esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Electromagnetic Characteristics of DLSRG
### Analysis of Magnetic Pulling Force  
In a generation cycle, the position of the right pole tip of the follower tooth aligned with the left pole tip of the stator tooth, and the alignment position between the center line of the actuator tooth and the center line of the stator tooth is called the excitation stage, and the transverse magnetic pull force of the actuator is the same as the direction of the external mechanical force. In this process, the generator stores the magnetic field. The position of the center line of the actuator tooth aligned with the center line of the stator tooth to the position of the left pole tip of the stator tooth and the right pole tip of the stator tooth is called the power generation stage, and the transverse magnetic pull of the actuator is opposite to the direction of the external mechanical force. Transverse magnetic pull overcomes the work done by external mechanical force. The greater the transverse magnetic pull is, the more work is done to overcome the external mechanical force, thus the efficiency of generator generation can be improved. Therefore, in the process of DLSRG running, the transverse magnetic pull force is as large as possible, and the tangential transverse magnetic pull force changes with the current, as shown in Figure 5, and the transverse magnetic tension force increases with the increase of the current. Therefore, the transverse magnetic tension can be increased by increasing the excitation current properly, and then the generation efficiency can be improved.  
Figure 4: Magnetic density distribution cloud image of typical position in one cycle operation of DLSRG. (**a**) The generator is in the excitation phase; (**b**) The generator is in the critical state; (**c**) The generator is in the power generation stage.  
Figure 5: When the winding current is 0 A, 2 A, 4 A, 6 A, 8 A, 10 A, the transverse magnetic pull force changes with current as shown in Figure 5.  
During the operation of DLSRG, two magnetic pull forces at a certain angle with the moving direction of the actuator are applied to the actuator, which are decomposed into transverse and radial directions. Without considering magnetic flux leakage, the two-sided structure is symmetrical and the radial magnetic pull force is zero. However, in practice, with the change of the relative position of the rotor and stator, there will be asymmetric magnetic leakage in the air gap. The radial magnetic pull produces fluctuations, and the simulation waveform of the radial magnetic pull is shown in Figure 6. With the increase of excitation current, the amplitude of radial magnetic pull increases exponentially, but the radial magnetic pull pulsation is relatively small, within controllable range. Moreover, the maximum radial magnetic pull force of the actuator is 293 N, the friction coefficient of the linear bearing is 0.002-0.003 and the maximum friction force is 0.879 N. The friction force has little effect on the practical running stability of DLSRG. Therefore, the normal magnetic pull of the DLSRG rotor is negligible.
## 3 Results  
### Scanning Electron Microscopy  
A scanning electron microscopy analysis of Co-Si LDHs was measured after coating with a platinum thin film to display clear images. Nanofibers and nanoneedles were observed for Co-Si LDHs, as shown in Figure 1. Figure 1b,d displayed individual fibers with diameters of 50 nm. Meanwhile Figure 1a,c revealed bundles of nanofibers. However, the morphology of the natural samples of layered double hydroxides, which consists of divalent and trivalent cations, is plate-like morphology [26; 27]. It means that the presence of the tetravalent cations of silicon caused distortion or curvature for the nanolayers of Co-Si LDHs. This distortion or curvature may be achieved through creating positive charges (+2) by silicon and the insertion of dual anions for neutralizing these positive charges.  
In case of the insertion of zirconium inside the Co-Si LDHs, SEM images showed plate-like morphology, which started to transform to fibrous morphology, as shown in Figure 2a. At the same time, other locations of the sample exhibited only fibrous morphology, as shown in Figure 2b. It is known that the crystals of hydrotalcite have a hexagonal plate-like morphology if carefully crystallized [26]. It means that the plate-like morphology of the LDHs, which was formed agreeing with the conventional LDHs, converted to nanofibers because of the presence of zirconium in the LDH structure. Energy dispersive X-ray spectrometer confirmed the presence of cobalt, zirconium, silicon, carbon, oxygen, and nitrogen on the structure of LDHs, as shown in Figure 2c.  
The presence of carbon, oxygen, and nitrogen suggests that a dual anions intercalation process has occurred inside the interlayered region of the structures of the LDHs. It means that both silicon and zirconium may have a role in producing and trapping cyanate and nitrate anions inside their LDHs.  
