esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Electromagnetic-Thermal Bidirectional Coupling Model Formulation of TPMLG
### Electromagnetic-Thermal Bidirectional Coupling Analysis  
During the operation of the mover, the stator core, the windings, and the PMs heat up and serve as the heat sources for the entire heat transfer process. After the establishment of the electromagnetic model and thermal-fluid model, a bidirectional iterative coupling relationship between the electromagnetic field and the thermal-fluid field is built.  
The coupling calculation process of electromagnetic and thermal-fluid fields is shown in Figure 6. In the first iteration, calculated temperature is assigned an initial value as the initial temperature to assign corresponding material electromagnetic characteristic parameters in the coupling analysis. After a preliminary electromagnetic analysis, electromagnetic loss data of different parts from the electromagnetic field is loaded into the thermal-fluid field as energy sources for temperature calculation through volumetric mapping. At the same time, temperature rise will also have an impact on the distribution of the electromagnetic field. The diagram of the electromagnetic-thermal coupling module connection is shown in Figure 7. The electromagnetic and thermal fields of the TPMLG interact with each other. New temperature field distribution data from the temperature field simulation is conversely transmitted to the electromagnetic field to update the material properties, thereby recalculating the electromagnetic loss under different temperature conditions. A feedback iterator module is added into the module of the electromagnetic-thermal analysis for iterative convergence. After several iterative processes, the temperature distribution of the LG finally converges within the margin of relative error \(\varepsilon\). The maximum temperatures of the major components, which are the windings and the PMs, are selected to calculate relative temperature differences \(\Delta T_{\rm w}\) and \(\Delta T_{\rm PM}\). Once both the temperature differences of the two adjacent iterations meet the permissible relative error \(\varepsilon\), the coupling calculation is done. The coupling iterative process greatly reduces the final temperature calculation error.  
Figure 6: Calculation flow chart of electromagnetic–thermal coupling analysis.
## 4 Results and Discussion
### Particulate Matter Formation  
For particulate matter formation (PMF), Figure 7 shows the contribution of the different powertrain technologies per kilometer driven. For this impact category, non-exhaust emissions are distinguished from the total TTW emissions, to highlight their significance relating to battery size. The specific impact regarding PMF from non-exhaust sources ranges from roughly one-fifth of the total PM emissions (petrol) to about one-tenth in case of the 90 kWh EV and the diesel car. Keep in  
Figure 5: Impact per kilometer on photochemical oxidant formation.  
Figure 6: Photochemical oxidant formation at a local level per powertrain technology.  
mind that the non-exhaust fraction originates from brake, tyre, and road wear and currently is not regulated within Europe. Again, we see the inherently high contribution of PM emissions during the use phase for diesel cars, while for the other ICEVs and EVs this impact increases with the applied battery capacity. What catches the eye is that for the entire life cycle, the 90 kWh EV's PM emissions actually exceed those of a diesel car, particularly if a battery replacement is considered.  
For the range-extender trailer combinations, brake wear is excluded as the trailer is assumed not to be provided with a braking system. The effect of towing a trailer--and thus an extra weight--on the non-exhaust sources is nonetheless encompassed, albeit that due to the occasional nature of using the trailer, these impacts are negligible. Notice how both the trailer combinations and the 60 kWh EV have a similar impact over the full life cycle, which is about 40 percent lower than for the 90 kWh EV. Compared to a PHEV, the trailers have a 14 percent higher contribution to PMF, although one must keep in mind that the discussed PHEV is based on petrol technology. In case of a diesel PHEV, the tank-to-wheel contribution would be significantly higher.  
If we look at the local emissions considering a refinery-to-wheel analysis, Figure 8 stresses the importance of regulation of non-exhaust emissions as they now have relative shares ranging up to 75 percent in case of the 40 kWh EV. Locally, the battery trailer performs about 30 percent better than the generator trailer and the 90 kWh EV, which have an equal contribution. Compared to the other EVs, the 90 kWh EV does represent a substantially higher local impact, which is mostly because of the higher vehicle mass' influence on non-exhaust emissions. Thus, we can confirm that the extra weight of the 90 kWh EV's battery has a negative effect seen for both its lifetime and local impact, although for the latter this effect is similar to the contribution of a hybrid powertrain. Whereas the trailer concept is designed in the light of a sharing economy and for covering longer distances, it is likely the trailer will be dropped off and picked up near highways. Therefore, the impact of the trailer combination is less relevant concerning local air quality levels.  
