esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Solution to the Problem
### The Case \(\beta\leq\alpha\)  
Equation (32) can be written in following equivalent form  
\[\widehat{\Omega}(\xi,s)=\frac{-k_{0}}{\xi^{2}}\sum\limits_{k=0}^{\infty}\sum \limits_{m=0}^{k}\frac{\frac{(-1)^{k}}{m!}\frac{k!}{(k-m)!}\frac{q_{2}^{m}(\xi )q_{3}^{k-m}(\xi)}{q_{0}^{k+1}(\xi)}}{\left(s^{\beta}+\frac{q_{1}}{q_{0}} \right)^{k+1}}\Big{[}p_{0}s^{(m+1)\beta-1-ka}+p_{1}s^{m\beta-ka-1}+p_{2}s^{(m +1)\beta-(k+1)a-1}\Big{]} \tag{39}\]  
whose inverse Laplace transform is  
\[\hat{\Omega}(\xi,t)=\frac{-k_{0}}{\xi^{2}}\sum\limits_{k=0}^{\infty}\sum \limits_{m=0}^{k}\frac{(-1)^{k}}{m!}\frac{k!}{(k-m)!}\frac{q_{2}^{m}(\xi)q_{3} ^{k-m}(\xi)}{q_{0}^{k+1}(\xi)}\Bigg{[}\begin{array}{c}p_{0}G_{\beta,(m+1) \beta-ka-1+1}\Big{(}t-\frac{q_{1}(\xi)}{q_{0}(\xi)}\Big{)}+p_{1}G_{\beta,\beta,\beta m-ka-1,k+1}\Big{(}t-\frac{q_{1}(\xi)}{q_{0}(\xi)}\Big{)}+\\ p_{2}G_{\beta,\beta(m+1)-a(k+1)-1,k+1}\Big{(}t-\frac{q_{1}(\xi)}{q_{0}(\xi)}\Big{)} \end{array}\Bigg{]} \tag{40}\]  
Applying the inverse Dirichlet-Weber transform to Equation (27), and using the Dirichlet-Weber transform of the function \(f(r)=\frac{1}{r},\ r\in[R,\infty)\), namely,  
\[\begin{array}{c}\hat{f}(\xi)=\int_{R}^{\infty}\frac{1}{r}r[I_{1}(\xi)Y_{1}( \xi)-I_{1}(\xi)Y_{1}(\xi)]dr=\frac{Y_{1}(\xi)}{\xi}\int_{R\xi^{2}}^{\infty}I_{ 2}(t)dz-\frac{I_{1}(\xi)}{\xi}\int_{R\xi^{2}}^{\infty}Y_{1}(z)dz=\\ \frac{Y_{1}(R\xi)}{\xi}[-J_{0}(z)]\big{|}_{R\xi}^{\inftywith the inverse Dirichlet-Weber transform,  
\[f(r)=\frac{R}{r}=\left\{\begin{array}{ll}1,&r=R\\ \int\limits_{0}^{\infty}\frac{I_{1}(r\xi)Y_{1}(R\xi)-I_{1}(R\xi)Y_{1}(r\xi)}{I_ {1}^{2}(R\xi)+Y_{1}^{2}(R\xi)}R\xi\Big{(}-\frac{2}{\pi R\xi^{2}}\Big{)}d\xi,&r> R\end{array}\right. \tag{41}\]  
we obtain  
\[u(r,t)=\left(\frac{-Q}{2\pi T_{f}}\right)\frac{1}{r}+\int_{0}^{\infty}\frac{I_ {1}(r\xi)Y_{1}(R\xi)-I_{1}(R\xi)Y_{1}(r\xi)}{I_{1}^{2}(R\xi)+Y_{1}^{2}(R\xi)} \xi\cdot\hat{U}(\xi,t)d\xi \tag{42}\]  
where \(\hat{U}(\xi,t)\) is given by Equation (34) for \(\alpha,\beta\in(0,1),\ \alpha\leq\beta\), respectively \(\hat{U}(\xi,t)\) is given by Equation (37) for \(\alpha=\beta=1\) and \(\hat{U}(\xi,t)\) is given by Equation (39) for \(\alpha,\beta\in(0,1),\ \beta\leq\alpha\).  
