esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Power Curves and High-Wind Events  
The available power in the wind comes from the kinetic energy in the air mass passing through a wind turbine and can be written as  
\[P(w)=\frac{\pi}{2}\rho r^{2}w^{3}, \tag{1}\]  
where \(\rho\) [kg/m\({}^{3}\)] is the air density, \(r\) [m] is the radius of the wind turbine blades, and \(w\) [m/s] is the wind speed. The theoretical maximum power that can be taken out of by the wind turbine is given by Betz's limit, but in general, a wind turbine-specific power curve \(PC(w)\) is used to describe the power output as blade shape, control systems, generator size and other design options that place additional limits on the power production for a given turbine. The power curve can be modelled based on the turbine hub height wind speed [31], and a simplified description of its shape following [32] is  
\[PC(w)=\begin{cases}0,&\text{if }w\leq w_{in}\text{ or }w\geq w_{out}\\ P(w)C_{P}(w),&\text{if }w\in\langle w_{in},w_{R}\rangle\\ P_{R},&\text{if }w\in[w_{R},w_{out}\rangle\end{cases} \tag{2}\]  
where the wind speeds \(w_{in}\) and \(w_{out}\) are the lowest and highest speeds at which the turbine produces power, i.e., the cut-in and cut-out wind speeds, respectively; \(P_{R}\) and \(w_{R}\) are the rated power and rated wind speed of the turbine, respectively; and \(C_{P}(w)\) is the power  
Figure 1: Gray scale showing terrain height from Global Multi-resolution Terrain Elevation Data (GMTED) at 30-arcsecond resolution by United States Geological Survey. **(a)** Scandinavia peninsula. **(b)** Regional terrain. **(c)** Aerial photo using www.norgeskart.no (accessed on 1 March 2025). Red O: Location of measurement mast. Yellow letters: Location of wind turbines.  
coefficient of the turbine. Note that \(C_{P}\) is limited in value to \([0,19/27)\), and that \(PC(w)\) in general can be said to follow an S- or sigmoid-shaped curve up to \(w_{R}\).  
Wind speeds around the rated wind are important to identify as these may result in large and rapid changes in power production. In this paper, we will focus on high-wind events defined as the 10 longest continuous events with wind speed measurements above the 90 th percentile wind speed.
## 3 Results
### Bending Test  
Figure 4 provides an overview of the mechanical properties of miniplates measured through bending testing, organized by groups of injection molding temperature variations and material weight compositions. It presents the statistical analysis of miniplate properties obtained through bending testing, categorized by groups of injection molding temperature variations and material weight compositions.  
In Figure 4a, the SPLA20P10 and SPLA20P20 sample groups demonstrated lower ultimate stresses for all injection mold temperatures compared to the remaining two groups. Notably, a significant difference was observed when comparing SPLA20P10 and SPLA20P10 sample groups. No statistical differences were found when comparing different temperature settings within each sample group, except for a significant reduction trend over the increasing injection temperature in the SPLA20P20 sample group, ranging from 5.109 \(\pm\) 0.57 MPa at 150 \({}^{\circ}\)C to 3.38 \(\pm\) 0.50 MPa at 180 \({}^{\circ}\)C.  
Figure 4b indicates that sample groups containing sago starch exhibited lower elastic modulus compared to the SPLA20P10 sample group. Statistical evidence supports this observation in the SPLA20P10 and SPLA20P20 samples. The modulus of elasticity behavior generally remained unclear, showing no significant difference with respect to the effect of  
Figure 4: Mechanical bending properties, including (**a**) ultimate stress, (**b**) bending modulus of elasticity and (**c**) elongation at break of miniplates obtained from bending testing, categorized by groups of injection molding temperature variations and material weight compositions (_p_ < 0.05; \(n\) = 5; annotations (*) and (*) for material compositions and injection temperature effects, respectively).  
injection molding temperature within each sample group. However, in the SPLA20P10 sample group, there was a clear trend of steady increment over the increasing temperature, specifically from 0.17 \(\pm\) 0.03 GPa at 160 \({}^{\circ}\)C to above 0.27 \(\pm\) 0.03 GPa at higher temperature settings.  
Figure 4c reveals that only the non-sago sample group (SPLA0P10) had significantly lower break elongation results across all injection mold temperatures, with the highest at 18.78 \(\pm\) 1.98% at 160 \({}^{\circ}\)C. The remaining temperatures exhibited break elongation ranging from 7.70 \(\pm\) 3.30% to 12.05 \(\pm\) 1.99% in that group. The remaining sample groups with sago blend consistently showed a range of break elongation between 20.44 \(\pm\) 2.95% to 24.9 \(\pm\) 2.46% without any substantial differences.  
