esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Results
### Improved IDW Interpolation and Mapping
#### 3.3.1 Development of an Improved IDW Interpolation Algorithm
Figure 8b shows the example of the application of the improved IDW interpolation with 250 m of the maximum horizontal search distance, 2 m of the maximum vertical search distance, 1 m of the assessment depth, 100 m of the elevation exaggeration factor, 12 of the maximum neighbor number and 2 power as the parameters. It is shown that as opposed to FD, one neighbor is added in the diagonal direction so that four neighbors are used in IDW interpolation. In the final stage, an Excel spreadsheet, which includes the assessment grid point coordinate, the z coordinate to which the elevation exaggeration factor was applied, IDW interpolation result, liquefaction assessment grade, and the number of neighbors, is returned. Here, the grade is 'High' if the IDW interpolation result is less than 1, 'Moderate' if it is between 1 and 2, and 'Low' if it is 2 or above. Figure 9 shows the algorithm developed in this process. Using the developed algorithm, 12,340 assessment grids and 9615 boreholes in Songpa-gu, Seoul were used in interpolation with different parameters by NP and FD, and the results are shown in Table 1. The area with NoData is the area with no borehole data around or is far from the assessment grid, and the result showed that in the study area, NP uses the nearest neighbors most, compared to FD, which searches for neighbors within the fixed distance, and could further reduce the frequency of NoData in the resulting outcome. However, such a difference is due to the different characteristics of the methodology, and thus, there is a limit over which to decide the criteria of the interpolation performance assessment based on the frequency of NoData alone. If data is widely distributed or the observation data is lacking, NP could be an alternative. However, as the estimation is performed using the value of too faraway neighbors, it is necessary to limit the suitable search distance, and if the data is sufficiently dense, FD could produce a better outcome. Nevertheless, frequency of 'NoData' was minimized by performing interpolation repeatedly over the estimation results when the results include 'NoData' in this study (Table 2). As the result of the experiment, repeated interpolation was applied from a  
Figure 9: Improved IDW algorithm using 3D search neighborhoods. minimum of 2 to a maximum of 5 times to all the results, excluding NP\(-\)III, and it shows that as the search distance shortens, the repetition of interpolation increases.
# Numerical Simulation of Red Blood Cell-Induced Platelet Transport in Saccular Aneurysms
## Mathematical Model  
### Governing Equations  
Without the electro-magnetic or chemical effects, for a two-component fluid system the governing equations contain the conservation equations for mass, linear and angular momentum. To consider the conservation of the platelets concentration, we apply an additional convection-diffusion equation.
## 4 Software  
### Communication Protocols  
The communication sequence is carried out as follows:  
1. The IoT-GL-Sensor generates the data, i.e., the percentage of the LPG level in the tank and the remaining percentage of the battery charge, and sends them to the Internet gateway using MQTT protocol [41]. MQTT is a lightweight, publish-subscribe-based messaging protocol. This means that clients, such as individual devices or gateways, publish their data to a centralized MQTT broker. In addition, the ESP maintains stable connections over MQTT that it offers smooth performance even in environments with unreliable and fluctuating network coverage and MQTT's low bandwidth requirements help the ESP to ensure reduced power consumption. Using libraries such as OpenSSL, the ESP handles safe transfer of data over MQTT (FTPS, HTTPS). It is essential for IoT applications as data privacy is a priority. As a consequence, MQTT is widely used in IoT applications.
2. The Internet gateway acts as a communication bridge between the IoT-GL-Sensor and the remote server or IoT cloud. For secure data transfer, it uses the HTTPS protocol. HTTP is a protocol to transfer data over the internet. When that data are encrypted with SSL/TLS, it is called HTTPS [42]. The HTTPS protocol encrypts the data transmitted between the client and the server using SSL/TLS encryption to ensure that it cannot be compromised or stolen by unauthorized parties, such as a hacker or cybercriminals [43].
3. The server warehouse stores the data and provides an API. The API_REST uses the HTTP protocol and uses the JSON format for data transfer. It allows the mobile app or gas company's web platform to make requests over the Internet to access the data. The interface uses security mechanisms such as authentication using a username and password. The authentication system consults a user directory, which is stored on the server. If the credentials match, the user is allowed to access the data.
