esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Work  
In this paper, the effect of the TBR-DR programs on reserve and energy scheduling in an isolated residential MG and in the presence of EVs were studied. A two-stage optimization model was developed to minimize the MG operation costs considering RESs and EVs uncertainties. The numerical results revealed that demand-side participation in energy and reserve scheduling reduces the total operating cost in different DR programs. The simulation results also demonstrated that in all TBR-DR programs, the participation of both responsive loads and EVs can reduce the energy cost of DGs and as the result the total operating cost of MG can decrease compared to the case where only DR actions are considered. Comparing the simulation results of TBR-DR programs also demonstrated that in CPP due to a great load reduction at peak price hours, the expected running cost of the system has its lowest value, and as a result, this program could be a proper alternative from the MG operator's viewpoint. In addition, the results showed that due to the uncertainty of EVs, the total cost of scheduling reserve of DGs and DR in each DR program is more than the one in case only with responsive loads. Moreover, it was shown that by increasing the participation of responsive loads in all TBR-DR programs (i.e., to more than 60%), the rate of decrement of expected cost may reduce due to the rebound peak problem.  
Our future efforts will be mainly focused on developing an optimal scheduling model based on real-world uncertainties of DR resources and assessing their effects on islanded MG voltage and frequency security.  
Figure 14: Expected cost of MG versus customers’ participation in different TBR-DR programs.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Attribute**} & \multicolumn{2}{c}{**Case 1**} & \multicolumn{2}{c}{**Case 2**} & \multicolumn{2}{c}{**Case 3**} \\ \cline{2-7}  & **No DR** & **RTP** & **TOU** & **CPP** & **RTP** & **TOU** & **CPP** \\ \hline Expected cost & 897.833 & 872.943 & 881.164 & 850.395 & 866.113 & 878.253 & 853.049 \\ Energy cost of DGs & 436.622 & 416.790 & 420.549 & 410.590 & 403.482 & 413.482 & 400.620 \\ Scheduling reserve cost of DGs & 19.752 & 25.076 & 19.172 & 21.600 & 23.482 & 21.085 & 22.801 \\ Scheduling reserve cost of DR & 0 & 23.856 & 0 & 10.048 & 33.856 & 0 & 10.048 \\ Energy cost of RESs & 443.206 & 443.206 & 443.206 & 443.206 & 443.206 & 443.206 & 443.206 \\ Deployed reserve cost of DGs & \(-2.365\) & 10.182 & \(-2.131\) & 1.536 & 9.923 & \(-2.081\) & 1.189 \\ Deployed reserve cost of DR & 0 & \(-56.785\) & 0 & \(-40.192\) & \(-56.785\) & 0 & \(-40.192\) \\ Start-up cost of DGs & 0.78 & 0.78 & 0.62 & 0.87 & 0.87 & 0.64 & 1.02 \\ Shut-down cost of DGs & 0.27 & 0.27 & 0.18 & 0.35 & 0.35 & 0.25 & 0.46 \\ \hline \end{tabular}
\end{table}
Table 3: MG operating costs (in $) in three cases considering TBR-based DR programs.
# Real-time Precise Point Positioning with a Xiaomi MI 8 Android Smartphone
## 3 Preliminary Analysis
This makes it difficult to assess the degree of agreement between different satellites observations, thus the difference values are calculated with a window of 100 epochs (take the average value of 100 epochs). There are a total of 26 lines in panel (a) of Figure 5, and a total of 23 lines in panel (b), the different colored lines represent different satellites. Although there are some deviations, most of the lines are coincident, and the lines of Xiaomi MI 8 are more coincident. Which indicates that the differences between the change rate of pseudorange observations and the change rate of carrier phase observations  
Figure 4: The observations (Galileo 03 satellite) and its change rate of a Xiaomi MI 8 smartphone. (a) The raw observations; (b) The change rate of observations. of all satellites are consistent. It also can be seen that the differences of change rate are slowly changing during the observation period, and the variation range is about several meters within one hour. Since the influence of the device clock bias on all satellites observations is the same, we believe that estimating two clock biases in positioning process can effectively weaken the impact of the phenomenon that the differences between the pseudorange and the carrier phase observations of mobile phone are not fixed.
