esa-sceva/satcom-chunk-collection huggingface dataset:
# Crack Detection of Concrete Based on Improved CenterNet Model
## 1 Introduction
[PERSON] [20] studied a decision-tree heuristic algorithm for crack detection and achieved satisfactory simulation results. [PERSON] [21] applied the SegNet network to surface crack detection and achieved satisfactory results. [PERSON] [22] proposed a concrete crack detection model based on artificial intelligence and smart phones and used a convolutional neural network in artificial intelligence deep learning to identify and locate cracks in pictures, achieving the purpose of crack detection. [PERSON] [23] studied a concrete surface crack detection method by combining the improved active contour model with the Canny iterative operator; however, the operation time was relatively long, and there were certain limitations. [PERSON] [24] used the Canny operator to extract the crack edge and the automatic generation technology to generate the finite element mesh of the crack structure to realize the analysis of the stress in the crack region. In the field of combining drone aerial photography technology and computer vision technology, many scholars have studied it. [PERSON] [25] conducted ground target detection using UAV and computer vision technology. [PERSON] [26] proposed a method in order to obtain more accurate detection boxes, in which the label encoding strategy and bounding box regression method of CenterNet were optimized, introducing localization quality loss to improve the localization quality of the detection boxes. With the continuous development of computer vision technology and UAV aerial photography technology, the images of concrete cracks are being collected by unmanned aerial vehicles and then processed as data. Finally, the cracks are identified by computer vision technology. The method has the advantages of simple principle, convenient operation, strong flexibility, high precision, low cost, and no contact. This article utilizes computer vision technology to process images collected by drones and achieves good results, which have been applied in practical engineering to a certain extent.
## 5 Discussion  
### Performance of VS and ER Models  
Performance analysis is crucial in measuring the quality of prognostics in different modeling fields and consists of different statistical calculations between measured (M) and predicted (P) data. In this paper, performance analysis allowed us to evaluate the prediction ability of the VS and ER models, providing comparable quotas of uncertainty, which could not be done directly using the errors provided by the geophysical software. In geosciences, there are no defined protocols or consensus on which statistics or group of statistics are the best to evaluate performance of models [65; 66; 67]. For variables VS and ER, multiple expressions for determining point distance between M and P data and comparable quotas of the relative error between models are used to produce a complete evaluation of models performance and error distribution, as proposed by [66; 67; 68]. Reliability of the performance and error expressions was analyzed, provided M and P data sets were normal or lognormal distributed [6; 52; 67]. The Kolmogorov-Smirnov goodness-of-fit was used for testing the data sets normality. The M and P data sets showed quite skewed distributions (rows 1 and 2 in Figure 7). Their logarithm proved to fit close-to-normal distributions (rows 3 and 4 in Figure 7). The logarithm M and P data pairs were plotted and the results proved to fit close to the theoretical linear 1:1 ratio (row 5 in Figure 7). After this analysis, normal M and P data sets were lognormal converted, the performance statistics and error expressions were applied to these lognormal data sets, and results were reverted to the original magnitude of variables. For VS and ER models efficiency criteria, Nash-Sutcliffe efficiency coefficient (NSE), logarithmic form of NSE (lnNSE), coefficient of determination (\(R^{2}\)), percent bias (PBIAS), root-mean-square error (RMSE), RMSE relative to standard deviation of the measured data (RSR), mean absolute error (MAE), and mean relative error (MRE) were used (Table 7). Description of statistics is detailed in [66; 68; 69; 70]. Normalizing the statistics facilitates the comparison of VS and ER models having different exploration scales, e.g., MASW and ERT surveys having equal spacing grid but different number of nodes determining different exploration length and depth. Though there is no consistent means of normalization in the literature, common choices are the mean, the range (the difference in maximum and minimum values), and the interquartile range (the difference in 0.75 and 0.25 quartiles) of the M data set. Here, the mean was selected as quotient despite that the interquartile range is less responsive to outliers in [68]. The term coefficient of variation (CV) refers to a normalization using the mean value, and may be used to avoid ambiguity regarding other normalizing procedures. This is analogous to the coefficient of variation with a given absolute error statistics, taking the place of the standard deviation of the measured data (STD), which is the most used statistics for normalizing errors [67]. CV facilitates
