esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimization Problem
### Solution Methodology  
In this study, the multi-population genetic algorithm (MPGA) is adopted to solve the proposed optimization problem. MPGA is an excellent probability search algorithm for global optimization. Based on the standard genetic algorithm (SGA), MPGA decomposes the SGA into several subpopulations by the idea of multi-population parallel evolution and increases the number of gene patterns by exchanging information among subpopulations (usually the optimal individuals) to avoid immature convergence. Figure 4 shows the algorithm structure of MPGA, where immigration represents the best individual replacing the worst individual in the target population and selection represents the best individuals in different groups being selected and stored in elite populations to ensure that the optimal  
Figure 3: Research structure of this paper.  
individuals generated by various groups are not destroyed or lost. Therefore, MPGA solves the shortcomings of SGA.  
The calculation process of MPGA can be described as:  
1. Population initialization and expansion: \(N\) populations are initialized, each of which has a binary chromosome, and the binary chromosome is transformed by the range of values of the \(x\), \(y\) variables as shown in Equation (25).
2. Fitness calculation: Fitness is applied to distinguish between individuals in a population. In this study, the objective function value is the maximum damping loss factor, so the objective function is employed as the fitness function. The larger the objective function, the greater the fitness and the better the individual.
3. Selection: From the old population, good individuals are selected with a certain probability to form a new population to reproduce the next generation of individuals.
4. Crossover and mutation: The crossover is the random selection of two individuals from the population to, through the exchange and combination of two chromosomes, produce new, excellent individuals. The mutation is to randomly select an individual from the population and select a point in the individual to mutate to produce a better individual.
5. Immigration: The immigration is to replace the worst individual in the target population with the best individual in the original population, so as to achieve the goal of multi-population co-evolution.
6. Convergence: MPGA determines the algorithm to terminate based on the elite population. Then, the optimized variables (\(x\), \(y\)) and damping loss factor \(\eta\) are obtained.
## 3 Result and Discussion
### Relation between Parameters
#### 3.5.3 Relation between Permeability and Fractal Dimensions  
Figure 9 displays the relationship between porosity and permeability of sandstone immersed in acid solution under different F-T cycles. As can be seen from Figure 9a, the fractal dimension of meso pores (D\({}_{2}\)) show good linear correlation with the porosity. This means that, with the pore structure of the meso pores, it became more complex--i.e., less uniform meso pores are more likely to merge and connect and water channels are more likely to be form; therefore, permeability increases with D\({}_{2}\). Figure 9b shows that linear correlation between porosity and fractal dimension of macro pores(D\({}_{3}\)) is not strong compared with D\({}_{2}\). This is probably because the fractal dimension of macro pores is affected by the peeling off of the sample surface.  
Based on the analysis above, it can be found that porosity has exponentially strong correlation with permeability. D\({}_{2}\) and D\({}_{3}\) have good linear correlation with permeability, while the correlation between porosity and fractal dimensions is not that obvious.  
Figure 8: Correlation between porosity and segment fractal dimensions for sandstone immersed in different acidic solution: (**a**) fractal dimensions D\({}_{2}\); (**b**) fractal dimensions D\({}_{3}\).  
Figure 9: Correlation between permeability and segment fractal dimensions for sandstone immersed in different acidic solutions: (**a**) fractal dimensions D\({}_{2}\); (**b**) fractal dimensions D\({}_{3}\).
## 2 Study Area and Data
### Data Sources  
#### 2.2.1 Field Measured Data  
Field experiments were conducted three times in 2015 during the crucial growing stages of winter wheat (Table 1). The observations mainly included dry aboveground biomass and yield. Only large and relatively homogeneous fields (>1 km\({}^{2}\)) were selected, and all measurements were made 200 m or farther from the field boundaries. In addition, a Global Positioning System (GPS, Trimble GeoExplorer 6000 Series GeoXH, Trimble Navigation, Ltd., Sunnyvale, CA, USA) was used to record the coordinates so that they could be accurately located in the satellite images.  
