esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of the Cross-Scale Multi-Feature Stereo Matching Algorithm
## 2 Algorithm Description
### Disparity Computation and Post-Processing  
Conventional algorithms typically adopt the winner-takes-all (WTA) strategy for disparity computation [23], with the calculation method formulated as follows:  
\[d_{p}=\textit{arg min}[\begin{array}{c}C(p,d)\end{array}](de[0,R_{d}]) \tag{10}\]  
where \(R_{d}\) denotes the range of disparity values, and \(C(p,d)\) represents the aggregated matching cost for pixel \(p\) at disparity level \(d\) after cost computation and aggregation.  
Occluded regions in the initial disparity map inevitably induce unmatched areas and erroneous pixel matches during disparity computation. This necessitates post-processing operations to refine disparity estimates and achieve precision-enhanced results.  
Therefore, our pipeline implements the following refinement stages:  
1. Left\(-\)right consistency check to eliminate mismatched pixels,
2. Guided hole filling with valid disparity values for occluded regions,
3. Median filtering to enhance smoothness consistency in weakly textured areas.  
This multi-stage approach effectively addresses:  
1. Discontinuous mismatches caused by residual noise in initial disparity maps
2. Smoothness degradation in low-texture zones
3. Boundary artifacts around occlusion boundaries  
The integrated post-processing framework ultimately yields high-precision disparity maps with subpixel accuracy. Figure 7 shows the flowchart of the stereo matching algorithm.  
Figure 6: Comparative analysis of the baseline versus proposed cost-aggregation maps.  
Figure 7: Schematic diagram of the stereo matching algorithm pipeline.
## 3 Results and Discussion  
### Absorption Properties of the Absorber  
We first investigate the absorption properties of the dielectric-metal metasurface absorber. The absorption (A, red line), and Figure 2a shows the reflection (R, black line) spectra of the absorber with normal incidence. For a thick aluminum plane with thickness larger than the penetration depth of electromagnetic waves in the infrared range, the transmittance of the structure is nearly zero and the absorptance is A = 1 - R. It can be seen that there is a sharp peak in the absorption spectrum with nearly total absorption at the resonant wavelength of 6.134\(\mu\)m, which is caused by the inevitable loss of the aluminum and it is known as perfect absorption at the resonant wavelength [34]. The full width half maximum (FWHM) of the spectrum is 29 nm with high quality factor [29] (Q = \(\lambda/\Delta\lambda\)) of 212, which can provide impressive performance for infrared optical sensing applications.  
Figure 1: Schematic of the surface-enhanced infrared sensor employing narrowband perfect absorber based on dielectric-metal metasurface.  
We further calculate the mode patterns of electric field, magnetic field, and power loss distribution of the absorber at the resonant wavelength to understand physical mechanism of the narrow linewidth and high absorption of the resonant spectra. Figure 2b obviously shows that one part of electric field is trapped inside the dielectric grating strip, while another part is concentrated at the ends on the topside surface and extended deeply into the surrounding medium. This significantly enhances the light-matter interaction at the dielectric grating strip surface and makes it sensitive to the refractive index change of the surrounding medium. Additionally, the maximum electric field intensity is located at the bottom and top of dielectric grating strip, indicating that the electric dipoles interacting with their images at the dielectric-metal interface. This circular displacement currents lead to the magnetic dipole resonance that is depicted in Figure 2c. The results show that the satisfaction of wavevector matching condition involves the surface lattice resonance mode of the strip array, and the guided mode in the dielectric grating [28]. The power loss pattern in Figure 2d shows that the power loss is distributed in the thin domain under the dielectric-metal interface. This can be attributed to the intrinsic loss of metal and the loss-free guided modes in dielectric strip, which results in the narrow linewidth of absorption spectra.