## 2 Theoretical Research on Indirect Measurement Method Based on Vehicle-Bridge Coupling
### Example Verification  
In order to verify the accuracy of the program, the vehicle response spectrum obtained in this paper is compared with reference [7]. When the verification program is correct in this section, the same bridge and vehicle parameters as those in the literature are selected. The schematic diagram of the model is shown in Figure 1. The span diameter of the simple branch girder bridge was 25 m; cross-sectional area A = 2 m\({}^{2}\); cross-sectional moment of inertia I = 0.12 m\({}^{2}\); bridge elastic modulus E = 2.75 \(\times\) 10\({}^{10}\) N/m\({}^{2}\); bridge unit length mass of 4800 kg/m; vehicle body mass \(m_{v}\) = 1200 kg; wheel stiffness \(k_{uv}\) = 500,000 N/m; vehicle speed \(v\) = 10 m/s; other parameters of the vehicle were set to 0. The extracted acceleration response was used as a fast Fourier transform. The spectrum of acceleration response obtained is shown in in Figure 3.  
It can be seen from Figure 3 that the extracted bridge frequency and vehicle frequency are 2.01 Hz and 3.22 Hz, respectively, which are very close to the bridge frequency and vehicle frequency of 2.08 Hz and 3.25 Hz in the literature. Input and output are the same, which proves the correctness of the program. This shows that the dynamic performance of the bridge can be identified and determined from the vehicle response.
# Synthesis of Si-Fe Chondrule-like Dust Analogues in RF Discharge Plasmas
## 1 Introduction
Using silicate and metal dust subjected to electric discharges (120-500 V) in 10 to 105 Pa, the authors concluded that lightning likely creates smaller crystallites--chondrule precursors--due to its destructive effects. [PERSON] et al. [18] investigated chondrule formation by nebula shock waves, using hot plasma to simulate shock waves in the protoplanetary medium. Significant progress includes the ISS-based EXCISS experiment [19], where forsterite particles were exposed to solar arc discharges in microgravity, showing smoothness under discharge influence and confirming the system's full functionality. The dusty plasma properties (in particular, the dust nanoparticles synthesised from the gas phase in the plasma medium) have been studied as an analogue to the interstellar dust [20]. Most of the research focused on the formation and various properties of carbonaceous dust particles [21]. The composition and structure of carbonaceous interstellar dust analogues have been explored using the plasma-polymerised nanoparticles in Ar and C\({}_{2}\)H\({}_{2}\) mixtures in RF discharge plasmas [22]. The researchers produced carbonaceous nanoparticles and analysed their infrared spectra. The results showed a strong 3.4 um aliphatic band, weak OH and carbonyl bands, and traces of aromatic compounds, similar to the characteristics of diffuse interstellar medium dust. These plasma-grown particles are found to be a strong candidate for laboratory studies aiming to replicate the spectra of diffuse interstellar dust medium. In another work [23], interstellar (IS) dust analogues made from amorphous hydrogenated carbon (a-C:H) were produced via plasma deposition using CH\({}_{4}\) and He mixtures. These samples were analysed by secondary electron microscopy, IR spectroscopy, and UV-visible reflectivity, alongside DFT calculations. Experimental data showed high hydrogen content (\(\approx\)50%) but differing sp\({}^{2}\)/sp\({}^{3}\) carbon ratios (1.5 from IR and 0.25 from reflectivity). The produced analogues exhibited infrared spectra similar to those observed in interstellar dust. Although significant effort has been devoted to the lab-scale growth of carbon-based analogues of interstellar dust in gas discharge plasma, the synthesis of silicate-based (chondrule-like) particles has not been explored. Here, we report the formation of dust particles in RF discharge plasma using a mixture of Ar gas with ferrocene and HMDSO vapour. HMDSO serves as a source of SiO\({}_{\rm x}\) molecules, while ferrocene provides iron-based molecules. The RF discharge plasma effectively dissociates precursor molecules through energetic electrons, UV photons, and Ar metastable atoms. The subsequent plasma polymerisation and condensation of these molecules simulate dust particle nucleation and accretion pathways.
# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 5 Experimental Results and Discussion
### Specific Emitter Identification Results  
Based on the 2D feature scatter maps described in Section 5.6, we present the corresponding confusion matrix in Figure 9. Similar to the abovementioned analysis, specific emitter J in A1\(\succ\)A2 and A1\(\succ\)A3 is easily identified as G. A small sample of specific emitter H is recognized as J. Specific emitters G, H, and J all come from the same navigation satellite system and the signal formats they transmit are exactly the same, which increases the difficulty of identifying similar individuals. For B1\(\succ\)B2, 57% of the samples from Specific Emitter A are identified as from B, which indicates that A and B are completely inseparable. Although the situation of B1\(\succ\)B3 is relatively optimistic, nearly one-third of the samples from Specific Emitter B are classified as from A. The existing domain adaptive methods suffer limited ability to solve the sample confusion problem. They still cannot provide reliable identification results for some long time span datasets, such as B1\(\succ\)B2 (with a considerable difference between the source domain and target domain). However, even if long time span presents considerable challenges, LTS-SEI can still identify different specific emitters with considerable accuracy. We can see that the classification accuracy of all specific emitter samples is nearly 100%, as shown in the third column of Figure 9. This predictive ability is crucial for practical applications when the labels of new data are not known.  
Figure 8: 2D feature scatter maps of the output features of the last fully connected layer of the classifier after t-distributed stochastic neighbor embedding dimensionality reduction: (**a**) A1\(\succ\)A2 (I/Q), (**b**) A1\(\succ\)A2 (shallow layer: CMD), (**c**) A1\(\succ\)A2 (LTS-SEI), (**d**) A1\(\succ\)A3 (I/Q), (**e**) A1\(\succ\)A3 (deep layer: HOMO), (**f**) A1\(\succ\)A3 (LTS-SEI), (**g**) B1\(\succ\)B2 (I/Q), (**h**) B1\(\succ\)B2 (shallow layer and deep layer: MMD), (**i**) B1\(\succ\)B2 (LTS-SEI), (**j**) B1\(\succ\)B3 (I/Q), (**k**) B1\(\succ\)B3 (shallow layer and deep layer: JDA), and (**l**) B1\(\succ\)B3 (LTS-SEI).  
Figure 9: _Cont._
# Value Creation with Digital Twins: Application-Oriented Conceptual Framework and Case Study
## 4 Application-Oriented Digital Twin Framework
### Product Life Cycle  
The life cycle of a product is commonly divided into three phases Beginning-of-Life (BoL), Middle-of-Life (MoL), and End-of-Life (EoL) [53]. Concerning the further subdivision into activities, there are different approaches with different granularity [54; 55]. As shown in Figure 4, we distinguish twelve activities along the product life cycle in our framework. In BoL we distinguish  
1. Customer and market analysis,
2. Product planning;
3. Product development;
4. Marketing and sales;
5. Production;  
Figure 3: Dimensions, specifications and subspecifications relevant for value creation with digital twins.  
Distribution and commissioning.  
Depending on the view, the activities in the areas of distribution and commissioning already belong to MoL, which is why this activity in the framework shows a transition from blue (for internal value creation during BoL) to gray (for external value creation during MoL). The other activities that are distinguished in the MoL phase are:  
Training;  
Service and maintenance;  
Updates;  
Upgrades;  
Resale.  
With regard to the last life cycle phase of BoL, there is only one activity that is counted exclusively as EoL in the framework:  
Recycling.  
In the sense of the closed-loop idea of PLM, however, some parts of the activity \"Customer & market analysis\" could also be counted as EoL. In particular, if valuable information and knowledge for the next cycle can be gained from the previous life cycle.  
