esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Alternating Temperature on the Effectiveness of EICP in Consolidating Aeolian Sand
## 4 Discussion
### Influence of Alternating Temperature on the SEM Images  
Crystal morphology was commonly used as one of the important markers to distinguish mineral types, and its differences ultimately affect the effectiveness of calcium carbonate crystal in solidification [50; 51]. There were usually three different amorphous calcium carbonate forms, namely vaterite, aragonite, and calcite, which were transformed from amorphous calcium carbonate [52].  
However, similar microscopic morphology of calcium carbonates was observed regardless of T1, T2, and T3. It could be found that vaterites were clearly observed for all test cases after magnifying the SEM images, as in Figure 9c,d; this appeared in the form of spherical or spherical aggregates. It has been shown that the growth kinetics of vaterite was superior to calcite at high urease activity in the calcium carbonate precipitation process [53]. In this regard, a commercial urease with high urease activity was used in this experiment. Similar observations regarding the dominant precipitation morphology of vaterite can also be found in the sand consolidation experiments of EICP by [PERSON] et al. [50].  
Furthermore, it could also be found that the calcium carbonate with irregular and disordered structure was generated around sand particles (Figure 9e,f). The sand particles served as nucleation sites, and calcium carbonates were attached to these particles. The thicknesses of the calcium carbonate were approximately 10-25 um, while the calcium carbonate connected the surrounding sand particles into an aggregate. This finding suggests that sand particles can be used as nucleation sites to separate calcium carbonate from solutions [25; 39; 54; 55], but this adsorption is not strong. Some pores still exist between the sand particles; the generated calcium carbonates are not enough to fill all the pores.
## 3 Results
### Results of the HLS-EU-Q16  
The HLS-EU-Q16 presented a good internal reliability (Cronbach's \(\alpha\) = 0.759).  
As demonstrated in Table 2, over the three evaluation stages, the scale scores showed an increasing trend: S0 with an average score of 30.36, S1 with a value of 37.19, and S2 with a value of 43.14.  
Analyzing the health literacy levels, presented in Table 3, there was a continuous increase over the three evaluation stages. Stage 0 was the only one where an inadequate level was found, and there were mostly problematic levels of health literacy. Stage 1 predominantly presented'sufficient' levels, and Stage 2 presented 'excellent' levels in more than half of the sample.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & & **N (\%)** \\ \hline \multirow{2}{*}{Sex} & Female & 24 (70.6\%) \\  & Male & 10 (29.4\%) \\ \hline \multirow{2}{*}{Age} & Mean \(\pm\) standard deviation & 41.06 \(\pm\) 4.98 \\  & Min–Max & 25–59 \\ \hline \multirow{4}{*}{Marital status} & De facto union & 5 (14.7\%) \\  & Married & 24 (70.6\%) \\  & Divorced & 3 (8.8\%) \\  & Widowed & 2 (5.9\%) \\ \hline \multirow{2}{*}{Employment status} & Employed & 25 (73.5\%) \\  & Unemployed & 9 (26.5\%) \\ \hline \multirow{4}{*}{Education} & Lower Secondary Education & 3 (8.8\%) \\  & Upper Secondary Education & 7 (20.6\%) \\  & Secondary Education & 12 (35.3\%) \\ \cline{1-1}  & Bachelor’s Degree & 7 (20.6\%) \\ \cline{1-1}  & Postgraduate & 2 (5.9\%) \\ \cline{1-1}  & Master’s Degree & 2 (5.9\%) \\ \cline{1-1}  & Doctoral Degree & 1 (2.9\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sociodemographic data of informal caregivers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Minimum** & **Maximum** & **Mean** & **Standard** \\ \hline Stage 0 & 16.67 & 44.79 & 30.36 & 6.93 \\ Stage 1 & 29.17 & 50.00 & 37.19 & 4.98 \\ Stage 2 & 32.29 & 50.00 & 43.14 & 4.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: HLS-EU-Q16 scores at three evaluation stages.  
