esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
We have analyzed the results of TD-DFT calculations of the absorption charge transfer band of viologen salts in solution of medium to low polar solvents. Comparison of the calculated results with experimental data, using several computational protocols, allowed us to select the TD-DFT = M06-2X(PCM)/6-31+G(d,p)/B3 LYP-D3(PCM)/6-31G+(d,p), as the protocol with the best accuracy to computational cost ratio among the ones investigated. A very good linear dependence of the calculated results in the UV-Vis region allowed us to propose the following empirical equation, \(\lambda_{\text{max}}\) = \(\lambda_{\text{max}}\)(TD-DFT) + 35.2 nm, as a tool to predict the absorption of the viologen salts in a given solvent.  
In this work, we have not included highly polar and protic solvents, such as methanol or water, where we expect our computational protocol to fail because the continuum model of solvation cannot capture the additional effect due to hydrogen bonds between water and the anions. Therefore, too tightly bound species would be predicted in this case not corresponding to the real structure in solution. On the other hand, considering explicit solvent molecules for these cases, is not expected to allow quantitative prediction of the kind discussed above. The reason, as shown in Ref. [9], is also related to the fact that when the ions are well solvated and separated, the CT band is hardly detectable and, even if a weak absorption can be observed at some relatively low wavelengths (high energy due to the large  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Solvent** & **Direct Effect 1 (\(\lambda_{\text{max}}\))** & **Indirect Effect 2 (\(\lambda_{\text{max}}\))** \\ \hline vacuum & 890 & 455 \\ n-hexane & 683 & 463 \\ CH\({}_{2}\)Cl\({}_{2}\) & 465 & 465 \\ Acetone & 436 & 466 \\ \hline \hline \end{tabular}  
* Fixed geometry obtained optimizing the structure with CH\({}_{2}\)Cl\({}_{2}\) as a solvent, TD-DFT calculation in vacuum and with the three solvents. 2 Fixed TD-DFT calculation using CH\({}_{2}\)Cl\({}_{2}\) as a solvent, geometries obtained after optimization in vacuum and with the three solvents.  
\end{table}
Table 4: Direct and indirect solvent effects on the absorption maxima, \(\lambda_{\text{max}}\), of the CT band of viologen iodide.  
separation of donor and acceptor), its position strongly depends on the solvent, as well as ion cluster, dynamics. Such dynamic behavior, however, cannot be properly described by DFT static calculations.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/22/8108/s1](http://www.mdpi.com/2076-3417/10/22/8108/s1)]([http://www.mdpi.com/2076-3417/10/22/8108/s1](http://www.mdpi.com/2076-3417/10/22/8108/s1)), xyz coordinates of the optimized geometries.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Mathematical Dynamic Model of the IRW
### Gravitational Restoring Force  
Due to the conicity of wheel tread, the normal forces of both wheels are not strictly in the direction of gravity, so they have lateral components generally named gravitational restoring forces. As shown in Figure 1a,b and marked as \(F_{Lgy}\) and \(F_{Rgy}\), these two forces will work in the lateral and yaw movement, so they should be considered in the IRW model.  
If a linear conicity wheel tread is adopted, the contact angle of the left wheel and the right wheel related to \(y\) can be defined as follows, \(\delta_{\rm L}(y)\) and \(\delta_{\rm R}(y)\), respectively  
\[\begin{cases}\delta_{\rm L}(y)=\delta_{0}-\frac{\lambda}{\dot{y}}y=\lambda \left(1-\frac{y}{b}\right)\\ \delta_{\rm R}(y)=\delta_{0}+\frac{\lambda}{\dot{y}}y=\lambda\left(1+\frac{y} {b}\right)\end{cases} \tag{5}\]  
where \(\delta_{0}\) is the nominal contact angle at the center position, and \(\lambda\) is the equivalent wheel-rail tread conicity at the center position.  
It is assumed that besides the whole gravity force of the IRW itself, there exist extra vertical loads virtually from the bogie or the vehicle acting on the left and right wheels, \(F_{\rm Lz}\) and \(F_{\rm Rz}\), respectively. The resultant gravitational restoring force of the IRW can be expressed as follows  
\[\begin{split} F_{gy}=F_{\rm Lgy}-F_{\rm Rgy}=\frac{(m_{\rm C}+2 m_{\rm W})g+F_{\rm Lz}+F_{\rm Rz}}{2}[\delta_{\rm L}(y)-\delta_{\rm R}(y)]\\ =-((m_{\rm C}+2m_{\rm W})g+F_{\rm Lz}+F_{\rm Rz})\frac{\lambda y}{b}\end{split} \tag{6}\]  
where \(m_{\rm C}\) is the carrier mass, \(m_{\rm W}\) is the wheel mass, and \(g\) is the gravitational acceleration.  
