esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
Through the Hf isotope mapping of granite-like zircon in the Sanjiang area, the properties and components of the crust in the Sanjiang area are revealed. It was concluded that the eastern part of the crust is dominated by the juvenile crust and the western part by the ancient crust. Meanwhile, from the Hf isotope mapping of granite-like zircon in the Sanjiang area, it is deduced that the lithospheric structure and its crustal type are the first-order factors that constrain the distribution of different minerals. Zircon Hf isotope regional mapping, combined with regional geological features, geophysical anomalies, and geochemical anomalies, can be used to predict regional mineralization. The northern part of the East Qiangtang area, the central part of the Jinsha River-Ailaoshan suture, the southern extension of the Ailaoshan area, and the Vietnam area may become the most potential porphyry Cu(-Mo-Au) metallogenic areas. There are potential areas for magmatic hydrothermal Sn-W deposits in some areas of the Tengchong-Baoshan block. The southern margin of the Yidun Island Arc, where the \(\varepsilon\)Hf(t) isotopic anomaly is transformed, is a potential prospective area for porphyry Mo-W(-Cu) deposits. This study shows that Hf isotope mapping can reveal the regional metallogenic rules and explore metallogenic prediction and metallogenic potential evaluation. Hf isotope mapping can be an expected new direction for studying regional metallogenic regularity, including, especially, studies of the detection and metallogenic background of deep crustal material.  
Conceptualisation, B.D., L.Y. and Z.Y.; methodology, B.D. and G.L.; software, L.W. and J.L.; validation, B.D.; formal analysis, B.D. and Q.C.; investigation, B.D., Z.Y., L.Y., Q.C., J.Z., K.S. and G.L.; writing--original draft preparation, B.D., Z.Y., K.S. and G.L.; writing--review and editing, B.D. and Z.Y.; visualisation, B.D. and Z.Y.; supervision, B.D. project administration, B.D. All authors have read and agreed to the published version of the manuscript.  
This research is jointly supported by the National Natural Science Foundation of China (Numbers 41872080, 92162101), the most Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences (Number NSFGPMR201804).  
Not applicable.  
The authors thank the team members at CUGB for their field support, data analysis, constructive discussions, and comments.  
The authors declare no conflict of interest.
## 2 Experimental Details  
### Methodology  
* The PV module temperature and thermal and electrical gains are calculated based on hourly variations of solar radiation and dry bulb temperature. Summation of hourly data is used to obtain daily outputs, and subsequently the daily outputs are summed up for calculating the monthly gains.
* The proposed PV/T system is designed to be used for all aforementioned flow schemes such as air, water, and air plus water. During single-fluid operation, the second fluid is kept stagnant, with a zero mass flow rate.
* In the case of the dual-fluid heat exchanger, the optimal flow rate for each fluid is identified in such a way that the flow rate of any of the two fluids is varied over a range from 0 to 0.03 kg/s for water and 0 to 0.06 kg/s for air, while during this variation the second fluid is kept fixed at different flow regions such as laminar, transitional and turbulent.
* Exergy efficiency is calculated based on solar radiation and the thermal and electrical outputs.
* An economic analysis is performed based on energy and exergy performance of a PV/T system with dual and single fluid heat exchangers, and considering life cost analysis parameters such as initial cost, annual savings, and return on investment.
# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 3 Materials and Methods
### Data Processing and Transformation  
Advanced computer vision techniques were employed for accurate data annotation using the PASCAL VOC XML format, followed by its transformation to TFRecord to ensure compatibility with TensorFlow. In addition, data augmentation strategies, such as rotation and scaling, were applied to robust convolutional neural network (CNN) training, achieving highly accurate localized feature maps.
