esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of Test Arch Based on Concrete Damage Plasticity Model and Damage Evolution Analysis
## 4 Simulation Model Construction  
### Model Construction and Validation  
Based on the dimensional parameters in one previous study [16], a simulation model was developed with the comprehensive finite element software ABAQUS 2022. The C3D8R solid element was used to represent the concrete, and the CDP model was employed for  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Expansion Angle [21]** & **Eccentricity [22]** & \
\begin{tabular}{c} **Compressive Strength** \\ **Ratio [23,24]** \\ \end{tabular} & **K-Factor [25]** &
\begin{tabular}{c} **Coefficient of** \\ **Viscosity [26]** \\ \end{tabular} \\ \hline
35 & 0.1 & 1.16 & 0.667 & 0.0005 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of concrete plasticity parameters.  
\begin{table}
\begin{tabular}{c} **Damage** \\ **Parameter** \\ \end{tabular} \\ \hline
2.317 & 0 & 0 & 5.127 & 0 & 0 \\
2.38 & 0.00003 & 0.16051 & 10.002 & 0.00001 & 0.0163 \\
1.38 & 0.00015 & 0.53002 & 14.389 & 0.000  
plastic damage. Spatial beam elements (B31 element) simulated the steel bars. The size of the reinforcement unit is 5 cm. The reinforcement is connected to the concrete by means of embedded fixing. The bond-slip effect between steel bars and concrete was not considered, and the loading was applied based on the amplitude data. The model is shown in Figure 6.  
Using preset parameters and experimental loads, horizontal and vertical displacement data for Column 2 were extracted from the referenced literature and were compared with our simulation model. The horizontal coordinates in Figure 7 are the live load multipliers, and the load values are shown in Table 2. The vertical coordinates are the displacement values. The displacement values are shown in Figures 3.4-3 and 3.4-5-4 in the literature [16]. As shown in Figure 7, the simulation results largely followed a linear trend without prominent deviations from the experimental data. The most significant discrepancy in horizontal displacement emerged in Load Cases 6 to 9, with a peak difference of 0.95 mm. Meanwhile, the greatest variance in vertical displacement appeared in Load Cases 4 to 6, peaking at 0.7 mm. Given potential inaccuracies in the simulation parameters, measurements, and loading, these deviations are within acceptable limits. Moreover, the damage distribution depicted in Figure 8 for the left half-span aligns closely with the experimental model, underscoring the reliability of our simulation results.  
Figure 6: Simulation model diagram: (**a**) bridge; (**b**) reinforcing steel skeleton.  
Figure 7: Comparison of displacement data of 2# columns: (**a**) horizontal displacement; (**b**) vertical displacement.
## 3 Results
### Temperature
#### 3.1.3 Historic and Future Distribution of Temperature
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Min Temp**} & \multicolumn{2}{c}{**Max Temp**} & \multicolumn{2}{c}{**Mean Temp**} & \multicolumn{2}{c}{**SD**} \\ \cline{2-9}  & **Historic** & **Future** & **Historic** & **Future** & **Historic** & **Future** & **Historic** & **Future** \\ \hline Chamba & 5.11 & 8.02 & 22.64 & 25.52 & 12.90 & 15.80 & 2.99 & 2.99 \\ \hline Lahul and Spiti & 5.14 & 8.05 & 6.20 & 