# A Study on Liquid Leak Rates in Packing Seals
## 1 Introduction
The current method used to correlate between different fluids (including liquids) is the simple viscosity ratio method, which is based on leaks conducted with a reference gas (usually helium). There are other more accurate methods that are based on porous structures, difficult to obtain for gaskets and packing seals under different operating conditions. Changes in the size and shape of the porous structure under load and temperature are difficult to measure in a test rig [26; 27; 28; 29]. In addition, the prediction of leakage requires the operating conditions, flow regime and fluid properties to be known. The pressure, temperature, fluid density, dynamic viscosity, surface tension, and multiphase flow are to name a few. In this study, an analytical model based on experimental determination of the porosity parameters used to accurately predict leak rates in packing seals under different operating conditions was proposed. The analytical model was based on fluid flow in capillaries, using the theory of Navier-Stokes, with the first slip boundary condition. The internal structure of the packing was simulated with rectilinear capillaries of unknown size and number, oriented in the stem axial direction. The objective was to predict liquid leak rates based on leakage measurements conducted on the same batch of packing seals under similar conditions, but with a reference gas (helium), from which the porosity parameters of the set of packing were deduced under isothermal steady-state conditions. In the last part of the study, liquid leaks at room temperature--measured experimentally--were compared to the predictions. A homemade liquid leak measuring device was developed to measure down to 0.0001 mL/s. Water and kerosene were selected for the experiments because they have different viscosities and are easy to manipulate.
## 3 Results and Discussion
### CuBTC@PET Characterization Analysis
The XPS results for CuBTC@PET composite fiber membranes at different treatment stages are shown in Figure 9, and Cu 2p, F 1s, O 1s, C 1s, F KLL, and O KLL peaks appeared in the XPS spectra of POTS-PET. This was attributed to the in situ growth of CuBTC bonded to POTS. To further confirm the chemical structure of SCP, peak splitting was fitted to the C 1s narrow spectrum. As shown in Figure 9, the spectra of C 1s fitted to six peaks appearing at 294.1 eV, 291.8 eV, 285.3 eV, 282.1 eV, 289.4 eV, and 286.3 eV, which is attributed to the combination of POTS with CF3, CF2, CC, C-Si, C=O, and C in CuBTC. -O. From the above, it can be demonstrated that the low-surface-energy material POTS has successfully formed a hydrophobic coating layer by immersion.
###### Abstract  
We conducted experiments to improve the electrical properties of the CNW (carbon nanowall), which has lower electrical properties than other carbon allotropes such as graphene and CNT (carbon nanotube), and report the results through this article. The carbon nanowall has an amorphous buffer layer, leading to low electrical properties, and MWCNT (multi-walled carbon nanotube) was used as a buffer layer to improve this issue, and then a CNW was grown on it by CVD (chemical vapor deposition). Then, the content of MWCNT was adjusted to 30 \(\upmu\)L, 50 \(\upmu\)L, and 70 \(\upmu\)L to analyze the electrical properties accordingly. Alteration in carrier concentration, carrier mobility and resistivity were observed as electrical properties. Dramatic changes in electrical properties with MWCNT were identified. The ohmic contact state between the MWCNT-based buffer layer and the CNW was investigated by analysis of the I-V and I-R characteristics and the electrical stability according to the linearity of the curve.  
carbon nanowall; Raman spectroscopy; hall measurement system; carrier concentration; carrier mobility; electrical characteristic +
Footnote †: journal: Applied Sciences
# Hiding Stealth Optical CDMA Signals in Public BPSK Channels for Optical Wireless Communication
## 4 System Performance Analysis with Stealth Signal
\begin{table}
\begin{tabular}{c c} \hline \hline \(L\): range & 1 km \\ \(\Omega\): attenuation & 3 dB/km (clear sky); 8 dB/km (heavy rain) [29] \\
**Dt: transmitter aperture diameter** & 4 cm \\
**Dr: receiver aperture diameter** & 8 cm \\
**\(\Omega\): beam divergence** & 1 mrad \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relative parameters employed for calculating power attenuation. Figure 17: BER versus transmission distance. The BER is found to increase as the transmission distance increases, and it is \(2.4\times 10^{-9}\) when the transmission distance is 320 m on a rainy day. When the sky is clear, a BER of \(1.62\times 10^{-9}\) is achieved at a transmission distance of 380 m.
# Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits
## Abbreviations \begin{tabular}{l l}
3D & Three-dimensional \\ DFIT & Diagnostic fracture injection tests \\ DOE & U.S. Department of Energy \\ DOFP & Date of first production \\ EUR & Estimated ultimate recovery \\ LSTM & Long short-term memory \\ ML & Machine learning \\ MLP & Multilayer perceptron \\ MSE & Mean square error \\ NETL & National Energy Technology Laboratory \\ \(d\) & Initial inter-well distance \\ \(c_{k}\) & Coefficients for the key features related to the frac hits used in the calculation \\ \(f\) & Fracking variable (0: non-tracking; 1: under fracking) \\ \(k\) & Number of features affecting frac hit \\ \(m\) & Number of datasets supplied to the model (either number of training/validation or testing datasets) \\ \(n\) & Number of input features \\ \(O\) & Oil production rate \\ \(o_{i}\) & Oil production rate with interpolation at missing data points \\ \(\dot{o}\) & Time derivative of oil production rate \\ \(\dot{o_{i}}\) & Time derivative of interpolated oil production rate \\ \(p\) & Pressure \\ \(p_{i}\) & Pressure with interpolation at missing data points \\ \(\dot{p}\) & Time derivative of pressure \\ \(\dot{p_{i}}\) & Time derivative of interpolated pressure \\ \(T\) & Size of the time series \\ \(\
u_{k}\) & \(k_{\text{th}}\) input feature \\ \(w\) & Water production rate \\ \(w_{i}\) & Water production rate with interpolation at missing data points \\ \(\dot{w}\) & Time derivative of water production rate \\ \(\dot{w}_{i}\) & Time derivative of interpolated water production rate \\ \end{tabular}   
**Table A2. ** Timeline for Pad 133 wells as counted from the Well 137-42006 DOFP. \begin{tabular}{c c c} \hline
**Well ID** & **Frac Start, Day** & **Frac End, Day** \\ \hline
133-40798 & 91 & 97 \\
133-40854 & 93 & 103 \\
133-40853 & 93 & 106 \\
133-40787 & 100 & 109 \\
133-40845 & 109 & 121 \\
133-40846 & 109 & 121 \\
133-40832 & 113 & 119 \\
133-40847 & 122 & 128 \\
133-41267 & 532 & 539 \\
133-41268 & 532 & 539 \\
133-41261 & 543 & 549 \\
133-41262 & 543 & 549 \\ \hline \end{tabular}   
**Table A3. ** Timeline for Pad 138 wells as counted from the Well 137-42006 DOFP. \begin{tabular}{c c c} \hline
**Well ID** & **Frac Start, Day** & **Frac End, Day** & **DOFP, Day** \\ \hline
138-42651 & 294 & 308 & 335 \\
138-42682 & 294 & 308 & 332 \\
138-42683 & 294 & 308 & 339 \\
138-42751 & 296 & 306 & 333 \\
138-42758 & 296 & 306 & 329 \\
138-42759 & 310 & 324 & 340 \\
138-42761 & 310 & 324 & 337 \\
138-42762 & 310 & 324 & 344 \\
138-42797 & 316 & 327 & 348 \\
138-42805 & 316 & 327 & 347 \\ \hline \end{tabular}   
The ML model architecture is illustrated in Figure A1. **Figure A1. ** Stacked long short-term memory architecture (small model). Figure 12: ML model’s workflow for frac-hit detection and monitoring (midsize model). For predictions, the test data were then fed to the trained model.
## 5 Discussion
### Prospects of the VRPC in Future Years  
Although the VRPC was funded by the National High-Tech Program (863) of China and the budget was limited, the four stations selected in the first stage have formed a solid foundation. Heihe Station, Huailai Station, and Jingyuetan Station were included in the Remote Sensing Experimental Research and Ground-based Observation Network of Chinese Academy of Sciences (CAS-RSON), which was established in April 2014. The Hulunber Station belongs to the CERN. Therefore, these four stations have fixed and continued funding support, which ensures the systematic and continued acquisition of data for regional remote sensing product validation in China.  
Additional efforts are still needed to promote future validation efforts. First, ground-based observation settings and validation strategies need to be discussed and optimized, and general and practical validation standards should be proposed by a global professional team for each remote sensingproduct. Second, the representation and deviation of ground-based observations need to be carefully analyzed, and the scale effect and the scale transformation method need to be resolved in order to properly prepare the validations of remote sensing products based on _in situ_ multi-point or footprint observation data [34]. Third, even though this paper only provides a prototype of the VRPC and there are only four stations in the first stage, more stations are under consideration for the VRPC's stations and core sites. Funding support is anticipated from the Ministry of Science and Technology of the Republic of China (MOST) and CAS. The expanded number of stations will increase the representativeness of typical land covers in China, which will improve the validation level of Chinese remote sensing products. Finally, the optimal use of observational resources from other existing observation networks is critical to increase spatial sample numbers. However, gaps exist between the observation strategies of these networks and the data demands of remote sensing product validation. We aim to reduce these gaps by processing and analyzing the data effectively, which may include optimizing special instrument settings or improving existing observation resources.
