esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 What Is Terrestrial Storage of Biomass (TSB)?
### Bioethanol from Corn
US bioethanol was originally framed as a way to help the US meet the 1997 Kyoto Protocol, which called for the major industrial countries to reduce their carbon footprint, and as a way to make the US independent of foreign oil. However, since the advent of tracking and the discovery of more oil in the US west, the US now exports about as much gasoline as it puts bioethanol into the gasoline pool, and it exports about five times as much crude oil, as shown in Figure 3 [38]. The bioethanol concentration in the US gasoline pool has reached the current blend wall limit of 10 vol%. Transportation accounts for about 30% of the total US energy demand, and transportation in the US is about 60% gasoline and 40% distillates (diesel, jet fuel, and fuel oil). Thus, bioethanol contributes only about 10% \(\times\) 30% \(\times\) 60% = 1.8% of US energy demand. Yet, 40% of the US corn crop is devoted to bioethanol (USDA (2023a) [39]). Also, about 30% of the US soybean oil is currently devoted to biodiesel (USDA (2023b) [40]). That is a lot of food for fuel in a world where people are starving. There is a question about where US bioethanol will go in the future as passenger cars move to EVs and, thus, the gasoline pool shrinks. In the short term, this may be addressed by increasing the blend wall limit. An increase to 15 vol% has already been proposed. However, this would mean a 50% increase in the fraction of the US corn crop devoted to bioethanol to 60%. This will only work for newer flex-fuel vehicles, since a high ethanol concentration is not compatible with the elastomers in older engines. It should be noted that ethanol provides a lower driving distance per volume consumed than hydrocarbon gasoline because the ethanol oxygen content means, in essence, that it is already partially burned. US pump prices in the US gasoline blends (10 vol% up to 85 vol% for E85) are generally about equivalent when placed on an equivalent driving  
Figure 3: US bioethanol build-up compared to gasoline and crude oil exports. Data source: EIA [38]. distance basis. However, consumers are really paying a higher price for ethanol blends and biodiesel when one considers that taxpayers pay for a tax per gallon, R&D, and capital incentives (RFA (2023) [41]). This discussion of corn bioethanol is provided to show that it is necessary to consider all of the impacts before setting new policy for net CO\({}_{2}\) reduction technologies.
## 4 3D FDEM Modeling of Rough Concrete-Rock Joint Failure in a Direct Shear Test
### Modelling Procedure
In this study, we determined the asperity height between 5 and 25 mm, and the specimen width, length, and height were determined as 125 mm, 300 mm, and 230 mm, respectively. The FDEM model consists of discrete 35 MPa concrete and granite blocks. Both the concrete and granite blocks have a saw-tooth shape at the one end of each block. Along the saw-tooth shaped ends, the two blocks are placed in a way that each end perfectly matches to form the C-R joint. To apply normal and shear loadings to the blocks with the rock-concrete joint, we considered two half-boxes (hereafter called loading platens) partially covering the concrete and granite blocks (Figure 9b). To provide a better understanding of the mechanical behavior of the C-R joint under self-weight loading, only a direct shearing test under CNL condition is considered here. The CNS conditions  
Figure 9: (**a**) A 3D FDEM model for simulating a direct shear test of the specimen with a rough concrete–rock joint with asperity angles \(\alpha=10\), 25, and 40 degrees, (**b**) cross-sectional view of example of direct shear simulation geometry and boundary conditions for the concrete–rock joint specimen with the asperity roughness (this case is _25 Deg._ roughness). employed in most of the previous research [27; 28; 29] for describing C-R joints were not considered because the main interest of this paper is in the joint between the foundation and gravity anchorage in which the normal stress of the rock joint is expected to remain constant during shear deformation. In such cases, it is recommended to adopt the direct shear test with a CNL [57]. In the 3D FDEM simulation, normal confinement stress \(\sigma_{\rm n}\) was applied to the top surface of the top loading platen (Figure 9b), while the bottom loading platen was kept in space to model the CNL condition. Then, the FDEM simulation continued until the static stress equilibrium was achieved in the C-R joint specimen. Note that the local damping scheme using a damping coefficient = 0.8 (coupled with mass scaling) [58] was utilized to accelerate the convergence rate to a static stress state. The cases of \(\sigma_{\rm n}\) = 1.0, 2.0, and 3 MPa were considered. After this, the top loading plate was moved along the shear directions (i.e., horizontal direction in Figure 9b) with a constant velocity \(v\) = 0.01 m/s, while the bottom loading plate was fixed in space. Accordingly, shearing the discrete concrete-rock blocks under the CNL condition can be realized along the saw-tooth shape joint. It is worth mentioning that the damping scheme was switched from the local damping scheme (with mass scaling) to the critical damping scheme [36] at this stage to achieve the quasi-static loading condition. Otherwise, unrealistic spurious fracturing occurs due to unrealistic stress pile-up near the loading region. The material properties of concrete and granite blocks are set to the calibrated parameters in the previous section (Tables 2 and 3).