Using the transformation \(u(r,t)=\frac{\partial H(r,t)}{\partial r}\) and the following properties of Bessel functions \(\int I_{1}(z)dz=-J_{0}(z)+C\), \(\int Y_{1}(z)dz=-Y_{0}(z)+C\), we obtain the expression of the normalized depth of groundwater as,  
\[H(r,t)=\left(\frac{-Q}{2\pi T_{f}}\right)\ln(r)-\int_{0}^{\infty}\frac{J_{0}( r\xi)Y_{1}(R\xi)-J_{1}(R\xi)Y_{0}(r\xi)}{J_{1}^{2}(R\xi)+Y_{1}^{2}(R\xi)}\cdot \hat{U}(\xi,t)d\xi \tag{43}\]
## 4 Simulation and Discussion
### Simulation Results of Energy Storage Participating in Demand Response  
**Demand response.** The results of the energy storage configuration and economic indexes are shown in Table 6 with different types of batteries. In addition, each cost and income term for the service with four different energy storage batteries are illustrated in Figure 8. From the results of Table 6, it can be seen that the user-side energy storage providing the demand response auxiliary service is economically profitable. Comparing the index of NPV, IRR and PI, the user can make a fortune by choosing the VRLA battery at the current stage. In the case of a certain revenue, the lower is the energy storage cost, the higher the profit index will be. Meanwhile, VRLA batteries have the lowest energy storage costs.  
**Energy Storage Charging and Discharging Results.** The load power with the best battery selection is shown in Figure 9. The charge and discharge power of VRLA is shown in Figure 10. The battery discharges in the peak period to response the grid command. Due to the low electricity price, the battery charges in the valley period and flat period.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Battery** & \begin{tabular}{c} **Energy** \\ **Storage Rated** \\ **Power (GW)** \\ \end{tabular} & \begin{tabular}{c} **Energy Storage** \\ **Capacity (GWh)** \\ \end{tabular} & \begin{tabular}{c} **Energy Storage** \\ **Cost in Full** \\ **Life Cycle (0)** \\ \end{tabular} & \
\begin{tabular}{c} **Income in Full** \\ **Life Cycle (0)** \\ \end{tabular} &
\begin{tabular}{c} **Recovery** \\ **Period (Year)** \\ \end{tabular} & **NPV (0)** & **IRR (\%)** & **PI (\%)** \\ \hline VRLA & 284.58 & 569.17 & 744,200 & 1,920,200 & 1.96 & 1,176,000 & 48 & 163.07 \\ NAS & 304.9 & 609.82 & 2,062,300 & 3,736,500 & 8.6 & 1,674,200 & 9 & 89.47 \\ LFP & 279 & 206.23 & 2,119,400 & 3,144,800 & 7 & 1,025,400 & 10 & 52.06 \\ V-redox & 304.9 & 609.82 & 2,548,700 & 2,999,300 & 8.7 & 450,600 & 4 & 15.88 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Energy storage configuration and economic evaluation results of demand response.  
Figure 8: Each cost and income term of four different energy storage batteries for demand response.  
Figure 7: Charging and discharging power of VRLA.
# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 3 Results  
This section presents the experimental results obtained from field tests of the RPSC prototypes. The analyses are structured around three key performance areas: GNSS positioning accuracy using RTK corrections from different vendors, trajectory tracking accuracy under various sensor configurations, and the effectiveness of the obstacle detection system. Each sub-section provides a detailed assessment of the corresponding system component, supported by quantitative metrics, graphs, and comparative insights, enabling an evaluation of RPSC performance. Figure 6a shows an RPSC used for testing navigation and motion control algorithms. This platform was a development unit for fine-tuning sensor integration and movement commands. Figure 6b shows an RPSC with a traffic cone mounted on the mobile base, performing positioning tests under real conditions.