# Utilization of By-Products from the Fruit and Vegetable Processing Industry in Pasta Production
## 3 Incorporation of By-Products
### Culinary Quality
#### 3.2.1 Cooking Time  
Table 1 shows the main results from studies on the inclusion of various by-products in pasta production. When evaluating the influence of by-product incorporation in pasta production, these differences also become evident. The observed differences can be attributed to various factors, such as the type of fiber and its water absorption capacity, the amount of by-product incorporated, the pasta shape, whether it is dried or fresh pasta, or the level of hydration used during production. Regarding optimal cooking times, the results show considerable consistency, generally indicating a reduction in cooking times when by-products in powder form are incorporated or no significant effect is observed, with few exceptions [49; 90; 97]. The optimal cooking time is determined visually by pressing the cooked pasta between two glass plates at different times until the appearance of a whitish central area, indicating ungelatinized starch, disappears. In general, the inclusion of by-products interferes with the gluten network, facilitating water penetration into the starch, thus reducing cooking times. This effect is less pronounced in fresh pasta than in dried pasta, as cooking times for fresh pasta are significantly shorter. This explains why some studies on fresh pasta do not detect significant differences in cooking time [62; 79] or observe changes only at the highest incorporation levels [53; 87]. However, the presence of a small amount of water or fibers with a high water retention capacity that compete with starch for water, could delay the optimal cooking time. For example, in the study by [PERSON] et al. [49], the inclusion of orange pomace and cucumber peel, by-products that may contain high amounts of water-absorbing fibers like pectins, is analyzed. In this study, the amount of water used in pasta production is not specified, so we must assume it is the same in all cases. In the study by [PERSON] et al. [97], the moisture content is adjusted according to the by-product incorporated (brewer's spent grain from einkorn or tritordeum), but it is not indicated on what basis this moisture content is modified. The same amount of wateris used for pasta production with different levels of brewer's spent grain. This study does not provide an explanation for the increase in cooking times. Interestingly, another study by the same group on barley brewer's spent grain found no changes in optimal cooking times or observed a reduction at higher percentages (20%) [95]. This reduction is explained by interference with the gluten network and better water absorption by the starch. In this case, dough hydration was increased as more by-product was incorporated.
# Numerical Analysis of a Moderate Fire inside a Segment of a Subway Station
## 4 Thermomechanical Simulation at Selected Time Instants
### Structural Response under Mechanical Loading  
Experimental data and simulation results referring to the state after the application of the external loading, but before the application of the thermal loading are compared in the following. As regards the numerical results, there are strains from two simulations: one is based on the linear-elastic (label: LE) and the other one on the elasto-plastic (label: EP) material behavior of concrete.  
The two simulations produce similar results; see Table 8. The EP simulation delivers strains that are by less than 3.5% larger than the strains from the LE simulation. This indicates that linear-elastic behavior of concrete governed the structural performance during and right after the application of the external loading, while inelastic effects played a significantly less important role.  
Both types of simulations delivered strains that agree with the measurements in terms of the mathematical sign, referring to tensile and compressive strains, respectively; see Table 8. Satisfactory agreement between simulation results and experimental data was obtained for the right wall. There, the difference between simulated and measured strains amounted to \(3.0\times 10^{-6}\) (LE simulation) and \(1.4\times 10^{-6}\) (EP simulation). This is equivalent to prediction errors of 6.1% and 2.8%, respectively. The absolute and relative differences at the other two positions, however, were significantly larger; see the last two columns of Table 8. These differences could be the result either of inaccurate measurements or of damage of the structure prior to testing. The latter could have resulted from restrained shrinkage of concrete or from the transport and maneuvering of the structure.  
Figure 19: Positions at which measurements of strains were carried out during testing: (**a**) perspective representation and (**b**) cross-section, coinciding with the plane of symmetry of the structure that contains the columns.  
Figure 18: Temperature-dependent behavior of steel rebar subjected to uniaxial tension, as recommended by Eurocode 2.
## 4 An Energy-Balanced Protocol Based on the Improved CFSFDP
### Algorithm Description
#### 4.3.2 Phase 2: Steady-state Phase  
This phase is the same as in LEACH [9]. The cluster head node sets up a TDMA schedule and transmits the schedule to the nodes in its cluster. After all nodes in cluster have received the TDMA schedule, the steady-state operation will begin. All the normal nodes send their data to their own cluster heads and the energy of normal nodes will be consumed in this process. Once the cluster head receives all the data, it performs data aggregation to enhance the common signal and reduce the energy consumption. The aggregated data are sent to the sink node by routing path. Hence the energy consumption of cluster head contains receiving data from cluster members and sending aggregated data to sink node.  
