esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Comparison of Hybrid Steel-Reinforced Concrete and Conventional Frames
## 4 Conclusions
The latter shows that steel structures experience more significant floor accelerations concerning strong ground motions, which could harm occupant comfort and the function of non-structural components. Also, regarding all structures analyzed under the selected near-fault earthquakes, the PFA/PGA ratio is generally higher along the \(Y\)-axis than the \(X\)-axis, showing directional sensitivity in seismic response. These findings emphasize the significance of considering the building type and the directional effects in the structural design for earthquake resilience. The dimensionless base shear ratio is generally higher in steel buildings than in reinforced concrete (r/c) and hybrid structures, implying that steel frames must resist greater lateral forces. In contrast, r/c and hybrid buildings have similar base shear ratios, reflecting similar seismic force distribution. These research results show the impact of material characteristics and structural systems on seismic load resistance, pointing out the importance of specialized design techniques to optimize the performance of the hybrid building form. By reviewing the structural failures, allowable inter-story drift ratio (IDR) restrictions, and overall seismic response, the hybrid steel-reinforced concrete buildings appear to be an appealing alternative to traditional constructions. In addition, based on the observed response parameters, hybrid steel-r/c buildings have advantages in resisting near-faultground motions compared to traditional ones. The improved efficiency and balanced seismic behavior of the hybrid building's form pose it as an accessible option for modern practical construction, providing both structural resilience and practical advantages. Future research could enhance connectivity mechanisms between the steel and reinforced concrete components to optimize the hybrid structure design and improve load transfer efficiency. In addition, research into innovative materials, such as high-performance concrete or shape-memory alloys, could boost seismic resilience. Using computational optimization models incorporating machine learning techniques could indicate optimal hybrid design configurations for particular seismic zones. Ongoing studies might broaden this study by investigating hybrid constructions under varied earthquake intensities, soil characteristics, and loading scenarios. Experimental validation of the numerical conclusions through large-scale testing would also provide further information on hybrid building performance. In addition, life-cycle cost analysis and sustainability investigations could better understand hybrid construction's longer-term advantages. The hybrid steel-reinforced concrete system may be further optimized by addressing these challenges, making it a more durable and efficient option for modern seismic-resistant building designs. This research received no external funding. Not applicable. Data Availability StatementData are contained within this article. The author declares no conflicts of interest.
## 4 Demanding Applications of Blockchain Technology and the Motivation behind Them
### Data on National Identity Is Being Protected Using Blockchain by Governments  
Furthermore, blockchain is being used by governments for the management of digital identities. For example, Estonia uses blockchain-based digital identity to digitize national identity records, secure citizen data, and to reduce the inefficiencies of legacy digital ID management platforms, such as high costs.
## 4 Results and Discussion
### Prediction Using ML Regression
The DE at the left lower lobe is lower than the remaining lobes in all cases. At the right upper lobe, the overall DE shows a fluctuating trend for different diameter particles. These specific findings are crucial for the health risk assessment of lung diseases and targeted drug delivery. The prediction model clearly indicates the deposition hot spot of the different diameter particles at various flow rates, which could potentially improve the efficiency of the targeted drug delivery. In conventional drug delivery tools, drug particles are mostly deposited in the upper airways. A minimum amount of drugs can reach the targeted areas of the lower part of the airways. The ML prediction model will improve the knowledge of the various diameter aerosol deposition hot spots and DE in the lower airways. An innovative drug delivery device can be developed for different lobes, and after diagnosis, a patient-specific drug delivery tool can be used, which will improve overall drug delivery efficiency. Figure 8: Particle DE comparison in the different lobes of the right and left lungs for different flow rates for (**a**) 2 \(\upmu\)m, (**b**) 4 \(\upmu\)m, (**c**) 6 \(\upmu\)m and (**d**) 8 \(\upmu\)m.  