Additionally, the moment generated by the gravitational restoring forces can be expressed as follows  
\[M_{g\psi}=F_{\rm Lgy}b\psi+F_{\rm Rgy}b\psi=((m_{\rm C}+2m_{\rm W})g+F_{\rm Lz }+F_{\rm Rz})\lambda b\psi \tag{7}\]  
According to Equation (6), force \(F_{gy}\) and lateral displacement \(y\) present a negative relation, while according to Equation (7), moment \(M_{g\psi}\) and yaw angle \(\psi\) present a positive relation. In view of this, force \(F_{gy}\) is generally regarded as a factor contributing to IRW's self-centering ability, and moment \(M_{g\psi}\) is usually considered a cause for the IRW's derailment at low speeds, as shown in Figure 2. These two viewpoints will be verified later.
## 3 Results  
### Results of the Delineation Between Built-Up and Non-Built-Up Areas  
In this study, the NP index was constructed based on night-time light data and POI kernel density. There are various ways to determine the delineation threshold, such as the density curve graphing method, to seek the mutation threshold [33], the empirical threshold method, and the method of cross-validation by drawing on statistical data [34; 35]. For the study area, after trying many methods, the cross-validation method was the most effective, and through many experiments, this study took 25.5 as the threshold to divide the built-up area and the non-built-up area; the results are shown in Figure 5. Based on this calculation, we calculated that the built-up area of Longquanyi District of Chengdu City in 2023 was 105.62 km\({}^{2}\), and compared with the actual statistical data of the built-up area of 103.63 km\({}^{2}\)[36], the current error rate of the measurement results is 1.92%, which fully shows the accuracy and reliability of the method. The results of the division are shown in Figure 7.  
In general, the important barrier of Longquan Mountain restricts the spread of built-up areas to the east, so that the built-up area of Longquanyi District is bounded by Longquan Mountain and concentrated in the middle and south of the east side of the whole area, while the northern part of the area is scattered in clusters. The non-built-up areas are mainly located in the western part of Longquanyi District, which are less developed and contain a more natural environment and non-urban land. The built-up areas are not entirely  
Figure 6: Selected sampling plots.  
homogeneous, and there are some undeveloped \"enclaves\", as well as some non-built-up areas surrounded by built-up areas. There are also smaller patches of built-up areas within the non-built-up area, creating small areas that are relatively independent of the larger built-up area.
# Process Discovery for Event Logs with Multi-Occurrence Event Types
## 3 Proposed Schema Induction Method
### Complexity Analysis  
For the execution complexity analysis of the proposed method, we decompose the algorithm into the following key modules:  
1. Pre-processing for eliminating trace duplications;
2. Pre-processing for fuzzy loop detection;
3. Calculating the similarity matrices;
4. Quasi-clique detection;
5. Schema generation;  
In our model, the cost complexity value depends on the following parameters:  
- N: number of traces in the train dataset.  
- L: average length of the traces.  
- E: number of distinct event types.  
- W: window size for the graph context description.  
In Step 1, we can convert the trace into string, and we use a hash structure or set to eliminate the duplication. The cost estimation is  
\[O(c_{1}NL)\]  
where \(c_{1}\) denotes the bucket density factor of the hash table.  
The cost of fuzzy loop detection can be given with  
\[O\Big{(}c_{2}L^{2}N\Big{)}\]  
where \(c_{2}\) is the cost factor of the CNN\({}_{1}\) module.  