# An Effective Directional Residual Interpolation Algorithm for Color Image Demosaicking
## 3 The Proposed Demosaicking Algorithm  
Our proposed demosaicking algorithm, which is a high-performance algorithm, is based on MLRI. We designed a new method that could effectively calculate the horizontal and vertical weights. It can also combine and smooth the horizontal and vertical color difference estimations. It improves the visual effect of the complex texture region. Compared with four directional weights in MLRI, the amount of calculation in the proposed demosaicking algorithm is efficiently reduced.  
It is vital to interpolate the missing G pixel values because the peak sensitivity in HVS lies in the wavelength of the green light. Since the missing G pixel values are the key to the performance of the proposed algorithm, we first estimate the G pixel values at each pixel. Then we estimate the R and B pixel values, which are based on the interpolated G pixel values.  
Similarly, compared with MLRI, the interpolation processes of the missing G pixel values can be divided into three steps in the same way as that in MLRI [21].  
1. The linear color difference interpolation in [PERSON]'s interpolation equation [15] can be replaced by MLRI [21]. Then the new equation estimates the G, R, and B pixels in horizontal and vertical directions, so we can get \(G^{\text{H}}\), \(G^{\text{V}}\), \(R^{\text{H}}\), \(R^{\text{V}}\), \(B^{\text{H}}\), and \(B^{\text{V}}\).
2. The horizontal and vertical color difference estimations are calculated, and we can generate the horizontal and vertical weights at each pixel.
3. To get color difference, the horizontal and vertical color difference estimations are combined and smoothed by two directional weights. As a result, the G pixel values at the location of R and B pixels are generated by adding final color difference to the observed R or B pixel values.  
To sum up, the outline of the interpolation processes of the missing G pixel values is shown in Figure 3.
## 2 Methodology  
The ABW model is chosen to adjust the optical parameters of the BDS-3 MEO satellites. Based on the adjusted parameters, an a priori SRP model of the satellite cuboid body is established. Finally, the ECOM1 model is used to absorb the remaining SRP acceleration introduced by the solar panels and other unmodeled accelerations. Both ISL observation and L-band measurements are used in the SRP modeling. The corresponding SRP model, observation model, and modeling strategy are described in this subsection.
# 3L-YOLO: A Lightweight Low-Light Object Detection Algorithm
## 2 Methods
### 3L-YOLO Model
#### 2.2.1 Improved C2f Module by Switchable Atrous Convolution  
In low-light environments, the object signal intensity in images is typically weak and often submerged in the background, making it challenging to extract rich local features. Therefore, leveraging global context information is crucial. Using convolutional kernels with a large receptive field helps enhance the model's ability to capture global information, thereby compensating for the lack of local details. To address this, we integrate switchable atrous convolution (SAConv) [41] into the C2f module of YOLOv8n to enhance its ability to extract global context features. Specifically, in the C2f module, all convolutions, except for the first and last, are replaced with SAConv, while the remaining structure of the C2f module remains unchanged. SAConv captures multi-scale contextual information by applying convolution kernels with varying dilation rates, thereby expanding the receptive field without increasing the number of parameters. The spatial attention mechanism improves the signal from low-light object regions, strengthens object features, and suppresses background noise. The enhanced C2f module is shown in Figure 3.  
Figure 2: The architecture of the proposed 3L-YOLO.  
SAConv [41] consists of three components: the pre-global context component, the switchable atrous convolution component, and the post-global context component, as shown in Figure 3. In the global context component, the input features undergo a global pooling operation to produce a feature map of size \(1\times 1\times\) C. This is followed by a \(1\times 1\) convolution for feature channel fusion, after which the features are sent to a residual module for further fusion. In the switchable atrous convolution component, a switching function is generated using an average pooling layer with a \(5\times 5\) kernel, followed by a \(1\times 1\) convolution. Two atrous convolutions with different dilation rates are applied to the input features, and the resulting features are combined. The switching function assigns different weightings to the two atrous convolutions, allowing for adaptive feature extraction at multiple scales. The principle of SAConv can be expressed by the formula:  
\[y=S(x)Conv(x,w,1)+(1-S(x)Conv(x,w+\Delta w,r)) \tag{1}\]  
where \(r\) is a hyperparameter of SAC, set to \(r=3\) in the experiment. \(\Delta w\) is a trainable weight, and \(S(x)\) is the switch function.
