esa-sceva/satcom-chunk-collection huggingface dataset:
# A Self-Calibration of Capacitor Mismatch Error for Pipeline ADCs
###### Abstract  
This study proposes self-calibration of capacitor mismatch errors for high-resolution pipeline analog-to-digital converters (ADCs). The proposed calibration circuit recursively amplifies the capacitor mismatch error by re-utilizing a multiplying digital-to-analog converter in a pipeline stage without increasing the circuit complexity, and the amplified error voltage is converted into digital code by utilizing the remaining pipeline stages. Error correction is performed by subtracting the digital code from the ADC output during normal operation. A prototype of a 12-bit pipeline ADC is fabricated in a 0.18 \(\mu\)m standard CMOS process. The ADC comprises eight 1.5-bit stages, followed by a 4-bit flash ADC as the final stage; the capacitor mismatch errors in the first two pipeline stages are corrected by utilizing the proposed self-calibration technique. Although the calibration method is employed in a 1.5-bit stage architecture, which uses a gain-of-two switched-capacitor amplifier, it is applicable to different bit-per-stage architectures. The ADC linearity significantly improves after calibration, and this is verified through simulations and measurements.  
capacitor-mismatch calibration; self-calibration; switched-capacitor circuit; pipeline analog-to-digital converter +
Footnote †: journal: Applied  
1
Footnote 1: institutetext: School of Electrical Engineering and Computer Science, Gwangqiu Institute of Science and Technology, Gwangqiu 61005, Republic of Korea; [EMAIL_ADDRESS] (D.-H.S.); [EMAIL_ADDRESS] (S.C.); [EMAIL_ADDRESS] (J.-G.K.)
## 4 Vibration Signal Analysis
### EEMD Frequency Analysis Result  
The EEMD method is used to analyze the vibrations of 16 test points, but due to the data being too large, this paper only shows the result of test point 1. As shown in Figure 7, IMF1 of the EEMD analysis has frequency components of 181.8 Hz, 184.0 Hz, 176.4 Hz, 187.3 Hz, 168.1 Hz, 197.7 Hz, 141.6 Hz and 203.9 Hz (The frequency components are sorted by descending order of amplitude.). Frequency components of 65.3 Hz, 59.3 Hz, 78.8 Hz, and 101.2 Hz exist in IMF2. The frequency components of 29.4 Hz, 45.2 Hz, 37.0 Hz, 32.6 Hz, and 21.9 Hz exist in IMF3. The above analysis shows that modal aliasing still exists in the decomposition result of EEMD, but EEMD analysis can decrease modal aliasing partly and improves analysis accuracy. The other 15 test points have the same conclusions.
## 3 Numerical Modeling Procedure
### Model Description and Simulation Procedure  
Considering that the longwall panel is symmetrical around its centerline, a 3D model is generated based on the study site, as shown in Figure 4. According to the geological column, this model is 190 m wide, 350 m long and 120 m high, and the model size and mesh density were determined based on a sensitivity analysis. The panel was 90 m wide and 250 m long, which left sufficient space to eliminate the boundary effect. It should be noted that, all parameters applied in the numerical simulation came from a previous case study presented in [28] and [30], the parameters for boundary conditions were based on in situ stress measurement, and the properties of the rock masses were based on the laboratory tests. To simulate the in situ stress state, a 15 MPa load was vertically applied to the top boundary; according to the in situ stress measurement and previous study [28], a 12 MPa horizontal load was applied perpendicular to the direction of retreat mining, and an 18 MPa horizontal load was applied along the direction of retreat mining. No displacement in the direction perpendicular to the model lateral boundaries was allowed. In situ stresses were applied as described in Section 3.1. The rock mass properties for the simulation were estimated from the intact rock properties, as listed in Table 1. A flowchart of the proposed numerical modeling approach for longwall mining is illustrated in Figure 5. The simulation process was as follows: (i) Generate the 3D model and assign the constitutive model and properties; (ii) simulate the in situ stress state; (iii) mine the coal seam in the retreat by 10 m for each cycle; (iv) fill the CZ with the double-yield model; (v) run the calculation with the tension-weakening model in the FZ to simulate the rock mass weakening induced by fracturing; (vi) after the calculation reaches equilibrium, repeat (iii) and (iv) until the entire panel is completely mined.  