Figure 7: The contribution of particulate matter per technology.
# Construction Schedule versus Various Constraints and Risks
## 3 Materials and Methods
### Time Coupling Method--TCM  
The Time Coupling Method (TCM) is one of the flow-shop methods. It is based on deterministic execution times of construction processes [34; 39; 40; 41; 42]. Scheduling construction projects using the Time Coupling Method allows for planning construction projects while taking into account technological and organizational limitations. The Time Coupling Methods are used in modern scientific solutions due to the algorithmic nature of calculations. The Time Coupling Method was developed by Professor [PERSON] [43; 44; 45; 46]. Subsequently, [PERSON] [41; 47], [PERSON] [39; 48; 49; 50], and [PERSON] [51; 52; 53; 54; 55] continued to develop Professor [PERSON]'s concept.  
Temporal couplings used in Time Coupling Methods are internal time connections between construction processes and work plots (sectors) [36; 39; 41; 56]. Time connections are defined between the earliest and latest start and end dates of individual construction processes or work tasks [57]. They may be obligatory, when the time between activities is specified, or conditional, when a minimum time break is specified between activities. Mandatory couplings enable modeling the following [57]:  
* Continuity of work of brigades (zero coupling between activities performed by individual brigades);
* Continuity of work on the plots (zero coupling between activities);
* Sequence of activities, assuming minimization of the construction project completion time, without maintaining the continuity of work of teams and continuity of work on working plots.  
In the calculations using the Time Coupling Method, the technology of performing the works is adopted, and the size and number of working plots are determined, as well as the resources required for their implementation. The method utilizes algorithmic notation to automate calculations and introduce restrictions. Traditional planning methods do not enable an automatic prioritization of tasks. Priorities are assigned to tasks based on engineering knowledge rather than through an objective evaluation. The Time Coupling Methods can be divided into six groups [41; 50]:  
* TCM I--continuity of work of individual work brigades is maintained;
* TCM II--continuity of work is maintained on working plots (sectors);
* TCM III--where the priority is to minimize the implementation time of the construction project, team downtime and failure to maintain continuity of work on work plots are possible;
* TCM IV, V, VI--minimum working time is taken into account with additional restrictions applied.
## 1 Introduction
Therefore, biochar is considered a valuable soil amendment to increase crop productivity and resilience to drought [21]. Because charcoal itself is decomposition-resistant, it can contribute to soil carbon sequestration and atmospheric carbon reduction [22], and is thus regarded as a feasible negative-emission strategy to mitigate climate change [19]. Incorporating biochar into good agricultural practices requires a better understanding of its effects on crop productivity, soil water usage, and land surface energy balance. Using a conceptual model, [PERSON] et al. [21] showed that biochar amendment--as evident by shifting the soil water retention curve--should increase soil water availability under water-limited conditions. As a result, the long-term mean evapotranspiration (ET) rate is expectedto increase after biochar amendment, which in turn should increase the gross primary productivity (GPP) due to the joint regulation of transpiration and photosynthesis by stomata. However, the impacts of biochar on plant water use efficiency (WUE = GPP/ET, the rate of GPP per unit water consumption) are not clear. Moreover, ET is not always increased in biochar-amended fields, in contrast to model predictions [21]. These divergent effects of biochar on crop growth and soil hydraulic properties highlight our limited understanding of the effects of biochar on crop growth and soil water usage [21; 23; 24; 25]. The apparent gap between the theory and empirical evidence on biochar effects may be also due to extrapolating documented biochar effects on soil properties from discrete in situ point measurements to the field or farm scale. Furthermore, as there are concurrent impacts of biochar on soil and plant properties, synthesizing them by combining both spatial and point measurements may provide a broader picture of crop responses after biochar application. This study explores the potential of UAV-borne hyperspectral and thermal sensing for a spatially improved understanding of the functioning of biochar in tropical dryland. We investigate the effect of biochar in a field experiment with upland rice (_Oryza sativa_ L.) in the North Pacific of Costa Rica to assess changes associated with energy, productivity, soil water availability, ET, and WUE. This is a drought-prone region, projected to experience more prolonged and severe droughts in the near future. A four-day field campaign was conducted using hyperspectral and thermal imaging cameras onboard a hexacopter at the end of the rice growing season. Combining the UAV snapshot images of surface variables, in situ measurements, and physical models, the study aims to (1) assess relative variations in dryland rice productivity, soil moisture content, and water use efficiency after biochar application, (2) test the applicability of drone-based hyperspectral and thermal imagery in cropland monitoring, and (3) develop a methodology combining data from UAV sensing, ground measurements, and models to support agricultural management.