## 3 Results and Discussion
### Flexural Strength  
The flexural strength test of cement mortars was conducted after 7 and 28 days of curing. The results are presented in Figure 8. The control series MS achieved a flexural strength of 7.5 MPa after 7 days of curing and 8.9 MPa after 28 days, representing the highest values among all series.  
The series modified with MSWI-FA waste in amounts ranging from 10% to 20% showed a reduction in flexural strength of 25-29% after 7 days compared to the MS series. After 28 days, these results were 22-28% lower than the control. For the series modified with CKD waste, a smaller reduction in the tested parameter was observed compared to the MS series. After 7 days, CKD-10, CKD-15, and CKD-20 achieved values that were 20%, 15%, and 11% lower, respectively. After 28 days, the differences were 19%, 18%, and 17.5%, respectively. In the series using a mixture of MSWI-FA and CKD, the greatest reduction in flexural strength was observed, both after 7 and 28 days. The reductions after 7 days  
Figure 7: Average compressive strength tested after 7 and 28 days of curing: series where each waste was added separately (**a**); series where wastes were mixed in the specified proportions (**b**).  
ranged from 31% to 39%, while after 28 days, the decreases were between 30% and 34%. For MSWI-FA-CKD/3-3 and MSWI-FA-CKD/3-17, the reductions after 7 days were 17% and 29%, respectively, compared to the MS series, while after 28 days, the decreases were 20% and 27%, respectively. The results indicate that the addition of CKD in combination with MSWI-FA significantly lowers the flexural strength of mortars, particularly in the early stages of curing.
# The Linac Coherent Light Source: Recent Developments and Future Plans
## 4 Future Developments and Science Opportunities--LCLS-II-HE
### LCLS-I-HE Science Opportunities
#### 4.2.5 Quantum Materials and Emergent Properties  
There is an urgent technological need to understand and ultimately control the exotic quantum-based properties of new materials--ranging from superconductivity to ferroelectricity to magnetism. These properties emerge from the correlated interactions of the constituent matter components of charge, spin, and phonons, and are not well described by conventional band models that underpin present semiconductor technologies. A comprehensive description of the ground-state collective modes that appear at modest energies, 1 meV-100 meV, where modern X-ray sources and spectrometers lack the required combination of photon flux and energy resolution, is critical to understanding quantum materials. High-resolution hard X-ray scattering and spectroscopy at close to the Fourier limit will provide important new insights into the collective modes in 5\(d\) transition metal oxides--where entirely new phenomena are now being discovered, owing to the combination of strong spin-orbit coupling and strong charge correlation. The ability to apply transient fields and forces (optical, THz, magnetic, pressure) with the time-structure of LCLS-II-HE will be a powerful approach for teasing apart intertwined ordering, and will be a step toward materials control that exploits coherent light-matter interaction. Deeper insight into the coupled electronic and atomic structure in quantum materials will be achieved via simultaneous atomic-resolution scattering and bulk-sensitive photoemission enabled by LCLS-II-HE hard X-rays and high repetition rate.
# Investigation of Laser Cutting Width of LiCoO\({}_{2}\) Coated Aluminum for Lithium-Ion Batteries
## 4 Conclusions  
During lithium-ion manufacturing processes, inefficient conventional cutting technology has been replaced by laser processing since it has many advantages such as a clean cutting edge, less deformation, applicability to almost all materials, possibility of precision processing, and an easy transition of cutting path. Since no clear definition of cutting widths has been presented and there is lack of knowledge to understand the effect of laser parameters on cutting widths in a few previous studies, this study examines the surface morphology of cathode cut by a laser with defining meaningful cutting widths, such as top width, melting width, and kerf width, to investigate the physical phenomena during laser cutting of cathodes. When the volume energy is less than \(6.0172\times 10^{10}\) J/m\({}^{3}\), no active electrode material is removed. When the laser power is greater or equal to 100 W, both the top and melting widths are clearly observed. The laser power of 50 W can selectively ablate the active electrode material with the material removal rate of 32.14-55.71 mm\({}^{3}\)/min. The threshold volume energy to fully penetrate the 50 \(\upmu\)m-thick current collector is between \(9.62752\times 10^{10}\)-\(8.02293\times 10^{10}\) J/m\({}^{3}\). All clearance widths are less than 20 \(\upmu\)m, while the clearance width interestingly exceeds 20 \(\upmu\)m when the laser power is 200 W. To explain the formation of clearance widths, one dimensional transient semi-infinite conduction model is proposed. In addition, five types of physical characteristics are defined and discussed with SEM images. Based on this approach, this study proposes the systematic analysis of laser cutting of electrodes for lithium-ion batteries. This method will be applied to the laser cutting of thicker cathodes for high power applications and transferability of this analysis will be tested for the future research.  