Figure 4: Layout of the three-dimensional panel-wide model.
# HI-Sky: Hash Index-Based Skyline Query Processing
## 5 Conclusions  
In this paper, we proposed a hash index structure and a special hash key for a skyline query method called HI-Sky, which reduces unnecessary dominance tests through a hash index and grid partitioning-based approach. For this purpose, we showed the theoretical background of HashGrid, a new hash index, and GLAD, a hash key used in the corresponding index structure. The theoretical background of HI-Sky, which efficiently computes the skyline using HashGrid, was presented. It was also tested through experiments against state-of-the-art methods. The results showed that HI-Sky can generate indices faster than other methods and can perform faster skyline query processing at higher dimensions by effectively reducing the number of dominance tests. This is because HI-Sky has an indexing method called HashGrid and a hash key called GLAD that enable data space pruning where a skyline cannot occur, eliminating unnecessary dominance tests when dominance cannot occur. Therefore, HI-Sky can process skyline queries more efficiently than other methods. To solve the data skewness problem, in the future work, we are planning to apply a method like box-counting [36]. The box-counting determines the data skewness based on number of grids that are penetrated by data points. This feature can be utilized in HI-Sky to make it choose the best np by itself. In addition, we are planning to utilize a system like SpatialHadoop [25; 26] to extend HI-Sky into a distributed environment.  
Figure 12: The experimental results using a real dataset.  
Author Contributions:[PERSON] designed the algorithm. [PERSON] also performed the bibliographic review and writing of the draft and developed the proposed algorithm. [PERSON] shared his expertise with regard to the overall review of this paper. [PERSON] and [PERSON] supervised the entire process. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A3B03035729). This work was also supported by the Industrial Strategic Technology Development Program (No.200003991, Development of Korean Wave Convergence Service for AI-based Motion Evaluation and Learning Technology) funded by the Ministry of Trade, Industry & Energy (MOTE, Korea). Conflicts of Interest: The authors declare no conflict of interest.
## 5 Predefined Path-Based Techniques  
Predefined path-based techniques reduce redundant traffic in HSR networks by forwarding unicast traffic through two separate predefined paths instead of duplicating and flooding the traffic as in the standard HSR protocol. There are several predefined path-based techniques, as follows:  
* Optimal Dual Paths (ODP): ODPs establish dual paths that have optimal link metrics and no common nodes based on network link information.
* Dual Virtual Paths (DVPs): DVP establish dual paths for each connection pair of terminal nodes by sending and receiving control messages.
* Ring-based Dual Paths (RDP): RDP establish dual paths by sending and receiving control messages for each connection pair of terminal rings (DANH rings) instead of terminal nodes.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 2 Principles of Grounding Systems
### Regulatory Standards and Guidelines
#### 2.2.1 IEEE Std 80-2013: Guidelines for AC Substation Grounding  
IEEE Std 80-2013 provides guidelines for grounding systems in AC substations, particularly focusing on safety from electric shock [9]. The standard includes detailed calculations for the safe touch voltage, step voltage, and ground potential rise (GPR) during fault conditions.  
The GPR can be expressed as follows:  
\[V_{GPR}=\frac{I_{fault}}{2\pi}\times\ln\left(\frac{R_{soil}}{re_{ electrode}}\right) \tag{4}\]  
where \(I_{fault}\) is the fault current, \(R_{soil}\) is the resistivity of the soil, and \(r_{electrode}\) is the radius of the grounding electrode. This equation models the potential rise in the surrounding soil, which is crucial for designing grounding systems that minimize hazards to personnel.
