esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Clustering and Analysis Methods  
To analyze workstation performance and identify process inefficiencies, a two-step clustering workflow was applied to the simulation data generated by the virtual factory simulation model. The first step involved outlier filtering using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm [18]. This technique was used to detect and remove anomalous data points, such as unusually long idle periods or sequences of very short cycles, which could distort cluster structure and introduce interpretation bias. In DBSCAN, a point \(x\) is considered a core point if it has at least a minimum number of neighboring points (minPts) within a specified radius (\(\varepsilon\)):  
**Equation (1).**  
\[N_{\varepsilon}(x)=\{y\in D\ ||x-y|\leq\varepsilon\},\ \text{and}\ |N_{ \varepsilon}(x)|\geq\text{minPts} \tag{1}\]  
After outlier removal, K-means clustering was applied to categorize the remaining workstation-level data into interpretable operational states [19; 20]. This enabled grouping of behaviors into clusters representing conditions such as \"stable,\" \"delayed,\" or \"high variation.\" The goal of K-means is to minimize the total intra-cluster variance:  
Figure 4: The DIMUSA platform interface is used for real-time workstation data monitoring and operator input collection [9].  
Equation (2). \[\min_{C_{1},\ldots,C_{k}\sum_{j=1}^{k}\sum_{x_{i}\in C_{j}}|x_{i}-\mu_{j}|^{\,2}}\] (2)  
where \(C_{j}\) Is the set of data points in cluster j, \(\mu_{j}\) It is the centroid of cluster j, and k is the predefined number of clusters.  
The clustering results were visualized to compare simulated performance across workstations over one one-month period. Figure 5 shows the Overall Equipment Effectiveness (OEE) values for each workstation over a month. This time-based view forms the basis for the next clustering analysis, which groups workstations by similar performance patterns. Each line corresponds to a specific workstation, as identified by the layout codes provided in Table 1. Additionally, this summary highlights the top five and bottom five stations based on overall efficiency, helping guide further investigation and process improvements.
# A New Method of Angle Measurement Error Analysis of Rotary Encoders
## 3 Analysis of Angle Measurement Error  
The parallel dual-joint coordinate measuring machine is a kind of articulated arm coordinate measuring machine that consists of two rotation joints and one linear rolling guide. Figure 2 shows the structure of the rotation joint. The mounting plate is fixed on the structural support, the reading head is fixed on the mounting plate, the grating disk and rotating shaft are connected with screws, and the grating disk is rotated with a rotating shaft relative to the reading head. The rotary encoder we use is the Mercury 3000 (Celera MOTION Company, Bedford, MA, USA, with resolution of 0.39\({}^{\prime\prime}\) for the measuring range of 360\({}^{\circ}\)). The angle measurement accuracy is influenced significantly by the installation eccentricity of the grating disk and the radial error motion of the rotating shaft.  
Section 3.1 provides the ideal working condition of a rotary encoder; Sections 3.2 and 3.3 provide the details of two types of error sources, installation eccentricity, and radial error motions; and Section 3.4 provides the comprehensive model based on the analysis discussed in Sections 3.2 and 3.3.
## 2 Methods
### Plant Density and Health Identification Framework
The accuracy was tested and validated by comparing the predicted plant density results from program codes and the calculated values from the Canopeo application. Three statistical parameters, average absolute error (AAE), average biased error (ABE), and coefficient of determination (R\({}^{2}\)), were employed to evaluate the accuracy and suitability of the program code [40]. Estimation errors, such as ABE, quantify the degree of overestimation and underestimation of models, while AAE measures the degree of closeness between the predicted and measured results. R\({}^{2}\) value is widely used in statistical and regression analyses to determine the degree of goodness and accuracy of program code. All the estimation errors and R\({}^{2}\) were derived from equations listed below:  
\[\text{AAE}=\frac{\left(\sum_{i=1}^{n}\frac{|\text{P}_{i}-\text{M}_{i}|}{\text{ M}_{i}}\right)}{\text{P}_{i}}\times 100\%, \tag{1}\]  
Figure 2: UAV-based RGB orthophoto of GSIs within Morgan State University campus. \[\text{ABE}=\frac{\left(\sum_{i=1}^{n}\frac{(\text{P}_{i}-\text{M}_{i})}{\text{M}_{ i}}\right)}{\text{n}}\times 100\%, \tag{2}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}(\text{P}_{i}-\text{M}_{i})^{2}}{\sum_{i=1}^{ n}(\text{M}_{i}-\overline{\text{M}})^{2}}, \tag{3}\]  
where P, M, i, and n represent predicted results, measured results, specific sample number, and total number of samples, respectively. Physical measurement of plant density in the field is challenging and labor-intensive due to the irregular shape and unclear boundaries. Therefore, the estimated results from the Canopeo application were assumed and considered as the measured results in this study because the Canopeo application is a commercial product with relatively high accuracy. Results from the Pycharm Software were used as predicted results. Prediction was considered accurate if the estimation errors, AAE and ABE, tended toward zero and the R\({}^{2}\) value approached 1. The ArcMap software was utilized to generate NDVI maps, classify NDVI ranges, and extract NDVI values based on the formula, NDVI = (NIR-RED)/(NIR + RED), where NIR stands for the near-infrared spectral band and RED stands for the red spectral band [27; 28; 29]. The NDVI maps were calculated and generated through ArcToolbox and the Image Analysis Function within ArcMap software.
