computing platform to develop fault diagnosis models. Unmanned plant protection machines and inspection robots are two examples of agricultural equipment that transports substantial resources. Consequently, using such equipment as a platform for edge computing does not increase costs and satisfies the requirements for fault diagnosis.
# Performance of a Supercritical CO2 Bottoming Cycle for Aero Applications
## 2 Materials and Methods
### S-CO\({}_{2}\) Fluid Property Model  
The strong real gas behaviour of S-CO\({}_{2}\) near its critical point, exemplified through rapid changes in specific heat for small changes in temperature and pressure (see Figure 2c), considerably reduces the compressor work necessary for a given pressure ratio. This contributes to the high power density of the S-CO\({}_{2}\) cycle. However, that behaviour also complicates simulations using 0 and 1D-simulation tools. Over the years, several equations of state (EOS) have been proposed to capture the real gas properties of S-CO\({}_{2}\), but each has its limitations. Two of the most commonly used EOS are the Peng-Robinson EOS (PREOS) [26; 27] and the Span & Wagner EOS (SWEOS) [28]. The latter is widely regarded as the most precise EOS for carbon dioxide. The SWEOS is the basis of REFPROP for CO\({}_{2}\). REFPROP is a commercial tool offered by the US National Institute of Standards and Technology (NIST), and the light version (REFPROPmini) was used to create look-up tables for the subsequent simulations. The tables have varying resolutions down to 0.1 K and 1 Pa near the critical point, which allows for linear interpolation between the points for simulation purposes while retaining reasonable precision. Figure 2a illustrates the differences in results given by PREOS and REFPROP around and beyond the critical point.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Range** & **Parameter** & **Range** \\ \hline \(T_{\text{cmp,in}}\) (K) & 305.15–320.15 & \(W_{\text{cmp,in}}\) [kg/s] & 15–30 \\ \(P_{\text{cmp,in}}\) (MPa) & 7.5–8.5 & PR [-] & 2.2–4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Investigated bottoming cycle parameters.
## 4 Conclusions  
In this study, we investigated the effect of iron oxide as a sintering aid on the performance of a highly conductive electrolyte based on lanthanum gallate and La\({}_{0.6}\)Sr\({}_{0.4}\)FeO\({}_{3-\delta}\) electrodes, which are promising components of symmetrical electrochemical devices on solids. It was demonstrated that the use of 0.5 wt.% Fe\({}_{2}\)O\({}_{3}\) as a sintering aid for the La\({}_{0.8}\)Sr\({}_{0.2}\)Ga\({}_{0.8}\)Mg\({}_{0.2}\)O\({}_{3-\delta}\) electrolyte leads to a significant increase in the grain boundary conductivity by forming better grain-to-grain contact. For the La\({}_{0.6}\)Sr\({}_{0.4}\)FeO\({}_{3-\delta}\) electrode material, a comparative study of the copper oxide sintering aid was additionally performed. It was shown for the first time that the nature of the sintering aid and the sintering temperature exert disparate effects on the activity of electrodes, depending on the gas environment (air or hydrogen). The findings demonstrate that the nature of the rate-determining stages of the electrode reactions of hydrogen oxidation and oxygen reduction can be altered based on the sintering temperature. Among the electrodes studied, La\({}_{0.6}\)Sr\({}_{0.4}\)FeO\({}_{3-\delta}\) with 3 wt.% Fe\({}_{2}\)O\({}_{3}\) at a sintering temperature of 1050 \({}^{\circ}\)C exhibited optimal activity in both the oxidizing and reducing media.  
Investigations, formal analysis, writing--original draft preparation, E.G. and E.A.; conceptualization, methodology, investigation, writing--original draft preparation, E.A. and D.O.; conceptualization, supervision, acquisition of funding, writing--review and editing, D.O. All authors have read and agreed to the published version of the manuscript.  
The study was funded by a grant of the Russian Science Foundation (grant number 24-19-00040), [[https://rscf.ru/project/24-19-00040/](https://rscf.ru/project/24-19-00040/)]([https://rscf.ru/project/24-19-00040/](https://rscf.ru/project/24-19-00040/)) (accessed on 4 September 2024).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.  
