esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Post-Processing of the 3 DP Implant Surgical Guides  
The 3 DP of the reference surgical guides was subdivided into groups depending on their location (upper versus lower arch), resin type, and post-processing (washing and curing method). Two different resin types were utilised, the Asiga DentaCLEAR 385 nm resin (Asiga, Alexandria, New South Wales, Australia. Lot number MO/08427) (\(n=20\)), and the Asiga DentaGUIDE (Asiga, Alexandria, New South Wales, Australia. Lot number MO/10567) (\(n=35\)), to print both lower \(n=10\) and upper \(n=45\) arches of implant surgical guides. Both resins were agitated thoroughly for one minute in the resin bottle before pouring them into a material tray. This was to ensure colour deviation was avoided and print success was achieved.  
All guides were post-processed through washing and post-curing. Five different post-processing methods were performed, with all washes, including pre- and post-washes, using 99.5% isopropanol (Thermo Fisher Scientific Australia, Victoria, Australia) with either hand wash or ultrasonic bath wash in dedicated baths for each resin (Table 2). For handwashing (\(n=20\)), the guides were placed in a plastic bag with 99.5% isopropanol and agitated by the same operator. For the ultrasonic bath, the guides were placed in a beaker with 99.5% isopropanol in the ultrasonic bath (BioSonic UC300 Ultrasonic Cleaner) (\(n=10\)). A combination of hand wash and ultrasonic bath was also used (\(n=25\)).  
Before post-curing, the samples were dried thoroughly using compressed air to ensure the printed parts were free of any alcohol residue. Lastly, post-curing with nitrogen (\(n=50\)) or without nitrogen (\(n=5\)) was performed using the same UV curing unit (Otoflash G171, 280-700 nm, Puretone 3D, Kent, United Kingdom). Each sample required a total of 4000 flashes (2 \(\times\) 2000 flashes), flipping the samples after 2000 flashes in the post-curing box and allowing them to cool for 5 min prior to repeating for a total of 4000 flashes.  
After post-processing, the supports were removed and trimmed with caution using pliers. All samples were stored until they were measured by the same operator using the coordinate measuring machine within two weeks post-printing.  
Figure 3: STL reference images in ASIGA MAX UV slicing software containing implant surgical guides with support structures in place. (**a**) Upper; (**b**) lower.
## 3 Results
### Assessment of Human Health Risks Associated with HMs  
The results of non-carcinogenic and carcinogenic risk assessments for HMs in the sediments are presented in Figures S1 and 7. A descending hierarchy of non-carcinogenic risk was observed across demographic groups: adult males < adult females < children. Quantitative analysis revealed non-carcinogenic hazard indices of 0.40, 0.45, and 1.79 for adult males, females, and children, respectively, with carcinogenic risk values quantified at 3.04 \(\times\) 10\({}^{-7}\), 4.54 \(\times\) 10\({}^{-7}\), and 1.16 \(\times\) 10\({}^{-6}\) for the corresponding groups, suggesting elevated health risks in pediatric populations from both non-carcinogenic and carcinogenic pathways (Figure S1). For all groups (adult men, adult women, and children), the ranking of non-carcinogenic risk indices of different HMs was As > Cr > pb > Ni > Zn > Cu > Cd, while the ranking of carcinogenic risk indices for different HMs was As > Cr > pb > Ni > Cd (Table S4).  
In terms of non-carcinogenic risks, the hazard quotient of arsenic (HQ\({}_{\text{As}}\)) was the highest via the hand-to-mouth ingestion pathway, while the hazard quotient of chromium (HQ\({}_{\text{Cr}}\)) was the highest via the dermal contact pathway, indicating that As and Cr are the primary non-carcinogenic factors (Table S4). The non-carcinogenic risks of HMs to adults are negligible (HI < 1). The non-carcinogenic risk index of As for children is 1.47, which is higher than 1, while the non-carcinogenic risks of the other HMs to children are negligible (HI < 1). This indicates that As poses a non-carcinogenic risk to children. Moreover, the non-carcinogenic risks of HMs to children are higher than those to adults via both exposure pathways, indicating that children are more susceptible to the impacts of HM pollution. The non-carcinogenic risks for different populations are higher via hand-to-mouth ingestion than via dermal contact, suggesting that hand-to-mouth ingestion is the main pathway affecting health risks. In terms of carcinogenic risks, only the carcinogenic risk index of As is significantly higher than 1.00 \(\times\) 10\({}^{-4}\) (Table S4). The carcinogenic risk indices of As for adult males, adult females, and children are 1.09 \(\times\) 10\({}^{-4}\), 1.13 \(\times\) 10\({}^{-4}\), and 1.28 \(\times\) 10\({}^{-4}\), respectively, indicating that As is the primary carcinogenic element.
