esa-sceva/satcom-chunk-collection huggingface dataset:
# A Traceable Spectral Radiation Model of Radiation Thermometry
### Grey Body Radiation Thermometry (GBRT)  
Grey body radiation thermometry (GBRT) is a simplification used for measuring grey bodies. It assumes that all influential quantities in the system of measurement are spectrally homogeneous and therefore independent of the observed wavelength. Based on this simplification, the spectral behaviour can be summarized with scalar values (constants), and their effect can be considered spectrally independent.  
While GBRT is theoretically similar to BBRT in terms of scalar calculation algorithm, it extends the application of radiation thermometry to idealized real bodies--grey bodies.  
GBRT requires scalar input parameters, generally referred to as instrumental parameters, which enable compensation of inevitable environmental influences. Input parameters should correspond to the entire system of measurement, which consists of the measured object (and its temperature), the environmental radiation, the transfer path and the measuring instrument. In GBRT, only the influence of actual grey bodies, which are spectrally independent, can be correctly compensated.  
The GBRT model and its variations are currently used by most radiation thermometers on the market with an adjustable emissivity parameter. This approach has been documented in numerous non-scientific (e.g., Fluke 4181 calibrator manual [20]) and scientific [21] sources and thoroughly by [PERSON] [22], who modelled the response of a fixed instrumental emissivity radiation thermometer in calibration with a black body calibrator. Although the article focuses on calibration, the mathematical model is also applicable to measurements. The grey body model by [PERSON] utilized radiances (total radiant powers throughout the spectrum, denoted \(M\)), obtained by the Sakuma-Hattori approximation of [PERSON]'s law. Spectral interactions are not considered later in this model, limiting model performance to spectrally homogenous influence properties of GBRT.
# Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article
## 3 Elastography Techniques
### A: Strain Imaging
#### 3.1.1 Acoustic Radiation Force Impulse (ARFI)  
Acoustic radiation force impulse (ARFI) is an alternative tactic used to measure strain in situations where no excitation or external stimulation is applicable. This technique involves the use of a high-intensity acoustic \"pushing pulse\" with a short duration of 0.1-0.5 ms. The spatial peak pulse average is 1400 W/cm\({}^{2}\), and the spatial peak temporal average is 0.7 W/cm\({}^{2}\). This pulse is used to displace tissue in the normal direction, which is perpendicular to the surface. The displacement of the tissue is approximately 10-20 \(\upmu\)m [32]. The displacement within a specified ROI is subsequently measured by the same methods as in strain elastography. Also, similar to strain elastography, the displacements may be displayed as an elastogram overlaid on the B-mode image [33]. The authors did not focus on this technique due to the underwhelming extent of research conducted around it and its recent development. However, it is important to note that ARFI imaging does not rely on transducer compression as other quasi-static elastography methods do, which can help in the assessment and imaging of deeper organs [6; 10; 34; 35; 36; 37].
# Example usage calculate_urban_resilience('input_file_name')
#### 5.4.4 Resilient Communities  
The resilience assessment, spanning across various Portuguese cities, reveals significant regional differences in their ability to withstand and recover from seismic events (Figure 7). This variation in resilience is influenced by several factors: (i) Revenue per Inhabitant: Municipalities with higher revenue per inhabitant tend to have better resources for post-disaster recovery, contributing to higher resilience; (ii) Upper Secondary Education Rate: Higher education levels in a community correlate with better preparedness and informed responses to disasters, enhancing resilience; (iii) Ageing Ratio: A higher ratio of elderly residents can limit resilience due to potential challenges in recovery and the need for more support services; (iv) Housing Density: A greater number of houses per capita, indicating less overcrowding, suggests better living conditions, which aid in quicker recovery and thus enhance resilience; (v) Population Density: High population density can challenge resilience due to the resource constraints and logistical difficulties inherent in post-disaster scenarios; (vi) Construction Projects: An increase in construction projects, especially those upgrading older structures, can indicate ongoing development and improved resilience against seismic activities; (vii) Fire Personnel: More firemen per capita suggests better emergency response capabilities, enhancing a municipality's resilience; (viii) Hospitals: A higher number of medical facilities per capita indicates stronger medical response capabilities, aiding community recovery and resilience; (ix) Purchasing Power: Higher per capita purchasing power reflects a stronger economic capacity at the individual and community levels, bolstering resilience against seismic threats.  