## 2 Data and Analysis Methods
### Satellite Measurements
The difference can exceed 20% or more, especially with intense rainfall [30; 31]. However, in high latitudes, the satellite-derived wind speed up to 30 m/s agrees with the anemometer measurements on oil platforms within the error of the satellite wind retrieval algorithms [29]. The vertical structure of the PL cloud system was analyzed using the Cloud Profiling Radar (CPR) measurements at 94 GHz frequency from the CloudSat satellite launched in 2006 as part of the \"A-Train\" satellite constellation [32], which also includes Aqua and GCOM-W1 satellites. The CPR is the first satellite radar designed to measure vertical structure of the macro- and microphysical characteristics of clouds. A distribution of droplets and crystals in the PL clouds was obtained by a joint analysis of nearly synchronous CPR measurements and the Q and Tb(89H) fields derived from the AMSR2 data. The CPR data were successfully used for analysis of the PL vertical structure over the water surface at the middle [33; 34] and high [35; 36] latitude areas.
# Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete
## 3 Materials and Methods
### Principal Component Analysis (PCA)  
In multi-variable problems, particularly in the case of applying AI models, PCA exhibits many advantages in the step of pre-processing data. PCA could help the training phase of AI models in: (i) assessing the feasibility of space reduction when the problem has too many inputs [71; 72], (ii) preventing the overtraining, as recommended in the work of [PERSON] et al. [73], and (iii) reducing the noise in the input data [74; 75; 76]. Using PCA technique, important statistical information in the data has been conserved without dropping any typical characteristics [77; 78]. The main steps of PCA technique are given below [71; 79; 80]:  
* Preparation and normalization of inputs;
* Calculation of the covariance matrix;
* Calculation of the eigenvalues and eigenvectors;
* Estimation of the proportion of total variance of each principal component;
* Identification of the loading of principal components and contribution of inputs.  
In this study, PCA was applied in order to reduce noise in the input space for training the models. Two datasets were prepared: one with raw inputs and the data with PCA pre-processed. These two datasets were used as inputs for the training and testing phases of the models as to demonstrate the effectiveness of PCA technique.
# A Numerical Analysis on Lateral Resistance of Pile-Bucket Foundation for Offshore Wind Turbines
## 5 Analysis on _p-y_ Curve
### Comparison of \(p\)-\(y\) Curves between PF and P-BF  
It has been proven above that the bucket greatly improves the lateral bearing capacity of the PF, and the \(p\)-\(y\) curve recommended by API specification seriously underestimates the lateral ultimate soil resistance of the soil below the 1 \(d\) of the large-diameter PF, which is too conservative. In order to study the reinforcement of the foundation for rigid-flexible piles, a large-diameter pile with a diameter of 6 m is used in the following calculations. Figure 14 shows the \(p\)-\(y\) curve of the P-BF. Because of the difference in bucket height, the \(p\)-\(y\) curve of the P-BF with depths of 2.25 and 3.75 m is compared as an example. In the same depth of shallow soil, the lateral ultimate soil resistance of the soil can be increased to more than seven times after the reinforcement of the bucket. At the same time, it has been found that increasing the height of the bucket has a limited improvement in the lateral ultimate soil resistance at a depth, as is shown in Table 7a,b. Moreover, the bucket of the same diameter increases with the height of the reinforced bucket, and the lateral ultimate soil resistance at the same depth is slightly lower. The lateral resistance of the whole foundation increases with the increase in bucket height.  
Figure 13: \(p\)-\(y\) curve of \(D=2\), 4, 6 m. (**a**) \(p\)-\(y\) curve of \(D=2\) m. (**b**) \(p\)-\(y\) curve of \(D=4\) m. (**c**) \(p\)-\(y\) curve of \(D=6\) m.
# New Methods for Assessing External Sulfate Attack on Cement-Based Specimens
## 4 Discussion
### Discussion Regarding ESA-Induced Expansion Measurement Using Optical Fibers  
The second experimental technique investigated in this paper is the use of OF sensors to measure ESA-induced expansion of laboratory specimens. For this application, the optical fibers do not require any special conditioning but only need to be implanted in the mold prior to casting the specimen. During the experiments, the expansion measurements obtained using OF sensors were comparable to those obtained using the traditional method (extensioner). As a result of this comparison, it can be concluded that OF-based techniques are able to follow the evolution of ESA-induced expansion. OF-based methods can then be considered as an alternative to measure ESA-induced expansion, thus providing insight into the behavior of cement-based materials.  