# An Improved Hybrid Control Scheme of a Switched Reluctance Motor for Torque Ripple Reduction
## 2 The Modeling of SRM and TSF Control  
### Mathematical Modeling  
Ignoring cross saturation, the voltage equation of SRM can be expressed as  
\[\begin{split} U_{p}&=R_{p}i_{p}+\frac{\mathrm{d} \phi_{p}(i_{p},\theta)}{\mathrm{d}t}\\ &=R_{p}i_{p}+\frac{\partial\phi_{p}}{\partial\theta}\cdot\frac{ \mathrm{d}\theta}{\mathrm{d}t}+\frac{\partial\phi_{p}}{\partial t_{p}}\cdot \frac{\mathrm{d}i_{p}}{\mathrm{d}t}\end{split} \tag{1}\]  
where \(\theta\) represents the rotor position angle, and \(i_{p}\), \(\psi_{p}\), \(U_{p}\), and \(R_{p}\) are phase current, phase flux linkage, phase voltage, and the phase resistance of phase \(p\), respectively. The phase number of the motor is \(N_{p}\) = 2 and \(p\) = [1,2] in this paper.  
The mechanical motion equation of the SRM is expressed as  
\[T_{e}=J\frac{\mathrm{d}\omega}{\mathrm{d}t}+D\cdot\omega+T_{L} \tag{2}\]  
where \(\omega\) = d\(\theta\)/d\(t\) is the angular speed, and \(T_{e}\), \(J\), \(D\), and \(T_{L}\) are the total torque, rotational inertia, coefficient of friction, and load torque, respectively.  
The instantaneous phase torque can be calculated according to the derivation of the magnetic coenergy, which is expressed as  
\[T_{p}=\left.\frac{\partial W_{c}(\theta,i_{p})}{\partial\theta}\right|_{i_{p}= \text{const}} \tag{3}\]  
where \(T_{p}\) and \(W_{c}\) are the phase torque of phase \(p\) and the magnetic coenergy, respectively.  
The phase torque based on the torque modeling can be established as [28]  
\[T_{p}=\left[\frac{L_{\text{b}}-L_{\text{c}}}{2}{i_{p}}^{2}+A{i_{p}}-\frac{A}{B }\Big{(}1-e^{-B{i_{p}}}\Big{)}\right]f(\theta) \tag{4}\]  
\[\left\{\begin{array}{l}A=\psi_{m}-L_{\text{b}}i_{m}\\ B=(L_{\text{a}}-L_{\text{b}})/(\psi_{m}-L_{\text{b}}i_{m})\\ f(\theta)=2N_{\text{r}}^{3}\theta^{3}/\tau^{3}-3N_{\text{r}}^{2}\theta^{2}/\tau^ {2}+1\end{array}\right. \tag{5}\]  
where \(N_{\text{r}}\), \(L_{\text{a}}\), \(L_{\text{b}}\), and \(L_{\text{c}}\) are the number of rotor poles, linear inductance before saturation, saturated inductance, and non-saturated inductance, respectively, and \(i_{m}\) and \(\psi_{m}\) are the maximum current and corresponding flux linkage, respectively. For a more intuitive expression, the magnetization curves can illustrate the related parameters obtained from the finite element model, as shown in Figure 1. Correspondingly, \(N_{\text{r}}\) = 14, \(A\) = 0.09, and \(B\) = 0.03 according to simulation calculation.
# Experimental Studies of Scale Effect on the Shear Strength of Coarse-Grained Soil
## 3 Results
### Relationship of Shear Strength Index and Grading Area  
The variation of internal friction angle \(\varphi\) and cohesion \(c\) with grading structure is shown in Figures 10 and 11. With the decrease of grading area \(S\), the cohesion \(c\) and internal friction angle \(\varphi\) gradually increase from 11.1 to 32 kPa and 44.83\({}^{\circ}\) to 59.88\({}^{\circ}\), respectively. The grading area \(S\) continues to decrease, and the cohesion \(c\) and internal friction angle \(\varphi\) show a slightly decreasing trend. The differences between the maximum and minimum values  
Figure 8: Relationship between internal maximum particle size and cohesion of test CGS.  
Figure 9: Relationship between maximum particle size and the internal friction angle of test CGS.  
of the cohesion \(c\) and internal friction angle \(\varphi\) are 65% and 25%, respectively. Obviously, the gradation structure \(S\) has a significant influence on the cohesion \(c\) of the shear strength index of CGS. This may be because when the grading curve area \(S\) is large or small, the filling degree of coarse and fine particles is low. When the grading curve area \(S\) is about 0.4, the filling effect of coarse and fine particles is prospective, and the shear strength of soil material is large.  