After the insertion of zirconium and titanium inside the structure of Co-Si LDHs, plate-like morphology was observed with a thickness of 100 nm and dimensions a few micrometers in width, as seen in Figure 3. The individual plates of the LDHs have a hexagonal platy morphology, agreeing with hydrotalcite crystals. The different elements in these plates were identified through the energy dispersive X-ray spectrum, as shown in Figure 3c. The peaks of cobalt, silicon, zirconium, and titanium were observed on these plates, confirming the insertion of zirconium and titanium in the Co-Si LDHs. In addition, carbon, oxygen, and nitrogen were detected inside the structure of LDHs.  
Figure 1: Scanning electron microscopy (SEM) images of Co–Si layered double hydroxides (LDHs). (a) Reaction time 16 h, (b) reaction time 18 h, (c) 16 h by magnification, (d) 18 h by magnification.  
Figure 2: Co–Si LDHs after zirconium inclusion: (a) SEM image, (b) SEM image after magnification, (c) energy-dispersive X-ray spectrometer (EDX).
## 2 Materials and Methods
### Projected Water Productivity of Rice  
Crop-water productivity is a ratio of crop yield to the amount of water required to produce that yield. The quantity of water can be the total water input (irrigation plus effective rainfall, IR + ER) or applied irrigation (_Irr_) or actual crop evapotranspiration (_ET\({}_{c}\)_). The variation of predicted water productivity, WP, of rice over different time periods is illustrated in Figure 10. WP is 0.46, 0.60, and 1.02 kg m\({}^{-3}\) during the baseline period when calculated based on (IR + ER), _Irr_ and _ET\({}_{c}\)_, respectively in the off-season. WP for the corresponding base in the main season is 0.49, 0.63, and 1.08 kg m\({}^{-3}\). The projected WP follows increasing trends under the three RCP emission scenarios over the future periods for both crop seasons. The WP of rice will likely be higher during the main season compared to the off-season; the difference is significant when WP is estimated based on applied irrigation, _Irr_. The WP will increase by 8% during the 2050s under RCP4.5 and 13% during the 2080s under RCP6.0 and RCP8.4 emission scenarios. Since increasing rainfall reduces irrigation requirements of the crop, the large increase in WP will be attributed to the increase in rainfall in the main season. The largest increase in _WP\({}_{ET}\)_ is projected under the RCP8.5 emission scenario in both crop seasons during the 2080s, since the projected increase in effective rainfall and CO\({}_{2}\) concentration will likely reduce crop evapotranspiration and improve crop-production efficiency. A shortened growth period of rice under increasing temperature will also contribute reducing crop evapotranspiration. Furthermore, reduced stomatal conductance under elevated CO\({}_{2}\) will reduce transpiration per unit leaf area [47]. The expected role of elevated CO\({}_{2}\) in reducing evapotranspiration and increasing water productivity for rice is consistent with other studies [48; 49]. Thus, the FAO AquaCrop model can be used to reliably simulate the effects of various irrigation management strategies for improving the water productivity of rice.  
Figure 10: Predicted changes in water productivity (WP) of rice based on irrigation plus effective rainfall, irrigation, and actual crop evapotranspiration under RCP4.5, RCP6.0, and RCP8.5 scenarios for the 2020s, 2050s, and 2080s.
# Sand and Dust Storms: Impact Mitigation
## 4 SDS Hazards and Their Mitigation
### SDS Source Mitigation  
There are numerous technical means of environmental management designed to mitigate SDS at source. Most of those detailed here are usually applied in places where wind erosion is predominantly a land-use issue. The main exceptions are in desert areas where naturally-occurring mobile sand dunes and blowing sand present challenges to human activities. Preventing wind erosion in some of the very large natural sources of desert dust is not practical, so hazards deriving from such areas are better managed when the dust is in the transport or deposition phase.  
SDS source mitigation can employ a wide array of techniques that have been used for wind erosion control, most developed to protect cultivated fields from soil loss [66; 67]. In any particular location, a range of measures is typically employed and [PERSON] et al. [26] distinguish between techniques designed to minimize actual risk (short-term, e.g., cultivation practices such as minimum tillage) from those that minimize potential risk (long-term, e.g., planting windbreaks).  