The obtained stress-strain curves of miniplate samples from the bending test process are illustrated in Figure 5, providing a visual representation from one of the tensile testing repetitions of miniplate samples material behavior under bending stress.
# Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM)
###### Abstract  
Building information modelling (BIM) is the first step towards the implementation of the industrial revolution 4.0, in which virtual reality and digital twins are key elements. At present, buildings are responsible for 40% of the energy consumption in Europe and, so, there is a growing interest in reducing their energy use. In this context, proper interoperability between BIM and building energy model (BEM) is paramount for integrating the digital world into the construction sector and, therefore, increasing competitiveness by saving costs. This paper evaluates whether there is an automated or semi-automated BIM to BEM workflow that could improve the building design process. For this purpose, a residential building and a warehouse are constructed using the same BIM authoring tool (Revit), where two open schemas were used: green building extensible markup language (gbXML) and industry foundation classes (IFC). These transfer files were imported into software compatible with the EnergyPlus engine--Design Builder, Open Studio, and CYPETHERH --in which simulations were performed. Our results showed that the energy models were built up to 7.50% smaller than in the BIM and with missing elements in their thermal envelope. Nevertheless, the materials were properly transferred to gbXML and IFC formats. Moreover, the simulation results revealed a huge difference in values between the models generated by the open schemas, in the range of 6 to 900 times. Overall, we conclude that there exists a semi-automated workflow from BIM to BEM which does not work well for big and complex buildings, as they present major problems when creating the energy model. Furthermore, most of the issues encountered in BEM were errors in the transfer of BIM data to gbXML and IFC files. Therefore, we emphasise the need to improve compatibility between BIM and model exchange formats by their developers, in order to promote BIM-BEM interoperability.  
building information modelling (BIM); building energy model (BEM); green building extensible markup language (gbXML); industry foundation classes (IFC); interoperability; digital twin; sustainable construction; intelligent buildings assessment; sustainability performance; simulation tools for building +
Footnote †: journal: _Sci. 2021_, 11, 2167. [[https://doi.org/10.3390/app11052167](https://doi.org/10.3390/app11052167)]([https://doi.org/10.3390/app11052167](https://doi.org/10.3390/app11052167))
# Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities
## 5 ICN-Based VANET Research Opportunities  
Generally, VANET communications are achieved on the basis of two key techniques, i.e., cellular networking and dedicated short range communications (DSRC) [37]. Through these two technologies, a vehicle obtains information from its own sensors and provides it to other vehicles or RSUs [38]. Currently, moving computing, management, and content caching in the Edge, ICN, and Cloud is the growing trend. This computation however brings a lot of new system requirements, which are presented in Figure 4 and discussed in the following subsections.
## 6 Discussion and Future Challenges
### Adaptive Intelligent Evaluation Model Adopting Deep Learning  
According to the above literature analysis, we can see that the application of the neural network to the effects evaluation model of the electromagnetic environment has been reported. However, the performance of the simple neural network-based model is not satisfactory due to the following reasons: a lack of standard electromagnetic environmental data, a small amount of testing data on the electromagnetic environmental effects, limited features of the data for learning, and other reasons. The deep learning method makes the model more adaptive, whereas the effect mechanism method is more in line with the actual interference effect. Subsequently, the effect mechanism and deep learning methods can be effectively combined; thereby, the basic model is built based on the effect mechanism method, and the model parameters are updated by the deep learning method to improve the quality and accuracy of the effect evaluation through the information fusion method. Although we can follow these steps to build the combination model, we still face challenges. The deep learning method requires a great deal of training data to achieve reliable training results through continuous data accumulation. In addition, a standard electromagnetic environmental effect evaluation database is necessary to verify the reliability of the proposed models.
## 5 Software Architecture
### Power Management  
Power management is a priority function of the system since the operation of the nodes is necessary for several weeks. Most of the time, the nodes are in an inactive mode, so the associated deep-sleep (time-out) function is used, which requires only 25 uA [56]. This function can be \"awakened\" through a timeout specified as a parameter of this function. This parameter is calculated at the end of the current process using Equations (2) and (3).  
Using Wi-Fi communication for information exchange is very convenient due to the high compatibility and the large amount of Wi-Fi equipment available. However, the high consumption of this type of communication makes it necessary to incorporate energy managers to optimize battery life. The best way to reduce the power consumption of Wi-Fi transceivers is to reduce the time they must be online to send and receive information.  
Regarding this issue, a time-synchronization algorithm was developed, so that the sensor nodes and the gateway nodes are synchronized with each other minimizing the connection time between them. The time pattern is always handled by the gateway node since it must \"wait\" for the sensor nodes to connect and send the information. The flowchart of the algorithm is presented in Figure 9.