There are many studies on the application of DTs along the product life cycle (e.g., [17; 19; 56]) and the integration of the DT into PLM (e.g., [57]). Research is still focused on the potential of DTs in the BoL and MoL phase, for example, to optimize product design, engineering, shop floor design, supply chain management, customer demand analysis, and service and value proposition design (e.g., [8; 17; 19; 21; 43; 56]). However, there is also a plethora of possibilities of DT applications in any other PLM discipline, which are not yet fully explored (e.g., [16; 28; 45]). In theory, DT-based services can provide value in every life cycle phase. Inherently, DT concepts embrace the whole product life cycle \"from cradle to cradle\" [58]. However, in a study from Switzerland, only 15% of the companies surveyed reported using DTs in the EoL phase, as the focus is mainly on the BoL (79%) and MoL phases (60%) [59].
## 3 Numerical Setup and Results
### Results and Comparisons  
We compared the proposed system with the typical single-loop RC. The parameter settings of the two systems are shown in Table 2. For a fair comparison, the total number of the virtual nodes should be identical between the double-loop and single-loop systems (i.e., \(N=150\) of a typical RC), and the rest of the parameters set at the best operating point of the respective system.  
Figure 8: Relationship between regularization parameter \(reg\) and BER.  
First, we used entropy [17] to compare the internal dynamics of the RC structure, as mentioned above, and the typical single-loop RC. The formula for normalized entropy is as Equation (8), where \(p_{i}\) is the probability of the node states being included in the \(i\)-th segment. As shown in Figure 9, the greater the entropy indicated the enhanced internal dynamics. As can be seen, the internal dynamics of the double-loop RC were enhanced more than the typical single-loop RC.  
\[h_{node}=\frac{1}{\log_{2}0.01}\sum_{i=1}^{N}p_{i}\log_{2}p_{i} \tag{8}\]  
The DPol-MZM-based double-loop optoelectronic RC enriched the internal dynamics of the reservoir and improved performance through the nonlinearity of the parallel loops superimposed on the transfer function. When simulating nonlinear channel equalization tasks, its BER was greatly improved compared with the single-loop scheme below the HD-FEC [21] and KP4-FEC [22], reaching a BER level of 1 \(\times\) 10\({}^{-6}\) at the SNR of 32 dB. The performance of decision feedback equalization (DFE) [23] and third-order Volterra filtering [24] methods are also shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters (unit)** & **Symbol** & **Typical RC** & **Proposed RC** \\ \hline Size of RC & \(N_{1}\) & 150 & 50 \\  & \(N_{2}\) & \(\backslash\) & 100 \\ \hline Input gain & \(\gamma\) & 0.5 & 0.7 \\ \hline Feedback gain & \(\beta\) & 0.47 & 0.11 \\ \hline Delay time (s) & \(\tau_{1}\) & 1.2 \(\times\) 10\({}^{-5}\) & \(4\times 10^{-6}\) \\  & \(\tau_{2}\) & \(\backslash\) & \(8\times 10^{-6}\) \\ \hline Bias voltage (V) & \(\varphi_{1}\) & \(-4\pi\) & 3.5\(\pi\) \\  & \(\varphi_{2}\) & \(\backslash\) & \(-2.5\pi\) \\ \hline Scale factor of feedback & \(\alpha_{1}\) & 0.9 & 0.7 \\  & \(\alpha_{2}\) & \(\backslash\) & 0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter values for the proposed RC and typical RC in our numerical simulation.  
Figure 9: Entropy versus the normalized feedback coefficient.
# Sulfuric Acid Resistance of CNT-Cementitious Composites
## 4 Conclusions  
This study aimed to evaluate the characteristics of deterioration caused by a sulfate in the CNT cementitious composites, and the following conclusions were drawn as follows;  
1. When CNT was added to cement mortar, the compressive and flexural strengths further decreased compared to those of the specimen without CNT due to an increase in the amount of pores inside. In particular, it was found that relatively large pores with sizes ranging from 370 to 80 \(\upmu\)m occurred due to the van der Waals force, and these pores resulted in the degradation of mechanical properties.