After the steady-state phase, the round ends. If total energy has not been exhausted or the current round \(r\) hasn't reached the maximum number of rounds, the next round will choose cluster heads and divide clusters by CFSFDP-E algorithm again, which will repeat Phase 1 and Phase 2.  
Figure 2 is the flow chart of CFSFDP-E algorithm implemented in WSN. \(r\) represents the current round of the network.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Nodes** & \(c\) & \(f\) & \(b\) & \(d\) & \(e\) & \(a\) \\ \hline \(\omega\) **(DESC)** & \(\omega_{c}\) & \(\omega_{f}\) & \(\omega_{b}\) & \(\omega_{d}\) & \(\omega_{e}\) & \(\omega_{a}\) \\ \(\delta\) & \(d_{cb}\) & \(d_{fc}\) & \(d_{bc}\) & \(d_{db}\) & \(d_{ef}\) & \(d_{ad}\) \\
**Class** & **1** & **1** & **2** & **2** & **1** & **2** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The density-energy and distance (determined by density-energy) of six nodes.
## 3 Development of a Novel Model Cutting Transport Ratio
Moreover, based on [50], the original lifting capacity (\(LC\)) can be calculated by Equation (26) and can be modified to Equation (27), which can be considered as the \(ECD_{m}\)[50]. \[LC=\frac{MW}{Wc} \tag{26}\]  
\[LC_{m}=\frac{ECD_{m}}{Wc} \tag{27}\]  
Thus, the transport ratio in Equation (28) also accounts for these real-time drilling parameters (see Figure 4). Since these parameters significantly impact the actual transport ratio, the obtained Equation (28) modified transport ratio (\(CTR_{m}\)) is more precise than conventional Equation (2), which is used to calculate the transport ratio for the vertical tubes and is based on experimental work by [PERSON] in 1974 [37]. Equation (28) can be used to compute and finalize the modified \(CTR_{m}\) as a consequence:  
\[CTR_{m}=\left(1-\frac{V_{scm}}{V_{ann.m}LC}\right)100 \tag{28}\]  
Figure 4: The input and output of the novel model \(\text{CTR}_{m}\) as a real-time evaluation for indicating the hole cleaning condition. Based on Figure 4, it is apparent that \(CTR_{m}\) can be calculated using the initial parameters \(CCA\) and \(MW_{eff}\). Additionally, the model incorporates factors such as hydraulic velocities, the rheological properties of drilling fluids, flow regime, drag coefficient, cutting properties, equivalent circulating density, and lifting capacity to generate a novel \(CTR_{m}\) model. The novel \(CTR_{m}\) model serves as an indicator for the condition of hole cleaning and is evaluated using two criteria. If the \(CTR_{m}\) is greater than 50%, this indicates proper hole cleaning. Conversely, a \(CTR_{m}\) value less than 50% signifies poor hole cleaning.
# Chronotype, Risk and Time Preferences, and Financial Behaviour
## 4 Results
### The Direct Effect of Morningness on the Likelihood of Investments in Stock (H5)  
Table 7 reports the average marginal effect of morningness on stock market participation using a logistic regression. The independent variable is morningness, indicating the morning type of circadian chronotypes. The results indicate that morning type is not positively associated with stock market participation (average marginal effect of morningness is 0.105, SE = 0.063, z-statistics = 1.66, \(p\) = 0.098). In other words, when comparing with other chronotypes, the percentage of owning stock for morningness people is not significant from zero. In summary, the results do not support Hypothesis 5.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Time Preference**} & \multicolumn{4}{c}{**Delinquent Credit Card Payment**} \\ \hline
**Variables** & **Coefficient** & **SE** & **t** & **Coefficient** & **SE** & **z** \\ \hline Constant & \(-4.245\)*** & \(1.053\) & \(-4.033\) & \(-0.047\) & \(0.866\) & \(-0.054\) \\ Age & \(0.532\)* & \(0.278\) & \(1.912\) & \(0.084\) & \(0.223\) & \(0.376\) \\ Male & \(-0.936\)*** & \(0.274\) & \(-3.413\) & \(-0.088\) & \(0.215\) & \(-0.410\) \\ Married & \(-0.213\) & \(0.328\) & \(-0.649\) & \(0.096\) & \(0.255\) & \(0.376\) \\ Education & \(0.954\)*** & \(0.262\) & \(3.643\) & \(-0.174\) & \(0.197\) & \(-0.884\) \\ Morningness & \(2.116\)*** & \(0.368\) & \(5.745\) & \(-1.118\)*** & \(0.392\) & \(-2.858\) \\ Monthly income & & & & \(0.191\)* & \(0.105\) & \(1.817\) \\ Time preference & & & & \(-0.106\)*** & \(0.038\) & \(-2.765\) \\ Monthly income \(\times\) Time & & & & \(0.062\)** & \(0.028\) & \(2.228\) \\ preference & & & & & \\ R2 & \(0.131\) & & & & \\ Pseudo R2 & & & & & \(0.047\) & \\ Number of observations & \(455\) & & & & \(455\) & \\ Moderator: Monthly income & Bootstrapping indirect effect & Boot SE & & & \(95\%\) CI (LL, UL) \\ Low (\(-1\) SD from mean) & \(-0.368\) & \(0.138\) & \(-0.666\) & \(-0.139\) \\ Average (0 SD from mean) & \(-0.224\) & \(0.094\) & \(-0.430\) & \(-0.062\) \\ High (\(+1\) SD from mean) & \(-0.079\) & \(0.097\) & \(-0.283\) & \(0.104\) \\ Index of moderated mediation & & & & & \\ Mediator & Index & Boot SE & & \(95\%\) CI (LL, UL) \\ Time preference & \(0.131\) & \(0.067\) & & \(0.012\) & \(0.270\) \\ \hline \hline \end{tabular} Note: Unstandardized regression coefficients are reported; bootstrap sample size = 5000. CI = confidence interval;  
LL = lower limit; UL = upper limit. * indicates \(p\) < 0.1, ** indicates \(p\) < 0.05, and *** indicates \(p\) < 0.01.  
\end{table}
Table 6: The conditional indirect effect of morningness on delinquent credit card payments.
# Analysis of Disinfection in Greenhouse Soils with Medium-Temperature Steam Produced by Solar Energy
## 1 Introduction  
Renewable energies are considered as those that are inexhaustible, considering the period of humanity's existence [1]. The use of these renewable energies in production processes has taken on greater importance in recent decades, as the production of so-called non-renewable energy such as coal, oil and natural gas is finite. Regarding oil, it is calculated as no more than 100 years, so there is a shortage or lack of this resource; however, this study is not new. The first serious study carried out was in 1866, where [PERSON] [2; 3; 4; 5; 6; 7] used parabolic cylindrical concentrators to heat water and produce steam. Currently, various alternatives are sought for the production of energy and especially the production that is used in various processes and applications, such as agriculture. In agricultural processes, it has been sought to use cutting-edge technology to streamline crop systems such as automated tractors and surveillance drones in the monitoring of crops without the presence of humans. In the case of crop products, one of the great challenges is to eliminate the various pests, fungi, arvaneses and bacteria that attack crops, without the use of toxic chemicals such as methyl bromide, which has been used for decades for soil disinfection, but in the long run, its toxicity affects soil and crop nutrients without raising the cost of soil and crop cleaning processes, which is why it is relevant topromote the use of soil disinfectants with an ecological approach, such as the solarization process [8]. This process allows for the destabilization of bacteria due to the increase in temperature; however, various disadvantages have been described such as the challenging removal of the plastic coverture after many hours of sun exposure. Although the method is efficient and manages to control pests and diseases transmitted by the soil (nematodes and weeds, etc.) there is no adequate control of the temperature, and therefore, this fluctuation causes some bacteria to resist disinfection. In this work, we propose a new method for disinfecting agricultural soils using medium-temperature steam produced by an automated PCC where the temperature, pressure and exposure time are controlled. These systems were developed to produce working steam using solar energy and produce electrical energy through turbines. Structural engineering changes were made to the PCC so that it was capable of injecting saturated water vapor into greenhouse soils in a controlled and directed manner. Thus, discharges of saturated steam (\(\leq\)120 \({}^{\circ}\)C) were carried out regarding the soil in the tomato and cucumber greenhouse to carry out disinfection. To control the pathogens and bacteria that could be eliminated, control soil that had not been disinfected by any method was used. Moreover, we demonstrate that the steam production method works even in times of low direct radiation, thus making this method efficient at any time of the year [9; 10].