As a result, the relationship of the cohesion \(c\), internal friction angle \(\varphi\), and grading curve area \(S\) can be expressed as:  
\[\left\{\begin{array}{l}c=\frac{P_{1}+c_{1}S}{d_{1}+e_{1}S_{2}}\\ \varphi=\frac{1}{1+d_{2}S+e_{2}S^{2}}\end{array}\right. \tag{10}\]  
where \(P_{1}\), \(c_{1}\), \(d_{1}\), \(e_{1}\), \(c_{2}\), \(d_{2}\), and \(e_{2}\) are all equation parameters, and the unit of \(P_{1}\) and \(c_{1}\) is kPa. In particular, \(P_{1}\) is 1 kPa, the unit of \(c_{2}\) is (\({}^{\circ}\)), and \(d_{1}\), \(d_{2}\), \(e_{1}\), and \(e_{2}\) are dimensionless.  
The test data of soil samples A1-4 to A12-4 are fitted according to Equation (10), and the fitting curve is given in Figures 10 and 11. It is found that the fitting curve is in good agreement with respect to the test points. Most of the errors between the fitting value and the test value of \(c\) and \(\varphi\) are less than 5% and 2%, respectively. In particular, the maximum errors are less than 10.52% and 4.59%, respectively, and the determination coefficients are greater than 0.93. Therefore, it can be considered that the influence of scale effect on \(c\) and \(\varphi\) could be quantitatively described by Equation (10).  
Figure 11: Relationship between gradation curve area and internal friction angle.  
Figure 10: Relationship between gradation curve area and cohesion.
## 2 Materials and Methods
### DFT Calculations  
To benchmark the pseudopotentials, first the bulk properties, such as equilibrium lattice parameters and electronic bandgaps, were calculated within accepted accuracy. With the optimized lattice parameters, surface-cells were built along the required directions for both the phases of the hetero-structures. Two types of terminations were considered for LAO substrate, e.g., AlO\({}_{2}\) and LAO terminations for the [001] LAO substrate. The interface model was relaxed using first-principle approaches within the scope of DFT in order to confirm experimental observations theoretically. The DFT calculations have been performed using the Quantum Espresso [24] code using the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional [25] as incorporated in the ultrasoft pseudopotentials. The valence electron configuration for the La, Ti, Al, and O atoms were 5s\({}^{0}\)5p\({}^{1}\)5d\({}^{2}\)6s\({}^{0}\)6p\({}^{1}\), 3s\({}^{0}\)3p\({}^{1}\)3d\({}^{2}\) 4s\({}^{0}\), 3s\({}^{0}\)3p\({}^{1}\), and 2s\({}^{0}\)2p\({}^{1}\), respectively. For the optimization of the heterostructure models, the plane-wave wavefunctions cutoff and the charge-density cutoff were set as 35 Ry and 210 Ry, respectively. The ionic positions and the lattice parameters were relaxed using the BFGS (Broyden-Fletcher-Goldfarb-Shanno) algorithm until the forces on each atom were converged below 1 m eV/A (1.6 \(\times\) 10\({}^{-12}\) N) for the bulk calculations. All atoms of the slabs were fully relaxed until the force on each atom reached less than 0.01 eV/A (1.6 \(\times\) 10\({}^{-11}\) N).
## Generation of Phase-Stable Sub-Cycle Mid-Infrared Pulses from Filamentation in Nitrogen  
**[PERSON] \({}^{*}\) and [PERSON]**  
Laser Research Center for Molecular Science, Institute for Molecular Science, 38 Nishigonaka,  
Myodaiji, Okazaki 444-8585, Japan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +81-564-55-7339; Fax: +81-564-53-5727.  