# Multi-Pass Stamping Forming a Concave Ring
## 3 Simulation Analysis of the Concave Ring Formed by Multi-Pass Bending  
### Establishment of Finite Element Model  
The material of the concave ring is 0 Cr18 Ni9 austenitic stainless steel. Its density is 7930 kg\(\cdot\)m\({}^{-3}\). The Young's modulus is 20,400 MPa. Poisson's ratio is 0.285. The yield stress is 205 MPa. The tensile strength is 730 MPa. The specific heat capacity is 502 J-(kg\(\cdot\)K)\({}^{-1}\). [PERSON] et al. [20] tested the tensile properties of 0 Cr18 Ni9 stainless steel at room temperature--true stress-true strain curves at different strain rates were obtained. The constitutive model of 0 Cr18 Ni9 stainless steel at room temperature is:  
\[\sigma=1397.5\times\varepsilon^{0.4215}\dot{\varepsilon}^{0.098} \tag{2}\]  
where \(\sigma\) is the stress; \(\varepsilon\) is the strain; \(\dot{\varepsilon}\) is the strain rate.  
Simufact.Forming software was used for the stamping simulation. The mesher was used to sheet mesh. The element type was hexahedral. The number of elements on the ring thickness was two. The size of a single finite element was set to 0.3 mm. An automatic remeshing tool was used to reconstruct the mesh if the effective strain increased by 0.4. The implicit finite element solver was used to solve the problem. The coefficient of heat transfer between the tools and the material was 15 W/m\({}^{2}\)K. All die types were set to rigid dies without heat conduction. The friction factor was used to 0.15. The punching velocity was set to 100 mm/s. The finite element model of multi-pass stamping of the concave ring is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Angle of inclined straight line of upper-1 groove \(\alpha\)/\({}^{\circ}\) & 30 \\ Radius of arc of upper-1 groove \(\tau\)/mm & 0.5 \\ Depth of upper-1 groove \(d_{1}\)/mm & 1.5 \\ Depth of lower groove \(d_{2}\)/mm & 0.5 \\ Length of straight edge \(h\)/mm & 1.5 \\ \hline \end{tabular}
\end{table}
Table 1: Die parameters.  
Figure 6: The cross-section of the concave ring.
## 3 Results and Discussion  
We obtained the analysis results of the radial compressor, whose experimental results are in the literature. The analysis results of the compressor are shown below. Table 4 shows the performance results of the radial compressor for which we performed numerical analysis.  
Figures 12 and 13 are given for the plane projection of the meridional section. Figure 12 shows 50% velocity vectors. It can be seen that the flow follows the airfoil, creating a regular flow field. It is seen that the speed decreases locally due to the vortices formed at the blade exits due to the tip gap. It does not appear to exhibit any unexpected behavior.  
The total pressure contour is given in Figure 13. It is seen that the total pressure is the lowest at the blade inlets, and the total pressure is at the highest level at the blade exit. It is seen that the total pressure is at the highest level where the speed at the blade exits is low. The consistency of the results obtained is important for the accuracy of the design.  