Regarding the three domains, it was observed that the healthcare domain consistently exhibited the lowest health literacy levels across all stages (problematic at S0 and sufficient at S1 and S2) when compared to the other domains. At Stages 0 and 2, the disease prevention domain showed the highest average levels (sufficient and excellent, respectively), whereas at Stage 1, the health promotion domain had the highest average (sufficient). These results are presented in Table 4.  
A repeated measures ANOVA test was performed, which revealed statistically significant differences in health literacy levels between the evaluation stages [F(2,56) = 75.55; \(p<0.05\)]. The Sidak post hoc test further indicated statistically significant differences (\(p<0.001\)) between all stages. These statistical test results are presented in Table 5.  
The remaining sociodemographic data did not correlate with the scales, indicating that the characteristics of the present sample did not influence the scores obtained on each scale. Furthermore, program duration also showed no correlation with the scale scores.
## 2 Materials and Methods
### ZnO-NPs Characterization
#### 2.3.7 Antioxidant Activity (DPPH FR\({}^{\bullet}\) Assay)  
The ability of the green-synthesized ZS-ZnO-NPs to scavenge free radicals (FRs\({}^{\bullet}\)), which reflects their antioxidant capacity, was evaluated by measuring their response to DPPH radicals using the method outlined by [26]. To gain a comprehensive understanding of this property, the ZS-ZnO-NPs were tested at varying concentrations (50-1000 ug/mL) and incubation times (0.5-24 h). For each experiment, 2 mL of ZS-ZnO-NPs/DMSO solution dispersed in different concentrations was mixed with an equal volume of DPPH solution (7 mg/100 mL methanol). The mixtures were then incubated for 0.5, 3, 6, 12, and 24 h, and the absorbance at 517 nm was measured. To serve as a control, a mixture of DPPH/DMSO was prepared. The inhibition percentage (IP\({}^{\prime}\)/o) of the tested ZS-ZnO-NPs was calculated using the following formula: IP\({}^{\prime}\)= ((A\({}_{\text{d}}-\text{A}_{\text{da}})/\text{A}_{\text{d}})\times 100\). In this equation, A\({}_{\text{d}}\) represents the absorbance value of the oxidized solution in the absence of ZS-ZnO-NPs antioxidant agent, while A\({}_{\text{da}}\) represents the absorbance value after adding ZS-ZnO-NPs antioxidant agent.
###### Abstract  
Batteries are everywhere, in all forms of transportation, electronics, and constitute a method to store clean energy. Among the diverse types available, the lithium-iron-phosphate (LiFePO4) battery stands out for its common usage in many applications. For the battery's safe operation, the state of charge (SOC) and state of health (SOH) estimations are essential. Therefore, a reliable and robust observer is proposed in this paper which could estimate the SOC and SOH of LiFePO4 batteries simultaneously with high accuracy rates. For this purpose, a battery model was developed by establishing an equivalent-circuit model with the ambient temperature and the current as inputs, while the measured output was adopted to be the voltage where current and terminal voltage sensors are utilized. Another vital contribution is formulating a comprehensive model that combines three parts: a thermal model, an electrical model, and an aging model. To ensure high accuracy rates of the proposed observer, we adopt the use of the dual extend Kalman filter (DEKF) for the SOC and SOH estimation of LiFePO4 batteries. To test the effectiveness of the proposed observer, various simulations and test cases were performed where the construction of the battery system and the simulation were done using MATLAB. The findings confirm that the best observer was a voltage-temperature (VT) observer, which could observe SOC accurately with great robustness, while an open-loop observer was used to observe the SOH. Furthermore, the robustness of the designed observer was proved by simulating ill-conditions that involve wrong initial estimates and wrong model parameters. The results demonstrate the reliability and robustness of the proposed observer for simultaneously estimating the SOC and SOH of LiFePO4 batteries.  