In fact, the proposed technique offers significant advantages, despite the fact that OF installation is more complicated than gluing the pins. A major perspective (not tested here) is to automate measurements on a very large number of prisms and at many different timescales, whereas monitoring with an extensometer would require a great deal of work. This could, for example, make it possible to monitor automatically, at the same time, series of specimens manufactured with different formulation parameters (different cements, different cement blends, different water-to-cement ratios, etc.).  
It is notable that in the present study, the only type of optical fiber tested (polyimide-coated OF) was damaged after 57 or 78 days of exposure to the Na\({}_{2}\)SO\({}_{4}\) solution due to uncertain reasons. In light of the above, one of the major improvements that can be considered for future studies is to test other coatings of OFs. In future validation investigations of OFs with other coatings, a set of samples equipped only with OFs and a set of specimens equipped only with stainless-steel pins will be considered in order to eliminate the risk of damaging the OFs during measurement with the extensometer.
# Non-Contact Heart Rate Detection Based on Hand Vein Transillumination Imaging
## 2 Materials and Methods
### Performance Evaluation of Heart Rate Detection Algorithm
#### 2.4.2 Quantitative Index  
The error between the measured heart rate (\(HR_{g}\)) and the actual heart rate (\(HR_{ero}\)) is shown in Equation (4).  
\[HR_{ero}=HR_{m}-HR_{g} \tag{4}\]  
(1) Mean Error (\(M\))  
\(M\) refers to the average heart rate measurement error, which reflects the deviation of the measurement result from the reference heart rate. It is negatively related to the accuracy of the heart rate detection method.  
(2) Standard Deviation (\(SD\))  
In order to improve the reliability of the average error index, as much sample data as possible should be used for the calculation in the analysis. \(SD\) represents the standard deviation of the measurement error value of the heart rate detection, which indicates the  
Figure 5: The pulse wave spectrum.  
degree of dispersion of the heart rate error values. It is inversely related to the stability of the heart rate detection algorithm.  
(3) Root Mean Squared Error (_RMSE_)  
_RMSE_ is the square root of the squared sum of the measurement error and the ratio of the number of video samples used for heart rate detection. The calculation formula is shown in Equation (5). _RMSE_ is an indicator that measures the degree of deviation between the detected heart rate detection value and the true value; thus, the _RMSE_ value reflects the accuracy of the heart rate detection algorithm, and is inversely related to the heart rate detection effect.  
\[RMSE=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}{(HR_{i}^{ero})^{2}}} \tag{5}\]  
where, \(N\) is the number of video samples used for heart rate detection, \(HR_{i}^{ero}\) represents the error value of the \(i\)-th video heart rate measurement, and \(M\) is the average error.  
(4) Pearson Correlation Coefficient (_r_)  
The Pearson correlation coefficient (_r_) is the ratio of the covariance of the measured heart rate and the real heart rate and the product of the standard deviation of the two. The closer its value is to 1, the stronger the positive correlation. The calculation formula is shown in Equation (6). The larger the absolute value of \(r\), the stronger the correlation between the measured value and the actual value, and the higher the accuracy of heart rate detection.  
\[r=\frac{Cov(HR_{m},HR_{g})}{SD(HR_{m})*SD(HR_{g})}=\frac{\sum\limits_{i=1}^{N}{ ((HR_{m}^{i}-\overline{HR_{m}^{i}})\sum\limits_{i=1}^{N}{(HR_{g}^{i}- \overline{HR_{g}^{i}})})}}{\sqrt{(HR_{m}^{i}-\overline{HR_{m}^{i}})^{2}*(HR_{ g}^{i}-\overline{HR_{g}^{i}})^{2}}} \tag{6}\]  
where, \(N\) is the number of video samples used for heart rate detection, \(Cov\) refers to the covariance, \(SD\) represents the standard deviation, \(HR_{m}^{i}\) and \(HR_{g}^{i}\) represent the measured and true values in the \(i\)-th video, respectively, \(\overline{HR_{m}^{i}}\) and \(\overline{HR_{g}^{i}}\) are the average value of the detected heart rate of all sample videos and the average of the actual values of the tested sample videos, respectively.