## 1 Introduction
This approach requires labor-intensive efforts and substantial time to obtain consistent radiance data from the reference target because not only is it challenging to find homogenous land cover in nature, but it is not easy to obtain reference radiance values in the field at the exact time when the satellite of interest passes under bright conditions. Moreover, making artificial reference targets or constructing the necessary instruments to obtain in situ data can be expensive [18]. Nevertheless, this radiometric calibration approach is employed in many studies because it provides highly accurate results using optimal information on ground truth data at the time of satellite imaging and excellent reference targets when using pseudo-invariant tarps, e.g., a black target can be made similar to a blackbody [6; 18; 19; 20; 21]. In this study, the vicarious radiometric calibration approach based on in situ data was applied to the Korea multi-purpose satellite-3 and -3A (KOMPSAT-3 and -3A/Arirang-3 and -3A), which are sun-synchronous orbit satellites launched on 25 May 2012 and 25 March 2015, respectively. Both satellites use the same satellite bus and payload. The main difference between them is that only KOMPSAT-3A has an infrared sensor to monitor day-and-night, and it has a higher spatial resolution than KOMPSAT-3. The objective of the satellites was to develop an Earth observation system using panchromatic, multispectral, and infrared (only for KOMPSAT-3A) imagery with very high spatial resolution for monitoring of the surface environment, vegetation, ocean, and natural disasters [22]. Initial radiometric calibrations of the satellites were previously reported for the initial satellite conditions during the in-orbit test (IOT) periods in 2013 and 2015, respectively [11; 22]. However, it is necessary to reexamine the sensor responses and update the DN to radiance coefficients, because four and two years have passed, respectively, since the initial radiometric calibration. Additionally, on 22 August 2015, the TDI (Time Delay Integration) values of KOMPSAT-3A multispectral bands changed due to their lower sensitivity to low surface reflectance area values. Therefore, the coefficients should also be adjusted by performing a new vicarious calibration for consistent radiance values. Although most studies related to these satellites have focused on the verification and calibration of the imaging sensors [23; 24; 25; 26; 27; 28; 29; 30], efforts to improve and conserve the quality of satellite images must be maintained. Therefore, this study performs an absolute radiometric calibration of KOMPSAT-3 and -3A multispectral images from electro-optical imaging sensors to obtain the latest DN to radiance coefficients.
## 4 Topology and Control Strategy for Proposed Power Converter  
### Nonlinear Model of DC/DC Converter  
A simplified circuit scheme of the PV system is presented in Figure 6. The circuit uses a boost converter because of the wide adoption of such a step-up DC/DC structure in PV systems; nevertheless, the analysis presented in this paper can be extended to other DC/DC topologies also. The scheme uses a voltage source as the system load, to model the DC links of double-stage structures in commercial PV inverters where the DC/AC stage regulates the DC-link voltage (C\({}_{dc}\) capacitor voltage) [36]. This voltage-source model is widely used to represent the closed-loop grid-connected inverters owing to its satisfactory balance between accuracy and simplicity, which is confirmed in [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40]. In the topological structure, \(P_{m}\) is the ANN output, namely, the reference power. The control goal of the boost converter is that the actual output power of the PV should be compatible with the \(P_{m}\) of the DC/DC controller.  
The dynamic behavior of the DC-DC converter is modeled by the Equations (5) and (6) [41],where \(i_{L}\) represents the inductor current, \(V_{apv}\) is the PV array voltage, \(I_{apv}\) represents the PV array current and \(P_{apv}=V_{apv}\times I_{apv}\), \(V_{dc}\) is the DC-link voltage, and \(L\) and \(C_{in}\) represent the inductor and capacitor values:  
\[\frac{\mathrm{d}i_{L}}{\mathrm{d}t}=\frac{V_{apv}-V_{dc}(1-u)}{L} \tag{5}\]  
\[\frac{\mathrm{d}V_{apv}}{\mathrm{d}t}=\frac{I_{apv}-i_{L}}{C_{in}}. \tag{6}\]
# Mission Planning of UAVs and UGV for Building Inspection in Rural Area
## 3 Design of Planning Algorithm for Cooperative Inspection
### Mission Planning Algorithm Based on ACO-GA
#### 3.2.3 Crossover Method  
The crossover operation of the genetic algorithm can lead to a qualitative leap in solution quality. Typically, a pair of chromosomes are selected, and with a certain probability, some of their genetic information is exchanged to create new individuals. However, due to the randomness of crossover, it is not conducive to algorithm convergence. Inspired by the convergence factor in whale optimization algorithms, a convergence factor \(A\) was introduced into the algorithm. Its calculation formula is:  
Figure 2: Encoding method for individual solutions.  