The results of our radial compressor, which we designed and numerically analyzed, and the radial compressor, whose experimental results are in the literature, are shown in Table 5.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Rotation speed (rad/s) & 1829.77 \\ Inlet volume flow rate (m\({}^{3}\)/s) & 0.6578 \\ Mass flow rate (kg/s) & 0.8056 \\ Work input coefficient & 0.5721 \\ Input power (W) & 19,438.8 \\ Total temperature ratio & 1.0834 \\ Total pressure ratio & 1.2999 \\ Total isentropic efficient & 93.3362 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Compressor performance results.  
Figure 11: NSGA-II algorithm flow chart.  
As seen in Table 5, there is agreement between the results we obtained from numerical analysis and the experimental results in the literature.  
As can be seen in Figure 14, our numerical analysis is compatible with the experimental results. Thus, it is accepted that the numerical analysis results of the tandem blade radial compressor to be designed will be correct.  
Figure 12: Velocity at span of 50%, blade to blade.  
Figure 13: Total pressure contour.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Mass Flow Rate from** & **Pressure Ratio from** & **Experimental Pressure** & **Experimental Mass** & **Errors \%** \\
**Analysis (kg/s)** & **Analysis** & **Ratio** & **Flow Rate (kg/s)** & **Errors \%** \\ \hline
0.341 & 1.414 & 1.386 & 0.341 & 1.98 \\
0.377 & 1.4011 & 1.385 & 0.377 & 1.15 \\
0.428 & 1.3878 & 1.383 & 0.428 & 0.35 \\
0.509 & 1.3691 & 1.38 & 0.509 & 0.80 \\
0.622 & 1.3383 & 1.355 & 0.622 & 1.25 \\
0.789 & 1.2999 & 1.306 & 0.789 & 0.47 \\
0.969 & 1.2397 & 1.223 & 0.969 & 1.35 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Percentage errors of pressure ratios.
###### Abstract
In recent years, some innovative techniques, such as X-ray Computed Tomography or Active Thermography, have emerged into the aerospace industry and some of them are already being employed in certain applications [12]. In the case of X-ray, this is difficult to implement during a fatigue test, whereas Active Thermography only provides surface damage-related information in metallic materials. Another approach involves the use of strain measurement [13] to monitor damage evolution. Fiber optic sensors (FOS) offer a viable alternative for measuring local strain in difficult-to-access areas. These sensors are embedded into composite sandwich structures between plies [14], enabling the estimation of initial failure and the rate of subsequent damage evolution. In this study, FOS were initially incorporated into the metallic sandwich structure, but their use had to be abandoned. This decision was made because they needed to be affixed to the external plate rather than the fins, owing to their thin thickness (100 \(\upmu\)m). Consequently, the local behavior of the sandwich structure could not be monitored using this approach. Due to this, in this work, two alternative and complementary technologies, Digital Image Correlation (DIC) and Acoustic Emission (AE), were employed to face the challenge of detecting damage in complex structures during testing, offering insights into the mechanical failure mechanisms of the investigated structures. Both DIC and AE have been previously implemented in high-temperature applications due to their non-contact nature [15; 16], although the temperature variable was not introduced in these tests for simplicity. In this work, AE, using optical microphones, was used to detect defects by positioning the sensor in the vicinity of the structure. The underlying physical phenomenon is based on the acoustic wave emitted after a breaking effect. Part of the released energy is transmitted through the air and is captured by the microphone as a pressure change. Even hidden ruptures could be detected provided that the distance and the resulting sound attenuation are low enough to capture sufficient signal level [17]. Within a conventional approach, structure-borne methods such as piezoelectric effect are commonly employed; however, in this particular case, and considering the complex geometry of the part (with fins as thin as 100 \(\upmu\)m, making the installation of AE sensors impossible), contact methods cannot be applied. Air-coupled membrane free microphones from XARION Laser Acoustics GmbH were employed instead, which offer more possibilities for complex geometries inspection. As a result, it was identified that AE served as an early damage indicator as the cumulative number of events was in good agreement with the severity of the damage. Furthermore, high peak-frequency signals were found to be related to cracking initiation and rupture. Digital Image Correlation, an image-based system, allows for measuring local strains through strain redistributions, identifying signs of damage. These redistributions gave clear indications of damage or deterioration but at a later stage compared to AE.