## 4 Analysis of Micro-Scale Failure Evolution of Deep Granite Based on IET Function
In this time period, the sample shows stable crack propagation, and the minority of macroscopic crack extension is limited because of the expansion of the macroscopic crack generation. The expansion of the macroscopic cracks of the frontier forms the surface for new strain energy dissipation. This strain energy is used to maintain the new microcrack generation at a high speed, which subsequently leads to the reduction of \(F(\tau)\). After a series of cracking events occur, the value of \(F(\tau)\) returns to a high level. After 3455 s, the rock sample moves to the post-peak failure stage, and \(F(\tau)\) initially remains at a high level, showing a typical unstable crack growth form. The function value \(F(\tau)\) also shows a continuous and compact U-shaped evolution trend. The continuous occurrence of large \(F(\tau)\) values indicates rapid increase in the AE activity simultaneously. Further, the accumulated energy of the granite sample begins to release during the loading process, and the internal cracks of the sample are connected under the action of sustained external force. This allows the rock sample to slide along the internal main fracture surface of the sample, resulting in local shear tensile failure and, finally, in the macroscopic failure of the sample. According to the AE signal value \(F(\tau)\) and the stress-strain curve of granite under compression, the loading process of the granite sample is divided into five stages: (1) crack closure, (2) linear elastic deformation, (3) microcrack generation and development, (4) macroscopic fracture generation and energy surge, and (5) post-peak failure. In stages 1 and 2, the rock sample experiences the compaction of primary pores, shear slip on the primary failure surface, relative slip between grains, and destruction of the rough particle surface at the microscale. After the end of the elastic loading stage, the stress-strain curve of the sample is no longer linear; the stress rapidly reaches the maximum value and a large number of microcrack propagation aggregates produce macroscopic cracks. In the post-peak stage, all three samples show high brittleness. In this stage, the large crack is quickly connected and formed. With continuously increasing loading, the main fracture surface inside the sample slides, causing local damage to the granite interior and complete damage eventually. Figure 4: Stress–strain \(F(\tau)\) evolution curves of granites. (**a**) A1, (**b**) B1, (**c**) C1.
## 4 Experimental Results
### Addressing RQ1: Predicting Participant Behavior Using K-Core and Activity Metrics
Notably, when act_level approaches zero, SHAP values rise sharply in the positive direction, indicating the presence of highly active contributors who often occupy central positions within the collaboration network. Conversely, as act_level moves away from zero, SHAP values trend negatively, suggesting that less frequent contributors are more likely to have shorter participation durations. These contributors are often located on the periphery of the network, indicating weaker integration into the collaborative framework. Figure 6: Dependence Plot for K-Core Value. Figure 7: SHAP Analysis of group and activity_level. In summary, the participant's network centrality (k value), the group they belong to, and their activity level (act_level) significantly influence their long-term participation in projects. SHAP analysis revealed that participants with higher k values tend to play more critical roles within the project and are more likely to remain active over a longer period. Additionally, participants involved in long-term projects (Group 5) are more likely to have longer participation durations, indicating a strong correlation between project longevity and network complexity. Lastly, participants with higher activity levels, meaning those who contribute more frequently, were predicted to have longer activity durations, emphasizing the importance of consistent contribution to project success. Thus, the answer to the research question is as follows: a participant's activity frequency and network centrality are key factors in predicting long-term contribution to GitHub projects. In particular, core participants play a crucial role in the long-term success of projects, with their network centrality being a decisive factor in the project's sustainability.