The total aboveground biomass (including stems, leaves and ears) was collected from five randomly selected small plots (1 m \(\times\) 1 m) at each sample site in the center of the field. All the  
Figure 1: The location map of the study areas in Shandong and Hebei, China (false color composite GF-1 images acquired on 15 and 20 August 2014, R/G/B vs. band 4/band 3/band 2).  
plant samples were heated to 105 \({}^{\circ}\)C and oven dried at 80 \({}^{\circ}\)C until they reached a constant weight in the laboratory. The final dry weight (DW) of the samples was recorded. The DW was then divided by the sample area and converted to g/m\({}^{2}\). The yield was measured following similar steps when the wheat was harvested; the difference was that only the grain portion was required for the yield calculation. In addition, we spent approximately two weeks in May conducting field surveys, during which the land use types, crop types and phenology were investigated.
# Sustainable Support Material for Overhang Printing in 3D Concrete Printing Technology
## 2 Experimental Details
### Evaluation of Printability  
The printability examination of the material includes the pumpability as well as the buildability characterization of the material. The pumpability of the mixtures was assessed by measuring the volumetric flow rate of the materials through the nozzle under a constant pump speed. A 4-axis gantry system (Mitsubishi) was used for all printing experiments. This gantry was attached with a progressive cavity pump (MAI(r)2 PUMP Pictor by MAI International), and used for the pumping operation to evaluate the mixture's pumpability. A 3 m long hose pipe was used to transport the material from the pump to the nozzle. The material was subsequently extruded from the nozzle, which had an orifice area of 20 mm by 35 mm. The mass of the material extruded from the nozzle for 30 s at a pump speed of 700 rpm was measured and used to compute the mass flow rate. The mass flow rate was converted to the volumetric flow rate through Equation (1).  
\[\text{Volumetric flow rate }=\frac{\text{Mass flow rate}}{\text{Density of mixture}} \tag{1}\]  
Cylindrical structures with a diameter of 200 mm were printed to evaluate the buildability of the material. A laser distance sensor was attached to the nozzle to detect the displacement of the extruded structure from the nozzle height due to the deformation of the filaments (Figure 8). The laser sensor has an operating range of 65 mm to 135 mm and is placed at a nominal distance of 100 mm from the nozzle opening. The printing parameters used for the printing of the buildability specimens are shown in Table 2. The buildability of the material is determined by the ability to build the maximum number of layers without excessive deformation of the filaments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mix Designs**} & \multicolumn{4}{c}{**Paste Content**} & \multirow{2}{*}{**Natural Sand/Paste Ratio**} & \multirow{2}{*}{**Water/Paste Ratio**} \\ \cline{2-2} \cline{4-5}  & **Fly Ash Powder** & & & \\ \hline FA60 RG40 & 60\% & 40\% & 0.8 & 0.3 \\ FA55 RG45 & 55\% & 45\% & 0.8 & 0.3 \\ FA50 RG50 & 50\% & 50\% & 0.8 & 0.3 \\ FA45 RG55 & 45\% & 55\% & 0.8 & 0.3 \\ FA40 RG60 & 40\% & 60\% & 0.8 & 0.3 \\ FA35 RG65 & 35\% & 65\% & 0.8 & 0.3 \\ FA30 RG70 & 30\% & 70\% & 0.8 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mix designs of mixtures.  
Figure 7: Particle size distribution of fly ash powder and recycled glass powder.
## 2 Related Work
### System Model
\[\mathrm{R}_{mk}\triangleq\mathbb{E}\left\{\mathrm{vec}\left(\widetilde{ \mathrm{H}}_{mk}\right)\mathrm{vec}\left(\widetilde{\mathrm{H}}_{mk}\right)^ {H}\right\}\in\mathbb{C}^{LN\times LN} \tag{2}\]  
\[\mathrm{R}_{mk}=\left(\mathrm{U}_{mk,t}^{*}\otimes\mathrm{U}_{mk,r}\right) \mathrm{diag}(\mathrm{vec}(\mathrm{W}_{mk}))\left(\mathrm{U}_{mk,t}^{*} \otimes\mathrm{U}_{mk,r}\right)^{H} \tag{3}\]  
We use mutually \(\tau_{p}\) normal pilot sequences as every pilot matrix contains N mutually orthogonal pilot sequences and is used for channel estimation [27]. \[P_{k}P_{k^{\prime}}=\left\{ \begin{array}{ll}\tau_{p}I_{N}&k^{\prime}=k\\ 0&otherwise\end{array} \right. \tag{4}\]  
where \(\mathrm{P}_{k}\in\mathbb{C}^{N\times\tau_{p}}\) denotes the pilot matrix of UE k.  