lithium-iron-phosphate battery; batteries modeling; state of charge (SOC); dual extend Kalman filter (DEKF); state of health (SOH) +
Footnote †: journal: Applied Sciences
###### Abstract  
A new method using a deep neural network (DNN) model is proposed to predict the ore production and crusher utilization of a truck haulage system in an underground mine. An underground limestone mine was selected as the study area, and the DNN model input/output nodes were designed to reflect the truck haulage system characteristics. Big data collected on-site for 1 month were processed to create learning datasets. To select the optimal DNN learning model, the numbers of hidden layers and hidden layer nodes were set to various values for analyzing the training and test data. The optimal DNN model structure for ore production prediction was set to five hidden layers and 40 hidden layer nodes. The test data exhibited a coefficient of determination of 0.99 and mean absolute percentage error (MAPE) of 2.80%. The optimal configuration for the crusher utilization prediction was set to four hidden layers and 40 hidden layer nodes, and the test data exhibited a coefficient of determination of 0.99 and MAPE of 2.49%. The trained DNN model was used to predict the ore production and crusher utilization, which were similar to the actual observed values.  
underground mine; deep learning; deep neural network (DNN); truck haulage system; ore production; equipment utilization +
Footnote †: journal: Applied Sciences
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 4 Flight Validation and Performance Analysis
### Flight Validation and Performance Analysis
#### 4.2.4 Key Insights and Implications  
The validation experiments highlight the significant advantages of the MADDPG-VSC algorithm in multi-UAV formation tasks. Key performance indicators such as trajectory accuracy, formation stability, and real-time responsiveness demonstrate its ability to maintain precise control and coordination under dynamic conditions. The system's low latency and efficient data transmission further reinforce its effectiveness in real-time decision making.  
These findings emphasize the innovations of the MADDPG-VSC algorithm, particularly in regards to adaptive sweep angle control and cooperation strategies. These enhancements provide a strong foundation for improving UAV performance in complex, real-world environments, ensuring reliable mission execution across a range of scenarios.
# Prediction Equation for Horizontal Ground Motion Peak Values in the Loess Plateau Region
## 6 Residual Analysis of Horizontal Peak Parameter
### Attenuation Relationships
The left chart displays the PGV residual distribution for Model I, ranging from \(-1.41\) to \(1.27\), with an average value of \(-0.01\), and the right diagram shows the PGV residual distribution for Model II, ranging from \(-1.28\) to \(1.00\), with an average value of \(-0.08\). The distributions reveal that Model II's fitting residuals are slightly lower than those of Model I, indicating that Model II's PGV fitting performance is marginally better than Model I's. Overall, whether for PGA, EPA, or PGV, Model II's predictive performance exceeds that of Model I, especially under conditions of low magnitude and large epicentral distance. Figure 25 displays the distribution of PGV fitting residuals based on magnitude. The chart reveals that the range of PGV residuals varies across different magnitudes and the average residual values differ per magnitude bracket. For Model I, the average residuals are \(M_{\rm S}\) 3.2 at 0.10, \(M_{\rm S}\) 4.2 at 0.19, \(M_{\rm S}\) 4.5 at 0.06, \(M_{\rm S}\) 5.0 at 0.01, \(M_{\rm S}\) 5.5 at \(-0.04\), \(M_{\rm S}\) 6.2 at  
Figure 23: Distribution of EPA fitting residuals by magnitude. Figure 24: Distribution of PGV fitting residuals by epicentral distance. \(-0.09\), and \(M_{\rm S}\) 6.3 at \(-0.12\). For Model II, the corresponding average residuals are \(M_{\rm S}\) 3.2 at 0.05, \(M_{\rm S}\) 4.2 at \(-0.05\), \(M_{\rm S}\) 4.5 at \(-0.12\), \(M_{\rm S}\) 5.0 at \(-0.17\), \(M_{\rm S}\) 5.5 at \(-0.16\), \(M_{\rm S}\) 6.2 at 0.09, and \(M_{\rm S}\) 6.3 at \(-0.09\). Overall, PGV fitting residuals are slightly smaller compared to PGA and EPA. For magnitudes less than \(M_{\rm S}\) 5.0, Model I's PGV predictions are somewhat lower than the observed values. For magnitudes greater than \(M_{\rm S}\) 5.0, Model I's predictions are slightly overestimated. Additionally, within the magnitude range of \(M_{\rm S}\) 4.0 to \(M_{\rm S}\) 6.5, Model II's PGV predictions tend to slightly overestimate compared to the observed values. This suggests that different models have varying predictive accuracies across different magnitude ranges, necessitating the selection of appropriate models based on specific magnitude ranges to enhance prediction accuracy.