Step 3 requires more resource consumption, as we must test all the possible event pairs belonging to the same event type. In this case, the number of pairs to be tested is  
\[O\Bigg{(}\bigg{(}\frac{NL}{E}\bigg{)}^{2}\Bigg{)}\]  
and the total cost for calculating the similarity values for all pairs is  
\[O\Bigg{(}c_{3}\bigg{(}\frac{NL}{E}\bigg{)}^{2}W^{2}\Bigg{)}\]  
Here, \(c_{2}\) is the cost factor of the CNN\({}_{2}\) module. Having the similarity graph, the next step is to detect the quasi-cliques inside the graph. We have separate graphs for each event type, having a node size  
\[O\bigg{(}\frac{NL}{E}\bigg{)}\]  
The general cost of clique detection can be approximated with  
\[O\Bigg{(}\bigg{(}\frac{NL}{E}\bigg{)}^{3}\Bigg{)}\]  
In the last step, we construct a graph from the cliques. As the number of cliques is less than the number of events, we obtain a cost as follows:  
\[O\Big{(}c_{4}NL^{2}\Big{)}\]Thus, taking only the dominant components, the final cost approximation can be given with  
\[O\left(c_{3}\left(\frac{NL}{E}\right)^{2}W^{2}+\left(\frac{NL}{E}\right)^{3}\right)\]  
This cost analysis shows that there are two critical components in the proposed methodology. The first is calculating the similarity values for each related event and the second component is the mining the quasi-cliques. In this version, we used the baseline implementation of both modules. The further optimization of these units will be next step of the project.
# Using Geospatial Information to Map Yield Gain from the Use of _Azospirillum brasilense_ in Furrow
###### Abstract  
The application of biological products in agricultural crops has become increasingly prominent. The growth-promoting bacterium _Azospirillum brasilense_ has been used as an alternative to promote greater yield in maize crops. In the context of precision agriculture, interpreting geospatial data has allowed for monitoring the effect of the application of products that increase the yield of corn crops. The objective of this work was to evaluate the potential of Kriging techniques and spectral models through images in estimating the gain in yield of maize crop after applying _A. brasilense_. Analyses were carried out in two commercial areas treated with _A. brasilense_. The results revealed that models of yield prediction by Kriging with a high volume of training data estimated the yield gain with a root-mean-square error deviation (RMSE%), mean absolute percentage error (MAPE%), and R\({}^{2}\) to be 6.67, 5.42, and 0.88, respectively. For spectral models with a low volume of training data, yield gain was estimated with RMSE%, MAPE%, and R\({}^{2}\) to be 9.3, 7.71, and 0.80, respectively. The results demonstrate the potential to map the spatial distribution of productivity gains in corn crops following the application of _A. brasilense_.  
biological product; geospatial data analysis; yield gain distribution map +
Footnote †: journal: _Agronomy_
# A Novel Markerless Lung Tumor-Tracking Method Using Treatment MV Beam Imaging
## 4 Discussion and Conclusions  
This novel lung tumor-tracking algorithm is capable of accurately detecting lung tumor motion during treatment delivery. All tumor tracking was based on the simulation/planning CT images, which typically have a voxel size of 1.1 by 1.1 by 3.0 mm. As a result, the uncertainties of DRRs could be estimated at about 3.4 mm. At the same time, MV images had a pixel size of 0.25 mm projecting back to the isocenter. Our phantom studies demonstrate that the maximum 2D errors were 1.8 mm, which is half of the sum of the image uncertainties. This means that the reported method reached the maximum detection accuracy. This motion management technique does not require implanted fiducial markers, adds no additional imaging dose, and provides a daily assessment of the tumor motion pattern as a means to ensure that the tumor motion is within prior estimates and is covered by the treatment planning margin. This method is particularly useful for lung SBRT with abdominal compression as a means to guarantee reproducibility of the compression application. This motion-tracking algorithm offers significant potential for future real-time adaptive radiotherapy strategies. It will enable clinicians to \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Patient \#**} & \multicolumn{1}{c}{**GTy Size**} & \multirow{2}{*}{**Tumor Location**} & **Abdomen** & **Motion Range** \\  & **(cm\({}^{3}\))** & & **Compression** & **(mm)** \\ \hline
1 & 5.1 & right upper lobe & No & 13.8 \\
2 & 32.0 & left upper lobe & No & 6.1 \\
3 & 17.6 & left lower lobe & Yes & 8.5 \\
4 & 86.4 & left lower lobe & Yes & 3.7 \\
5 & 93.7 & left upper lobe & Yes & 15.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of patients and their characteristics including tumor motion range obtained from four-dimensional CT scans. GTV, gross tumor volume. Figure 5: Lung tumor trajectories detected in the SI positions at a gantry angle of 270\({}^{\circ}\) for Patient #5 during three fractions/days (Fx 1, Fx 2, and Fx 3) of the treatment course. reduce margins and safely deliver higher radiation doses by accounting for the effects of respiratory lung tumor motion during treatment. [PERSON] developed the software and analyzed the data. [PERSON] carried out the experiments and collected the data. [PERSON], [PERSON], [PERSON], and [PERSON] helped to develop the algorithm and analyze the data. [PERSON] initiated and supervised this study.