## 1 Introduction
This makes the ON-FDT an ideal solution for implementing predictive maintenance strategies [14; 17]. Since the goal is to design a solution suitable for predictive maintenance, this paper will focus exclusively on ON-FDTs, which can be conceptually classified into three categories: signal-based (SB), model-based (MB), and data-based (DB) [17]. Signal-based online fault diagnosis techniques (SB-ON-FDTs) assesses the health status of capacitors by analyzing the analytical relationships between features extracted from measured signals. These features can be obtained using time-domain methods, frequency-domain methods, or hybrid approaches [18; 19]. SB-ON-FDTs offer a fast response and do not require prior knowledge of PC parameters or large datasets for model training. However, they rely on additional sensors, are typically invasive in nature, and may produce false fault alarms when the PC is operating under extremely dynamic conditions [20; 21]. Model-based online fault diagnosis techniques (MB-ON-FDTs) rely on the converter's model, which represents a mathematical representation of the system's expected behavior. A fault may be acknowledged when there is a discrepancy between the model output and the actual data from the physical system. Unlike the previous approach, MB-ON-FDTs are model-dependent and require a deep understanding of the system's underlying model [13]. Data-based online fault diagnosis techniques (DB-ON-FDTs) evaluate the capacitor's health status by directly analyzing real-world data. However, this approach requires a training dataset to enable the machine learning (ML) model to learn how to identify faults [17]. This paper proposes a solution that aims to overcome some of the identified constraints, which will be detailed and discussed in Sections 4 and 5. Section 2 will focus on aluminum electrolytic capacitors (al-caps), a common technology used in the design of PCs. A brief overview of the current state of the art of FDTs in PCs will be provided in Section 3. In Sections 6 and 7, the proposed method is evaluated on an entirely new dataset to demonstrate its effectiveness and adaptability across different scenarios. Finally, Section 8 will summarize the main conclusions.
# Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems
## 3 Cascaded Fuzzy Systems Based on Greenshields Models for Vehicle Speed and Travel-Time Prediction  
The relationship among vehicle speed, traffic flow, and traffic density on highways can be described by Greenshields models. This study focuses on the design and implementation of a fuzzy logic system for vehicle speed prediction based on Greenshields models. In the proposed approach, traffic flow and traffic density serve as input parameters, with their corresponding membership functions defined according to realistic traffic conditions. A fuzzification process transforms these inputs into meaningful fuzzy values based on the specified membership functions. The fuzzy inference mechanism then applies logical rules derived from Greenshields models to the input information, establishing decision-making laws for predicting vehicle speed as the output parameter. In the defuzzification stage, the centroid method is employed to convert the fuzzy inference results into a precise vehicle speed prediction. For practical application, the models were developed using traffic data from the southern section of the Sun Yat-Sen Highway, specifically the segment between the Dingjin System and the Rende System. The speed limit for passenger cars on this highway ranges from 60 to 110 km/h, with a buffer zone permitting speeds up to 120 km/h. Traffic flow and density data were obtained from the Freeway Bureau [42] and integrated into the model design to ensure that the simulation results are stable and representative of actual traffic conditions. Cascading fuzzy systems in sequence along each highway section enables both vehicle speed prediction and travel-time estimation for specified distances. The procedure for designing the fuzzy logic system is illustrated in Figure 8. Finally, we constructed an additional model using polynomial regression based on the collected data and compared its performance with that of the fuzzy logic model to verify the feasibility of the proposed approach.