## 3 Materials and Methods
### SPME-Arrow and GC/MS Analysis  
The SPME-Arrow extraction was carried out using RSH Triplus autosampler (Thermo Fisher Scientific Inc., Brookfield, MO, USA). The appropriate sample weight was placed in 20 mL headspace screw-top vials sealed with PTFE/silicone septum containing caps.  
Separation and detection of the samples was carried out by TRACETM 1300 Gas Chromatographer coupled to ISQ 7000 TriPlus quadrupole mass spectrometer (Thermo Fisher Scientific Inc., Bartlesville, OK, USA) equipped with TG-WAXMS A capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness; Thermo Fisher Scientific, Bartlesville, OK, USA). The volatile compounds injected into the inlet were delivered to the column at a split-less mode and helium was used as a carrier gas at a constant flow rate 1 mL/min. The oven temperature program was as follows: initial temperature of 40 \({}^{\circ}\)C was maintained for 5 min, increase 2 \({}^{\circ}\)C/min to 210 \({}^{\circ}\)C and hold for 10 min. The MS spectra were recorded in the electron impact ionization mode (EI) at an ionization energy 70 eV. The mass spectrometer was performed in full scan mode in the range 30-300 \(m\)/z. The data obtained were processed using ChromeleonTM Data System (Thermo Fisher Scientific Inc., Bartlesville, OK, USA). Identification of volatile compounds was achieved by comparing the recorded mass spectrum with the data available in Wiley Registry 12 th Edition/NIST Spectral Library. The Retention index (RI) was calculated using alkane standards C8-C20 (Sigma Aldrich, St. Loius, MO, USA) according to the equation described in [PERSON], et al. [25] and compared with previously reported in the literature [39; 40] (Supplemental Tables S1 and S2).
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 8 Challenges and Future Directions
### Strengthen Uncertainty Analysis  
Uncertainty analysis is an important prerequisite for formulating effective measures to improve the measurement accuracy. The spatio-temporal dynamics of rainfall, multi-path propagation, atmospheric absorption, sampling strategy, and quantization error are all sources of uncertainty. The combination of data resources, computing power, and machine learning provides an opportunity to discover the spatial and temporal uncertainties of the rainfall and the relationship between the rain-induced attenuation and rainfall intensity. Moreover, the input-output relationship derived from these data-driven models can provide inspiration and guidance to explain and understand the mechanism in the physical world. In addition, for uncertainty study, we can also build a dedicated microwave link experimental environment to carry out targeted analysis for a certain interference factor. Because the CML network covers a wide area, and the amount of CML data is huge, many of the uncertainties are difficult to quantify when the CML data are directly applied to actual rainfall measurement.
## 5 Finite Element Results and Discussion  
Figure 19 shows the Chaboche model simulation results for the uniaxial and biaxial loadings, using ratcheting parameters presented in Table 4. The value of \(\gamma_{3}(=3)\) had been calibrated using uniaxial loading simulation. For different ratcheting parameter sets, the results are evaluated in terms of two main concepts. The first evaluation concept involves simulation results obtained from Sets A and B. The second concept involves simulation results obtained from Sets C and D.  
Figure 18: Finite element model of biaxial specimen: (a) Inside surface symmetric about X-Y plane, (b) through thickness symmetric about Y-Z plane, (c) boundary condition.  
When the parameters in the model are determined using uniaxial loading tests, the numerical simulation results show closer correlation to the uniaxial test rather than the biaxial test, but all simulation results overpredict test results. The uniaxial and biaxial ratcheting simulation results using Sets A and B are closer to test results in comparison to those obtained from Sets C and D. The good correlation is due to reduction in transient Bauschinger effect, because the yield point of the shifted curve is positioned lower than the monotonic and post-stabilized monotonic curves, as shown in Figure 8 and discussed in Section 2.4. The ratcheting parameters obtained from the hysteresis curve present the same trend with the shifted curve in FE simulation for monotonic section. The curve accordance or matching between \((\sigma,\ \varepsilon)\) from the post-stabilized monotonic test with \((\sigma_{shift},\,\varepsilon_{shift})\) from shifted curve depends on material and strain amplitude of pre-hardening strain control test and this match occurs only for one value of strain range for every different material. Finding this matching requires many trial-and-error tests. Even so, this match is suitable only for limited ratcheting tests that are within the same strain range of the matched hysteresis curve. Hence, the hysteresis curve is not suitable to be used in generating Chaboche ratcheting parameters compared to post-stabilized monotonic curve.  
Adopting an accurate elastic limit in Sets B and D gives a more precise yield stress surface, compared to Sets A and C, respectively. The ratcheting rate predicted in uniaxial loading is quite close to experimental results with parameters obtained from Set B, with slight overestimation but with similar trend of the curve.