\[A=2a\cdot r-a \tag{9}\]  
In Equation (9), \(r\) is a random number between \([0,1]\), and \(a\) is the weight factor, which will gradually decrease with the progress of iteration. Its specific value is as follows:  
\[a=2-2t_{iter}/t_{iter,max} \tag{10}\]  
In Equation (10), \(t_{iter}\) is the current number of iterations and \(t_{iter,max}\) is the maximum number of iterations.  
Firstly, the solution with the maximum fitness value in the previous iteration is set as the parent chromosome \(R_{0}\). Next, the value of A calculated by Equation (9) is judged. If \(\mid A\mid\leq 1\), the chromosome \(R_{1}\) is crossed with the parent chromosome \(R_{0}\) to obtain a new chromosome \(R_{1}\)\({}^{*}\) and reserve it; if \(\mid A\mid>1\), another chromosome \(R_{2}\) is randomly selected to cross with \(R_{1}\) and reserve \(R_{1}\)\({}^{*}\). With this setting, as the iteration proceeds, the value of \(\mid A\mid\) approaches 0, which greatly improves the probability of crossing with the parent chromosome, thus accelerating the convergence of the algorithm.  
During the crossover operation, two random positions are selected along the length of the chromosomes, as illustrated in Figure 3. The genes at these positions on chromosome \(R_{2}\) are found and cleared on chromosome \(R_{1}\). Then, the genes at these two positions on chromosome \(R_{2}\) are exchanged with those on chromosome \(R_{1}\). Finally, the genes originally located at these positions on chromosome \(R_{1}\) are inserted into the blank positions. The specific process of chromosome \(R_{1}\) during this crossover operation is depicted in Figure 4.
## 3 Research Method  
### Development of the Research Hypotheses  
#### 3.1.1 Relationship between E-Service Quality and Customer Satisfaction  
The relationship between e-SQ and CS is fundamental in digital environments. Conventionally, e-SQ has been identified as a crucial determinant of CS. When a business consistently delivers high-quality services that meet or exceed customer expectations, it positively influences CS [37].  
Numerous studies have demonstrated that e-SQ is an antecedent to CS in various domains [38; 39]. [PERSON] et al. [5] highlighted the effects of e-SQ on CS in websites. [PERSON] et al. [40] noted a significant impact of e-SQ on CS within e-marketing, while [PERSON] and [PERSON] [41] found this relationship in the context of e-retail. Consequently, the current study hypothesizes the following relationship between e-SQ and CS in e-commerce marketplace platforms:  
**Hypothesis 1 (H1).**_E-SQ is positively related to CS._
## 3 Geophysical Techniques and Geophysical Characteristics
### Geophysical Techniques
#### 3.1.1 High-Density Resistivity Method  
The high-density resistivity method is an improvement of the traditional electrical sounding method. It has high field data collection efficiency and is most widely used in urban geological survey. In this study, in order to detect the formation structure within 100 m of the shallow layer near the surface, the point distance is selected at 10 m, and 60 electrodes are arranged in a row, at a total distance of 600 m, and the continuous rolling mode is implemented. The acquired parameter is the apparent resistivity, because the apparent resistivity is the overall response of formation information; Res2 Dinv software is used to invert the results, in which finite difference is used for forward calculation, and the least square method based on the smooth constraint is used for inversion. The WGMD-4 resistivity data acquisition system developed by the Chongqing Pentium Numerical Control Technology Research Institute was used in this study. A Wenner device with a high longitudinal resolution was selected [16] in order to detect stratum stratification.  
Figure 1: Location map of geological and geophysical test sections in the study area (revised from 1:50,000 Chang-de regional geological map).