# 3D-Printed Accessories and Auxiliaries in Orthodontic Treatment
## 4 Discussion
### Contemporary Use of 3D-Printed Accessories in Orthodontics
#### 4.1.6 Devices for Craniofacial Disorders  
Craniofacial disorders (CDFs) represent one-third of congenital defects and affect the shape and function of the head, face, airways, occlusion, and aesthetics. In the treatment of CDF, multidisciplinary cooperation is required, including orthodontists [56]. Treatment strategies involve a comprehensive approach, incorporating both nonsurgical and surgical interventions to promote the most natural development possible for infant patients with CDF [57]. Some studies suggest that thanks to the advance of digital technologies and the availability of additive manufacturing methods, it is only a matter of time before pre-surgical treatment with 3D-printed appliances becomes a standard [58]. Digital scans provide a safer alternative to conventional impressions for infant patients with craniofacial disorders and the production of clinically relevant devices is possible [59].  
The utilization of computer-aided design in the presurgical treatment of infants with cleft lip and cleft palate relies on digitally altering scanned models of the upper jaw to create a sequence of adjusted models. These models are the base for constructing plates that incorporate the designed alveolar movements. Utilizing these plates sequentially has been shown to achieve presurgical alignment of the alveolar ridge while minimizing the time spent in the dental chair and the number of appointments. CAD/NAM treatment was claimed to be a more effective method that lessens the overall demand for patient care [60; 61; 62; 63; 64]. Other papers studied customized appliances in the treatment of airway constriction due to micrognathia associated with the Pierre Robin sequence while demonstrating an additive manufacturing process, using a 3D integration of intraoral scans combined with segmented CT or CBCT images [56; 57; 59; 65; 66].
## 1 Introduction
Until today a variety of procedures have been adopted for the synthesis of N-TiO\({}_{2}\) such as gas annealing processes (using gases such as ammonia), or by a wet chemical approaches employing thiourea, liquid ammonia, hydrazine or nitric acid as a nitrogen doping source [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40]. Although all such approaches provide efficient photocatalysts with improved performance, however they utilize somewhat complex procedures for the nitrogen doping during the synthesis process. Hence, to overcome the key issue of complex synthesis procedure, and use of risky and unsafe doping precursors such as acids and highly basic ammonia, it is important to develop a synthesis approach which is facile, less complicated and employs no or less toxic doping precursors. In the present study, a facile two-step synthesis strategy has been proposed, employing urea as an inexpensive nitrogen source for nitrogen doping, which can lower the cost of doped TiO\({}_{2}\) for textile wastewater treatment. Urea interestingly also intensifies the anatase phase of TiO\({}_{2}\) when its amount is increased. As well-established, TiO\({}_{2}\) anatase phase is the most photocatalytic-active phase, thus the synergistic effect of urea as a dopant source leads to the formation of narrowed band gap nitrogen-doped TiO\({}_{2}\) with an enriched anatase phase. The photocatalytic performance of the prepared samples was evaluated by their tendency to degrade a simulated MB dye-containing wastewater.
# Valuing Resilience Benefits of Microgrids for an Interconnected Island Distribution System
## 3 Microgrid Case Study
### Sizing and Sensitivity Analysis  
Using the valuation analysis, a microgrid was analyzed to determine the different sensitivities. Different objectives were considered when deciding on the sizes of the solar photovoltaic (PV) generation and energy storage to be installed in both small islands. The microgrid optimization tool HOMER was used to size the DERs.  
The analysis considered two objectives:  
1. Reduction in existing diesel consumption when either Vieques or Culebra is separated from the main island grid.
2. Reduction in excess energy when Vieques and/or Culebra are separated from the main island grid.  
These objectives are considered under four scenarios:  
1. [label=S0., ref=S0., ref=S1]
2. Vietues is isolated.
3. [label=S2]
4. Culebra is isolated.
5. Vietues and Culebra are separated from the main island but interconnected to one another.
6. Blue sky operation (i.e., both islands are connected to one another and to the main island grid).  
To reach a recommendation, a comprehensive search of various microgrid scenarios was conducted. The loading for both islands for one year, using a one-hour resolution, was used. The sizing results are shown in Table 5. These results demonstrate that renewable energy curtailment is unavoidable under practical DER sizes, but scenarios can be optimized to reduce both objectives.  
Figure 3: A simplified representation of the island system (not to scale).  