# Impacts of Arc Length and ECOM Solar Radiation Pressure Models on BDS-3 Orbit Prediction
## 3 Results and Analysis  
The accuracies of BDS-3-predicted orbits with different arc lengths are compared to the IGS final precise orbits and SLR data. Three-month data from 1 July 2021 to 30 September 2021 are used in this experiment. The BDS-3 satellite orbit prediction accuracy during and outside of eclipse seasons is provided. Finally, the impacts of different ECOM SRP models on the predicted orbit accuracy of the BDS-3 satellite are discussed. Because of the poor orbital accuracy of the BDS-3 GEO satellites, the BDS-3 GEO satellites will not be considered in this experiment. It should be noted that in Sections 3.1-3.3, we use the ECOM9 SRP model for orbit prediction.
## 7 Experimentation and Analysis
### Algorithm Performance Validation
#### 7.1.5 Results and Discussion  
In summary, in most cases, the GLSOAG has significant advantages in convergence accuracy, convergence speed, and algorithmic stability, and the algorithmic complexity is within tolerance. Through the improvement strategy proposed in this paper, the global exploration ability of the algorithm in the early stage is effectively improved, the phenomenon of the premature maturity of the algorithm is improved, and the stability of the algorithm is maintained while meeting the needs of the algorithm to deal with high-dimensional complex problems.
## 3 Fitted Frequency Range Comparison
It is clear the selection of frequency band for the optimization procedure does impact the coefficients. Therefore, the frequency band should be considered when employing optimization fitting processes to design a fractional-order filter. Designers must weigh the trade-offs that result between selecting a wider frequency band (necessary to capture the DC or high frequency gain) or a smaller specific band (necessary to capture the ripple characteristics); and decide based on which design features are most important for their specific application. Additionally, further studies should investigate the sensitivity of the fractional-order transfer functions magnitude characteristics to the coefficients, similar to that presented in [7]. This is important to understand during the physical implementation.
# GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest
## 4 Results
### Experimental Methods and Results

Table 1: The parameter settings for the RF model experiments conducted at the P041 station, P037 station, and LM station. Figure 7: Random Forest model training error. The blue line shows the trend of the model training error with the number of decision trees, and red line is the number of decision trees when the model training error stabilizes. that, at the P041 station, the RBF neural network and CNN models showed significant deviations from the reference values. In contrast, the RF and SVM models more accurately reflected the peak changes in soil moisture. Specifically, compared to the soil moisture reference values, the RBF neural network exhibited significant differences and random trends during abrupt changes in soil moisture, while the CNN model performed well in the early stages but deteriorated in later stages. The RF and SVM models demonstrated better overall replication capabilities of the soil moisture reference values. At the P037 station, the RBF model showed significantly better fitting results compared to the P041 station, with stable retrieval results from the RF, SVM, and CNN models. In the smaller dataset of the LM station, the RBF model significantly outperformed the previous two stations, suggesting that the RBF model may be more suitable for handling smaller datasets. Additionally, the CNN model showed noticeable deviations from the reference values, with retrieval results increasingly diverging from the reference values in the last few days. The SVM model performed well initially, but also diverged in the final days. The RF model accurately reflected the changes in soil moisture reference values. Notably, across all three sites, the RF model consistently showed the best retrieval performance. Figure 8: Comparison of soil moisture for each model retrieval at site P041, with reference data provided by PBO. Figure 9: Comparison of soil moisture for each model retrieval at site P037, with reference data provided by PBO.
# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 4 Application of Image Processing Techniques to Underwater Crack Detection in Dams
### Problems in Underwater Image Processing
Figure 7: Comparison of above-water and underwater crack images on the dam surface: (**a**) the above-water crack image; (**b**) the under-water crack image; (**c**) the above-water crack image; (**d**) the under-water crack image; (**e**) the above-water crack image; (**f**) the under-water crack image. Deep learning methods are based on massive crack data to analyze crack features and thus detect cracks in unknown images. Most deep learning-based crack detection methods are supervised learning methods, which require corresponding labels to extract crack features [72; 73]. However, due to the peculiarities of the underwater environment, the images obtained by underwater devices are under-calibrated and the fracture images are uncertain and unstable. This leads to the transmission of ambiguous information in the crack images and labels. The crack detection effect of underwater images is uncertain. In addition, the images in different scenes are not similar due to the complex underwater environment, which further reduces the reliability of underwater crack detection. In summary, the commonly used image processing methods for underwater cracks are difficult to apply directly to underwater crack image detection. Therefore, it is necessary to develop methods for underwater crack identification and feature extraction from dam surfaces.