# Experimental Study on Shear Strengthening of RC Beams with an FRP Grid-PCM Reinforcement Layer
## 4 Analytical Model of Shear Capacity
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Series**} & \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Experimental Value \(P_{\text{exp}}\)**} & \multicolumn{2}{c}{**Calculated Value \(V_{\text{cal,rod}}\)**} & \multirow{2}{*}{\(P_{\text{exp}}\)/\(V_{\text{cal,rod}}\)**} \\ \cline{3-3} \cline{5-6}  & & **\(P_{\text{exp}}\)** & **\(\varepsilon_{\text{p,max}}\)** & **\(V_{\text{cal,rod}}\)** & **\(\varepsilon_{\text{u,rod}}\)** & \\  & & **(kN)** & **(\textgreater{10}\textasciitif\)** & **(kN)** & **(\textgreater{10}\textasciitif\)** & **(\textgreater{10}\textasciitif\)** \\ \hline \multirow{2}{*}{Series A} & RWC5 & 640 & 10,533 & 552 & 3449 & 1.16 \\  & RHWC5 & 727 & 7853 & 559 & 3449 & 1.30 \\ \hline \multirow{4}{*}{Series B} & RHWC4 & 881 & 10,757 & 636 & 5494 & 1.39 \\  & RHWC6 & 949 & 7188 & 684 & 3374 & 1.39 \\ \cline{1-1}  & RHWC8 & 943 & 5467 & 712 & 2747 & 1.33 \\ \cline{1-1}  & RHWC4\({}^{\prime}\) & 950 & 7412 & 674 & 3172 & 1.41 \\ \cline{1-1}  & RHWC64 & 860 & 6981 & 684 & 3374 & 1.26 \\ \hline \multirow{2}{*}{Literature [50]} & SB1 & 376 & 2100 * & 234 & 2428 & 1.61 \\ \cline{1-1} Literature [36] & L3 & 373 & 4004 & 237 & 1833 & 1.57 \\ \cline{1-1} \cline{2-6}  & SB2-3 & 409 & 2500 * & 246 & 1684 & 1.66 \\ \cline{1-1} \cline{2-6}  & SB3-5 & 351 & 3400 * & 254 & 1218 & 1.38 \\ \hline \hline \end{tabular} Note: symbol * means the actual value beyond the value shown in this table.  
\end{table} 
Table 9: Comparison between calculated value \(V_{\text{cal,rod}}\) results and the corresponding experimental values. (also called effective strain of CFRP grid [38; 40]) and has been explored through regression analysis, in two series of tests. The analysis is shown in Figure 12 and the result is given as:  
\[\varepsilon_{\text{u,grid}}=\left(\frac{1}{0.0352\rho_{\text{g}}+0.0079}\right)^ {2}\times 10^{-6} \tag{13}\]  
where \(\rho_{\text{g}}\) is defined as the cross-sectional area of the CFRP grid per unit length in the horizontal direction. Considering the new definition for the strain, the values for shear can be re-evaluated (labelled as \(V_{\text{cal,grid}}\) and shown in Appendix A) and compared with those obtained previously (\(V_{\text{cal,rod}}\)) in Table 9. The comparison is plot as the chart of Figure 13a. Again, the safety ratios of \(P_{\text{exp}}\) to \(V_{\text{cal,grid}}\) are beyond 1.00 so they satisfy the safety requirement. Similarly, the strain value (\(\varepsilon_{\text{u,grid}}\)) can be re-evaluated, and the values result much closer to the measured ones, as shown in Figure 13b. It can be deduced that the latter calculated system, which is proposed to evaluate the shear capacity for concrete beams externally bonded with a CFRP grid-PCM reinforcement layer, is demonstrated to be a more reasonable approach than the previous method. Figure 12: Effective strain of vertical CFRP grid. Figure 13: _Cont._
# Experimental Study of Interfacial Bond Properties between CGM and Existing Concrete
## 3 Results and Discussion
### Interfacial Bond Strength Analysis
#### 3.2.2 Influence of Existing Concrete's Strength  
Table 5 indicates that the bond strength of the interface increased with the increase in the existing concrete's strength, but the increase rate decreased. For example, for the direct shear specimen with a triangular interface without shear key, when the existing concrete strength increased from C35 to C45 and C55, the interface shear bond strength increased from 3.15 MPa to 3.50 and 3.73 MPa, and the increase rate decreased from 11.11% to 6.57%. For the slant shear specimen with shear key at a shear angle of 45\({}^{\circ}\), when the existing concrete strength increased from C35 to C45 and C55, the interface shear bond strength increased from 11.52 MPa to 14.47 and 15.54 MPa, and the increase rate decreased from 25.61% to 7.39%.  