9.11 & 5.47 & 8.38 & 0.29 & 0.29 \\ \hline Anantnag & 5.95 & 8.91 & 19.14 & 22.07 & 13.22 & 16.15 & 3.43 & 3.42 \\ \hline Badgam & 8.93 & 11.83 & 19.90 & 22.82 & 15.44 & 18.36 & 3.02 & 3.03 \\ \hline Bandipore & 5.88 & 8.85 & 17.56 & 20.50 & 10.33 & 13.28 & 2.75 & 2.74 \\ \hline Paramulla & 10.04 & 12.95 & 19.25 & 22.18 & 15.91 & 18.83 & 2.05 & 2.06 \\ \hline Doda & 10.79 & 13.70 & 20.47 & 23.36 & 16.00 & 18.89 & 2.83 & 2.83 \\ \hline Ganderbal & 6.83 & 9.79 & 19.25 & 22.18 & 11.41 & 14.36 & 3.73 & 3.72 \\ \hline Jammu & 24.56 & 27.42 & 29.58 & 32.37 & 28.24 & 31.07 & 0.86 & 0.85 \\ \[PERSON] & 4.13 & 7.06 & 8.15 & 11.11 & 5.49 & 8.43 & 1.04 & 1.06 \\ \[PERSON] & 13.44 & 16.32 & 29.16 & 31.96 & 24.51 & 27.37 & 4.32 & 4.29 \\ \[PERSON] & 4.13 & 7.06 & 17.47 & 20.37 & 10.00 & 12.92 & 3.44 & 3.44 \\ \[PERSON] & 8.13 & 11.03 & 19.27 & 22.21 & 14.33 & 17.23 & 3.85 & 3.85 \\ \[PERSON] & 8.60 & 11.56 & 18.60 & 21.53 & 14.47 & 17.40 & 1.52 & 1.52 \\ \[PERSON] & 10.04 & 12.95 & 24.00 & 26.89 & 16.78 & 19.68 & 4.50 & 4.50 \\ \[PERSON] & 10.30 & 13.25 & 19.90 & 22.82 & 17.13 & 20.07 & 3.28 & 3.28 \\ \[PERSON] & 13.66 & 16.55 & 28.53 & 31.37 & 22.76 & 25.63 & 3.34 & 3.33 \\ \hline Ramban & 13.59 & 16.49 & 21.57 & 24.43 & 17.17 & 20.06 & 2.05 & 2.04 \\ \hline Reasi & 11.90 & 14.79 & 28.38 & 31.21 & 20.40 & 23.27 & 4.51 & 4.50 \\ \hline Samba & 25.85 & 28.70 & 29.37 & 32.16 & 28.25 & 31.07 & 0.80 & 0.79 \\ \hline Shupiyan & 8.32 & 11.22 & 19.90 & 22.82 & 16.98 & 19.90 & 3.28 & 3.29 \\ \hline Srinagar & 10.71 & 13.66 & 19.25 & 22.18 & 16.71 & 19.65 & 2.70 & 2.70 \\ \hline Udhampur & 13.44 & 16.32 & 27.99 & 30.81 & 21.88 & 24.74 & 3.91 & 3.90 \\ \hline Pathankot & 22.33 & 25.22 & 28.87 & 31.69 & 26.98 & 29.82 & 2.27 & 2.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Zonal distribution of temperature both historically and for the future.
# A Deep Learning Framework for Signal Detection and Modulation Classification
## 4 Results
### Performance on Modulation Recognition
With the increase of symbol number and eye number, the overall accuracy of the model  
Figure 15: The network performance on different frequency offsets range. (**a**) classification accuracy for different frequency offset versus SNR; (**b**) normalized confusion matrix in 3 dB when the frequency offset is \([-0.01,\,0.01]\); (**c**) normalized confusion matrix in 3 dB when the frequency offset is \([-0.02,\,0.02]\); (**d**) normalized confusion matrix in 10 dB when the frequency offset is \([-0.1,\,0.1]\). is gradually increasing. But we also can see that when the symbol number is 1000 and the eye number is 5, the improvement of performance is not obvious. Therefore, we finally choose 800 symbols and 4 eye numbers to generate the eye diagram and the vector diagram. Finally, we compare the performance of the single input network with the multi-inputs network in this work. The results are shown in Figure 17. The modulation recognition algorithm based on a single eye diagram has poor performance. The performance of the I-eye diagram is lower than that of the Q-eye diagram, which may be due to the setting of the initial phase in the same modulation format. And the performance of the vector diagram based method is also inferior to our method, since it does not make full use of the signal waveform information.
# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 5 Satellite Precipitation Products
### Precipitation Products with Low Spatiotemporal Resolution  
The GPCP dataset (V2.3) comprises monthly (2.5\({}^{\circ}\)\(\times\) 2.5\({}^{\circ}\)), pentad (2.5\({}^{\circ}\)\(\times\) 2.5\({}^{\circ}\)), and daily (1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\)) products. The monthly product is generated based on the weighted average-based method and AR-based method [117]. Subsequent enhancements have been periodically implemented over the years [118; 119; 136]. The daily product [137] is accessible from late 1996 onward, while the pentad product [138] covers the period from 1979 to present. These GPCP products can be obtained from National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) via WMO's World Data Center website ([[https://www.nccs.nasa.gov/services/data-collections/atmosphere-based-products/gpcp](https://www.nccs.nasa.gov/services/data-collections/atmosphere-based-products/gpcp)]([https://www.nccs.nasa.gov/services/data-collections/atmosphere-based-products/gpcp](https://www.nccs.nasa.gov/services/data-collections/atmosphere-based-products/gpcp)), accessed on 1 December 2024).  
The CMAP dataset comprises monthly precipitation (2.5\({}^{\circ}\)\(\times\) 2.5\({}^{\circ}\)) and pentad precipitation (2.5\({}^{\circ}\)\(\times\) 2.5\({}^{\circ}\)). This dataset integrates estimates from diverse satellite observations, monthly gauge analyses from the Global Precipitation Climatology Centre (GPCC) and the Global Historical Climatology Network (GHCN), as well as precipitation data from the NCEP-NCAR reanalysis using a weighted-average-based approach [127; 128]. These CMAP products can be accessed through the Earth System Research Laboratory at NOAA
# A Trust-Influenced Smart Grid: A Survey and a Proposal
## 3 NIST Conceptual Domain Model
### Markets Domain  
The sale and purchase of grid assets are conducted in the Markets domain, hence its importance to ensure that communications within this domain are transparent and reliable. There is the balance of supply and demand as well as the exchange price within the power system that is ensured by this domain. It must also be noted that due to the evolving nature of the Smart Grid, the market domain is bound to evolve, which in turn will define the Smart Grid in the future. The market domain communicates with the entity that controls the assets (operations domain), the customer domain and the other domains that supply the assets. The efficient matching of demand for power with the consumption of power is dependent on the domain of the market; thus, the communication flow between that domain and the domains that supply the power is critical. Bulk generation and DERs (which are usually served through aggregators) are examples of power suppliers, with DER more likely to become greater partakers as the interactive nature of the grid increases. Typical applications in the market domain include market management, DER aggregation, market operations, trading, ancillary operations and retailing.
## 5 Results
### Model Selection  
According to the procedure described in Section 4.4, five prediction models were evaluated. The results of the model evaluation procedure were collected and presented in Table 7. They indicate that, in the case of identifying failures in hydraulic pumps, tree-based models exhibited the highest prediction error. Among the tree-based models, the Gradient Boosted Trees algorithm achieved the best results, with an accuracy of 84.55%. The kNN algorithm ranked next, showing a 3-percentage-point improvement in accuracy compared to the GBT algorithm. The best results were obtained by the neural network, which achieved an accuracy of 88.68%.  
It is also worth noting that the use of autoregressive data, including up to three historical samples, did not positively affect the quality of the obtained results. These findings indicate no significant impact on model quality, and in many cases, a minimal degradation in model quality can be observed with an increasing window size.  