2. The electrical resistance of the CNT cementitious composites was significantly reduced by to up to 90%. In addition, the decrease in resistance value was greater in SWCNT cementitious composites than in MWCNT mixtures, indicating that SW exhibited more excellent electrical properties. The specimen subjected to deterioration resulting from sulfate ions showed a slight increase in the resistance compared to that before  
Figure 13: Cumulative pore volume of CNT cementitious composites before and after immersion in 5% sulfuric solution: (**a**) before damage and (**b**) after damage.  
the damage. This signifies that there will be no major complications in exhibiting self-sensing performance.
* The incorporation of CNTs led to large pores forming in the cement mortar. However, as CNT particles with excellent chemical resistance prevented contact between cement hydrates and sulfate ions, the relaxation and breakage of hydrates affected the weight reduction rate and compressive strength decrease ratio, thereby improving the resistance to sulfate attack. In future studies, it is necessary to analyze the effect of the contact area between sulfuric acid and the cement structure on the pore size and pore distribution created by the incorporation of CNTs.  
Conceptualization, G.-C.L., S.-Y.S., and H.-D.Y.; methodology, G.-C.L. and S.-Y.S.; validation, G.-C.L., S.-Y.S., H.-D.Y., Y.K. and S.H.; formal analysis, G.-C.L., H.-D.Y., and S.H.; investigation, G.-C.L., S.-Y.S., and S.H.; resources, G.-C.L. and Y.K.; data curation, G.-C.L. and Y.K.; writing--original draft preparation, G.-C.L., Y.K., S.-Y.S., and S.H.; writing--review and editing, G.-C.L., S.-Y.S., H.-D.Y., and S.H.; visualization, S.H.; supervision, G.-C.L., S.-Y.S., and S.H.; project administration, G.-C.L. and S.-Y.S.; funding acquisition, G.-C.L., S.-Y.S., H.-D.Y., and S.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (No. 2018R1A4A1025953). The views expressed are those of authors and do not necessarily represent the sponsors.  
Not applicable.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2018R1A4A1025953).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Microarray Data  
A microarray data matrix containing the gene expression profile of 40 HMCLs was used to study the genes involved in arachidonic acid metabolism, to detect genes expression profile patterns likely connected to the different multiple myeloma types. All the numerical results were obtained by implementing the algorithms in Matlab 8.4 codes and running them on a machine equipped with an Intel(r) core 2 Duo CPU with RAM 8.00 GB.  
Figure 1: Non-negative matrix factorization decomposition schema. Each column of X contains a different human myeloma cell lines (HMCL) described by the expression profiles of the selected genes; each row represents the expression profile of the corresponding gene, through the HMCLs.  
Microarray data of multiple myeloma cell lines available on the public database ArrayExpress [41] were analyzed.  
The microarray data were collected using the Affymetrix GeneChip Human Genome U133 Plus 2.0 [HG-U133-Plus-2]. The analysis was conducted on a subset of the original data starting from 40 human myeloma cell lines (HMCLs) that provided a signature for stratification of patient risk [41] and 64 genes involved in arachidonic acid metabolism as depicted in Figure 2.  
Figure 2: Arachidic acid (AA) metabolism, released from the membrane upon mechanical mitogen stimuli. Eicosanoids are the major products of AA metabolism. In this figure, the biosynthetic pathways of the main eicosanoids together with genes coding for proteins are differently colored: cyclooxygenase (COX) (red), 5-lyporoxygenase (5-COX) (purple), 15-LOX (green), 12-LOX (yellow), cytochrome P (CYP) epoxygenase (orange), CYP w-hydroxyylase (cyan). Modified image from [35].  
A data matrix was built by using 64 rows (genes) and 40 columns (HMCLs) (Figure 3). It was evident that the dimensionality of the data was too big to directly identify any GEP pattern. Hence, a dimensionality reduction was needed to bring out useful information that could be easily visualized through the part-based representation.