Figure 6: Typical failure modes of slant shear specimens: (**a**) pure interfacial failure; (**b**) interfacial failure with shear key cut off; (**c**) interfacial failure with substrate and partial CGM failure.
## 6 Systematic Differences Between the LT-1 and Sentinel-1 Satellites
### MDDG of Different Satellites  
The magnitude of MDDG, measured in mm/m, indicates the maximum displacement (in mm) detectable over a 1 m ground distance along the LOS using InSAR technology [34]. A larger MDDG value signifies better InSAR detection performance, enabling the detection of more substantial displacements [36]. To comprehensively compare the differences in MDDG across different wavelengths and resolutions, we analyzed the data, as shown in Figure 12, where the satellite imaging mode is consistent with that in Table 4. It is evident that L-band SAR satellites exhibit significantly larger MDDG values compared to C-band and X-band satellites. Among the L-band satellites, the LT-1 satellite in STRIP1 mode stands out, achieving a maximum MDDG value of 69 mm/m. This value is notably higher than that of other L-band satellites, demonstrating LT-1's superior performance in detecting deformation gradients.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite** & **Imaging Mode** & **Repeat Cycle** & **MDDR (mm/d)** & **Band** \\ \hline \multirow{2}{*}{LT-1} & 3 m/50 km (STRIP1) & 8 d & 4.41 & \multirow{2}{*}{L} \\  & 12 m/100 km (STRIP2) & 20 d & 1.77 & \\ \multirow{2}{*}{ALOS-2} & 3 m/50 km (UB) & 28 d & 1.26 & \\ \multirow{2}{*}{Gaofen-3} & 1 m/10 km (FS1) & 29 d & 0.7 & \\  & 5 m/125 km (SLA) & 24 d & 0.22 & \\ \multirow{2}{*}{Sentinel-1} & 20 m/250 km (IW) & 12 d & 0.1 & \\ \multirow{2}{*}{TanDEM-X} & 3 m/30 km (SL) & 11 d & 0.52 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: MDDR of the common SAR satellites.  
Figure 12: MDDG distribution from different SAR satellites under the variations of wavelength and resolution.  
Moreover, the MDDG of LT-1 in STRIP1 mode, reaching 69 mm/m, is five times as big as that of Sentinel-1, underscoring LT-1's superior capability in detecting larger deformation gradients. This also explains why the interferometric quality of the LT-1 data is higher than that of the Sentinel-1 data in the same region in Figure 7.  
However, it is important to recognize that the theoretical MDDG values, as discussed in Figure 12, represent the maximum possible under ideal conditions. In practical applications, the real MDDG will likely be reduced due to factors such as temporal and geometrical decorrelation, thermal noise, phase noise, and atmospheric water vapor, all of which can significantly impact MDDG. The effects of interferometric coherence and multi-looking during SAR data processing must also be considered, as these factors further influence the MDDG.
# ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition
## 4 Results
### Finger Vein Image Pre-Processing  
In the process of finger vein image acquisition, accurate identification of finger veins is a very difficult task due to the influence of uneven illumination, equipment noise, and other factors. Therefore, before finger vein recognition, it is necessary to pre-process the finger vein datasets to improve the recognition performance. To test the performance of our proposed model to the maximum extent, we only performed region of interest (ROI) localization processing on the finger vein image to maximize the retention of the vein feature information in the original image.  