## 7 Simulation
### Comparing EQV-Architecture with Other Protocols  
In order to evaluate the EQV-Architecture effectively, it is compared with another architecture composed of three different protocols. In the set of selected protocols, there were protocols compatible with real-time transmission and multi-path routing. The chosen protocols were: MPEG-2 in application layer, MRTP [24] in transport layer, and MMSPEED [31] in network layer. The simulation performed in 5 sample of mixed environment.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|} \hline Hierarchical Level & 3 & 6 & 9 & 12 & 15 & 18 \\ \hline Transmitted Data with Dropping & 39.12 & 79.51 & 114.85 & 146.54 & 175.45 & 202.21 \\ Scheme (MB) & 44.88 & 95.99 & 144.50 & 190.72 & 234.88 & 277.17 \\ \hline \end{tabular}
\end{table}
Table 3: The average Transmitted Data in transmission with Dropping Scheme and without it  
Figure 23: The average PSNR for Mixed Environment with and without Dropping Scheme  
Figure 25, shows the comparison of average energy consumption in these two architectures. EQV-Architecture saved more energy than Mixed-Architecture does. That is due to the fact that Mixed-Architecture sends I-Frames periodically [34] and delivers packets in multi-paths, while EQV-Architecture utilizes dynamic period for transmitting M-Frames in single-path hand in hand with dropping scheme. Therefore, EQV-Architecture saves approximately _75%_ of energy.  
Another point for comparison is video quality based on average PSNR which is illustrated in Figure 26. It shows that however EQV-Architecture provides less PSNR than Mixed-Architecture, the resulted PSNRs are satisfactory.
# Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks
## 1 Introduction
To the best of our knowledge, we are the first to use a CASAE in surface defect detection applications. Due to the cascaded architecture, more accurate and consistent defect detection results are obtained compared with other methods under complex lighting condition and ambiguous defects. Moreover, only one threshold parameter needs to be adjusted after the CASAE is trained. (2) The entire defect detection and recognition task is formulated as a segment and classification problem via the proposed architecture. This two-staged architecture joins two sub-tasks together, which can not only obtain accurate defect outlines, but also obtain defect categories. (3) Successful metallic surface defect detection and classification using the proposed approach is evaluated using a real-world industrial dataset. Moreover, the proposed approach is a generic methodology that can be directly applied to the detection of other materials, such as the spot detection of nanofibrous material. The remainder of this paper is organized as follows. Section 2 introduces the system framework. The proposed detection module is illustrated in Section 3. In Section 4, we explain the classification methods in detail. Section 5 presents the experimental results conducted to evaluate the proposed method. Other applications of this method and a summary of results are also discussed in Section 5. Finally, conclusions are presented in Section 6.
## 2 Methods
### Oxygen and Nutrient Flux Measurements  
After 30 d, sediment-water column fluxes of oxygen, nitrate (NO\({}_{3}^{-}\)), ammonium (NH\({}_{4}^{+}\)), and phosphate (PO\({}_{4}^{3-}\)) were measured in the dark and light. These fluxes of nitrogen- and phosphorus-containing solutes were used to elucidate shifts in nutrient cycling. By measuring sediment uptake or release to the water column, and the variation between rates in the light and dark, we can infer changes in microbial and algal uptake and transformation [75]. Similarly, the sediment-water column flux of oxygen in the light and dark can be used as a proxy for benthic ecosystem metabolism (carbon cycling) and microalgal gross primary production [76]. Each microcosm was filled completely with artificial freshwater and tightly sealed with a clear polycarbonate lid fitted with a rubber o-ring. The rubber stopper inserted into the sampling port in the center of the lid was fitted with a small swivel that held a magnetic stir bar. During the flux measurements, microcosms were placed around a central core tube fitted with a motorized magnetic stir bar that spun each small magnet at approximately 60 rpm in order to prevent the build-up of a diffusion gradient at the sediment interface. Microcosms were tightly wrapped with aluminum foil to create a dark environment for the first half of the experiment.  