Figure 9 presents the comparison of DE for the 17-generation model. The ML prediction data is compared with the CFD measurements at a 25 L/min flow rate. The overall DE from the ML model shows a good agreement with the CFD data. A second-order polynomial equation is developed from the trendline of the CFD and ML DE curves. The R\({}^{2}\) value for the CFD and ML trendline is 0.9637 and 0.9751, respectively. The curve fitting indicates the accuracy of the ML model.
## 6 Discussion
### Muscle Efforts  
Muscle effort has been defined based on muscle activation during two activities. \(E\) is the normalized EMG signals to the MVC signals. \(E_{w}\) and \(E_{S}\) denote the \(E\) measured during walking and sitting, respectively, acquired from the results of Figure 10.  
\[F=\int_{0}^{60-t_{s}}\left(E_{w}\right)d\tau+\int_{0}^{t_{s}}\left(E_{s}\right)d\tau \tag{16}\]  
The relative muscle effort with the KARE-1 compared to the case without it is defined as \(G\) in Equation (17).  
\[G=F^{W}/F^{U} \tag{17}\]  
where the superscripts '\(W\)' and '\(U\)' stand for the case with the KARE-1 and the case without assistance, respectively. It is shown that G drops down, or the KARE-1 becomes more efficient, as the period of sitting duration increases. The \(G\) value drops when the body assistance ratio \(\Re\) increases. However, as the period of walking time increases, the efficiency drops and \(G\) becomes more than one (see the gray-colored circle, Figure 11), which means that the KARE-1 is not efficient anymore.  
While \(G\) gives a rough estimation on how the efficiency of the KARE-1 with respect to the body assistance ratio and task ratio, it has limitations in that: (1) the estimation is based on the extrapolation of measured data, and (2) changes of muscle by adaptation or fatigue are not considered.  
Finally, the performance specifications of the proposed assistive robotic exoskeleton are summarized and compared with top commercial products in Table 6. This verifies the feasibility and validity of the proposed exoskeleton.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline - & **Noonee [3] (Early Type)** & **BWAS [5]** & **KARE-1** \\ \hline Actuation & Passive & Active & Active \\ Assist Capacity & Full support/100 kg/leg & Partial Assist/10 kg/leg & Full support/42.5 kg/leg \\ Weight & 1 kg/leg(Target) & 3.25 kg/leg & 3.0 kg/leg \\ Mechanism & Damper and Locking System & 1 Motor/leg & 1 Motor/Leg \\ Operation time & 8 h & 2 h & Over 8 h \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with Commercial Knee-Assistive Devices.  
Figure 11: A contour plot of effectiveness factor G with various body assistance ratios \(\Re\) from 0 to 1. (Walking speed is set at 4 km/h).
## 3 Results
### PLS-DA and PLSR Model Validation  
The classification and regression models (PLS-DA and PLSR) were established to further validate class separation under multiple factors. Since the origin of EVOO is the most important source of difference in the UV-Vis spectra of adulterated samples, it may significantly affect the accuracy of the adulteration detection model. Therefore, eight models were established to compare the differences in the sample under various situations. Specifically, the eight models consisted of four groups. The global models included all 10 EVOO producers. The European model excluded the Australian sample, while the Spanish and Italian models only included samples originated from Spain and Italy, respectively. For the three local models, the applicability of the external test set was applied to evaluate the performance of these local models, for which the external test set is defined as the independent test sets that were from the selected countries of origins only. Figure 4: ANOVA-PDR mapping of the three main factors. (**A**) EVOO Origin; (**B**) adulteration Level; (**C**) adulteration Type. \(R_{s}\), geometric average of all PDR scores. From the prediction results of the local models in Table 1, it can be observed that the local models generally yielded good performance by using internal training and the test set. The prediction accuracies of the local models by PLS-DA on training and test sets were above 97%. The RMSEs of the PLSR local models on training and test sets were also less than 2.03%. However, these models have a poor predictive effect on the external test sets from other origins. The prediction accuracies of the three PLS-DA local models for these different external test sets dropped to 59.58-87.92%. Meanwhile, the RMSEs of PLSR models also raised significantly. Considering the results of ANOVA-PDR, among the EVOO origin, the difference between A1 and others is the largest. Its RMSE reached 31.39% in the PLSR model of the European model, and lack of quantitative prediction power. Moreover, compared to the external test set of other origins, when A1 is used as the external test set, the RSME of the PLSR model of the Spanish model and the Italian model both increased by more than 50%, and the worst prediction effect is obtained. A more intuitive presentation of the local PLSR model on the external test set from other origins can be observed in Figure 5. In the process of establishing the EVOO adulteration detection model, the EVOO origin is an important influencing factor, but it was typically neglected. For instance, [PERSON] et al. established PLSR models of EVOO adulterated with corn oil, soybean oil, and sunflower oil by UV-Vis, in which the RSMEs yielded as low as 0.001% [5]. However, these models are based on only one EVOO, so that validation at a larger scale might still be needed.
# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 1 Introduction
### Random Forest  
Random Forest is a group learning method that builds multiple decision trees to improve classification accuracy by aggregating the predictions of individual trees [10; 11]. This technique is particularly effective in handling high-dimensional data and is less prone to overfitting compared to individual decision trees. The diversity of trees is achieved by training each tree on a random subset of the data, both in terms of the samples (bagging) and the features used for splitting at each node.  
For event classification in road event detection, Random Forest offers a robust solution to identify patterns in vibration and acceleration data. The algorithm begins by creating multiple decision trees, each trained on a random subset of the data. Each tree makes a prediction based on the input features, and the final classification is determined by the majority vote of all trees in the forest. This process enables the model to handle noisy, high-dimensional sensor data and to classify events accurately without requiring extensive feature engineering.  
The strength of Random Forest lies in its ability to reduce the risk of overfitting by aggregating the results of many decision trees, each of which captures different aspects of the data. Furthermore, it is computationally efficient, making it suitable for real-time applications in road event detection. The robustness of the model also ensures that it can adapt to the variability in sensor data across different urban environments, providing reliable event detection even in the presence of noise or inconsistencies in the data.  
In addition, Random Forest can handle both numerical and categorical data, making it highly versatile for multi-modal sensor inputs, such as vibration and acceleration data. This capability improves its performance for classifying diverse events in complex systems, where the patterns of sensor data can vary greatly depending on factors such as road conditions, vehicle types, and driving behavior [12].
###### Abstract  
This study investigated whether pulsed electric field (PEF) treatment can induce structural changes of whole, intact red onion bulb (_Allium cepa_ L. var. Red Pearl). Onion bulbs were treated at electric field strengths of 0.6 and 1.2 kV/cm combined with energy inputs of 6 and 60 kJ/kg at different onion orientations with respect to the high voltage electrode. Results showed that onion cells across all fleshy scales experienced uniform cell damage with a higher proportion (>80%) of non-metabolically viable cells after PEF treatment at 1.2 kV/cm when the root end was positioned facing toward the PEF electrode. The findings were supported by cryogenic-scanning electron micrographs (cryo-SEM), where the underlying storage circular cells were completely damaged owing to the PEF treatment. In this study, it was found that the treatment intensity of PEF to induce structural damage across all the scale layers of an onion bulb coincided with an increase in dipropyl disulfide (DPDS) released from the onion bulbs. Therefore, DPDS was used as a volatile marker indicating cellular disruption within whole, intact onion bulbs. A considerable increase of DPDS, up to 52-fold, was detected from PEF-treated onion bulbs compared to untreated bulbs.  