For the MMCBNU and FV-USM datasets, we used the ROI images included in the datasets for model training, which ensured the reliability of the vein image information. For HKPU and SDUMLA datasets, we adopted the ROI extraction method proposed by [PERSON] et al. [36]. We retained the finger vein area in the image to the greatest extent and conducted ROI localization along the outer tangent direction of the finger. We only removed irrelevant background information in the image, but not the part with unclear vein lines in the original image. Although there were still some problems, such as unclear partial vein texture information and missing vein texture information caused by severe exposure in some images after ROI extraction, the vein image after ROI localization retained the complete vein information of the original image and such data could better show the robustness of the model we proposed. Figure 6 shows the comparison of ROI-localized finger vein images with the original finger vein images from four public datasets.
## 7 Electric Field and Charged Particles Distributions  
Figure 8 shows the electric potential morphology inside the thruster, with black arrows representing the electric field vectors at each grid point. For argon (Figure 8a), the electric potential attains the highest value in the central region with the field lines mostly confined inside the nozzle; the equipotential lines' distribution is such that the electric field is radially predominant at the entrance of the cathode, which constitutes a less-than-ideal case for accelerating the ions in the axial direction.  
For xenon (Figure 8b), the electric equipotential morphology extends further in the azimuthal direction, but a significant electric field radial component weakens the gas flow exhaust speed all along the height of the cylinder. Notice that the cross-sectional area of both cathodes is not equal, and although, for argon, the gas speed is higher than xenon, at the end the highest thrust is observed for xenon due to a higher mass and therefore a more effective momentum transfer.  
The spatial distributions for electrons and ions for the two ionized gases are presented in Figures 9 and 10, respectively. In the case of electrons, the spatial distributions in both working gases are different, but diffusion from anode to cathode is observed. In argon, electrons concentrate mostly near the cathode, while in xenon, electrons tend to the anode and are also constrained in a small central region inside the cylinder nozzle. In the case of the ions distributions, Ar\({}^{+}\) concentrates near the cathode's entry and for Xe\({}^{+}\), they also tend to be constrained in the same small region inside the cylinder cathode as the electrons, and it appears a small concentration at the entrance of the cathode. This is a consequence of the electric potential morphology.  
Figure 7: Fluid velocity components at exit of the EHD thruster (cm/s) for the optimal thruster output: (**a**) thruster with argon as propellant gas; (**b**) thruster with xenon as propellant gas.  
Figure 8: Electric potential distribution (in V units) and electric field (black arrows in V/m units) for the optimal thruster output: (**a**) thruster with argon as propellant gas; (**b**) thruster with xenon as propellant gas.  
Figure 10: Spatial distributions of ion density (m\({}^{-3}\)) for the optimal thruster output: (**a**) density of Ar\({}^{+}\) in the thruster with argon as propellant gas; (**b**) density of Xe\({}^{+}\) thruster with xenon as propellant gas.  
Figure 9: Spatial distributions of electron density (m\({}^{-3}\)) for the optimal thruster output: (**a**) thruster with argon as propellant gas; (**b**) thruster with xenon as propellant gas.
# Generation of Complete SAR Geometric Distortion Maps Based on DEM and Neighbor Gradient Algorithm
## 3 Experiments and Results  
### Study Area and Used Data  
As shown in Figure 3, the study area is in Chongqing, southwest of Sichuan, China [18; 19; 20]. Influenced by the subtropical humid monsoon climate, rainfall is abundant, which has triggered thousands of landslides. However, the landslides are generally located in a mountainous area, which is difficult to monitor by the InSAR technique. Therefore, it is essential to analyze the SGD mechanisms in the study area to select the optimal SAR pairs.  
To test the proposed P-NG method, 1 arc-sec Shuttle Radar Topography Mission (SRTM) DEM data was acquired for the study area. In addition, ascending and descending Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) image pairs were selected to provide the actual SGD map used for comparison (see Table 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Orbit** & **Master** & **Slave** & **B\_Temp (day)** & **B\_Para (m)** \\ \hline
1 & Ascending & 20081201 & 20090116 & 46 & 290 \\
2 & Descending & 20080908 & 20081024 & 46 & 146 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the formed ALOS PALSAR image pairs. B\_para and B\_temp represent the spatial baseline and time interval between the two acquisitions, respectively.  
Figure 2: Interactions of the SGD.