## 4 Results
### Econometric Results  
The results of the regression model are presented in Table 2. Model (1) shows that RGTI has a negative impact on CEICI at the 1% significance level, with a regression coefficient  
Figure 2: Comprehensive development levels of GTI, NQP, and CRCI across 30 provinces. of \(-0.478\). After including control variables, Model (3) indicates that this negative impact remains significant at the 1% statistical level, with the regression coefficient increasing to \(-0.813\). Upon the inclusion of the NQP variable, RGTI continues to exhibit a negative effect on CEICI at the 1% significance level, with the regression coefficient reaching \(-0.759\). This indicates that regions can significantly reduce the carbon emissions intensity of the construction industry and promote carbon reduction by enhancing the level of green technology innovation, which also verifies previous findings on the carbon reduction effects of green technology innovation, including in the construction industry [24; 45]. NQP in Models (2) and (4) exhibits a similar situation, although after incorporating control variables, the negative impact of NQP on CEICI decreases from the 1% level to the 5% level; it continues to show a robust negative effect. This demonstrates that the development of NQP can not only enhance carbon emissions performance [44] but also effectively promote carbon reduction in the construction industry. Further analysis reveals that, according to Model (5), the levels of government governance (DGI), degree of openness (LO), regional economic development (LRE), and industrial structure (IS) all exhibit significant negative impacts on CEICI. This indicates that regions can enhance the implementation of green technology innovation, improve the application and promotion of low-carbon technologies, and accelerate the green transition by strengthening policy support for carbon reduction, promoting market openness, and optimizing industrial structures [46], thereby further improving overall carbon reduction performance in the construction industry. It is noteworthy that, according to Model (6), RGTI has a positive impact on NQP at the 1% significance level, with a regression coefficient of 0.250. Combining the results of Model (3), it is evident that NQP plays a significant mediating role in the effect of RGTI on CEICI. This suggests that NQP can facilitate RGTI's further reduction of carbon emissions intensity in the construction industry, which is consistent with previous research findings on the carbon reduction effects of new productivity [44].
## 8 Conclusions  
In a GEO-LEO BiSAR system with the characteristics of complex geometry and obvious orbit difference, the signal of the illumined area is spatial variant because the relative position of the transmitter and the receiver changes with time. By conventional NLCS method, azimuth perturbation function is introduced by local fit method on condition that the signal is not aliasing in time and frequency domain. However, the method becomes invalid for the bistatic SAR configuration of geostationary transmitter with LEO receivers working in sliding spotlight or TOPS modes.  
In this paper, a unified algorithm for the GTLR BiSAR sliding spotlight and TOPS modes data processing is proposed. Firstly, the GTLR-BiSAR imaging geometry and signal properties are introduced. Then, the azimuth Doppler aliasing problem is solved by deramping operation. Furthermore, the spatial variance problem is corrected by our proposed strategies. In addition, the modified chirp scaling function is proposed to complete the RCM correction and the azimuth compression is operated in frequency domain. Finally, simulation and real data experiments are carried out to validate our algorithm. In the future, interferometry processing methods will be studied in the GTLR-BiSAR system for the generation of digital elevation model.  
Figure 16: The navigation signal and the imaging result. (**a**) The continuous signal is transferred to segmentation form. (**b**) The focused image of the repeater signal. (**c**) The pulse response of the target in range direction. (**d**) The pulse response of the target in azimuth direction.  
**Author Contributions:** Conceptualization, F.T., Z.L. ([PERSON]) and Y.W.; software, F.T.; investigation, F.T. and Z.W.; methodology, F.T., Z.L. ([PERSON]) and Z.S.; validation, F.T. and Z.W.; writing--original draft preparation, F.T. and Y.W.; writing--review and editing, Z.S. and Z.L. ([PERSON]); funding acquisition, Z.L. ([PERSON]) and Z.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (grant no. 62031005) and the Beijing Nova program (Z201100006820103) from the Beijing Municipal Science and Technology Commission.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank anonymous reviewers for their helpful comments and suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.