Considering the obtained results, in Aveiro, Murtosa demonstrates strong resilience with a score of 0.548, while Sao Joao da Madeira shows a lower resilience at 0.469. In the Azores, Corvo stands out with a high resilience index of 0.625, contrasting with Praia da Vitoria's lower score of 0.351. In Beja, Barrancos leads with a resilience score of 0.607, while Serpa shows a moderate level at 0.508. In Braga, Esposende has a notably lower resilience index of 0.403, whereas Amares and Barcelos exhibit similar levels around 0.48-0.49.  
These data provide very important insights for guiding regional policy, infrastructure planning, and community development initiatives aimed at enhancing resilience across Portugal.
# BDS-3 Time Group Delay and Its Effect on Standard Point Positioning
## 3 Experimental Data and Processing Strategies
Hence, the TGD correction of the BDS-2 system has less effect on the accuracy of the BRCH station. From the average RMS values of the six stations in Table 5, compared with the \"non-corr\" scheme of the B1I-based solutions, the positioning accuracy of the \"tgd-corr\" scheme is notably improved by 52.4% from 2.69 m to 1.28 m and by 57.8% from 2.3 m to 0.97 m in the horizontal directions. In addition, for the vertical direction, the B1I-based positioning results are improved by 45.6% from 4.08 m to 2.22 m. Interestingly, the B3I-based positioning results are not affected by the TGD parameter because the satellite clocks of BDS-2 in the broadcast ephemeris refer to the B3I signal. Under the \"tgd-corr\" scheme, the RMS values of single- and dual-frequency signals for the horizontal and vertical components are 1 m-2 m. For the \"tgd-corr\" scheme, the RMS values of the B1I/B3I combination are significantly reduced by 70.1% from 5.36 m to 1.6 m, by 74.2% from 5.12 m to 1.32 m and by 47.4% from 5.67 m to 2.98 m for the E, N and U components, respectively. Table 4.1 Daily RMS values of BDS-2 with different schemes for the BRCH station. Table 4.2 Average global PDOP of BDS-2 on day of the year (DOY) 10 2019 with an elevation cut-off angle of 5\({}^{\circ}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Scheme** & **B11** & **B31** & **B11/B31** \\ \hline \multirow{3}{*}{**E**} & \multirow{3}{*}{tgd-corr} & 1.28 & - & 1.60 \\  & & non-corr & 2.69 & 1.27 & 5.36 \\  & Improvement (\%) & 52.4 & - & 70.1 \\ \hline \multirow{3}{*}{N} & \multirow{3}{*}{tgd-corr} & 0.97 & - & 1.32 \\  & & non-corr & 2.30 & 1.12 & 5.12 \\  & Improvement (\%) & 57.8 & - & 74.2 \\ \hline \multirow{3}{*}{U} & \multirow{3}{*}{tgd-corr} & 2.22 & - & 2.98 \\  & & non-corr & 4.08 & 2.75 & 5.67 \\ \cline{1-1}  & Improvement (\%) & 45.6 & - & 47.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The mean root mean square (RMS) values of the six selected stations SPP solutions using 41-day observations with and without TGD correction in different BDS-2 frequency combinations (m). Figure 9: Improvements in the E, N, and U components of the “tgd-corr” scheme compared to the “non-corr” scheme at different stations. Note that the mean RMS of 41 days at each station with and without TGD correction is first obtained. The improvement from the “tgd-corr” scheme is then calculated and compared to that from the “non-corr” scheme.
## 2 System Model
### User Throughput Performance  
The user throughput calculation for the proposed model in this work, i.e., WHT-NOMA and IRC, is based on BER performance [36]. As user throughput and BER are related to each other, in this paper, in both cases, i.e., conventional NOMA and WHT-NOMA, throughput is calculated based on the BER performance as follows:  
\[T=(1-BER)\times\mathbf{BW}\times log_{2}(Q), \tag{37}\]  
where, \(\mathbf{BW}\) is the bandwidth and \(Q\) is the modulation order of the transmitted signal.  
After a detailed description of the system model, the next section gives the simulation results conducted to validate the presented work in this paper.
## 1 Introduction
Two well-behaved NWP centers in the TIGGE archive, i.e., ECMWF and NCEP, were evaluated in this study. Two popular postprocessing methods, i.e., GPP and BMA, were used to postprocess EPFs. Both deterministic and probabilistic metrics were used for the evaluation. Specifically, the objectives of this study are to answer the following two questions: (1) How do the ECMWF and NCEP perform in precipitation forecasting and streamflow prediction over a mountain river basin? (2) How do the two postprocessing methods, GPP and BMA, perform in improving the skill of EPF and ESP?