Samples were taken every two hours, with the first three measurements in the dark and the last two in the light. Oxygen concentrations were measured using a self-stirring dissolved oxygen probe (Hach LDO-BOD with HQ40D meter), and water samples were taken using a 60-cc syringe. Water removed during sampling was replaced with a known volume of artificial freshwater, and this dilution was accounted for in flux calculations. Water samples were immediately filtered through a 0.45 \(\upmu\)m PES membrane filter and stored at \(-20\,\mathrm{\SIUnitSymbolCelsius}\) until analysis. Nitrate was analyzed using a vanadium-based method [77], ammonium was measured using the phenol-hypochlorite method [78], and phosphate was measured using the ammonium molybdate method [79]. Flux rates were calculated from the change in concentration over time [75], with daily rates based on the sum of light and dark measurements scaled to a 14:10 h light:dark diel cycle. Gross primary production (GPP) was calculated using the difference between the hourly oxygen flux in the light and dark, assuming respiration is the only oxygen-consuming process occurring in the dark.
# Methodology Proposal and 3D Model Creation of a Car Steering Wheel
## 2 Methodology of Vehicle Steering Wheel Creation
### Preparatory Phase of the Proposal for the Vehicle Steering Wheel Creation
#### 2.1.1 Definition of Dimensional Characteristics  
Due to the fact that the steering wheel is part of the vehicle's interior, it is not possible to choose arbitrary dimensions. The dimensions of the steering wheel also depend on the type of car. Different dimensions are, for example, in an SUV car, others in a classic sedan or van. In general, the wider the vehicle, the larger the steering wheel can be, but it must meet the condition that it will not limit the driver by its dimensions and will not significantly affect the reach of the driver's hands on the steering wheel and also the driver's cone of visibility from the seat to the interior and to the immediate surroundings of the vehicle. Manufacturers of steering wheels for the automotive industry strictly guard production drawings with exact dimensions, so we determine steering wheel dimension intervals based on our steering wheel measurements on the following vehicles: MPV/Van, hatchback, 2x SUV, 2x Sedan. Table 1 provides the steering wheel values outer diameter, total height, handle circumference and the presence of a cover on the handle.  
Dimensional characteristics have been determined on the basis of measurements for a sedan type vehicle as follows:  
* the outer diameter was determined in the interval 37-39 cm;
* the total height was determined in the interval 9-12 cm;
* handle circumference was determined in the interval 10-10.9 cm.
## 2 Materials and Methods
### Feature Extraction  
A large set of ultrasound features does not necessarily guarantee the precise classification of breast tumors; rather, it sometimes degrades the performance of the classifier. Moreover, most of the time it would require a high configuration system for all the computation. In this work, several statistical, geometrical, and texture features are investigated on B-mode US image, B-mode parametric image, Contourlet transformed image, parametric version of Contourlet transformed (CP) image, and weighted parametric version of Contourlet coefficient (WCP) image. The prior features were employed on the B-mode US image, share wave elastography, parametric version of US image, and mammogram images in various earlier works but never on the weighted parametric version of Contourlet coefficient images. To ascertain the feasibility and to assess the dissimilarity of the extracted features, ANOVA \(p\)-value analysis has also been performed where the \(p\)-values are less than 0.1 for all the features utilized in this work, which proves that the features are useful  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PDL** & **DDL** & **ANOVA \(p\)-Value** & **PDL** & **DDL** & **ANOVA \(p\)-Value** \\ \hline
2 & 1 & 0.077 & 4 & 5 & 0.071 \\
2 & 2 & 0.054 & 4 & 6 & 0.052 \\
2 & 3 & 0.078 & 4 & 7 & 0.073 \\
2 & **4** & **0.022**  
and non-redundant. The features are summarized in Table 4 with corresponding references and _p_-values.  
Figure 6: WCP images modeled by RiIG PDF at Contourlet sub-bands: pyramidal decomposition level-2 directional decomposition level-4 (P2D4), as well as P2D8, P3D8, P3D16, P4D16, and P4D32.  
Figure 7: Example of Contourlet Coefficient at sub-band P4D32 (**A,D**); corresponding CP image (**B,E**); corresponding WCP image (**C,F**). The first row represents a benign case, and the second row represents a malignant case.