In practical CF M-MIMO systems, due to limited system resources, multiple UE may have to use the same pilot matrix. Let \(\mathcal{P}_{k}\) denote the set of indices of UE that use the same pilot matrix as UE k, including UE k itself. In this scheme, all UE sends their pilot signals to the AP, and the received signal at the AP can be expressed as \(\mathrm{Y}_{m}^{p}\in\mathbb{C}^{L\times\tau_{p}}\); at the AP, \(m\) is obtained by Equation (5). \[\mathrm{Y}_{m}^{p}=\sum_{k=1}^{K}\mathrm{H}_{mk}\mathrm{F}_{k}\mathrm{P}_{k}+ \mathrm{N}_{m} \tag{5}\]  
where \(\mathrm{F}_{k}\in\mathbb{C}^{N\times N}\) is the UL precoding matrix of UE \(k\), which is designed to maximize the received signal power at the AP while satisfying the maximum transmit power constraint \(\mathbb{E}\left\{\parallel\mathrm{F}_{k}\parallel_{F}^{2}\right\}\leq p_{k}\), with \(p_{k}\) being the maximum transmit power of UE \(k\) and \(\mathrm{N}_{m}\in\mathbb{C}^{L\times\tau_{p}}\). It represents the noise and interference at the AP, which is modeled as complex Gaussian noise with zero mean and variance with \(\mathcal{N}_{\mathrm{C}}\left(0,\sigma^{2}\right)\) elements. The integration of the precoding matrix with the user data represents a simple vector multiplication, which performs spatial precoding (or beamforming) at the UE side to improve transmission efficiency and reduce interference. To find an estimate of h\({}_{mk}\), AP \(m\) matches Y\({}_{m}^{p}\) with P\({}_{k}^{H}\), as shown in Equation (6). \[\mathrm{Y}_{mk}^{p}=\mathrm{Y}_{m}^{p}\mathrm{P}_{k}^{H}=\sum\
olimits_{l\in \mathcal{P}_{k}}\tau_{p}\mathrm{H}_{ml}\mathrm{F}_{l}+\mathrm{N}_{m}\mathrm{P}_ {k}^{H} \tag{6}\]  
Since \(\mathrm{\hat{f}}_{mk}\), \(\mathrm{R}_{mk}\), and \(\varphi_{mk}\) are available for AP m, the PA-MMSE estimate of h\({}_{mk}\) is obtained by Equation (7). \[\mathrm{\hat{f}}_{mk}=\mathrm{vec}(\mathrm{\hat{f}}_{mk})=\mathrm{\overline{h} }_{mk}e^{i\varphi_{mk}}+\mathrm{R}_{mk}\mathrm{\widetilde{F}}_{k}\mathrm{ \Psi}_{mk}^{-1}\mathrm{vec}\left(\mathrm{Y}_{mk}^{p}\right) \tag{7}\]  
where \(\mathrm{\overline{h}}_{mk}=\mathrm{vec}\left(\mathrm{\overline{H}}_{mk} \right)\mathrm{\widetilde{F}}_{k}=\mathrm{F}_{k}^{T}\otimes\mathrm{I}_{L}\), and \(\mathrm{\Psi}_{mk}=\sum_{l\in\mathcal{P}_{k}}\tau_{p}\mathrm{\widetilde{F}}_ {l}\mathrm{R}_{ml}\mathrm{\widetilde{F}}_{l}^{H}+\sigma^{2}\mathrm{I}_{LN}\).