Ultimately, the MLP model, which achieved the highest accuracy, was used for the verification of the test data. The quality of the results obtained from the Failure 2 and Failure 3 datasets is presented in Table 8. Complementing the results is the confusion matrix obtained for both datasets, which is shown in Figure 7, where class 0 corresponds to the correct pump operation, and class 1 corresponds to the failure state. The results obtained differ by 13%. Such a large difference in accuracy can be explained by the levels of damage. In the Failure 2 dataset, the damage is the smallest, and in the Failure 3 dataset the damage is the largest and appears in two holes. Also notable is the difference between the results obtained in the cross-validation test using the Failure 1 dataset where the estimated accuracy was 88% and for the Failure 2 dataset the accuracy was 80%. The difference in accuracy can also be explained by the size of the damage in the valve plate. More details on the datasets are presented in Section 3.  
In Figure 7 identical values can be seen for the negative class (0). They are justified because the same set of values that describe the normal pump operating condition was used for both datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**LagModel** & **RandomForest** & **RotationForest** & **GradientBoosted Trees** & **kNN** & **MLP** \\ \hline
0 & 0.8442 & 0.8335 & 0.8455 & 0.8739 & **0.8868** \\ \hline
1 & 0.8223 & 0.8224 & 0.8440 & 0.8742 & 0.8803 \\ \hline
2 & 0.8180 & 0.8295 & 0.8429 & 0.8737 & 0.8865 \\ \hline \end{tabular}
\end{table}
Table 7: Comparison of the models’ performance using grid search procedure on failure 1 dataset.  
\begin{table}
\begin{tabular}{c c c} \hline
**Failure 2** & **Failure 3** \\ \hline MLP & 0.8016 & 0.9313 \\ \hline \end{tabular}
\end{table}
Table 8: Model performance obtained on the test datasets: the Failure 2 and Failure 3 datasets.
## 4 Numerical Simulation of Damaged Steel Wire  
### Effect of Corrosion Defects on the Mechanical Stress of the Steel Wire  
Fatigue tests on corroded steel wires indicate that the influence of corrosion on wire stress and fatigue life primarily involves changes in stress distribution due to surface  
Figure 6: Geometry model of coplanar dual crack steel wire. (**a**) 3D view. (**b**) Crack section. (**c**) Equivalent crack.  
damage and defects, leading to stress concentration and increased average stress, thereby reducing mechanical performance. Finite element models are established to simulate the morphology of corrosion defects in steel wires and to analyze the stress distribution near the damaged areas.  
The surface damage caused by actual corrosion on steel wires is complex and random. Based on the measurement results presented in reference [32], an analysis and discussion of the simplified idealized representations of defects are conducted. Models were established for steel wires featuring semi-spherical pit defects, groove-shaped pit defects, single-crack defects, and defects characterized by both pits and cracks, as illustrated in Figure 7. The semi-spherical defect has a radius of 0.2 mm, the groove-shaped defect has a length of 0.4 mm and a depth of 0.2 mm, and the crack size is a semicircular crack with a radius of 0.3 mm.  
The four steel wire models undergo elastic analysis, and Figure 8 provides corresponding Mises stress distribution contour plots near the defects. From the figure, it can be observed that the peak stress for the steel wire with spherical pit defects is 905.3 MPa, for the wire with groove-shaped pit defects, it is 1016.6 MPa, for the wire with a single crack, it is 1155.5 MPa, and for the wire with cracks and pits, it is 1356.3 MPa. The stress concentration in the groove-shaped pit is higher than that in the spherical pit, and the stress concentration in the wire with cracks and pits is higher than that with a single crack. As the distance from the damage location increases, the stress decreases sharply, and the concentrated area becomes very small. The stress reduction gradient for the wire without cracks is steeper compared to the wire with cracks.  
Figure 7: Finite element meshing with cracks. (**a**) Hemispherical pits. (**b**) Groove-shaped pits. (**c**) Cracks without pits. (**d**) Cracks with pits.
# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 3 Discussion about Articles
### Energy Expenditure
#### 3.5.1 Thermal Imaging  
The introduction of thermal imaging in exercise monitoring also allowed for the development of reliable contactless techniques for energy expenditure measurements. Thermal cameras capture infrared radiation in the mid or long-wavelength infrared spectrum, depending on sensor type, emitted from any object. Therefore, the pixel values in the images are converted to temperature values and finally mapped with energy expenditure.  