## 3 Engineering Example
### Seismic Response Analysis of Shield Tunnel Based on Time-History Analysis Method
#### 3.3.3 Result Analysis  
(1) Internal Force Analysis  
Table 6 provides the internal force distribution and variation of different cross sections of the shield tunnel under the action of an SL-2-level ground motion via the dynamic time-history analysis method. It suggests that the maximum bending moment is 222.5 kN-m, the maximum shear is 1448.3 kN, and the maximum axial force is 3456.3 kN. The strength requirement of lining segment can be satisfied by proper reinforcement.  
It can also be seen that consistent with the regulation of the response displacement method, the geological condition has a certain influence on the internal force of the lining segment. Section 1 is positioned at a thick silt layer with a relatively poor geological condition, so the calculated internal force is larger.  
(2) Analysis of the Rate of Diameter Change and Joint-Stretching value of Lining Segment  
Table 7 and Figure 23 show the rate of the lining segment's diameter change at the cross section of the shield tunnel in which the maximum rate of diameter change for the shield tunnel under the action of an SL-2-level ground motion is 1.336%. Table 8 and Figure 24 show the opening of a lining segment in the shield tunnel in which the maximum joint-stretching value of the lining segment under the action of an SL-2-level ground motion is 0.529 mm.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Working** \\ **Condition** \\ \end{tabular} } & \multirow{2}{*}{**Position**} & \multirow{2}{*}{**Tunnel Thickness**} & \multicolumn{2}{c}{**Axial Force (kN)**} & \multicolumn{2}{c}{**Shear (kN)**} & \multicolumn{2}{c}{**Bending Moment**} \\  & & & **(mm)** & & **Max** & **Min** & **Max** & **Min** \\ \cline{3-8}  & Normal X & 450 & 180.7 & \(-\)3456.3 & 1361.6 & \(-\)1021.7 & 222.5 & \(-\)9.4 \\  & Normal Z & 450 & 616.2 & \(-\)1270.9 & 89.3 & \(-\)77.9 & 25.5 & \(-\)24.3 \\ \hline \multirow{2}{*}{Section 2} & Normal X & 450 & 47.5 & \(-\)3368.2 & 1448.3 & \(-\)1118.4 & 204.1 & \(-\)1.6 \\  & Normal Z & 450 & 686.7 & \(-\)1351.0 & 97.0 & \(-\)55.1 & 40.2 & \(-\)40.7 \\ \hline \multirow{2}{*}{Section 3} & Normal X & 450 & 85.4 & \(-\)3247.4 & 1259.7 & \(-\)931.6 & 192.6 & \(-\)4.8 \\  & Normal Z & 450 & 386.6 & \(-\)1434.8 & 101.3 & \(-\)108.6 & 25.1 & \(-\)21.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Internal force distribution of lining segments under influence of ground motion.  
Figure 22: Positions of longitudinal monitoring sites in tunnel.  
It can also be seen that the geological condition has some influence on the deformation of the lining segment. The silt layer at the Section 1 position is thick and the geological condition is relatively poor, so the calculated rate of diameter change and joint-stretching value are larger.
# The Impacts of Climate and Human Activities on Grassland Productivity Variation in China
## 4 Discussion
Our research indicates that in the early stages of the study, i.e., 2000, there was a strong relationship (including positive and negative) between livestock (sheep and large livestock) and grassland, while in the late stages of the study, this relationship showed a weakening trend in most provinces. This might have occurred because the Chinese government began implementing a large-scale project of \"returning grazing land to grassland\" in 2003 to curb the deterioration of grassland ecosystems, and a series of measures like grazing prohibition, rotation, and rest were implemented on grassland [73; 74]. It is worth noting that sheep have a higher contribution to the dynamics of grassland GPP in the Loess Plateau, especially in SAX and GS provinces. In the Loess Plateau, the main area for the implementation of ecological restoration policies in China, the scattered animal husbandry method was the main way of raising sheep before the implementation of ecological restoration policies. However, after the ecological restoration projects were implemented, the scattered animal husbandry method shifted to intensive farming, and the proportion of feed usage increased, effectively alleviating the pressure on the grassland [75; 76]. Primary industry has a positive relationship with grassland GPP in the northwest region of China, such as SAX, GX, NX, QH, and XJ, and studies by [PERSON] [46] and [PERSON] [77] have reached similar conclusions. Livestock husbandry is an important primary industry that relies on pasture areas. Although primitive grazing can generate economic income, it can also lead to grassland degradation. In recent years, with traditional grazing shifted to intensive farming and government ecosystem subsidies being directly disbursed, the dependence of herdsmen on grasslands for their production and livelihoods has decreased while their income has increased [78; 79]. In addition, the coefficient of the forbidden grazing effect has had a consistent trend with the area of forbidden grazing. Although there was a negative relationship between forbidden grazing and grassland GPP in this research, when the area of forbidden grazing reached a certain level, the coefficient then had a positive effect. The area of rodent and pest control was closely related to the area where rodent and pest diseases occurred that year, so the area of rodent and pest control was negatively related to the GPP of grassland. This study aimed to reveal the impact of human activities on grassland GPP using various socioeconomic data. However, due to the lack and inconsistency of statistical data at the county and city levels over many years, the collection of some indicator data has been limited. Therefore, the characteristics of spatial heterogeneity within the province cannot be reflected. In future research, we will strive to collect more accurate municipal or even county-level indicators to reveal the direct or indirect impact of socioeconomic factors on grassland ecosystems.