# At the End of a Slippery Slope: A Pilot Study of Deceleration Mats for Snow Tubing
## 4 Discussion
### Measurement Values
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Coefficient of Friction** & **Reference** \\  & **Mean (\(\pm\)std dev) or Range** & \\ \hline
**Snow Tubes** & & \\ No mat & 0.08 \(\pm\) 0.03 & _Current study_ \\ Flat mat & 0.11 \(\pm\) 0.07 & _Current study_ \\ Folded mat & 0.26 \(\pm\) 0.14 & _Current study_ \\
**Skis and Snowboards on Snow** & & \\  & 0.03–0.07 & _[PERSON] et al._, _2016_[3] \\ Skis—alpine & 0.02–0.14 & _[PERSON] et al._, _2021_[4] \\ Skis—Nordic (cross-country) & 0.02–0.22 & _[PERSON] et al._, _2021_[5] \\ Snowboards & 0.03–0.14 & _[PERSON] et al._, _2021_[4] \\ Snowboards—boarder-cross & 0.04–0.05 & _[PERSON] et al._, _2016_[6] \\
**Ski Suit Fabrics on Snow** & & \\ Racing suit, smooth & 0.19–0.33 & _[PERSON] et al._, _2016_[7] \\ Racing suit, dimpled & 0.35–0.48 & _[PERSON] et al._, _2016_[7] \\ Ski overall & 0.26–0.31 & _[PERSON] et al._, _2016_[7] \\
**Footwear on Ice** & & \\ Natural rubber (tread) & 0.27 \(\pm\) 0.02 & _[PERSON]_ et al._, _2004_[8] \\ Polyurethane (smooth) & 0.22 \(\pm\) 0.03 & _[PERSON]_ et al._, _2004_[8] \\ Synthetic rubber (tread) & 0.28 \(\pm\) 0.03 & _[PERSON]_ et al._, _2004_[8] \\
**Rubber on Ice, Snow, and Sand** & & \\ On snow covered ice & 0.02–0.15 & _[PERSON] et al._, _2010_[9] \\ On sand-snow on ice & 0.29–0.40 & _[PERSON] et al._, _2010_[9] \\ Tires on snow & 0.32–0.62 & _[PERSON] et al._, _2013_[10] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Coefficients of friction for snow tubes on deceleration mats compared to other snow sports equipment, and footwear material, and tires on snow and ice. If the mean and standard deviation were not provided, the range is presented. of the rider were related to the compression of the snow tube as the rider weight shifted during deceleration and that inflation pressure of the tubes could influence this motion. In this study, we attempted to keep constant the speed of the snow tube riders entering the run-out area in order to compare the effects from the deceleration mat conditions. The results, however, exhibited a large range of speeds (5.0 to 14.1 m/s) entering the run-out area; it is unclear what caused the variation, but changes in the snow conditions on the downhill portion of the tubing run and foot drag by the riders are two likely contributing factors. The Pearson's correlation coefficient (r) between the speed entering the run-out area and peak deceleration was 0.33; there was little relationship between speed entering the run-out area and the peak deceleration. A similar result was obtained for effective COF (r = 0.12). Because speed did not influence significantly the deceleration and effective COF across our tests, it is expected that additional, consecutive mats would further slow snow tube riders and would be recommended when shorter slowing distances are desired; additional tests at lower speed would be prudent to check this hypothesis.
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 5 Performance Analysis
### Computation Overhead for [PERSON] et al. [23]  
#### 5.3.1 Computation Cost of Sender  
This algorithm uses a decentralized server instead of a client-server model. To reduce the communication cost, the authors used a quantized model in which federated averagingdepended on the local training of the node data, and they used a graph to connect all nodes in the network.  