The research described herein was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP; Ministry of Science, ICT & Future Planning) (No. 2017R1C1B507916). This work was also supported by the Hana Technology Co., Ltd, Republic of Korea. The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors. The authors appreciate the support from our sponsors.  
[PERSON] and [PERSON] wrote this paper together.  
The authors declare no conflict of interest.
## 4 Dynamic Response Analysis of the RPC Frames Under Coupled Fire and Explosion  
The finite element analysis software ABAQUS was used to establish the structural finite element model of the RC frame and RPC frame, and the three-story, two-span frame model was established to compare and analyze the dynamic response of the RC frame and RPC frame. The frame model establishment process is as follows: first, the concrete and steel reinforcement skeleton models of beam, slab, and column components are established, and the temperature field is preloaded; among them, the concrete adopts the C3D8R three-dimensional reduced integral unit, and the reinforcement adopts the T3D2 truss unit, and the mesh size of 20 mm is selected for mesh delineation of the model. Next, the dynamics-explicit analysis stage is completed, and the contact type of contact is used to analyze the dynamic response of the RC frame and RPC frame, and the contact type of contact between the steel model and the concrete model is excluded. Bond-slip and the contact type \"embedded\" are used to simulate the interaction between the concrete and reinforcement. Lastly, based on GSA guidelines [33] (the U.S. Public Service Administration developed guidelines for the analysis and design of continuous collapses in 2000), constant and live loads are applied to the model before applying the blast load to the concrete frame model, and the combination of the load is shown in Equation (20). The boundary conditions are fully fixed at the bottom of the column. The predefined field is loaded on the established frame model to simulate the temperature loading, and the high temperature-explosion coupling simulation can be realized by selecting the previously completed frame temperature field simulation file for import:  
\[\text{Load}=\text{DL}+0.25\text{LL} \tag{20}\]  
Figure 13: Plot of horizontal displacement with reinforcement ratio in RPC column span.  
where LL is the structural live load, in kN/m\({}^{2}\); DL is the constant load generated by the self-weight of the structure, in kN/m\({}^{2}\); the constant load of the floor and roof is taken as 5 kN/m\({}^{2}\); and the live load is taken as 4 kN/m\({}^{2}\).  
The frame model is 6 m long in the x-direction and 5 m long in the y-direction. The frame model comprises two floors with a floor height of 3 m. The cross-section size of the columns is 400 mm \(\times\) 400 mm, and the cross-section size of the beams is 300 mm \(\times\) 600 mm. The thickness of the reinforced concrete slab is 120 mm, and the size of the slab is 5 m \(\times\) 6 m. The reinforcement rate of the slab is 1.5%, the reinforcement rate of the beams is 1.5%, and the reinforcement rate of the columns is 1.5%. The explosive equivalent is taken as 50 kg of TNT. The other parameters are identical to those outlined in Section 2, and the column footings are all fully fixed. The meshing of the concrete frame numerical model is shown in Figure 14.
# Application of Virtual Reality in Learning Quantum Mechanics
## 2 Virtual Teaching Modules
### Photoelectric Effect Experiment  
The black body radiation experiment started the study of quantum effects, and then scientists established the foundation of quantum mechanics through several well-known experiments. Among them, the photoelectric effect experiment is the most famous experiment of light quantization, and it is also an important basis for understanding modern physics. A photon with an energy higher than the binding energy of a metal has the opportunity to eject electrons from a metal surface, thereby generating photocurrent, which cannot be explained by classical electromagnetic theory. According to [PERSON]'s formula, if the frequency of a photon is \(\
u\), then its energy is \(E=h\
u\). When a photon hits a metal, if the electron is to be ejected from the metal surface, the binding energy of the metal surface must be overcome for the electron to escape; if there is any excess energy, it will be converted into the kinetic energy of the electron.  