## 3 Case Study
### Slope Unit Extraction  
Using the Yangloushi Town DEM as the data source, this study employs both HPAM and SUD-CGHP to identify slope units. Partial results of the segmentation are illustrated in Figure 5. Figure 5 shows the corresponding slope unit divisions for the study area: the HPAM applied an 800 mm flow threshold, resulting in 8267 identified slope units, whereas the SUD-CGHP method, which requires no flow threshold adjustment, yielded 8378 slope units.
## 3 Results and Discussions
### Sensitivity Analysis
It is found that the most dominant relationship with the output is the beam length variable (R = 0.667), followed by the flange width (R = 0.375), web thickness (R = 0.332), flange thickness (R = 0.209), opening diameter (R = 0.107), section height (R = 0.092), inter-opening distance (R = 0.072), and beam end-opening distance (R = 0.023), respectively. It can be observed that there is a good agreement between the results of the ANFIS-SFLA algorithm with the classical regression approach (Table 7). The classification order of importance obtained by ANFIS-SFLA is also indicated in Table 7. On the basis of the results from ANFIS-SFLA and linear statistical correlation, it can be concluded that the beam length highly affects the critical buckling load of circular openings I-shaped beam, followed by the flange width, flange thickness, and web thickness, respectively. Figure 9: Evaluation of deviation in output \(\theta\) (%) for eight input variables, respectively. Overall, without solving complex equations, an approach using hybrid ML models could also indicate these highly nonlinear relationships in an efficient manner and avoid too much computational cost.
## 2 Materials and Methods
### Model Driving Data  
#### 2.3.1 Meteorological Data  
The maximum temperature (K), minimum temperature (K), mean temperature (K), precipitation (kg\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)),wind speed (ms\({}^{-1}\)), relative humidity (%), shortwave radiation (W m\({}^{-2}\)), and pressure (Pa) were all from the GFDL-ESM4 product released by CIMP6 ([[https://esgf-node.llnl.gov/projects/cmip6/](https://esgf-node.llnl.gov/projects/cmip6/)]([https://esgf-node.llnl.gov/projects/cmip6/](https://esgf-node.llnl.gov/projects/cmip6/)), accessed on 2 July 2022) [66] (Table 3), and because the model performs better in regional temperature and precipitation simulations in China, the monthly bias correction/spatial decomposition (BCSD) method was downscaled to generate day products with a horizontal resolution of 25 km [67] and then bilinearly interpolated to 10 km \(\times\) 10 km, including meteorological variables under the SSP scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios from 2020 to 2060. The unit conversion formulae involved are as follows:  
\[T_{FORCCHN}=T_{GFDL}-273.15 \tag{13}\]  
where \(T_{FORCCHN}\) and \(T_{GFDL}\) represent the required temperature (\({}^{\circ}\)C) for the FORCCHN model and temperature (K) in the GFDL model, respectively.  
\[PR_{FORCCHN}=PR_{GFDL}\times 86400 \tag{14}\]where \(PR_{FORCCHN}\) and \(PR_{GFDL}\) represent the required precipitation (mm) for the FORCCHN model and precipitation (kg\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) in the GFDL model, respectively.  
\[PS_{FORCCHN}=PS_{GFDL}/100 \tag{15}\]  
where \(PS_{FORCCHN}\) and \(PS_{GFDL}\) represent the required air pressure (hPa) for the FORCCHN model and air pressure (Pa) in the GFDL model, respectively.
# Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
## 4 Experiments and Results  
We combined the aforementioned methods regarding data, datasets in the form of sessions, train-validation-test splits, and NN architectures to perform SoC estimation of the deployed EV battery. There were a total of three experiments:  
* **E1: SoC Estimation using Different NN Architectures**: this experiment serves as a benchmark of NN architectures for accurate and robust SoC estimation.
* **E2: SoC Estimation using Fusion Hybrid Model**: this experiment utilizes NNs and the fusion of static and dynamic features from EV batteries to conduct SoC estimation.
* **E3: SoC Estimation Guided by xAI and Importance Estimates**: this experiment builds on E1 by applying xAI and importance estimate methods to evaluate the significance of features for SoC estimation.