1. Secret Key Sharing Cost: In this algorithm, clients communicate with their neighboring clients. However, they communicate only after multiple FL sessions. This reduces the cost involved in comparing communications after every FL session. However, the communication between clients is still proportional to the number of clients in the network. We take the number of clients in the network as 1000; hence, the total number of computations for client-to-client communication would be 1000 \(\times\) 999. Suppose that these clients are communicating after a maximum of 100 FL sessions in their model. The need for communication would be 100-fold less. In this case, the client-to-client interaction complexity would result in a computation time of at least 9,990,00/100 = 9990 ms, which is still very high.
2. Encryption and Digital Signature Cost: The authors have not mentioned the name of the cryptography algorithm used for encryption and decryption. For signature also, no algorithm is mentioned. Therefore, we assume that it is signed by the sender's private key. Hence, the cost of signature in this algorithm is the same as the cost of signature in paper [5].  
The total cost of the sender in this algorithm can be given as below formula given in Equation (27):  
\[C_{Sender}=9990+C_{ENC}+C_{SIG}, \tag{27}\]  
and similarly the total cost of the receiver can be given as Equation (28):  
\[C_{Receiver}=C_{VER}+C_{DEC}+C_{AGG}. \tag{28}\]
## 5 Results and Discussion
### Result Comparison  
This study conducted quantitative evaluation and comparative analysis of TOC for five sub-layers of shale in the Qiongzhusi Formation using three methods: the \(\Delta\)logR method, multi-attribute joint inversion, and a RFR-based TOC prediction model. The analysis focused on 12 cored wells with measured TOC data (cumulative interval: 410 m) in the study area. Results demonstrate that the RFR model significantly outperforms traditional methods in prediction accuracy.  
As shown in Figure 9, the TOC curve predicted by the RFR model exhibits higher consistency with the measured values. Quantitative evaluation reveals that the RFR model achieves optimal performance, with a RMSE of 0.171 and a coefficient of determination (R\({}^{2}\)) of 0.920. The multi-attribute joint inversion method ranks second, yielding an RMSE of 0.193 and R\({}^{2}\) of 0.904, while the \(\Delta\)logR method shows relatively lower accuracy (R\({}^{2}\) < 0.9, RMSE > 0.224), failing to meet practical application requirements (see API RP 86 (2023) for shale gas resource assessment). Scatterplot analysis (Figure 10) further confirms that the RFR predictions cluster closely around the 45\({}^{\circ}\) reference line, with the smallest relative mean error and significantly stronger correlation with measured TOC data compared to the \(\Delta\)logR and multi-attribute inversion methods.  
While the \(\Delta\)logR method, multi-attribute joint inversion, and RFR model all enable effective TOC prediction, the RFR model demonstrates clear advantages in accuracy, stability, and correlation with experimental data. This approach provides robust technical support for shale gas resource assessment in the study area.
# Mining Pool Selection under Block WithHolding Attack +
## 6 Conclusions  
In this paper, we have investigated the mining pool selection problem in the presence of a Block WithHolding (BWH) attack and obtained the stable population states from the perspective of evolutionary game theory. In particular, we have focused on the scenario with two mining pools and provided simulation and theoretical results to show the correctness of the analysis. The results in this paper have shown that pools can attract miners to join by launching the BWH attack, and devoting more hash power (computation power) for attack attacks more miners, when the attack power is small. However, too large of an attack  
Figure 8: Both-side attack case with \(a_{12}=0.015\) and \(a_{21}=0.015\).  
Figure 9: Stable population states vs. attack sizes \(a_{12}\) and \(a_{21}\).  
power will discourage miners to join. These observations indicate the significant impacts of BWH attack on the mining pool selection of miners. Note that this paper considered the two-pool scenario as the case study, which is good enough for us to obtain fundamental findings but may provide less insights to the general case with more mining pools. Therefore, we will focus on the general scenario in the future work. In addition, we will also consider conducting experiments with more practical parameter settings or in real-world blockchain networks like Bitcoin to help us understand the problem more deeply.  
Scheme Design, K.F., Y.Z., M.S. and S.K.; implementation, K.F.; writing--original draft preparation, K.F.; writing--review and editing, Y.Z., M.S. and S.K.; supervision, S.K.; project administration, S.K.; funding acquisition, Y.Z., M.S. and S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by KAKENHI (A) under Grant 19H01103, SCAT Research Grant and The Telecommunications Advancement Foundation.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results and Discussion
### Thermodynamics Studies  
To obtain complementary information on the feasibility of the process, standard thermodynamic parameters were calculated for the adsorption of either MB or MG on 4 AZW at temperatures varying from 25 to 50 \({}^{\circ}\)C: The Gibbs free energy change (\(\Delta G^{0}\)), enthalpy change (\(\Delta H^{0}\)), and entropy change (\(\Delta S^{0}\)).  