[PERSON] [63] validated the thermal imaging method to estimate energy expenditure using oxygen uptake as a comparative. Fourteen endurance-trained subjects completed an incremental exercise test on a treadmill. Heart rate, gas exchange, and mean accelerations of the ankle, thigh, wrist, and hip were measured throughout the exercise. A linear correlation was found between the energy expenditure calculated using the optical flow of the thermal imaging and the oxygen uptake values. The contactless measurement of energy expenditure during exercise was also presented by [PERSON] [64]. The authors used thermal video analysisto automatically extract the cyclic motion pattern in walking and running. The results indicated a linear correlation between the proposed method and oxygen uptake.
# Automatic Extraction of Adverse Drug Reactions from Summary of Product Characteristics
## 2 Materials and Methods
### ADR Extraction  
#### 2.3.1 SmPC Processing  
In the second phase of our NLP pipeline is ADR extraction. It starts with processing the above-mentioned ADR relevant HTML excerpts. As we described before, the section 4.8 in the SmPC contains ADR information in diverse formats: plain text, structured text,  
Figure 2: An example of a Summary of Product Characteristics (SmPC) excerpt.  
and tables. Tables used in describing ADR information also come with different structural styles. Therefore, we classify the 4.8 section into separate categories in terms of its content structure. Figure 3 elaborates on the process of categorization. Each category has its own ADR extraction technique.  
First, we identify a list of structural features in the HTML excerpts, for instance, the counts of '<table>', the counts and positions of some MedDRA terms [10], such as the frequency and SOC (System Organ Classes) [11]. Table 1 shows some specific examples of such words. Secondly, the features are engineered from the scraped HTML files. Based on the features, we obtain three main structural categories: structured text, free text, and tabular.  
* Free-text: ADRs are explained in free-text, as shown in Figure 4a. ADRs are hidden in sentences, which thus requires named entity recognition (NER) to extract them.
* Structured text: ADRs are described in a structured text, as shown in Figure 4b. It depicts a list of ADRs and their frequencies in an organized format.
* Tabular: ADRs are presented in a very structured way, namely a table. Figure 4c shows an example of such tables. These tables are further split into two groups: tabular horizontal and tabular vertical.  
Figure 3: A decision tree for classifying the ADR content during SmPC processing.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c} \hline \hline
**Grid Dimension (\(X\times Y\))** & \(Nu_{avg}\) \\ \hline
61 \(\times\) 61 & 8.892393 \\
81 \(\times\) 81 & 8.848909 \\
101 \(\times\) 101 & 8.821577 \\
121 \(\times\) 121 & 8.802795 \\
141 \(\times\) 141 & 8.789614 \\
161 \(\times\) 161 & 8.784837 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Impact of grid size on \(Nu_{avg}\) when Ra = 10\({}^{5}\), Ha = 50, \(\phi\) = 0.04, \(N\) = 4 and \(m\) = 16.1576.  