# A Survey of Secure Time Synchronization
## 2 Time Synchronization
### Common Challenges for Time Synchronization
#### 2.3.3 Convergence Speed  
Wireless sensor networks always contain a large number of sensor nodes and two nodes can contact to each other through many hops, which makes it difficult to reduce the convergence speed in time synchronization algorithm design [23]. The tiny-sync and mini-sync protocols both provide the benefits of minimum usage of storage space and computing resources, require low bandwidth, and tolerate message loss. However, they have the disadvantage of high convergence time of achieving synchronization [34]. The RBS protocol has a large number of message exchanges to achieve synchronization and resynchronization which takes a large amount of the available bandwidth. The FTSP is slow in convergence because it requires an initiation period due to linear regression [32].  
These challenges need to be analyzed carefully and corrected to avoid the nondeterministic delay in radio messages delivery which can affect the precision of time synchronization. In addition to these challenges, there are several requirements that determine what type of synchronization method should be used.
## 1 Introduction
Moreover, this paper will elaborate the potential benefits of sensor collaboration, and discuss the challenging issues and future research opportunities in this area, focusing on distributed sensing and collaborative information processing through WSNs. We propose a collaborative weighted clustering algorithm (CWCA) in this paper. It uses a weighted sum of several metrics such as the degree, the Euclidean distance, the relative mobility, and the lifetime of a node as a cluster head [9]. The advantage of such an algorithm is that the weight parameters can be adjusted according to the mobile wireless sensor networks requirements. The flexibility in changing the weight factors is helpful for the algorithm's application in various types of networks. Some simulation experiments have been carried out to evaluate the performance of our proposed algorithm. Both the static sensors and mobile sensors are taken into account. The experimental results show that the LEACH and CWCA can achieve a longer network lifetime than the static clustering algorithm. Although the difference between performances of these two algorithms was limited, with only static sensors in the application that evolves mobile sensors, the CWCA would behave better. The rest of this paper is organized as follows: various effective collaborative strategies are reviewed in detail in WSNs. Section 3 introduces the background of this research, including the basic idea of application scenario and traditional WSNs routing protocols. Section 4 proposes a collaborative weighted clustering protocol. Section 5 presents illustrative simulation results evaluating the performance of the proposed algorithm for some system configurations and network topologies. Section 6 finally summarizes the research work of the full paper, and discusses future research challenges and opportunities.
# Ankle Joint Dynamic Stiffness in Long-Distance Runners: Effect of Foot Strike and Shoes Features
###### Abstract  
Foot strike mode and footwear features are known to affect ankle joint kinematics and loading patterns, but how those factors are related to the ankle dynamic properties is less clear. In our study, two distinct samples of experienced long-distance runners: habitual rearfoot strikers (n = 10) and habitual forefoot strikers (n = 10), were analysed while running at constant speed on an instrumented treadmill in three footwear conditions. The joint dynamic stiffness was analysed for three subphases of the moment-angle plot: early rising, late rising and descending. Habitual rearfoot strikers displayed a statistically (\(p\) < 0.05) higher ankle dynamic stiffness in all combinations of shoes and subphases, except in early stance in supportive shoes. In minimal-supportive shoes, both groups had the lowest dynamic stiffness values for early and late rising (initial contact through mid-stance), whilst the highest stiffness values were at late rising in minimal shoes for both rearfoot and forefoot strikers (0.21 \(\pm\) 0.04, 0.24 \(\pm\) 0.06 (Nm/kg/\({}^{\circ}\)-100), respectively). In conclusion, habitual forefoot strikers may have access to a wider physiological range of the muscle torque and joint angle. This increased potential may allow forefoot strikers to adapt to different footwear by regulating ankle dynamic stiffness depending upon the motor task.  
running; biomechanics; footwear; joint work; loading +
Footnote †: journal: Applied Sciences
## 4 Accident Control Strategy
### Core Principles for Accident Control  
The response methods for different kinds of accident are different. However, there are two core principles for accident control, and they are applicable to the response methods for all kinds of accidents.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Accident Level** & **Accident Description** \\ \hline Level 1 & Failure of pumps \\  & Failure of the three-phase separator \\  & Overpressure \\  & Over-heating \\  & Failure of the heater \\  & Opening of pressure relief valves \\  & Burst or leakage of pipelines or equipment \\  & Power outage \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation of possible accidents with different risks that could occur in an SCWO system.  