The study was, in part, carried out using the equipment of the Shared Access Center of \"Composition of Compounds\" at the IHTE UB RAS. The authors are grateful to [PERSON] and [PERSON] for their research assistance.  
The authors declare no conflicts of interest.  
Figure 7: Impedance spectra and DRT functions for 1050-Fe electrode in various atmospheres at 800 \({}^{\circ}\)C (**a**). DRT functions for the investigated electrodes in the atmosphere of wet hydrogen at 800 \({}^{\circ}\)C (**b**).
## 1 Introduction
### Contributions of This Study
#### 1.1.1 Comparative Analysis of Traditional and Advanced Models  
* We compare traditional ML models (Support Vector Machines, Naive Bayes, Logistic Regression, and Random Forest) against deep learning (CNN and Bi-LSTM) and state-of-the-art transformer models (LLaMA 2 and ModernBERT) in a rigorous manner.
* When comparing classical and modern NLP frameworks for use in detecting offensive Roman Urdu text, a comparison of classical and modern concepts of natural language processing for offensive roman word identification.
## 2 Results and Discussion
### Evaluation and Optimization of Dominant Volatile Compounds
Predicted mean was calculated for benzaldehyde (11.3%), 2-phenylacetaldehyde (8.9%), undecane (1.5%), nonanal (1.4%), 2-phenyllethan-1-ol (14.1%), 2-phenylacetonitrile (10.8%), 1-phenylbutane-2,3-dione (6.0%), methyl nonanoate (2.0%), 3-hydroxy-4-phenyl-2-butanone (12.0%), (Z)-3-hydroxy-4-phenylbut-3-en-2-one (1.0%). A test set of 80 monofloral thym samples was submitted for validation of the optimization model and their quantitative analysis are presented in Table S1. These samples  
Figure 5: Desirabilities of overall volatile compound responses based on optimum isolation conditions. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Response** & **Volatile Compound** & **A** & **B** & **C** & **D** & **E** & **F** & **Desirability** &
\begin{tabular}{c} **Predicted** \\ **Mean** \\ **(\%Area)** \\ \end{tabular} \\ \hline R1 & benzaldehyde & 60 & 5 & 15 & 700 & 6 & 1:1 & 0.810 & \(14.9\pm 1.6\) \\ R2 & 2-phenylacetaldehyde & 60 & 15 & 15 & 400 & 6 & 1:1 & 0.975 & \(13.2\pm 1.0\) \\ R3 & undecane & 30 & 15 & 15 & 400 & 2 & 3:1 & 0.940 & \(15.2\pm 0.9\) \\ R4 & nonanal & 45 & 15 & 30 & 700 & 4 & 3:1 & 0.933 & \(2.4\pm 0.4\) \\ R5 & 2-phenyllethan-1-ol & 60 & 15 & 15 & 400 & 6 & 1:3 & 0.923 & \(15.4\pm 1.9\) \\ R6 & 2-phenylacetonitrile & 60 & 15 & 30 & 400 & 6 & 1:3 & 0.957 & \(11.1\pm 1.3\) \\ R7 & 1-phenylbutane-2.3 dione & 60 & 30 & 30 & 700 & 6 & 1:3 & 0.966 & \(6.3\pm 0.6\) \\ R8 & methyl nonanoate & 60 & 30 & 30 & 400 & 2 & 3:1 & 0.814 & \(12.5\pm 2.3\) \\ R9 & 3-hydroxy-4-phenyl-2-butanone & 60 & 30 & 30 & 700 & 4 & 1:3 & 0.841 & \(14.5\pm 1.2\) \\ R10 & (Z)-3-hydroxy-4-phenyl-3-en-2-one & 60 & 5 & 30 & 700 & 6 & 1:3 & 0.947 & \(1.5\pm 0.3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Optimum conditions, desirabilities, and predicted mean for each response. were analyzed under optimum conditions of SPME using GC-MS. Dominant volatile compounds were isolated in all validation samples. Confirmation data mean calculated for benzaldehyde (7.1%), 2-phenylacetaldehyde (20.9%), undecane (1.0%), nonanal (3.8%), 2-phenylethan-1-ol (4.9%), 2-phenylacetonitrile (5.8%), 1-phenylbutane-2,3-dione (8.2%), methyl nonanoate (2.3%), 3-hydroxy-4-phenyl-2-butanone (9.6%), (_Z_)-3-hydroxy-4-phenylbut-3-en-2-one (3.2%). Most results, based on the developed RSM models, confirm that they operate at the 95% prediction interval. However, due to the nature of honey and its variability as food, we should be wary and more tolerant of the results of the prediction interval. Nevertheless, the total results are satisfactory and the above RSM model could provide suitable conditions for the isolation of dominant volatile compounds for Greek monofloral thyme honey.