_Received: 10 December 2012; in revised form: 26 January 2013 / Accepted: 29 January 2013 / Published: 6 February 2013_  
**Abstract:** Sub-single-cycle pulses in the mid-infrared (MIR) region were generated through a laser-induced filament. The fundamental (\(\omega_{1}\)) and second harmonic (\(\omega_{2}\)) output of a 30-fs Ti:sapphire amplifier were focused into nitrogen gas and produce phase-stable broadband MIR pulses (\(\omega_{0}\)) by using a four-wave mixing process (\(\omega_{1}+\omega_{1}-\omega_{2}\rightarrow\omega_{0}\)) through filamentation. The spectrum spread from 400 cm\({}^{-1}\) to 5500 cm\({}^{-1}\), which completely covered the MIR region. The low frequency components were detected by using an electro-optic sampling technique with a gaseous medium. The efficiency of the MIR pulse generation was very sensitive to the delay between the fundamental and second harmonic pulses. It was revealed that the delay dependence of the efficiency came from the interference between two opposite parametric processes, \(\omega_{1}+\omega_{1}-\omega_{2}\rightarrow\omega_{0}\) and \(\omega_{2}-\omega_{1}-\omega_{1}\rightarrow\omega_{0}\). The pulse duration was measured as 6.9 fs with cross-correlation frequency-resolved optical gating by using four-wave mixing in nitrogen. The carrier-envelope phase of the MIR pulse was passively stabilized. The instability was estimated as 154 mrad rms in 2.5 h.  
**Keywords:** filamentation; infrared; ultrashort pulse; four-wave mixing
## 3 Results and Discussion
### PCDD/F and DL-PCB Concentrations
Compared with the PCDD/Fs, the DL-PCBs had almost the same congener pattern among the 11 sampling sites. The most dominant congener was 2,3\({}^{\prime}\),4,4\({}^{\prime}\),5-pentaCB (#118: 39.5%-54.8%) after 2,3,3\({}^{\prime}\),4,4\({}^{\prime}\)-pentaCB (#105: 15.0%-23.9%) (Figure 4, Table S2). This trend indicated that the contamination source of DL-PCBs was almost similar among the 11 sampling sites. This congener concentration composition was similar to that of mussels in previous studies [28, 43, 44]. However, 3,3\({}^{\prime}\),4\(A^{\prime}\)-TeCB (#77: 7.6%-22.4%) existed in a relatively higher percentage compared with the previous research. In the study by [PERSON] [28], a higher concentration of 3,3\({}^{\prime}\),4\(A^{\prime}\)-TeCB (#77) was observed from sediment compared with the analyzed fishes. The wharf reach lived on a supratidal zone (terrestrial field); therefore, the exposure pathway may be different from the marine organisms. Exposure via soil has a higher importance with regards to 3,3\({}^{\prime}\),4\(A^{\prime}\)-TeCB (#77) pollution in terrestrial organisms compared with marine organisms. This constant trend was not correlated with the log P\({}_{\rm ow}\) value of each DL-PCBs among the 11 sampling sites ([PERSON]'s rank correlation, \(p\) = 0.215-0.675). heights between the two sites is high (100). The NOR sample was also separated from the other sites. The separation of these three sites from other sites was induced by high relative concentrations of Al. The other eight sites were more closely clustered. In particular, there are two small clusters: NOT, SAD, and ONA; and TAT, USU, and ISH. The clustering with dioxins (Figure 5b) also indicate that HAC and GAM were different from the other nine sites. These differences were likely the result of significantly higher concentrations of DL-PCBs (total DL-PCBs: 9222 and 11459 pg/g-dw, respectively). The ONA sample was secondly separated from other eight sites, and completely far from HAC and GAM. Compared with the results of heavy metals, the other eight sites were more clustered, forming two groups: NOT, SAD, TAT, AOM, and NOR; and USU, ISH, and MAT. The different grouping patterns that emerge when the analyses are performed with heavy metals and dioxins suggest differing contaminant sources, accumulation patterns, and mobilities in water. This is particularly the case with the HAC and GAM sites.
# Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
## 2 Materials and Methods
### Burn Severity Indices
For Sentinel, this means all indices derived from NBR (i.e., dNBR, RBR, and RdNBR) were mapped at a pseudo-resolution of 10 m using a combination of the 10 m _NIR_ band and the 20 m _SWIR2_ band resampled to 10 m, while RdNDVI was mapped at true 10 m resolution. Indices derived from Landsat were mapped at the native 30 m resolution. All indices were calculated in the cloud-based remote sensing platform Google Earth Engine (GEE) [67] for both the paired scene (Section 2.3) and composite approach (Section 2.4).
## 3 Methods
### Decoder Block  
The decoder block involves two stages. In the first stage, the feature map is obtained by up-sampling through a 2\(\times\) bilinear up-sampling and convolution block operation. This feature map is then divided into multiple 1 \(\times\) 3 \(\times\) 3 convolution blocks of the same size, which are spliced according to the depth dimension. The processed feature maps are obtained by fusing the residual tensor through the convolution layer. Subsequently, the SE (Squeeze-and-Excitation) module is applied to the processed feature map to calibrate the feature map and reduce the depth dimension feature to 1, resulting in the final feature map. The SE module utilizes the squeeze-excitation mechanism to adaptively adjust each feature by weighting the channel features. This adaptive channel recalibration enhances the network's response to important features and improves the quality of feature representation. Additionally, the SE module reduces the dimensionality of feature depth by compressing the channel dimension of the original feature map to 1 dimension. This compression is achieved through a global average pooling operation on each channel feature value, significantly reducing feature dimension while preserving key channel-level information. In summary, the SE module serves two key roles: enhancing the quality and expressive ability of feature maps through adaptive channel recalibration and compressing the feature depth to 1 dimension. The final 1-dimensional feature map produced by the SE module is used as input for the next step.  