## 4 Results and Discussion
### The Predictive Performance
While XGBoost still demonstrated a statistically significant superiority over NB in terms of AUROC, and SVM-RF, XGBoost, and NB all showed significant differences in Accuracy for certain pairs, the overall trend was towards more consistent and robust performances, often reflected by narrower IQRs (Table 3). This suggests that hazard-informed sampling effectively reduces noise and label ambiguity, leading to a higher baseline performance across models, even if some statistically significant differences persist. Based on the results, model selection should consider the quality of the sampling strategy and the associated data noise:  
* When using hazard-informed sampling methods such as GLHM, which reduce label noise and improve data quality, multiple models, including SVM-RF, XGBoost, SVM, and NB, demonstrate comparable and robust performance. In this scenario, model choice can be guided by other factors such as computational cost, interpretability, and ease of deployment, since performance differences are minimal, and models exhibit consistent robustness. * When data are sampled randomly without hazard-level constraints, resulting in noisier and less reliable labels, more complex models like SVM-RF and XGBoost should be preferred. These models show significantly better predictive performance and greater robustness compared to simpler models, which are more sensitive to label noise and exhibit lower accuracy and stability.
## 3 Static Analysis of REJ on Suspension Bridge
### Displacement and Pier Force  
For Schemes 1-3, the rail-beam relative displacements and pier longitudinal forces under expansion, bending, and braking loads are respectively calculated, as shown in Figures 11 and 12.  
According to Figure 11, the rail-beam relative displacements from REJ laying position to beam end are large under the action of expansion, bending, and braking loads. For expansion load, as shown in Figure 11a, the maximum rail-beam relative displacement for each scheme is 251.83 mm, 233.05 mm, and 236.37 mm, respectively. Compared to Scheme 1, the peak values of rail-beam relative displacement of Schemes 2 and 3 are reduced by 7.46% and 6.14%. For bending load, as shown in Figure 11b, there is little difference between the maximum rail-beam relative displacements, which are 31.56 mm, 31.19 mm, and 31.70 mm, respectively. For braking load, as shown in Figure 11c, the maximum rail-beam relative displacements are respectively 18.34 mm, 7.58 mm, and 8.90 mm. The closer the laying position of the regulator is to the beam end, the greater the rail-beam relative displacementsat the beam end area. Compared with Scheme 1, the relative displacements of Schemes 2 and 3 are reduced by 58.67% and 51.47%, respectively.  
Figure 11: Rail-beam relative displacement: (**a**) Under expansion load; (**b**) Under bending load; (**c**) Under braking load.  
Figure 12: Pier longitudinal force.  
According to Figure 12, the longitudinal forces of the side span continuous beam bridge fixed pier for the three schemes are greatly different. For expansion load, the longitudinal force of bridge pier in Schemes 2 and 3 decreases by 38.40% and 38.34%, respectively, compared to Scheme 1. For bending load, compared to Scheme 1, the longitudinal force in Schemes 2 and 3 decreases by 25.74% and 25.94%, respectively. For braking load, the longitudinal force of bridge pier in Schemes 2 and 3 both increase by 81.65% compared to Scheme 1. According to the above comparison, the longitudinal force of the piers can be greatly reduced under expansion and bending load, and it is beneficial for the whole structure to share the braking force when REJ is laid at a certain distance from the beam end.
# Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
## 2 Data and Methods
### Methodology
#### 2.2.3 Assessment of Rainwater Resources  
The areal rainwater resources (i.e., precipitation) is evaluated by the mean value. The variation trend of the areal precipitation is predicted by the improved Mann-Kendall method [63; 64; 65].  
Corresponding to the time series \(X\) with \(n\) sample sizes, the order column is constructed as follows:  
\[S_{k}=\sum_{i=1}^{k}r_{i}\quad r_{i}=\begin{cases}1&x_{i}>x_{j}\\ 0&x_{i}\leq x_{j}\end{cases}j=1,2,\cdots,i. \tag{7}\]  
It can be seen that the order sequence \(S\) is the cumulative number of values at the \(i\)th moment greater than that at the \(j\)th moment. Under the assumption that the time series is randomly independent, the statistics are defined as follows:  
\[UF_{k}=\frac{S_{k}-E(S_{k})}{\sqrt{var(S_{k})}}k=1,2,\cdots,n, \tag{8}\]  
Figure 2: The artificial neural network (ANN) structure diagram.  