## 2 Experimental Measurements
### Uncertainty Analysis  
An uncertainty analysis was conducted within the apparatus's components used in the experimental data collection. The error-propagating components of the device are the flowmeter and the T-type thermocouples. For the flowmeter, the error was determined, through the calibration processes, to be equal to 0.44% US gallon per minute (USGPM). The uncertainty was obtained through the calibration processes for the T-type thermocouple, yielding an uncertainty of 0.75% (\({}^{\circ}\)C). The uncertainty of the non-dimensional parameters, such as the Nusselt number and the temperature, was detected as the temperature and the flow rate fluctuate. For instance, the Nusselt number is expressed by  
\[\mathrm{Nu}=\frac{\mathrm{h}_{\mathrm{x}}\cdot\mathrm{D}}{\mathrm{k}_{f}} \tag{1}\]  
and the Reynolds number is expressed by:  
\[\mathrm{Re}=\frac{\mathrm{u}_{\mathrm{in}}\cdot\mathrm{D}}{\mathrm{\gamma}} \tag{2}\]  
Figure 3: Temperature variation as a function of time. (**a**) Aluminum TPMS structure. (**b**) Silver TPMS structure.  
Additionally, there exist several parameters that are dimensional, such as the local heat transfer coefficients expressed in W/m2C expressed by:  
\[\mathrm{h_{x}}=\frac{\mathrm{q^{\prime\prime}}}{\mathrm{T_{x}-Tin}}=\frac{ \mathrm{q^{\prime\prime}}}{\mathrm{\Delta T_{x}}} \tag{3}\]  
As we can see, the calculations in all the cases above rely on the experimental results. The uncertainty of the average Nusselt number can be obtained as follows:  
\[\mathrm{\delta Nu}=\sqrt{\left(\frac{\mathrm{\partial Nu_{x}}}{\mathrm{ \partial x}}\cdot\mathrm{\delta x}\right)^{2}+\ldots+\left(\frac{\mathrm{ \partial Nu_{x}}}{\mathrm{\partial y}}\cdot\mathrm{\delta y}\right)^{2}+ \ldots+\left(\frac{\mathrm{\partial Nu_{x}}}{\mathrm{\partial z}}\cdot\mathrm{ \delta z}\right)^{2}} \tag{4}\]  
where \(\mathrm{h_{x}}\) represents the local heat transfer coefficient over the heater surface, \(\mathrm{T_{x}}\) represents the local surface temperature, \(\mathrm{T_{in}}\) represents the water inlet temperature, D represents the inlet diameter of the tube, and \(\mathrm{u_{in}}\) represents inlet water velocity throughout the test section. The kinematic viscosity of the fluid is \(\
u\), and \(\mathrm{k_{f}}\) represents the thermal conductivity of water. The maximum value of the uncertainty of the local Nusselt number was 2.6%. Table 1 shows the physical properties of the fluids used in the experimental setup, such as the distilled water and the nanofluid.  
As observed in Table 1, the nanofluid generally has an increase in thermal conductivity, density, and heat capacity. On the other hand, the higher the viscosity coefficient, the higher the pressure drop, which will be examined in detail.
## 4 ANN Modeling Procedure
### ANN Optimization and Performance Evaluation  
For the traditional approach and each of the four different levels of the assisted approach, the ANN's configuration is optimized using the validation datasets. The optimization is done to identify the combination of learning algorithm, hidden nodes activation function, and the number of nodes in the hidden layer that gives the best prediction accuracy. Three learning algorithms are used in the optimization, namely; BR, LM and SCG. The difference between the algorithms lies in the way that each algorithm sets the internal parameters of the ANN (i.e., weights and biases). The weights and biases are initially set randomly and then updated iteratively by calculating the error on the training outputs and distributing it back to the ANN layers. Though more learning algorithms are available, the three algorithms used here are the most commonly used in literature and they are known to give good performance in comparison to the other available learning algorithms in different industrial applications [59; 60; 61; 62]. For the activation functions, all the 12 different activation functions available in the MATLAB ANN-toolbox are used in the optimization [63]. The difference between these functions lies in the way that each function calculates the layer's output from the received inputs. Finally, for the number of nodes in the hidden layer, from 1 up to 20 nodes are used in the optimization.  