The research conducted to obtain these results also serves as a sensitivity exercise, as it displays the impact of changing the configuration on the diesel consumption, shown in Figure 4. This exercise considers any excess energy that may result from each case. It suggests that both battery energy storage systems (BESS) and PV systems must be increased in size simultaneously to provide significant benefit; otherwise, we observe diminishing returns. This analysis reveals that for the allotted project budget (not discussed in detail in this paper for confidentiality), the proposed size meets the project requirements.  
Figure 4: Sensitivity analysis results for diesel consumption reduction of different scenarios, (**a**) Viewes microgrid, (**b**) Culebra microgrid, and (**c**) combined Viewes + Culebra integrated microgrids.  
The analyses were conducted assuming a total loss of the subtransmission 38 kV supply (black sky event), which results in major PV generation curtailment, as suggested by Figure 5. However, the curtailed PV energy could be used under blue sky operation to further offset the total energy consumption of both islands when the subtransmission supply is available. For blue sky days, this microgrid will result in a substantial positive flow of renewable energy into the main island grid system.  
Figure 5: Initial feasibility analysis results showing excess energy of different islanded scenarios, (**a**) Viewes microgrid, (**b**) Culebra microgrid, and (**c**) combined Viewes and Culebra integrated microgrids.
# A Switching Observer for State-of-Charge Estimation of Reconfigurable Supercapacitors
## 2 Modeling and Motivation
### System Modeling
#### 2.1.2 Reconfigurable Circuit Modeling  
Let us begin by examining the reconfigurable circuit illustrated in Figure 2. This circuit features two switches, \(s\) controlling the capacitor branch and \(\bar{s}\) governing the bypass path. These switches operate in a complementary fashion, ensuring that only one path is active at any given time. When \(s\) is ON (\(s=1\)), the supercapacitor is connected to the main circuit, allowing for charging or discharging. Conversely, when \(\bar{s}\) is ON (\(\bar{s}=1\)), the supercapacitor is bypassed, effectively isolating it from the circuit and preventing any current flow.  
To develop a comprehensive model for cell balancing, we need to integrate the mathematical representations of both the supercapacitor and the reconfigurable circuit. This integrated model will provide a framework for analyzing and controlling the system's behavior.  
Figure 2: The reconfigurable circuit considering ESR.  
By combining Equations (5) and (7), which describe the supercapacitor's dynamics, with Equations (4) and (6), which capture the circuit configuration, we arrive at the following state-space representation for the supercapacitor within the reconfigurable circuit:  
\[\left\{\begin{array}{l}\dot{SOC}=\frac{s}{c_{eq}v_{rated}}i\\ v=v_{rated}SOC+sri\end{array}\right. \tag{8}\]  
In this representation, \(i\) represents the input current, \(v\) represents the output voltage, and SOC serves as the state variable, reflecting the supercapacitor's charge level. This model reveals that the system exhibits switching behavior, transitioning between two distinct linear subsystems depending on the state of the switches.  
To further clarify the system's dynamics, we can compare Equation (8) with the general form of a state-space representation:  
\[\left\{\begin{array}{l}\dot{x}=A_{\sigma}x+B_{\sigma}u\\ y=C_{\sigma}x+D_{\sigma}u\end{array}\right. \tag{9}\]  
In this general form, \(x\) represents the state vector, \(u\) represents the input vector, and \(y\) represents the output vector. The matrices \(A_{\sigma}\), \(B_{\sigma}\), \(C_{\sigma}\), and \(D_{\sigma}\) define the system's behavior in each subsystem, with the subscript \(\sigma\) indicating the active subsystem.  
By mapping the variables in our specific model (\(x=SOC\), \(u=i\), \(y=v\)) to the general form, we can readily extract the system parameters for each subsystem:  
\[A_{1}=0,B_{1}=0,C_{1}=v_{rated},D_{1}=0 \tag{10}\]  
\[A_{2}=0,B_{2}=\frac{1}{C_{eq}v_{rated}},C_{2}=v_{rated},D_{2}=r \tag{11}\]  
Here, \(\sigma=s+1\) serves as a switching signal, determining which set of parameters is active at any given time. When \(\sigma=1\) (\(s=0\)), the supercapacitor is bypassed, and the system's behavior is governed by the parameters in Equation (10). Conversely, when \(\sigma=2\) (\(s=1\)), the supercapacitor is connected, and the system's behavior is dictated by the parameters in Equation (11).