Keywords: onion microstructure; pulsed electric fields; cell viability; volatiles; dipropyl disulfide +
Footnote †: journal:
# Vulnerability Analysis of the China Railway Express Network Under Emergency Scenarios
## 3 Methodology
### Cascading Failure Model of CRN Based on CML
\begin{matrix}(1-\epsilon_{1}-\epsilon_{2})f(x_{i}(t-1))+\frac {\epsilon_{1}\sum_{j=1,j\
eq i}^{N}a_{ij}f\big{(}x_{j}(t-1)\big{)}}{\epsilon_{ i}}\\ +\frac{\epsilon_{2}\sum_{j=1,j\
eq i}^{N}w_{ij}f\big{(}x_{j}(t-1)\big{)}}{s(i)} \end{matrix} \right|+R \tag{8}\]  
When node \(i\) fails, the edges connected to node \(i\) will also fail simultaneously; that is, the node will be removed from the network. The cargo flow between node \(j\), which is adjacent to node \(i\), will be undertaken by the adjacent nodes of \(j\). Considering the transportation distance and cost limitations between different nodes on the CRN, allocation rules based on the distance and remaining capacity between adjacent nodes are proposed as shown in Equations (9) and (10). \[F_{jp}^{i}=F_{jp}+\Delta F_{jp} \tag{9}\]\[\Delta F_{jp}=F_{ij}\cdot\left(\gamma\cdot\frac{\frac{1}{d_{jp}}}{\sum_{n\in P} \frac{1}{d_{jn}}}+(1-\gamma)\cdot\frac{C_{p}-f_{p}}{\sum_{n\in P}C_{n}-f_{n}}\right) \tag{10}\]  
\(F_{jp}^{i}\) represents the cargo flow from node \(j\) to node \(p\) affected by the failure of node \(i\); \(\Delta F_{jp}\) refers to the transferred cargo flow from node \(j\) to node \(p\), \(F_{ij}\) denotes the cargo flow from node \(i\) to node \(j\), and \(F_{jp}\) indicates the original cargo flow from node \(j\) to node \(p\). Assuming that the set of adjacent nodes for node \(j\) is P, \(d_{jp}\) represents the distance between node \(j\) and node \(p\), and \(\gamma\) is a distribution coefficient, where \(\gamma\in(0,1)\). When distributing the cargo flow, both the distance between node \(j\) and node \(p\), as well as the remaining capacity of the adjacent nodes of node \(j\) are considered. The schematic diagram of cargo flow distribution is shown in Figure 2.  
In order to analyze the vulnerability of CRN under sudden incidents, sites or edges are selected based on different indicators to simulate the occurrence of these incidents. If a site in the network experiences a sudden incident, that site and the edges connected to it will be removed from the network. If an edge in the network experiences a sudden incident, this edge will be removed from the network, and the number of removed edges is represented by \(E\). Unlike point failures, edge failures directly remove edges from the network, and reallocate the flow on that edge according to Equations (9) and (10). Based on different sudden incidents, the cascade failure ratio of the entire network is defined to evaluate the vulnerability of the CRN. \[\mathrm{V}=\frac{N_{0}}{N} \tag{11}\]  
where \(N_{0}\) represents the number of failed nodes in the network after cascade failure occurs, and \(N\) represents the total number of nodes in the network.
# NOx Formation Mechanism and Emission Prediction in Turbulent Combustion: A Review
## 2 NOx Formation Mechanism
### NNH Route  
The NNH route, as a new pathway, was discovered in 1995 [33]. Through radical [O-] and [NNH-] combining and decomposing rapidly, it would finally generate the radical [NH-] and NO.  
\[\mathrm{N_{2}+H\cdot}\rightarrow\mathrm{NNH\cdot} \tag{8}\]  
\[\mathrm{NNH\cdot+O}\rightarrow\mathrm{NO+H\cdot} \tag{9}\]  
With the environment around oxygen atoms, the NNH is a dominant way to generate NO. In addition, the NNH pathway at lower mixture temperatures and short residence times contributes more to NO production [34]. Especially for the short residence times, the NNH route plays a dominant role in NO generation [35].
# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 4 Formation Damage Diagnosis and Technology Development Direction Prediction
### Numerical Simulation Techniques for the Spatial and Temporal Evolution of Formation Damage  
Production practice demands higher requirements for formation damage diagnosis, i.e., not only quantitative diagnosis of damage extent and causes, but also the dynamic simulation of the process of damage generation and developmental changes, thus helping production decision-making by making the diagnosis of strong practical value. It is necessary not only to realize the simulation of the spatial and temporal evolution of each damage type and the total damage degree, but also to provide data on the sensitivity of each damage type in proportion to total damage, and to provide a quantitative value of the percentage of each damage type in the degree of total damage. For wells where formation damage has occurred, the use of historical parameters to achieve quantitative formation damage simulation and spatio-temporal evolution is of great significance for the optimal design of deconstruction measures and the improvement of numerical simulation accuracy. For wells where formation damage has not yet occurred, quantitative prediction of formation damage and spatial and temporal extrapolation of damage patterns can be performed using physical and engineering parameters. This would guide the field of petroleum industry practitioners towards a correct approach to the prevention or avoidance of formation damage and the formulation of formation development plans and subsequent production enhancement measures.  
Therefore, in the future, a model considering the coupling relationship of multiple damage factors can be established based on the material balance theory, combined with probabilistic processes based on previous studies. By systematically studying the temporal and spatial evolution of formation damage on a large scale (a spatial area extending from the center of the wellbore to the formation for several meters), the simulation process is extended to four dimensions, and the changes in formation permeability and skin coefficient due to the interaction of formation permeability, rock surface properties, fluid properties, and external factors (e.g., pressure difference, temperature, foreign fluids, and others) are simulated in space and time dimensions. This would make the description of formation damage more \"transparent, digital, dynamic, visualized, and intelligent\", providing an immersive experience for those working in the oil field industry.
## 3 Results
### PHASE 1--Modeling  
#### 3.1.1 Stage 1--Generation of the GIS-3D Site Environment  
In step 1, _Data Management and Initial Configuration_, orthophotos were utilized as the base map alongside a digital terrain model that included relief. To represent the latter, we employed point clouds from the LiDAR flights conducted as part of the 2012 National Aerial Orthophotography Plan of Spain (PNOA), which were used to generate the aforementioned digital terrain model (DTM). Both the orthophotos and the corresponding point clouds were obtained from the _Mapas Cantabria-Espana_ download center [27] and the headquarters of the _Catastro-Espana_[28]. To develop step 2, _Definition of Terrain and Building Footprint_, the process began with the cadastral base of the parcels for each site. This base, referred to in Spain as \"_Constru_\", was obtained directly from the Spanish Cadastre headquarters [28]. The 2D layer \"_Constru_\" included an attribute table containing information about the parcels, sub-parcels, and the polygonal shapes of the building footprints. Given that the site was expected to have terrain with relief, it was necessary to create both the digital terrain model (DTM) and the digital surface model (DSM). To achieve this, the points corresponding to the terrain in the LiDAR point cloud were filtered out, as shown in Figure 7a. These points were then combined with the previously assigned orthophoto using algorithms implemented in GIS software (_ArcGIS Pro v2.7_), resulting in a terrain that featured realistic relief and texture, as illustrated in Figure 7b. In step 3, _Generation of the GIS-3D Environment of Buildings_, the \"Constru\" layer created in step 2 included an attribute table with a field that specified the height of each building. For this project, an average height of 3 m per floor was adopted, enabling the generation of the environment at level of detail 1 (LoD 1), as illustrated in Figure 8a. To achieve LoD 2, a Python function was automated to identify building heights and elevation differences at the highest points (roofs) within the digital height model (DHM). This process defined the slopes of the roofs accurately. For LoD 3, it was essential to enhance the realism of the building envelopes by incorporating textures (_multipaths_). Figure 8b illustrates the GIS-3D model that integrated buildings at LoD 2 alongside those at LoD 3. Finally, LoD 4 was achieved through the integration of BIM models in subsequent stages of the methodology.  
Figure 6: Location: (**a**) Santander campus of _“Las Llamas_” of the University of Cantabria. (**b**) Civil Engineering School.