## 4 Simulation and Results
### The Simulation Data in Different States  
In the photovoltaic array simulation model, the fault of abnormal aging is simulated by connecting a series resistor. The model of photovoltaic modules in the simulation model is CHN310-72P, and the main parameters of the photovoltaic module at standard test conditions (STC) are shown in Table 1.  
The outputs of the simulation model are the operating voltage \(U\) and the output current \(I\) of the photovoltaic array; thus, we can obtain the V-I curve of the photovoltaic array under the STC, which further gets the external parameters \(U_{\infty}\), \(I_{\text{sc}}\), \(U_{\text{m}}\), \(I_{\text{m}}\), and the equivalent circuit internal parameters \(I_{\text{ph}}\)', \(R_{\text{s}}\)', \(R_{\text{sh}}\)' [26]. From the simulation results, we can know that the external parameters of photovoltaic array in normal state are: \(U_{\text{oc}}=121.00\) V, \(I_{\text{sc}}=15.85\) A, \(U_{\text{m}}=107.00\) V, and \(I_{\text{m}}=15.90\) A, and the equivalent circuit internal parameters are: \(I_{\text{ph}}=16.68\) A, \(R_{\text{s}}\)' = 0.21 \(\Omega\), and \(R_{\text{sh}}\)' = 8.12 K\(\Omega\). The comparison studies show that the parameters obtained by the simulation are basically consistent with the parameters that are provided by the manufacturer, which shows that the model can simulate the actual photovoltaic array well.  
The photovoltaic array runs in the states of normal, short circuit, open circuit, and abnormal aging; 40 sample groups in each state (160 sample groups altogether) were obtained, of which 30 sample groups were used as the training set, and the remaining 10 groups were used as the testing set.
# The Smart Buildings Revolution: A Comprehensive Review of the Smart Readiness Indicator Literature
## 4 Results and Discussion
### Research Gaps and Future Directions in the SRI's Dominant Themes
#### 4.2.1 Research Gap in Applicability of the SRI to Different Contexts  
Further development and refinement of the SRI methodology are necessary to adapt the measure to various types of buildings and environments. [PERSON] et al. [16] suggested exploring different metrics or indicators to accurately measure buildings' smartness levels across diverse contexts.  
[PERSON] et al. [54] proposed broadening the detailed calculations to include a wider range of buildings of various types and sizes to validate the SRI methodology. [PERSON] et al. [55] recommended enhancing the algorithmic outcomes for residential systems (RSs) by incorporating cost considerations. For instance, in the thermal envelope RS, including the cost of insulating materials would enable users to align suggestions with their financial limitations. In addition, establishing a central database at the national level to streamline the collection of SRI data for future researchers in the field would also address the subjectivity of the SRI framework by including quantitative elements [55]. [PERSON] [49] noted that for case studies, conducting supplementary cost analyses is necessary to illustrate the actual level of savings in relation to the required investments.  
Future research topic in reviewed literature  
Future studies could explore the potential expansion of the SRI framework to other resource-sharing dimensions beyond energy, such as building facilities and spaces. Research could investigate how the SRI framework can be adapted to address challenges related to resource sharing, climate conditions, and flexible energy resource sharing to include aspects such as the utilization of building facilities [38; 45; 79; 80].  
Figure 5: Current and future research areas in the SRI literature categorized according to the dominant themes.