## 3 Results
### Cluster Risk to Transmission Owner (TO)  
#### 3.2.1 Distribution of Clusters by TO  
The average annual number of clusters per company (TO) with TADS inventory above 200 kV was 2.4 from 2013 to 2019. It did not change significantly from year to year--similarly to the number of clusters. However, there is a large variability in the number of clusters by TO; this variability primarily reflects a company size. Figure 5 illustrates the distribution of number of clusters per TO for 7 years.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cluster** & **Year** &
\begin{tabular}{c} **Duration** \\ **(Hours)** \\ \end{tabular} & **TADS Elements** & **Initiating Causes of Outages** \\ \hline
9 & 2013 & 2.3 & 9 ac & Lightning \\
9 & 2017 & 11.6 & 8 ac, 1 trans & Power system condition, Human error, Unknown \\
9 & 2016 & 1.6 & 9 ac & Failed AC substation equipment \\
11 & 2017 & 2.0 & 4 ac, 7 trans & Human error, Power system condition \\
11 & 2018 & 1.6 & 4 ac, 7 trans & Lightning, Power system condition \\
12 & 2018 & 11.9 & 12 ac & Human error, Fire, Power system condition \\
12 & 2013 & 48.7 & 12 ac & Contamination \\
14 & 2019 & 35.1 & 13 ac, 1 trans & Fire, Power system condition \\
16 & 2019 & 5.9 & 13 ac, 3 trans & Fire, Power system condition (wildfires in California) \\
18 & 2013 & 116.0 & 18 ac & Weather-initiated (extreme rainfall) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ten largest clusters (2013–2019).  
The 39 entities (about 22% of companies with TADS elements of 200 kV and above) have not experienced a cluster from 2013-2019. The 39 TOs had at least 20 clusters each. Out of these, four companies have more than 100 clusters each (with two more than 300 clusters each) over the 7 years. These outliers are the TOs with large inventory of TADS elements above 200 kV.
## 3 Case Study
### Calculations
#### 3.1.2 Yearly Plan Development  
Having developed the calibration model, a model was created for each of the months within the yearly plan, applying first a stochastic methodology. Among the information given with the yearly plan, the different destinations could be found where the tonnages would end up, the number of days for each month, and the planned mechanical availability for the truck fleet. Additionally, due to a change in the material handling, stockpiles 1 and 2 stopped receiving trucks, and stockpiles 3 and 7 were deposited with ore as new designs allowed the expansion of those piles, thus increasing their total capacity.  
Step 1. Creation of the yearly model  
The month-to-month planned tonnages varied, as shown in Figure 9. Furthermore, the overall daily tons moved month-to-month was increased from 325,000 tons in January to 386,000 tons in December, maintaining this value consistently since July.  
It is worth noting that the number of days in the plan for each month was based on the Asiatic calendar, which is different from the Gregorian calendar, as the number of days per month is not regular, see Table 8.  
Moreover, the month-to-month mechanical availability for the truck fleet varied, as shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Month** & **Number of Days** & **Month** & **Number of Days** \\ \hline January & 25 & July & 30 \\ February & 31 & August & 31 \\ March & 29 & September & 31 \\ April & 31 & October & 30 \\ May & 30 & November & 31 \\ June & 31 & December & 36 \\ \hline \end{tabular}
\end{table}
Table 8: Number of days per month in the Asiatic calendar.  
Figure 9: Tons moved per day on average during 2023.  
Figure 11: Truck productivity in the stochastic and deterministic models.  
Figure 12: Overall cycle time in the stochastic and deterministic models.
# LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments
## 4 Multi-Sensor Data Fusion Framework
### Manifold-Based Unscented Kalman Filter
#### 4.3.3 Design of Point Cloud Distortion Correction and Relocalization  
During the motion of a robot, the lidar sensor cannot simultaneously capture a complete frame of point cloud data when the robot employs turning or other locomotion methods. As a result, the point cloud data may exhibit a degree of distortion. Because the sensor itself cannot perform distortion correction, IMU (Inertial Measurement Unit) information is required for specific correction and compensation. For systems equipped with high-frequency IMUs, only the time difference between two consecutive IMU data frames needs to be calculated (\(\mathbf{R}_{d}=\frac{t_{i}-t_{k+1}}{t_{k}-t_{k+1}}\mathbf{R}_{o}\)) and interpolation is unnecessary. Integration of the angular velocity is achieved through the simplified process shown below.  
\[\mathbf{R}_{x,y,z}^{\mathrm{t}}=\mathbf{R}_{x,y,z}^{\mathrm{t-1}}+\mathbf{A}_{ x,y,z}^{\mathrm{i}mu}*\Delta t \tag{26}\]  
For stable systems, relying solely on point cloud matching is insufficient. To ensure environmental stability in various conditions, fusion of multiple sensors is necessary. In this paper, we introduces an adaptive relocalization algorithm, Realtime Array Calibration (RAC), which addresses the reduction in point cloud matching performance when there are numerous similar feature points (see Algorithm 1).  
```
Input:\(P_{b},P_{rac},A_{imu},R_{cal}\) // Robot pose, RAC pose, acceleration, Quaternions Output:\(\hat{P}_{b}\) // Predict pose
1 fordatainputdo \(oft=IterationOfTheNearestPoint(Source,Target)\); // Source,Target are Point clouds at some point \(P_{cal}\leftarrow\mathit{CompensateForRACData}(P_{rac},oft)\); \(R\leftarrow\mathit{CompensateForQuaternions}(R_{imu},oft)\); \(e_{d}\leftarrow\mathit{EuclideanDistance}(P_{b},P_{rac})\); \(e_{a}\gets ErrorAngle(P_{b},P_{rac})\); if\(e_{a}>e_{a}mod(2*pi)\)then \(e_{a}=e_{a}mod(2*pi)\); if\(e_{a}<limit1\) and \(e_{d}<limit2\)then  return \(\hat{P}_{b}\);  end if
2  end if
3  end if
4  end for return
```  
**Algorithm 1**Adaptive Relocalization Algorithm.
# Haskap Juicing Method Effects on Haskap Juice Quality
## 2 Materials and Methods
### Antioxidant Capacity Assays  
#### 2.9.1 Total Antioxidant Capacity Assessment (DPPH)  
The antioxidant activity of juice was evaluated using DPPH radical scavenging assay [38]. For the analysis, 0.02 mL of juice extract was mixed with 0.02 mL of DMSO, then subsequently mixed with 2.96 mL of freshly prepared 0.1 mM DPPH solution. The mixture was reacted in the dark for 30 min at room temperature. Spectrophotometric absorbance at 516 nm was measured afterwards. Three mL of DPPH was taken as the control. The radical scavenging activity was calculated with the equation:  
RSA (%) = (Absorbance of control \(-\) Absorbance of sample)/Absorbance of control \(\times\) 100.
# Evaluation of pH and Optical Properties of Dual Ringe HEDP Irrigating Solution
## 3 Results  
### pH Values of Endodontic Irrigants  
The pH measurements of different irrigating solutions showcased significant variations in acidity and alkalinity (Figure 1, Table 2). Distilled water (DW) presented a nearly neutral pH, while sodium hypochlorite (NaOCI) demonstrated a markedly alkaline pH. Dual Ringe HEDP and the HEDP + NaOCI combination displayed substantial alkalinity, with HEDP presenting the highest pH value out of all tested irrigants. Similarly, EDTA exhibited an alkaline pH, while CA showed high acidity. Chlorexidine (CHX) displayed a slightly acidic pH. Concurrently, the potential difference (mV) readings underscored the electrochemical variances between the solutions, with each irrigating solution demonstrating distinctive millivolt values as presented in Table 2.