The Gibbs free energy of adsorption \(\Delta G^{0}\) is derived from:  
\[\Delta G^{0}=\ -\text{R.T.}\ln\left(\text{K}_{\text{L}}^{0}\right) \tag{16}\]  
where R= 8.314 J/mol.K is the universal gas constant, T is the absolute temperature (K), and K\({}_{\text{L}}\)\({}^{0}\) is the (dimensionless) 'thermodynamic' Langmuir constant for the adsorption process. This value is calculated from K\({}_{\text{L}}\) (L/mg) in the Langmuir model (Equation (12)), after changing all concentrations to molar form and considering the standard state C\({}^{0}\) = 1 mol/L [61; 62; 63]:  
\[\text{K}_{\text{L}}^{0}=\text{K}_{\text{L}}(\text{L/mg})\.1000\ (\text{mg/g}). \text{M}\ (\text{g/mol})\.\ \text{C}^{0}\ (\text{mol/L}) \tag{17}\]  
Figure 10: Diagrammatic optimization of MB removal parameters.  
Figure 9: 3-D response surface plots for MB removal efficiency (%) onto 4 AZW: (**a**) Effect of initial concentration/adsorbent dose; (**b**) effect of initial concentration/pH; (**c**) effect of adsorbent dose/pH.  
where M = 319.85 g/mol or M = 364.91 g/mol are the molar mass of MB or MG, respectively, and the factor 1000 converts g to mg.  
The enthalpy (\(\Delta\)H\({}^{0}\)) and entropy (\(\Delta\)S\({}^{0}\)) parameters were estimated from the classical thermodynamic relationships:  
\[\mathrm{K}_{\mathrm{L}}^{0}=\exp\left(\frac{\Delta\mathrm{S}^{0}}{\mathrm{R}}- \frac{\Delta\mathrm{H}^{0}}{\mathrm{R.T}}\right) \tag{18}\]  
\[\Delta\mathrm{G}^{0}=\Delta\mathrm{H}^{0}-\mathrm{T.}\Delta\mathrm{S}^{0} \tag{19}\]  
The non-linear plots (not shown) of the van't Hoff Equation (18) allowed us to calculate \(\Delta\)H\({}^{0}\) (kJ/mol) and \(\Delta\)S\({}^{0}\) (J/mol.K). The values of \(\Delta\)G\({}^{0}\) (kJ/mol) were again calculated from these values of \(\Delta\)H\({}^{0}\) and \(\Delta\)S\({}^{0}\) (see Table 6).  
The negative values of \(\Delta\)G\({}^{0}\) slightly increase as temperature increases from 298 to 323 K; while they are negative at different temperatures, the adsorption process of MB and MG on 4 AZW is favored and spontaneous. These values of \(\Delta\)G\({}^{0}\) increased in the order 4 AZW-MB < 4 AZW-MG indicating that the adsorption was more spontaneous for the 4 AZW-MG system than for the 4 AZW-MB one [62].  
Furthermore, both \(\Delta\)H\({}^{0}\) values are positive, the characteristic of an endothermic process; for each dye, \(\Delta\)H\({}^{0}\) < 40 kJ/mol, suggesting that their adsorption is a physically driven process [64]. With positive \(\Delta\)S\({}^{0}\) for both dyes, there is an increase in the randomness of the system due to the desorption of water molecules from the adsorbent surface; similar results have also been reported by other research groups [51; 65].
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 4 Methodology
### Experimental Procedure
#### 4.4.7 Real-Time Performance Analysis  
To assess the model's capability for real-time applications, we conducted additional experiments focusing on data acquisition, processing speed, and model inference time. Methodology:  
* Used a high-speed camera (1000 fps) for data acquisition.
* Implemented parallel processing for feature extraction.
* Measured inference time on both CPU and GPU. Results:  
1. Data Acquisition: 1 ms per frame.
2. Feature Extraction: 5 ms per frame.
3. Model Inference:  
* CPU: 2 ms.
* GPU: 0.5 ms.  
Total Processing Time:  
* CPU pipeline: 8 ms per frame.
* GPU pipeline: 6.5 ms per frame.  