Figure 6 depicts the network structure of the De-1 module, which is designed to accept 4 input features. The input feature out1 undergoes three 1 \(\times\) 3 \(\times\) 3 convolutions, while out2 undergoes two 1 \(\times\) 3 \(\times\) 3 convolutions. Out3 undergoes a single 1 \(\times\) 3 \(\times\) 3 convolution and is then concatenated with out4 in the channel dimension. The combined features are then processed through the se module to produce the output of the De-1 module.  
As shown in Figure 7, the output of decoder block De-1 is combined with the MFCF-3 module processed feature graph through two \(1\times 3\times 3\) convolutions and then enters the SE attention module to obtain the final feature map.  
As shown in Figure 8, The output of decoder block De-2 is combined with the processed feature graph from the MFCF-2 module through two \(1\times 3\times 3\) convolutions. Subsequently, it enters the SE attention module to generate the final feature map, as illustrated in Figure 8. In Figure 9, the output of decoder block De-3 is combined with the MFCF-1 module processed feature graph through two \(1\times 3\times 3\) convolutions and then enters the SE attention module to obtain the final feature map.  
Figure 6: First stage decoder block De-1.  
Figure 7: Second stage decoder block De-2.
# Electronic and Transport Properties of Epitaxial Graphene on SiC and 3C-SiC/Si: A Review
###### Abstract  
The electronic and transport properties of epitaxial graphene are dominated by the interactions the material makes with its surroundings. Based on the transport properties of epitaxial graphene on SiC and 3C-SiC/Si substrates reported in the literature, we emphasize that the graphene interfaces formed between the active material and its environment are of paramount importance, and how interface modifications enable the fine-tuning of the transport properties of graphene. This review provides a renewed attention on the understanding and engineering of epitaxial graphene interfaces for integrated electronics and photonics applications.  
Keywords:epitaxial graphene; SiC; 3C-SiC on Si; substrate interaction; carrier concentration; mobility; intercalation; buffer layer; surface functionalization +
Footnote †: journal: Applied Physics Letters B
## 2 Materials and Methods
### Datasets
#### 2.2.4 Ancillary Data  
The global land-cover maps with 30 m spatial resolution were made using Landsat time-series data [34]. An exclusive land-cover classification system was developed that contains ten primary biomes (e.g., cropland, forest, grassland, shrubland, wetland, water, tundra, impervious surface, bare land, and snow/ice). The first five biomes are considered as vegetation type pixels and other species are considered as nonvegetation type pixels. Subsequently, the ratio of vegetation pixels (RVP) [36] is calculated by using the spatial aggregation method under a 500 m spatial resolution grid, which reflects the LCH characteristic more comprehensively.  
DEM Data  
The terrain data were derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version 2 (ASTER-GDEM V2) with a spatial resolution of 30 m. To reflect the complexity of the topographic features in the TRSR, this study introduces the terrain niche index (TNI) [37] with the following equation:  
\[T=\log[(\frac{E}{E}+1)\times(\frac{S}{S}+1)] \tag{2}\]  
where \(T\) is the TNI; \(E\) and \(\overline{E}\) represent the elevation with 30 m spatial resolution and the elevation aggregated to 500 m resolution using the spatial averaging method, respectively.  
Similarly, \(S\) and \(\overline{S}\) perform the slope with 30 m and 500 m spatial resolution, respectively. By employing the above equation, the original topographic properties (i.e., elevation and slope) can be described by TNI. The areas with low TNI values represent low elevations and flat terrains, and the areas with high TNI values indicate high elevations and sloping terrains, whereas regions with middle TNI values mean the other cases.  
The terrain and land-cover data were firstly projected, mosaicked, and aggregated to match the GLASS grid, then terrain and surface heterogeneity metrics were extracted based on a 500 m grid by using zonal statistics. Subsequently, the RVP metrics were then divided into 10 (0-10%, 10-20%,..., 90-100%) groups based on an interval of 10% (Figure 3a). Similarly, the TNI was divided into 12 groups (0-0.2, 0.2-0.4,...,>2.2) based on an interval of 0.2 (Figure 3b).