## 3 Recurrence Quantification Analysis
### Estimation of the Embedding Parameters
#### 3.1.3 Optimal Values of \(m\) and \(\tau\)  
\(r(m,\tau)\) is a bivariate function whose expression leads to  
\[\lim_{(m,\tau)\rightarrow(m_{0},\tau_{0})}r(m,\tau)=1 \tag{8}\]  
So we define the cost function \(f(m,\tau)\):  
\[f(m,\tau)=\bar{a}(m,\tau)-\bar{a}(m+1,\tau) \tag{9}\]  
The optimal values \(m_{0}\) and \(\tau_{0}\) become  
\[(m_{0},\tau_{0})=\underset{(m,\tau)\in\mathbb{N}^{*}}{argmin}\left\{f(m,\tau)\right\} \tag{10}\]  
Numerical results show that values of \((m_{0},\tau_{0})\) obtained from (10) become independent from modulation schemes, SNR, and the number of samples per symbol \(N_{s}\). Figure 2  
Figure 1: The proximity coefficient \(r(m,\tau)\) based on the embedding dimension \(m\) for a 16-QAM signal.  
illustrates the cost function \(\mathbf{f}(m,\tau)\) with respect to \((m,\tau)\) for a noisy 16-QAM signal. We can notice that  
\[\forall\ \tau\geq 6,\quad m\geq 1,\quad\mathbf{f}(m,\tau)\to 0 \tag{11}\]  
The number \(K\) of state vectors is given by  
\[K=N-(m-1)\tau\geq K_{min} \tag{12}\]  
Based on [PERSON] theorem (in [PERSON]'s theorem, the phase space of a system can be reliably reconstructed if and only if \(m\geq 2D+1\), where \(D\geq 1\) is the dimension of the system attractor [36, 44]) and the minimum number \(K_{min}\) of state vectors for reliable detection, we conclude that \(m_{0}\) and \(\tau_{0}\) should be chosen in the following ranges:  
\[(m_{0},\tau_{0})\in\left[\ 3\ ;\ \frac{N-K_{min}}{\tau_{0}}\ \right]\times \left[\ 6\ ;\ \frac{N-K_{min}}{2}\ \right] \tag{13}\]  
However, we notice that in practice \(K_{min}\ll N\). Consequently, we can  
\[(m_{0},\tau_{0})\in\left[\ 3\ ;\ \frac{N}{\tau_{0}}\ \right]\times \left[\ 6\ ;\ \frac{N}{2}\ \right] \tag{14}\]  
The recurring states in the phase space can be highlighted by the Recurrence Plot.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 4 Experimental Comparison
### Disturbed Scenario  
In the presence of interference, the process of correcting the Kalman filter with a neural network can be divided into the following steps:  
Figure 9: Two-dimensional and Z-direction error analysis plots for loosely coupled closed-loop system vs. tightly coupled system.  
Data Collection and Processing: Initially, data required for training the neural network need to be collected. This includes measurement data from INS, observation data from GNSS, etc. These data undergo appropriate preprocessing, such as noise removal and normalization, to facilitate the training of the neural network.  
Definition of Neural Network Structure: Based on specific application scenarios and requirements, an appropriate neural network structure is defined. Here, the networks used for comparison are a conventional CNN and a combination of a CNN and LSTM. The input to the network comprises observation data and state data, while the output is measurement error.  
Training of the Neural Network: The collected data are utilized to train the neural network. The training objective is to enable the neural network to learn the mapping relationship between the Kalman filter parameters and the INS state estimation and actual GNSS observation data. During training, the network's performance is optimized by adjusting parameters such as weights and biases.  
Neural Network Correction: In the Kalman filter process, the trained neural network is employed to adjust the covariance matrix and other parameters of the Kalman filter.