The optimum ANN configuration is determined based on the average MAE\({}_{\text{P}}\) for the 80 random combinations of the validation datasets. The MAE\({}_{\text{P}}\) is used for the optimization since it is more meaningful for reflecting the level of the error. In addition to the MAE\({}_{\text{P}}\), two other performance metrics are also calculated and reported to get more insights into the performance of the assisted-ANN modeling approach as compared to the traditional approach. The additional performance metrics are the root mean square percentage error (RMSE\({}_{\text{P}}\)) and the coefficient of determination (R\({}^{2}\)). The MAE\({}_{\text{P}}\), RMSE\({}_{\text{P}}\) and R\({}^{2}\) are calculated here as:  
\[\text{MAE}_{\text{P}} = \frac{1}{\text{N}}\sum_{i=1}^{N}\left|\frac{\sigma_{\text{Prdct} }-\sigma_{\text{Exp}}}{\sigma_{\text{Exp}}}\ \%_{i}\right|_{i} \tag{7}\] \[\text{RMSE}_{\text{P}} = \sqrt{\frac{1}{\text{N}}\sum_{i=1}^{N}\left(\frac{\sigma_{\text{ Prdct}}-\sigma_{\text{Exp}}}{\sigma_{\text{Exp}}}\ \%_{i}\right)_{i}^{2}}\] (8) \[R^{2} = \left(1-\frac{\sum_{i=1}^{N}\left(\sigma_{\text{Exp}}-\sigma_{ \text{Prdct}}\right)_{i}^{2}}{\sum_{i=1}^{N}\left(\sigma_{\text{Exp}}-\sigma_{ \text{Exp}}\right)_{i}^{2}}\right)_{\%} \tag{9}\]
## 2 Materials and Methods
### Datasets
#### 2.2.2 Influencing Factors of Line Loss Rate  
Taking into account of both the possible influencing factors and the information recorded, a total of twelve factors are selected as inputs of the regression models, as shown in Table 2. Among those, the third and fourth factors are one-bit codes, while the others are numerical values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Factor** & **Remarks** \\ \hline
1 & Date & - \\
2 & Capacity of the transformer & - \\
3 & Type of the transformer & Public transformer (=0); special transformer (=1) \\
4 & Type of the gird that the region belongs to & City grid (=0); country grid (=1) \\
5 & Monthly line loss rate & Three inputs, including the last three months \\
6 & Daily load rate & Daily load rate = daily power \\
7 & Daily maximum load rate & - \\
8 & Daily average power factor & - \\
9 & Number of customers & - \\
10 & Average transformer capacity per customer & Average capacity = transformer \\
11 & Rate of residential capacity & Rate = residential capacity/transformer capacity \\
12 & Power supply duration & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Influencing factors of line loss rate utilized in this study.  
Figure 3: The component result of data quality analysis (84.61% normal values, 8.67% outliers and 6.72% missing values).  
Figure 2: The distribution box-plots of original dataset and the dataset after interpolation operation.
## 4 Failure Criteria
### Lamina Level Hashin Criteria  
Lamina level Hashin failure criteria [29] were also used to predict the mechanical response of the test coupons to compare with micro-level Tsai-Hill in combination with maximum stress criteria based on GMC. Hashin criteria utilized take four different failure modes into account, which are matrix cracking, matrix crushing, fiber buckling and fiber fracture. The expressions of lamina level Hashin criteria are represented, as shown below.  
1. Fiber rupture in tension (\(\sigma_{11}\geq 0\)): \[d_{ft}=\left(\frac{\sigma_{11}}{X_{T}}\right)^{2}+\left(\frac{\tau_{12}}{s_{12} }\right)^{2}+\left(\frac{\tau_{13}}{s_{13}}\right)^{2}\geq 1\] (20)  
Figure 4: Flow chart of multiscale failure analysis using GMC.  
Figure 3: \(2\times 2\) architecture of the generalized method of cells (GMC) used to represent the composite material.  