(1) The oxygen system should be cut off immediately once an accident happens. Oxygen is a combustion-supporting gas. Considering the fact that there are many kinds of flammable organic matter in feedstock, the quantity of oxygen in the system should be strictly limited. Once an accident happens, the regular oxidation reaction cannot be maintained. Therefore, the oxygen that is injected into the reactor cannot be thoroughly consumed, resulting in an increasing of the concentration of oxygen in the effluent. Moreover, the effluent may flow backward to oxygen pipelines if an overpressure accident happens, resulting in the pollution of the oxygen pipeline. It is notable that the oxygen pipeline should be kept scrupulously clean during the transportation of oxygen to prevent ignition of fire.  
(2) The flow of feedstock in the system should not be stopped in any circumstance. The feedstock of our commercial-scale SCWO system is sludge. As introduced in Section 2.2 and the previous article [20], there are considerable amounts of insoluble solid particles in sludge. The solid particles are surrounded by some organic matter at first. After SCWO reactions, they are separated from the organic matter, and flow with the effluent. The flow rate of feedstock should be kept at a relatively high value to guarantee that the solid particles can be carried with the bulk fluids. Otherwise, the solid particles may accumulate in pipelines or facilities, leading to blockage of the system. If the flow of feedstock is interrupted and the system cannot be resumed because of accidents, the high-temperature and high-pressure sludge feedstock should be discharged from the system. The methods to discharge the feedstock from the system corresponding to a Level 2 accident and a Level 3 accident are different, and will be introduced in the next section.  
The two rules mentioned above should be strictly obeyed; otherwise, irreversible damage to the facilities, or even casualties, could be caused by some accidents.
## 3 Experimental Set-Up  
This section presents the configuration characteristics of the proposed VLC positioning system, followed by a detailed description of the methodology. The experimental system designed to evaluate indoor conditions consists of a platform perpendicular to the table, located 38 cm above the top photodiode of the designed structure. On this platform, the transmitting LEDs are arranged in a square with sides measuring 20 cm and a diagonal of 28.28 cm. The transmitter also includes the drivers for modulating the FSK signals, which are generated by Arduino microcontrollers.  
A stepper motor system was incorporated to enable automated scanning with the receiver, enhancing efficiency and minimizing the risk of human error. The area covered by this system comprises 30 \(\times\) 30 cm, and we take 10 samples along each axis, enabling the capture of 100 data points across the scanned area.  
The receiver comprises three main components: an array of photodiodes arranged in a semi-spherical configuration to ensure angular diversity, a printed circuit board (PCB) for signal conditioning tailored to the specific conditions, and a TM4C1294 NCPDT microcontroller that manages system control, performs analog-to-digital conversion, and transmits the collected data to a computer via Ethernet for processing in Matlab. The detailed system diagram is described in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Quadrant** & **Condition** & **Angle** \\ \hline
1 & \(x_{p}>x_{\text{LED}}\) \& \(y_{p}<y_{\text{LED}}\) & \(\alpha-(0+(90^{\circ}-\theta))\) \\ \hline
2 & \(x_{p}>x_{\text{LED}}\) \& \(y_{p}>y_{\text{LED}}\) & \(\alpha-(90^{\circ}-\theta)\) \\ \hline
3 & \(x_{p}<x_{\text{LED}}\) \& \(y_{p}>y_{\text{LED}}\) & \(\alpha-(180^{\circ}+(90^{\circ}-\theta))\) \\ \hline
4 & \(x_{p}<x_{\text{LED}}\) \& \(y_{p}<y_{\text{LED}}\) & \(\alpha-(270^{\circ}-\theta)\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Conditions for calculating the orientation angle.  