The phototube structure of the virtual photoelectric effect experiment is shown in Figure 4. The electrode that irradiates the light source is called the emitter, where electrons come out. Connecting the cathode of the DC power supply to the emitter and the anode of the DC power supply to another electrode, the electrons will move to the positive electrode to form a photocurrent, so the positive electrode is called a collector. Reversing the voltage between the emitter and the collector of the photocell results in a retarding voltage. If the potential difference between the two electrodes by the retarding voltage is greater than the maximum kinetic energy of the electrons, they are not able to reach the collector, and the photocurrent will become zero. The applied retarding voltage is called the stopping potential, which also depends on the material of the metal surface.  
From the experimental results of the photoelectric effect, it can be found that the photon energy \(h\
u\), binding energy \(\varphi\), and the deceleration potential energy \(eV\) have the following relationship: \(eVh=-\
u\varphi\), where \(\mathrm{e}\) is the electron charge, \(V\) is the deceleration voltage, \(\
u\) is the frequency of incident light, h is [PERSON]'s constant, and the binding energy \(\varphi\) will vary with different metals.  
The virtual experiment of the photoelectric effect designed in this study allows learners to change the frequency of incident light and intensity of the photocell and adjust the deceleration voltage to observe the current change. Learners can determine the metal in the  
Figure 3: Virtual oil drop experiment.  
photocell from the binding energy measured in the experiment and understand that the frequency of light must be higher than the cut-off frequency of the phototube to produce the photoelectric effect, regardless of the intensity of the light source.
# A Comprehensive Study on EDFA Characteristics: Temperature Impact
## 2 Analytical Model
### Gain and Optimum Fiber Length Calculation  
EDFA gain (G) along the fiber length (L) is defined as the ratio of the signal output power at the fiber end (z = L) to the input signal power driven inside the fiber (z = 0), i.e.,  
\[\mathrm{G}=\frac{\mathrm{P}_{\mathrm{s}}(\mathrm{L})}{\mathrm{P}_{\mathrm{s}} (0)} \tag{11}\]  
The optimum fiber length is defined as the typical fiber length designed in order to get maximum EDFA gain. It is important for evaluating the EDFA performance, and can be obtained using the model proposed by [PERSON], [PERSON] et al., and [PERSON] et al. [3; 21; 30]. Another simpler model that fits our simulation parameters and results in a complete accordance between analytical and graphical results is [15]:  
\[\left[\left.\mathrm{G}\right|_{\mathrm{Lopt}}\right]^{\mathrm{\beta}_{ \mathrm{o}}}\mathrm{e}^{-\mathrm{\beta}_{\mathrm{o}}\mathrm{\Gamma}_{ \mathrm{s}}\mathrm{\sigma}_{\mathrm{s}}^{\mathrm{e}}\ \mathrm{N}\ \mathrm{L}_{\mathrm{opt}}}=\frac{\mathrm{A}_{ \mathrm{eff}}\mathrm{h}\ \mathrm{v}_{\mathrm{p}}\mathrm{o}_{\mathrm{s}}^{\mathrm{a}}}{\mathrm{\tau} \mathrm{P}_{\mathrm{p}}(0)\mathrm{\Gamma}_{\mathrm{p}}\mathrm{\sigma}_{ \mathrm{p}}^{\mathrm{a}}\mathrm{\sigma}_{\mathrm{s}}^{\mathrm{e}}}\] (12a) where: \[\mathrm{\beta}_{\mathrm{o}}=\frac{\mathrm{\Gamma}_{\mathrm{p}}\mathrm{\sigma}_{ \mathrm{p}}^{\mathrm{a}}}{\mathrm{\Gamma}_{\mathrm{s}}(\mathrm{\sigma}_{ \mathrm{s}}^{\mathrm{e}}+\mathrm{\sigma}_{\mathrm{s}}^{\mathrm{a}})} \tag{12b}\]