2. Fiber buckling (\(\sigma_{11}\) < 0): \[d_{fc}=\left(\frac{\sigma_{11}}{X_{c}}\right)^{2}\geq 1\] (21) 3. Matrix cracking under transverse tension and shearing (\(\sigma_{22}\geq 0\)): \[d_{mt}=\left(\frac{\sigma_{22}}{Y_{T}}\right)^{2}+\left(\frac{\tau_{12}}{s_{12 }}\right)^{2}+\left(\frac{\tau_{23}}{s_{23}}\right)^{2}\geq 1\] (22) 4. Matrix crushing under transverse compression and shearing (\(\sigma_{22}<0\)): \[d_{mc}=\left(\frac{\sigma_{22}}{2S_{12}}\right)^{2}+\left(\frac{\sigma_{22}}{ Y_{c}}\right)\left[\left(\frac{Y_{c}}{2S_{12}}\right)^{2}-1\right]+\left(\frac{ \tau_{12}}{S_{12}}\right)^{2}+\left(\frac{\tau_{23}}{S_{23}}\right)^{2}\geq 1\] (23) where \(d_{fl}\), \(d_{fc}\), \(d_{mt}\) and \(d_{mc}\) are damage variables corresponding to each damage mode.  
The value of each damage variable is 0 before damage occurs. \(X_{T}\), \(X_{C}\) are, respectively, the tensile and compression strength in the fiber direction of each ply, and \(Y_{T}\) and \(Y_{C}\) are, respectively, the transverse tensile and transverse compression strength, which are perpendicular to the fiber direction. The last parameter \(S_{ij.}\) is the shear strength. Once any equation above is met, the damage at that integration point occurs. When the stress states of any element satisfy multiple Equations in (20)-(23) at the same time, multiple damage modes corresponding to that element are considered to have occurred, and thus the material properties embedded in the damaged elements will degrade accordingly. Herein the local degradation model, Camanho [30], is used to describe the stiffness reduction after the element is damaged, as shown in Table 6.  
Where \(Q\) denotes elastic material parameters required to reduce according to the different failure modes. Specifically, \(Q\) represents the elastic modulus \(E_{11}\) to be reduced when fiber rupture or fiber buckling occurs, and in the case that matrix-cracking or matrix-crushing happens, \(Q\) is set to \(E_{11}\), \(G_{12}\), \(G_{23}\). \(Q_{d}\) indicates the corresponding elastic properties that will be degraded. If more than one failure mode has occurred simultaneously at an integration point, the stiffness of the material would be degraded cumulatively in terms of the degradation scheme.
## 4 Discussion
### Pro--Information or Outcome Data?  
Since PRO's purpose has broadened, it is ambiguous to determine whether PRO is best thought of as information, outcome data or maybe a combination of both? Thus, it is noticeable that a single PRO questionnaire logically is unable to produce outcome data on its own. If a PRO is to provide outcome data, it requires comparison with a baseline measurement, either being individual health status progression or aggregated population data.  
Moreover, even though there might be a correlation between a healthcare intervention and the subsequent outcome, their causality is quite uncertain as PROs are measuring possible effects in an uncontrolled environment consisting of a multitude of potential confounders. Arguably, it is not due to their enhancement of the internal validity concerning healthcare interventions and subsequent outcomes that PROs are attractive tools, but due to their ability to strengthen the ecological validity, thus, PROs provide a more authentic impression of patients' disease situations as part of everyday settings.  
Furthermore, in cases where PRO is considered an independent variable for example when used as an empowerment tool to increase patient participation, their actual potential is still relatively unclear; hence, further studies elucidating PROs capacity in this respect are demanded. Besides eliciting patient outcomes, PRO is also perceivable as an information tool, since it provides healthcare professionals with useful knowledge through the eyes of patients; subsequently, enabling improved patient management. Therefore, PRO may also be labeled as information regarding patients' health status and HRQoL, which through comparison with baseline measures (formerly collected PRO data), and thorough interpretation elicits patients' outcomes and current health situation [31]. Thus, it is no coincidence that researchers are dedicated to determining how to interpret PRO data, which is an imperative endeavor since inconsistent and inaccurate interpretations of the information PRO data provides might have counterproductive effects. In other words, turning the PRO data into clear and useful information, both on a general level but also as part of clinical practice is an important task to handle henceforth.