Figure 2: Mathematical model used to determine the orientation of a receiver toward an optical transmitter using the \(V_{rms}\) from three lateral adjacent photodiodes.
## 4 Results and Discussion
### Results Comparison and Analysis
#### 4.2.3 Results of ML Models  
Table 10 presents the performance indices for both the training and testing datasets. These indices include critical evaluation metrics such as \(MAE\), \(MAPE\), \(RMSE\) and \(R^{2}\). As can be seen from Table 10, the XGBoost model demonstrated superior training performance, exhibiting a superior fit in comparison to the GBDT and RF models. The XGBoost model demonstrated optimal performance in the testing dataset, as evidenced by its highest \(R^{2}\) value of 0.776 and lowest \(RMSE\) of 0.243. This suggests that the model exhibited mini  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Parameter** & **Grid Search Scope** &
\begin{tabular}{l} **Determine the** \\ **Numerical Value** \\ \end{tabular} \\ \hline XGBoost & max\_depth & [3, 4, 5, 6, 7, 8, 10, 12] & 8 \\  & n\_estimators & [50, 100, 150, 200, 220, 250, 300, 350, 400, 450, 490, 500, 510] & 300 \\  & learning\_rate & [0.001, 0.01, 0.05, 0.1, 0.2] & 0.05 \\  & gamma & [0.0, 0.1, 0.2] & 0.0 \\  & reg\_alpha & [0.001, 0.01, 0.1] & 0.001 \\  & reg\_lambda & [0.01, 0.1, 1] & 0.01 \\  & min\_child\_weight & [2, 3, 4, 5, 6] & 3 \\  & colsample\_bytree & [0.6, 0.7, 0.8, 0.9] & 0.6 \\  & subsample & [0.6, 0.7, 0.8, 0.9] & 0.7 \\ \hline Random Forest & n\_estimators & [50, 100, 150, 200, 220, 250, 300, 350, 400, 450, 490, 500, 510] & 220 \\  & max\_depth & [3, 4, 5, 6, 7, 8, 10, 12] & 5 \\  & min\_samples\_split & [2, 5, 8, 10] & 2 \\  & min\_samples\_leaf & [1, 2, 3, 4, 5] & 1 \\  & max\_features & [Sqrt\^{\prime}, Log2\^{\prime}] & Sqrt \\  & bootstrap & [True, False] & False \\ \hline GBDT & n\_estimators & [50, 100, 150, 200, 220, 250, 300, 350, 400, 450, 490, 500, 510] & 500 \\  & learning\_rate & [0.01, 0.05, 0.1, 0.15, 0.2] & 0.05 \\  & max\_depth & [3, 4, 5, 6, 7, 8, 10, 12] & 7 \\  & min\_samples\_split & [2, 5, 10, 15] & 10 \\  & min\_samples\_leaf & [1, 2, 4, 5] & 2 \\  & subsample & [0.6, 0.7, 0.8, 0.9, 1.0] & 1.0 \\  & max\_features & [Sqrt\^{\prime}, Log2\^{\prime}, None] & Sqrt \\ \hline \hline \end{tabular}
\end{table}
Table 9: Parameters of hyperparametric optimization.  
mal deviation from the actual values. And the XGBoost model demonstrates a marginal decline in error when comparing the training and test datasets, suggesting that it is not experiencing a significant overfitting issue. In addition, the training and prediction times of ML models are shown in Table 15 in Appendix A. The XGBoost model has a distinct advantage in prediction speed, which is crucial for applications where real time is needed.  
Figure 7 shows the scatter plots, which present the relationship between the predicted and actual values for the test dataset. The scatter plot for XGBoost shows a strong linear trend with most points clustering closely around the red dashed line, indicating superior accuracy. The XGBoost model outperforms both the RF and GBDT models in terms of prediction accuracy and generalization. Thus, this study performs key influencing factors' interpretable analysis on the basis of an XGBoost-based prediction model.