Action taken to mitigate anthropogenic sources of SDS contributes towards the global aspiration to halt and reverse land degradation by 2030 (Sustainable Development Goal target 15.3) and will be in line with the concept of Land Degradation Neutrality, or LDN [68]. These goals stretch over the longer-term and are synonymous with the aims of sustainable land management or SLM [69] while in many cases they also embody the ecological restoration of degraded lands. SLM in particular contributes towards the resolution of issues surrounding the need to achieve social, economic and environmental objectives in areas where productive land uses often compete with environmental and biodiversity goals: the concept of integrated landscape management [70].
## 4 Experimental Results and Discussions
### Magnetic Localization Evaluation
#### 4.2.1 Magnetic Fields Derived from the \(T\)-Type PM
(**a**) Arrangement illustration of the \(T\)-type PM and analyzed magnetic field distribution; (**b**) \(B\) derived from the combined \(T\)-type PM; (**c**) \(B\) derived from the magnet PM\({}_{h}\); (**d**) \(B\) derived from the magnet PM\({}_{v}\). To further discuss the effects brought by the \(T\)-type PM to the measurements acquired by the proposed sensing system, the magnetic field magnitude distribution \(B\), including the contributions of the magnetic fields derived from PM\({}_{v}\) and PM\({}_{h}\) that make up the \(T\)-type PM marker, as measured by sensors S1 and S8 distributed on the edge of the sensor array, and by sensor S4 distributed on the center of the sensor array, are presented in Figure 8. Figure 8a-f is the measurements while tracking the spiral line and the folding line trajectory, respectively. The variation of the sensor measurements changes with the magnetic moment directionality, and can be analyzed accordingly. Figure 8: Measurements of the magnetic field magnitude \(B_{v}\) and \(B_{h}\) derived from different PM by sensors distributed on the sensor array edge and center. **(a)** The magnetic field magnitude measured by sensor S1 on the sensor array edge of the spiral line trajectory; **(b)** The magnetic field magnitude measured by sensor S4 on the sensor array center of the spiral line trajectory; **(c)** The magnetic field magnitude measured by sensor S8 on the sensor array edge of the spiral line trajectory; **(d)** The magnetic field magnitude measured by sensor S1 on the sensor array edge of the folding line trajectory; **(e)** The magnetic field magnitude measured by sensor S4 on the sensor array center of the folding line trajectory; **(f)** The magnetic field magnitude measured by sensor S8 on the sensor array edge of the folding line trajectory. By comparison of the magnetic field of the sensors located on the sensor array edge derived from PM\({}_{v}\) and PM\({}_{h}\) in Figure 8a,c,d,f, we can see that the contributions from PM\({}_{v}\) and PM\({}_{h}\) exchange, implying the effect brought by the \(T\)-type PM marker. In Figure 8a, the contribution from PM\({}_{h}\) is larger than that from PM\({}_{v}\) for samples 1 to 32, and the situation reverses afterwards. The same performance can be found in Figure 8c,d,f, where the sensor is located on the sensor array edge. In contrast, when the magnetic fields are observed by the sensor located on the sensor array center, the contribution from PM\({}_{v}\) remains dominant. The variation among the different contributions of the measurements by the sensors located in different regions verifies the role of the combined \(T\)-type PM in weakening the influence of the magnetic moment's directionality.
###### Abstract  
Accurate extraction of impermeable surfaces is important for assessing land use change and improving the urban heat island effect. Nighttime light imagery has the advantage of being efficient and cost effective, providing a new perspective for monitoring and extracting impermeable surfaces and analyzing urban expansion processes. However, for the vast Karst terrain fragmentation area located in southwest China, the extraction of impermeable surface information faces many challenges due to surface landscape fragmentation and nighttime light image resolution. These challenges include light spillover, oversaturation and limited understanding of spatial links with surface types at fine scales. This study uses Luojial-01, NPP-VIIRS, and Flint as remote sensing data sources to examine the applicability of nighttime light images in extracting impermeable surfaces from geomorphologically complex areas. The results show that Luojial-01 data can provide finer spatial details and more accurate impermeable surface extraction results than NPP-VIIRS and Flint data. The relative error of extracted area in regions with large topographic relief is higher than that in regions with flat topographic cuttings. The extraction results of the three images are spatially similar; however, the overall accuracy is poor, and a single nighttime light image is not the best solution for obtaining impermeable surface information in large scale terrain fragmentation areas. However, the integrated application of multi-source light images is a trend for future regional research and development, and the best way to extract impermeable surfaces in complex terrain areas should be explored in conjunction with other remote sensing data sources in the future.  