shape factor. One can easily surmise, then, that both the local and average Nusselt numbers increase with increasing Ra and decreasing Ha. These fluctuations of \(Nu_{ave.}\) and \(Nu_{loc. }\) with Ha and Ra can be explained as follows: lower values of Ra lead to a weakened buoyancy force in the system. So, in this case, the conduction mode of heat transfer governs the system. However, the convection mode dominates over the conduction mode at higher Ra because of the increased buoyancy force. Furthermore, introducing a magnetic field can lessen the power of the buoyancy force in the cavity. So, at higher Ha, the conduction mode will govern the system. Finally, we defined the relationship for the average Nusselt number and the influencing parameters in the current study. The relationship is defined as follows:  
\[ \begin{array}{ll}Nu_{ave}&=2.18015+5.79302\times 10^{-5}\times Ra-0.067176 \times Ha\\ &+1.37548\times N+6.56049\times\phi-1.34977\times 10^{-6}\times Ra\times Ha\\ &+6.23829\times 10^{-6}\times Ra\times N-0.024939\times Ha\times N\\ &+1.49480\times 10^{-3}\times Ha^{2}\end{array}  \tag{16}\]  
This correlation's R-squared equals 0.9927. The above correlation is valid for Ha = 10-30, \(\phi\) = 0.01-0.03, \(N\) = 1-3, and \(m\) = 3. Figure 2: _Cont._  
Figure 3: _Cont._  
Figure 2: Streamline contours for different values of Rayleigh and Hartmann numbers when \(N=1\), \(m=3\), and \(\phi=2\%\). Figure 3: Isotherm contours for different values of Rayleigh and Hartmann numbers when \(N=1\), \(m=3\), and \(\phi=2\%\). Figure 4: _Cont._  
Figure 4: (a) Local Nusselt (Nu) numbers for different values of volume fractions of the nanofluid and radiation parameters when \(Ra=10^{4}\), Ha = 25, and \(m=3\), (b) average Nu number for different values of volume fractions of the nanofluid and radiation parameters. Figure 5: _Cont._  
Figure 5: Local Nu number for different values of Rayleigh and Hartmann numbers and shape factors when \(N=1\) and \(\phi=2\%\). Figure 6: Average Nu number for different values of Rayleigh and Hartmann numbers and shape factors when \(N=2,\phi=2\%\), and \(m=3\).
# [PERSON]'s Classification for Colon Polyps Assessment Using a Deep Learning Approach
###### Abstract  
Colorectal cancer (CRC) is the second leading cause of cancer death in the world. This disease could begin as a non-cancerous polyp in the colon, when not treated in a timely manner, these polyps could induce cancer, and in turn, death. We propose a deep learning model for classifying colon polyps based on the [PERSON]'s classification schema, using basic colonoscopy equipment. We train a deep convolutional model with a private dataset from the University of Deusto with and without using a VGG model as a feature extractor, and compared the results. We obtained 83% of accuracy and 83% of F1-score after fine tuning our model with the VGG filter. These results show that deep learning algorithms are useful to develop computer-aided tools for early CRC detection, and suggest combining it with a polyp segmentation model for its use by specialists.  
colon cancer; deep learning; image processing; medical dataset; VGG +
Footnote †: journal:  
non malignant. Type III and IV have a low risk factor of being carcinogenic. Type V has a 56% of being malignant [8; 9].  
Multiple techniques originated from the area of computer vision have been used in the area of computer assisted polyp classification. Most of these are based on hand-crafting feature extraction methods to train a more basic classifier such as support vector machine (SVM) or a k-nearest neighbor (k-NN) classifier. Some known methods used are wavelet based methods [10; 11], texture analysis methods based on computing the local fractal dimension [11] and energy maps obtained from the intensity of valleys [12].  
Some authors have already experimented using deep learning for polyp detection and classification. In [13] they trained a large, deep convolutional model to classify between \"adenomatous polyp-positive\" and \"adenomatous polyp-negative\" classes. Using 1225 polyp images extracted from various digital sources they obtained an accuracy of 80% with a recall of 90% and a precision of 78%. In [14] the authors used transfer learning to obtain a feature extractor for a SVM that classifies between hyperplasia and adenoma polyps. They obtain an accuracy of 85.9% with 87.6% recall and 87.3% precision, however as this method uses support vector machine for classification, training when new data is available consume significant resources. The benefits of using learned features compared to hand-crafted methods is their ability to generalize better to multiple situations and to unfamiliar datasets. This has been proved in other areas such as pedestrian gender recognition [15].  
To provide a computer-aided diagnosis tool for CRC prevention, we propose a deep learning algorithm based on any standard colonoscopy equipment that classifies polyps into high risk and low risk of malignancy based on pit patterns described by [PERSON]. Those classified as Type V are considered high-risk, and those classified as Type IV or below are considered as low-risk.