# Response of Corroded Steel Pipeline Without and with CFRP Reinforcement to Reverse Fault Movement
## 4 Results and Analyses
### Effectiveness of CFRP Reinforcement
#### 4.4.3 Comparison of Corroded Pipeline Before and After CFRP Reinforcement  
Figure 15 shows the Mises stress diagram of the pipeline reinforced with CFRP when the top corrosion occurs at the footwall with 0.9 m fault displacement and residual wall thickness of 0.6 t. Figure 15 demonstrates that the overall stress of the pipeline before and with reinforcement remains unchanged; the top of the footwall pipeline is under pressure and the bottom is under tension, while the top of the hanging wall pipeline is under tension and the bottom is under pressure. Furthermore, the overall pipeline exhibits an S-shaped deformation. However, the pipeline presents different buckling patterns at the corrosion  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Failure Modes**} & \multicolumn{4}{c}{**Reinforcement Lengths**} \\ \cline{2-5}  & **0 m** & **5 m** & **10 m** & **15 m** & **20 m** \\ \hline Local buckling & 0.25 & 0.47 & 0.47 & 0.47 & 0.47 \\ Tensile fracture & 1.3 & 1.3 & 1.3 & 1.3 \\ Elliptic deformation & 0.68 & 0.87 & 0.91 & 0.93 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Critical fault displacement of the pipeline with different reinforcement lengths.  
Figure 14: A mechanical response of the pipeline with different reinforcement lengths. (**a**) Peak axial strain. (**b**) Peak ellipticity.  
position without and with reinforcement, which is manifested as local buckling without the reinforcement, while there is no obvious stress concentration with the reinforcement. The Mises stress at the corrosion position is significantly reduced after the CFRP reinforcement.  
Figure 16 shows the cross-section deformation diagram of the corrosion position of the pipeline before and after CFRP reinforcement. Figure 16 shows that without the reinforcement, the corrosion position has a large extent of depression and the shape is greatly distorted. However, after applying the reinforcement, the section deformation changes moderately. The irregular deformation of the pipeline is improved, and the maximum deformation occurs at 0.84 m displacement instead of 0.78 m. The reinforcement at the corrosion position improves the deformation resistance by 7% and the cross-section becomes more circular.
## 2 Materials and Methods
### Irradiation Procedure and Thermogravimetric Analysis  
Irradiations of SC4A and SC4A/CHCl\({}_{3}\) were carried out at 25 \(\pm\) 0.2 \({}^{\circ}\)C in air using a \({}^{60}\)Co radiation source (China institute of atomic energy, Beijing, China) with a total absorbed dose ranging from 1.0 \(\times\) 10\({}^{4}\) to 1.1 \(\times\) 10\({}^{6}\) Gy.  
Weighed quantities of SC4A were subjected to thermogravimetric analysis (TGA) in a nitrogen atmosphere using a TG/DTA6200 instrument (Seiko instruments inc, Tokyo, Japan) at a heating rate of 10 \({}^{\circ}\)C\(\cdot\) min\({}^{-1}\).  
The structure of SC4A was analyzed using a Nicolet iS10 infrared (IR) spectrometer (Thermo Fisher, Waltham, MA, USA), Bruker DPX-400 nuclear magnetic resonance (\({}^{1}\)H-NMR) spectrometer (Bruker, Karlsruhe, Germany), and Trace DSQ electrospray ionization mass spectrometry (ESI-MS) instrument (Thermo Fisher, Waltham, MA, USA).  
Figure 1: Structures of (**a**) _p-tert_-butylsulfonylclax[4]arene (SC4A); (**b**) _p-tert_-butylthiacalix[4]arene (TC4A); and (**c**) _p-tert_-butylsulfonylclax[4]arene tetraacetate (SC4 AOEt).