where \(U\!F_{k}=0\), \(E(S_{k})\) and \(Var(S_{k})\) are the mean and variance of the cumulative number \(S_{k}\), respectively. When \(x_{1}\), \(x_{2}\),..., \(x_{n}\) are independent of each other and have the same continuous distribution, they can be calculated by the following formula:  
\[E(S_{k})=\frac{n(n+1)}{4}Var(S_{k})=\frac{n(n-1)(2n+5)}{72}, \tag{9}\]  
where \(U\!F_{i}\) is the standard normal distribution, which is the statistical sequence calculated according to the time series \(X\) (\(x_{1}\), \(x_{2}\),..., \(x_{n}\)). The significance level \(\alpha\) is determined and the normal distribution table is checked. If \(|U\!F_{i}|>U_{\alpha}\), it indicates that there is a significant trend change in the sequence.  
Repeating the above process according to the time series \(X\) in inverse order \(x_{n}\), \(x_{n-1}\),..., \(x_{1}\), while letting \(U\!B_{k}=-U\!F_{k}\), \(k=n\), \(n-1\),..., \(1\), \(U\!B_{1}=0\). If the calculated value of \(U\!B_{k}\) or \(U\!F_{k}\) is greater than 0, it indicates that the sequence has an upward trend; if lower than 0, it indicates a downward trend. When they exceed the critical line, it suggests that the upward or downward trend is significant.
## 3 Results and Discussion
### Analysis of Gin, Rum, and Whiskey Samples by HS-SPME-GC-Ms
This method is particularly adapted to micro-distilleries, that can, for a limited investment in apparatus and technical skills, provide a reliable way of analyzing their samples and comparing them to other products. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Chemical Class** & **Compound** & **exp. RI** & **th. RI a** & **Gin b** & **Rum b** & **Whiskey b** \\ \hline \multirow{7}{*}{alcohol} & 2-methyl-1-propanol & 623 & 624 & - & + & + \\  & 3-methyl-1-butanol & 736 & 736 & - & ++ & + \\  & 2-methyl-1-butanol & 738 & 739 & - & ++ & + \\  & octanol & 1068 & 1070 & - & tr & tr \\  & 2-phenylethanol & 1109 & 1116 & - & - & + \\  & decanol & 1268 & 1272 & - & - & + \\  & dodecanol & 1452 & 1474 & - & tr & + \\  & tetradecanol & 1678 & 1676 & - & - & + \\  & hexadecanol & 1870 & 1880 & - & - & tr \\ \hline \multirow{7}{*}{alcohol} & ethyl acetate & 611 & 612 & - & + & + \\  & ethyl propanoate & 716 & 710 & - & tr & tr \\  & ethyl 2-methylpropanoate & 754 & 756 & - & - & tr \\  & 2-methylpropyl acetate & 771 & 772 & - & tr & tr \\  & ethyl butanoate & 801 & 802 & - & tr & tr \\  & ethyl 2-methylbutanoate & 847 & 849 & - & - & tr \\  & ethyl 3-methylbutanoate & 849 & 853 & - & tr & tr \\  & 3-methylbutyl acetate & 873 & 872 & - & + & + \\  & 2-methylbutyl acetate & 875 & 879 & - & tr & tr \\  & ethyl (E)-2-methyl-2-butenoate & 937 & 939 & - & - & tr \\  & ethyl hexanoate & 995 & 999 & tr & ++ & + \\  & hexyl acetate & 1010 & 1011 & - & - & tr \\  & ethyl heptanoate & 1094 & 1098 & - & + & tr \\  & 3-methylbutyl angelate & 1144 & 1153 & tr & - & - \\  & 2-methylpropyl hexanoate & 1146 & 1149 & - & - & tr \\  & benzyl acetate & 1153 & 1164 & + & - & - \\ ester & ethyl benzoate & 1157 & 1172 & - & tr & tr \\  & diethyl succinate & 1169 & 1181 & - & tr & - \\  & ethyl octanoate & 1195 & 1196 & - & ++++ & ++++ \\  & octyl acetate & 1206 & 1210 & tr & - & tr \\  & 3-methylbutyl hexanoate & 1238 & 1250 & - & tr & tr \\  & ethyl phenylacetate & 1239 & 1247 & - & - & tr \\  & 2-phenylethyl acetate & 1244 & 1258 & - & tr & + \\  & 2-methylbutyl hexanoate & 1249 & 1247 & - & - & tr \\  & ethyl nonanoate & 1284 & 1295 & - & + & + \\  & 2-methylpropyl octanoate & 1344 & 1348 & - & - & tr \\  & ethyl 9-decenoate & 1370 & 1388 & - & ++ & + \\  & ethyl decanoate & 1392 & 1396 & - & ++++ & ++++ \\  & decyl acetate & 1421 & 1409 & - & - & + \\  & 3-methylbutyl octanoate & 1424 & 1446 & - & + & + \\  & 2-methylbutyl octanoate & 1427 & 1449 & - & + & + \\  & ethyl undecanoate & 1473 & 1494 & - & tr & + \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Volatile compounds by chemical class identified in 3 commercial liquors analyzed by HS-SPME-GC-MS.
# Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm
## 3 New Node-Based Tomographic Approach
### Determination of a Tomographic Region  
In this study, the dimensions of the tomographic region are determined by searching for the minimum bounding box enclosing the intersections between the paths of all GNSS signals and the plane at different height of the tomographic planes (the intersection points are named pierce points). [PERSON] and [PERSON] [28] demonstrate clearly that the minimum bounding box of a set of points is also the minimum area rectangle of the points' convex hull polygon. Thus, the primary goal is to determine the convex hull of the pierce points on each tomographic plane.
## 4 Simulation Test
### Comparison with Other Algorithms  
To evaluate the performance of the NSGA-II-V solver designed in this paper for the HFSP based on the parallel sequential movement mode, the benchmark proposed by [PERSON] [38] was used for testing. Other current algorithms with a better performance were chosen as comparison algorithms, including DSOA [39], DFWA-LS [13], and IWO [17]. However, the above studies in the literature only stayed in the parallel sequential movement mode-based flow-shop scheduling, so the NSGA-II-V was applied to the parallel sequential movement mode-based flow-shop scheduling. The experimental results are shown in Table 4. To facilitate the comparison of the NSGA-II-V with other algorithms, the average relative percentage deviation (ARPD) and standard deviation (SD) were used to measure the algorithm performance.  
\[\text{ARPD}=\frac{1}{N}\sum_{i=1}^{N}\frac{C_{max}(i)-C^{*}}{C^{*}} \tag{20}\]  
\[\text{SD}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left[C_{max}(i)-C_{avg}\right]^{2}} \tag{21}\]  
Note: \(N\) is the number of simulation tests. \(C_{max}(i)\) is the solution obtained by the algorithm in the \(i\)th simulation. \(C^{*}\) is the optimal solution used in the literature. \(C_{avg}\) is the average value obtained from \(N\) simulations.  
Table 4 shows the performance of the four algorithms for solving Taillard examples of different sizes, and \(C^{*}\) was selected to compare the optimal solutions used by the algorithms. As shown in Table 4, the average ARPD value of the NSGA-II-V was 0.158, which was lower than 0.362 and 1.774 for the DFWA-LS and IWO, and slightly larger than 0.152 for the DSOA. The average SD value of the NSGA-II-V was 0.028, which was lower than 0.029, 0.051, and 0.185 for the DSOA, DFWA-LS, and IWO. The above results indicate that the NSGA-II-V outperformed the remaining three algorithms in terms of the stability of the optimization search. In addition, the NSGA-II-V had the lowest average SD, indicating that the NSGA-II-V has excellent stability in solving problems of different scales. In summary, the effectiveness and applicability of the NSGA-II-V in solving parallel sequential move or no-idle scheduling problems were demonstrated by comparing it with three different algorithms.
# MSEN: A Multi-Scale Evolutionary Network for Modeling the Evolution of Temporal Knowledge Graphs
## 2 Related Work
### TKG Reasoning under the Extrapolation Setting
#### 2.2.2 Entire Graph-Based Models  
The methods focus on obtaining the history of the latest KG of fixed length. Glean [26] introduces unstructured event description information to enrich entity representations, but description information is not available for all events in real applications; TANGO [13] extends neural ordinary differential equations to multi-relational graph convolutional networks to model structural information, but the model's utilization of long-distance information is still limited; RE-GCN [12] and CEN [7] both model the evolution of representations of all entities by capturing fixed-length historical information, but neither of these two models considers the evolutionary features of historical information from different perspectives; TiRGN [14] introduces a new recurrent graph network model that incorporates local and global historical patterns in temporal knowledge graphs to improve the accuracy of link prediction over time.  
Some recent works have incorporated both local and global modeling for TKG reasoning. However, MSEN proposes new designs for each module and their integration, achieving better results. The hierarchical design in the local module enriches semantic learning, while the periodic and aperiodic temporal modeling in the global module improves temporal encoding.