These results demonstrate that our system can operate at over 100 Hz, which is suitable for real-time analysis in badminton where typical shot durations range from 50-200 ms. The low latency ensures that predictions can be made within the timeframe of a single shot, allowing for potential real-time feedback during training or matches.  
Implications for Real-world Applications:  
1. Training Enhancement: The real-time capability allows for immediate feedback during practice sessions, enabling players to adjust their technique on the fly.
2. Match Analysis: Coaches can receive instant insights during matches, potentially informing strategic decisions between points or games.
3. Personalized Coaching: The system's speed allows for the accumulation of large datasets over time, facilitating more personalized and adaptive training programs.
4. Integration with Wearable Technology: The low latency opens possibilities for integration with smart glasses or other wearable devices, providing players with immediate visual or auditory feedback.
5. Automated Refereeing Assistance: While not replacing human judgment, the system could provide additional data points to assist in close calls or performance tracking. Limitations and Future Work: While these results are promising, further optimization may be needed for seamless integration in match conditions. Future work will focus on:  
* Testing the system under various lighting conditions and environments.
* Optimizing the feature extraction algorithms for even lower latency.
* Exploring edge computing solutions to reduce dependency on centralized processing.
* Conducting user studies to assess the impact of real-time feedback on player performance and decision-making.  
This real-time analysis capability significantly enhances the practical applicability of our QE-based predictive model, bridging the gap between laboratory research and on-court implementation in badminton training and competition.
# Synthesis of Mesoporous MWCNT/HKUST-1 Composite for Wastewater Treatment
## 3 Results and Discussion
### Adsorption Study  
The effect of pH has been found to be an important parameter in the adsorption of organic and inorganic wastewater treatment. This is due to its ability to influence both the adsorbent structure and the pollutant species [44; 45]. [PERSON] et al. [46] have studied the effect of pH on the adsorption of MB in an AC-HKUST-1 MOF and found that the highest adsorption occurred at pH 4. In this work, we examined the effect of pH on methylene blue (MB) adsorption in FMWCNT/HKUST-1 at pH 2, 4, 6, 8, and 10.  
In order to determine the optimal wavelength for the analysis of FMWCNT/HKUST-1, a plot of absorbance as a function of wavelength was generated. Figure 9a shows the adsorption band maxima of the MB solution on the adsorbate at different pH values. Maximum absorption bands of 665, 665, 665, and 664 nm were obtained at pH 2, 4, 6, 8, and 10, respectively. It can be noted that there was no shift in the adsorption band of aqueous MB in the FMWCNT/HKUST-1 composites. Furthermore, the effectiveness of the FMWCNT/HKUST-1 composite in the removal of MB dye was examined by evaluating its partition coefficient (PC). A partition coefficient is a solid-liquid adsorption system that has been defined as the ratio of the adsorbate per unit mass of the adsorbent to its concentration in the liquid phase at equilibrium [47; 48]. The partition coefficient (PC) was calculated according to the report of [PERSON] et al. [49] and [PERSON] [50] using different dye concentrations with  
Figure 8: Thermogravimetric analysis (TGA) of HKUST-1 and FMWCNT/HKUST-1.  
100 mg of adsorbent. Partition coefficients (PCs) of 18.89, 12.73, and 10.29 mg/g/\(\upmu\)M were obtained for 20, 100, and 200 ppm, respectively. The performance of the FMWCNT/HKUST-1 composite could be attributed to the synergistic effect of FMWCNTs in the virgin HKUST-1 MOF in enhancing the pore structure, which provided more channels for the adsorption of MB molecules. In addition, a maximum adsorption capacity greater than 100 mg/g was obtained for 100 ppm at 25 \({}^{\circ}\)C. Figure 9b shows photographs of the MB solution treated with the adsorbent at pH 2 and pH 8.  
The pictures reveal that MB adsorption on the FMWCNT/HKUST-1 synthesized in this work favored a basic medium, and this was evident in the residual dye solution (Figure 9b). This result is reasonably consistent with the adsorption of methylene blue on an Fe\({}_{3}\)O\({}_{4}\)/Cu\({}_{3}\)(BTC)\({}_{2}\) MOF, where the efficiency of removal of MB was found to be directly proportional to pH in the range of 2-11 [47]. On the other hand, an electrostatic interaction between the negatively charged surface of FMWCNT/HKUST-1 and the positively charged MB could have been responsible for the high adsorption [51]. In addition, the remarkable removal efficiency obtained could be attributed to the presence of a carboxylate group and a hydroxyl site in the adsorbent [14]. Furthermore, the textural properties of FWMCNT/HKUST-1 also contributed to the adsorption capacity value obtained in this work [14; 52].