# Use of Indoor Location Technologies in Healthcare Contexts: A Scoping Review
## 3 Results
### General Characteristics  
Although no time restriction was applied, the articles included in the analysis, after using the inclusion and exclusion criteria, were published between 2005 and 2022. The highest publication peak occurred between 2011 and 2013, with an average of nine articles published each year, as shown in Figure 2.  
The average number of authors per article was 5.07, with a maximum of 13 authors and a minimum of 1. This relatively high average indicates that research on indoor location technologies in healthcare typically involves multidisciplinary collaboration, reflecting the complexity and the need for diverse expertise in this field. The median and mode were both four authors, totalling 421 contributors across the analysed studies. Indoor location technologies were applied in various hospital areas. Following the classification  
Figure 1: Prisma diagram for the scoping review.  
proposed by [PERSON] et al. [12], the study areas were characterised as General Hospital/Non-specified (\(n=25\)), Emergency Department (\(n=15\)), and Surgery (\(n=10\)). Other areas included Outpatient (\(n=5\)), Trauma Department and Radiology (\(n=4\) each), Pathology and ICU (\(n=3\) each), and Primary Care, Ophthalmology, and Oncology (\(n=2\) each). The remaining areas had only one study each. A small number of studies have focused on more than one department [26; 27; 28; 29]. There is a noticeable predominance of publications from the United States, with 36, followed by Taiwan and Germany, each with 5. The most published authors were [PERSON] and [PERSON], with four articles each. Highlighting these key contributors helps identify influential researchers and research networks driving advancements in RTLS, which may guide future collaborations and indicate centres of expertise.  
Most articles were classified as Case Studies (68), followed by Literature Reviews (12). There was also one System Development article without a technology application, one Framework Development article, and one Survey article. No scoping reviews were found. Most articles (77) were published in full text, while only six were published as abstracts. In the practical articles, the motivation for implementing RTLS and the characteristics of the institution involved in the case study are consistently reported; however, few justify the choice of a specific technology, lacking a comprehensive literature review on the available options.
## 2 Materials and Methods
### Pre-Processing and Identification of the Forested Areas
#### 2.2.2 Hyperspectral Dataset  
PRISMA data are made up of 66 bands in the visible and near-infrared (VNIR) range (400-1010 nm), 174 bands in the shortwave infrared (SWIR) range (920-2505 nm), which partially overlap, and one panchromatic (PAN) band (400-700 nm). The spatial resolution of the VNIR and SWIR is 30 m, while it is 5 m for the PAN. This study uses a level 2-D product. The data were first processed with a Python script (Prisma Tool) [49] developed by the authors and freely available.  
Using the Prisma Tool, the HDF5 file provided by ASI was converted into the Geo-Tiff format while simultaneously improving the georeferencing of the four corners of the view. As expected from the literature, three windows in the infrared range were completely opaque due to water vapor and other atmospheric gasses, causing a low signal-to-noise ratio. Thus, the data were filtered based on a statistical analysis using R software (v. 4.4.1), by removing the bands in the intervals from 1361 nm to 1449 nm, 1803 nm to 1949 nm, and 2483 nm to 2497 nm. This resulted in a hyperspectral dataset composed of 205 bands (HS_DS).
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 4 Catalytic Applications of Mesoporous Silica Materials
### Organic Synthesis
#### 4.2.11 Ullman Coupling  
The Heck, Suzuki-Miyaura, Sonogashira, and Ullmann reactions have been shown to be excellent methods for generating carbon-carbon bonds through catalyzed cross-coupling reactions [152; 153; 154; 155]. Aryl-aryl bond formation reactions are amongst the versatile strategies involved in the synthesis a wide range of products, including agrochemicals, pharmaceuticals, natural products, and conducting polymers [156; 157; 158]. [PERSON] et al. reported that Pd-poly(N-vinyl-2-pyrrolidone)/MCM-48 NP is an efficient, heterogeneous, recyclable catalyst for the Ullmann cross-coupling reaction. This environmentally friendly catalyst exhibited high catalytic activity, easy separation, and reusability [159]. This reaction was carried out in a DMF medium at 100 \({}^{\circ}\)C and produced good yields. The activation of the C-Cl bond proved to be more difficult than the activations of the other alkyl-halide bonds, and it mostly requires harsher conditions in heterogeneous catalytic systems.