Luojial-01; NPP-VIIRS; flint; impermeable surface; karst mountains; geomorphologically complex area +
Footnote †: journal: Applied Sciences
## 5 Simulation Results and Discussion
### Results and Discussion under Different Scenarios
#### 5.2.4 Discussion  
The results from the case study under different EV penetration levels show that the voltage deviation problem gets worse when EV penetration ranges from 30% to 100% in uncoordinated charging, and the voltage unbalance problem get worse when EV penetration ranges from 75% to 100% with uncoordinated charging. The results show that all  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**100\%** & **Winter** & \
\begin{tabular}{c} **Uncontrolled** \\ **Charging** \\ \end{tabular} & **Method 1** & **Method 2** & **Method 3** &
\begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular} \\ \hline \multirow{4}{*}{Weekday} & Max VUF (\%) & 2.1 & 2.2 & 4 & 2 & 1.98 \\  & Min Voltage (p.u) & 0.825 & 0.94 & 0.94 & 0.95 & 0.97 \\  & Max Voltage (p.u) & 1 & 1 & 1 & 0.98 & 1.01 \\  & Total Cost (EUR/day) & \(-3.00\) & \(-2.70\) & \(-2.47\) & \(-4.15\) & \(-1.723\) \\ \hline \multirow{4}{*}{Weekend} & Max VUF (\%) & 2 & 2.25 & 3.8 & 2 & 1.98 \\  & Min Voltage (p.u) & 0.89 & 0.96 & 0.97 & 0.97 & 0.99 \\ \cline{1-1}  & Max Voltage (p.u) & 1.01 & 1.03 & 1.01 & 1.01 & 1.02 \\ \cline{1-1}  & Total Cost (EUR/day) & \(-2.33\) & \(-2.11\) & \(-2.42\) & \(-4.18\) & \(-1.76\) \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of result of the case study under different load profiles.  
Figure 20: Bus 846 (**a**) voltage and (**b**) VUF for weekend electric load profiles.  
of these problems are reduced with the proposed method and that the final desired SOC is maintained.  
In the case study under different EV price signals, the impact of voltage deviation and the voltage unbalance problem is worst with uncoordinated charging. The proposed method schedules times for charging/discharging of both the active power and reactive power of EVs in such a manner that it largely improves voltage profile and reduces VUF with customer benefits compared to the uncoordinated charging.  
In the case study under a weekend load profile, the voltage unbalance problem does not occur with uncoordinated charging but the bus voltage is below the desired limit. The proposed method is capable of scheduling EVs to improve both the system and customer requirements.  
However, battery degradation costs with the proposed method, in all case studies, are a bit higher than with uncoordinated charging, Method 2, Method 3, and Method 4, but the benefits from the total costs are better with proposed method, which helps to outweigh this drawback.  
Method 2 is exclusively designed to minimize the charging costs and battery degradation costs and thus the total cost is the second lowest after the proposed method. However, as the researchers do not control VUF, its value is high compared to other methods for all seasons. This shows that EVs with V2G have to consider VUF failure, which can lead to deterioration of the system.  
This study shows that battery degradation has little impact on voltage deviation. However, it is an important objective to consider as it directly affects battery lifetime.
# Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models
## Materials and Methods
### Machine Learning Models
#### 2.2.1 Extreme Gradient Boosting  
Gradient boosting is a meta-algorithm built on the basis of weak learners, like decision trees [19], with the aim of obtaining a strong ensemble learner [46; 47]. Specifically, XGBoost is a scalable and efficient way to carry out the implementation of a gradient boosting algorithm. It is remarkable for its ease of implementation and its high accuracy in predictions [20]. This algorithm focuses on the idea of combining, in a final step, all the predictions made by a set of weak learners (_additive training strategy_[48]). Moreover, XGBoost is capable of simplifying the objective function with different combinations of predictive and regularization terms without loss in the optimal computational speed [19; 20]. The specific learning process is summarized as follows [18; 20]:  
* An initial learner is fitted to the whole sample of inputs.
* A second model is fitted to the residuals of the first model to reduce its deficiencies.
* These two learning steps are repeated until a particular stop criterion is reached.
* The final predictions are obtained from the sum of the individual predictions of the learners used. The general function to obtain a prediction at step \(t\), with additive training, is presented in Equation (4). \[G_{i}^{(t)}=\sum_{j=1}^{t}G_{j}(\mathbf{x}_{i})=G_{i}^{(t-1)}+G_{t}(\mathbf{x }_{i})\] (4) where \(G_{t}(\mathbf{x}_{i})\) is the learner at step \(t\), \(G_{i}^{(t-1)}\) the prediction at step \(t-1\) and \(\mathbf{x}_{i}\) the input variable.  
On the other hand, to prevent the problem of overfitting while maintaining an optimal computational speed, XGBoost takes into account Equation (5) to evaluate the suitability of the model [17; 20]:  
\[O^{(t)}=\sum_{j=1}^{n}L(\mathbf{\hat{y}}_{i},\mathbf{y}_{i})+\sum_{j=1}^{t} \Psi(G_{i}) \tag{5}\]  
\(n\) being the number of observations, \(L\) a differentiable loss function and \(\Psi\) a regularization term that penalizes the complexity of the model [18].  
XGBoost expands the loss function into the second order to be able to optimize the problem quickly. Furthermore, several specific techniques can reduce the possible overfitting of the algorithm [18]. One of them consists of, after each step of boosting, the weights recently added being scaled by a factor \(\eta\) (_shrinkage_). This process reduces the influence of the individual trees built, and therefore, the training will be slower and more efficient.  
Lastly, XGBoost optimization requires the control of multiple parameters [19], and finding the best combination of them is important. In this analysis, the optimal combinations of parameters (_max_depth_,_min_child_weight_, subsample, _colsample_bytree_ and _learning_rate_) are found through a \(k\)-fold cross-validation method (\(k=10\)) [49] carried out on the different validation samples studied.
## 4 Graphene/Graphene Oxide-Based RF Biosensing Platforms
### Graphene-Based Wireless Mobile Health System  
#### 4.2.1 Graphene-Based Contact Lens  
In general, wireless systems for sensing platforms combine RF modulation, sensing terminal, and analog memory to record the history of various chemical events, even with a single graphene transistor. This is, however, not possible with conventional solid-state electronic devices. In recent years, the advancement of RF biosensors has proposed a practical wireless sensing platform based on graphene for real-time and continuous monitoring of surface chemical events. This biosensing scheme with a back-gated graphene device showed a distinct conversion of an RF signal when introduced to reduce or oxidize chemical gases or more complex substances, such as protein biomarkers [69].  
Figure 6 shows a type of eye-wearable device, smart contact lens, based on an all-graphene harmonic sensor, which could detect biomaterials such as glucose, pathogens, bacteria, and infectious keratitis, for point-of-care (POC) monitoring and real-time wireless biosensing.  
In this sensing scheme, [PERSON] et al. [70] presented the concept of a transparent, flexible, and battery-free wireless sensor consisting of a graphene FET-based circuitry and a transparent graphene antenna (Figure 6a). This integrated RF biosensor benefits many wireless sensing and diagnostic applications, particularly for smart  
Figure 5: (**a**) Schematic of rGO-based sensor and measurement parameters of transmission (S\({}_{21}\)) and reflection (S\({}_{11}\)) coefficient in the two-port measurement system and (**b**) chemical reaction between phenylbutyric acid (PBA) and glucose [66].  
Figure 6: A graphene-based harmonic sensor on a soft contact lens. (**a**) Schematics of a fully passive, transparent, and conformal all-graphene harmonic sensor designed for various point-of-care monitoring and wireless biosensing and (**b**) an eye-wearable device (smart contact lens) based on the all-graphene harmonic sensor (reprinted with permission from [70]).  
and microscope slides that require high sensitivity, high flexibility, and high transparency and must be lightweight (Figure 6b). Moreover, once multiple graphene transistors are used to build a more complicated circuitry, such as a quad-ring topology, the sensor can be activated by wireless power transfer without any need for a battery. Additionally, this biosensing scheme represents numerous possibilities for pervasive health care Internet of Things (IoT) devices and ubiquitous sensor networks [70].  
In addition, graphene can protect eyes from electromagnetic (EM) waves that may cause eye diseases such as cataracts [71]. The lens coated with graphene absorbs the EM energy, and the graphene is released in the form of thermal radiation. Consequently, [PERSON] et al. demonstrated that the damage to egg whites could be minimized (Figure 7a,b) [72; 73; 74].