esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Bubble-Particle Interaction
### Bubble-Particle Attachment
[152; 153; 154] discovered that surface roughness (ranging from 1 to 100 \(\upmu\)m) effectively decreased the time for the TPC formation and liquid film drainage on Teflon surfaces. Specifically, the t\({}_{\text{TPC}}\) shortened from 105 ms to just a few milliseconds as the surface roughness increased from around 1 \(\upmu\)m to 80 \(\upmu\)m, a reduction that was further enhanced in distilled water with low fother concentrations (\(\alpha\)-terpineol and n-octanol). Additionally, [PERSON] et al. [128] found that the TPC formation time increased from 2.5 ms (in distilled water and 3 \(\times\) 10\({}^{-5}\) M \(n\)-octanol solution) to about 18 ms at a higher \(n\)-octanol concentration of 6 \(\times\) 10\({}^{-4}\) M. Lastly, [PERSON] et al. [162] observed that as the surface roughness of low-rank coal samples increased, the diameter of the wetting film decreased while the expansion velocity of the TPC line increased, indicating that the wetting film ruptured more easily and that a stable TPC line formed more quickly on rough surfaces. Numerous investigations show that surface roughness significantly affects the bubble-particle collision and attachment process; however, some findings remain contradictory. Some researchers argue that surface roughness stimulates and accelerates the thinning and rupturing of the liquid film [102; 127; 163], while also noting that larger cavities in rough surfaces can accommodate more gas, leading to a faster formation of the three-phase contact (TPC) required for bubble attachment [130; 155; 164; 165]. In contrast, some other researchers suggest that surface roughness can hinder the bubble-particle attachment. This occurs because the pores and grooves on rougher hydrophilic surfaces can be entrapped and filled up with water, preventing the formation and expansion of the three-phase contact line on these rough surfaces [83]. Figure 13: Kinetics of TPC expansion of Teflon surfaces of different roughness [155]. Differences in the measurement methods and parameters used to determine the attachment time have resulted in significant variations in the reported values. The effect of surface roughness on the attachment time between air bubbles or water droplets and particle surfaces can only be explained through comparative analysis, and the relationship cannot be directly explained by the attachment time [166]. Currently, there is no accurate model that explains the correlation between attachment time, wettability, and floatability. Predicting these factors through modeling remains challenging as the colloid and surface chemistry aspects of thinning and wetting liquid films have yet to be fully quantified, and mineral surfaces are typically heterogeneous. It is essential to establish a standardized measurement method or instrument for determining the attachment time and to develop a feasible predictive model that relates the attachment time to surface roughness.
# Inferring TLB Configuration with Performance Tools
## 3 Results
### Analysis Results
While the heatmap result is not detailed in this study, the capacity of the STLB was noted to be 1024 entries. Therefore, an XOR-7 mapping with 1024 entries implies an 8-way association. Table 1.0 The i9 ITLB heatmaps and gradient plots. In Figure (**a**), the red square highlights the configuration of 16 ‘sets’ and 8 ‘ways’. In Figure (**b**), the red line for 16 ‘sets’ shows an increase at 4 ‘ways’; however, ‘32’ and ‘64’ also show increases in the same area. This is another example where the gradient plot supports the heatmap but does not independently provide the exact configuration. 3. IBM Power9 DERAT, IRAT, TLB: This study aimed to assess whether the methodology applied to Intel CPUs could be transferred to another ISA. The objective was to execute the C programs and shell scripts designed to collect and analyze data. The first challenge involved identifying the HPCs that are equivalent to those on Intel, which provides insights into the TLB configuration. To determine the configurations of DERAT and IRAT, we hypothesized that the data collected by pm_tlb_hit could serve as the equivalent to Intel's dtlb_misses.sthb_hit. Similarly, for TLB configurations, we assumed that pm_tablewalk_cyc could be the counterpart to Intel's event dtlb_load_misses.walk_active. In Figure (a)a, the pm_tlb_hit event was used to determine the size of the TLB. The capacity of the TLB appears to be a combination of 'ways' and'sets' that totals 1024. Given that the unit under test operates in Radix mode, it can be inferred that the actual size of the TLB is half of this number, equating to 512 entries. However, from the heatmap alone, we can only deduce that the total capacity is 1024, as indicated by the red squares. Table 1.1 The i9 STLB heatmaps and gradient plots. Figure (**a**,**b**) indicate that the STLB employs an XOR-7 mapping function. The gradient plot, Figure 9b, does not offer additional insights. It confirms the same combinations resulting in a total of 1024, allowing us to ascertain the capacity of the TLB, but it does not provide specific details regarding the configuration of 'ways' and'sets'. In Figure 10a, pm_tablewalk_cyc shows a pattern at'sets' 512 and 1024. However, the TLB configuration of'sets' 512 and 'ways' 4 cannot be deduced from this heatmap alone. Additionally, the gradient plot shown in Figure 10b does not provide any further insights into the correct configuration. Even though this study did not successfully reverse engineer the Power9 TLB configuration, the patterns observed in the heatmaps suggest that further modifications in the code, along with the exploration of additional events, could yield improved results. Table 1.2 Power 9 tlb hit heatmap and gradient plots. From Figure (a), we can only deduce that the total capacity is 1024, suggesting that the linear mapping function is not the one used by the system. Figure (b) does not provide the same insights as those observed with the Xeon or i9.
## 2 Materials and Methods
### Response Surface Parameters
#### 2.2.5 Distance between Heat Source and Thermal Camera--\(d_{heat-cam}\)  
The second mentioned influence of the source velocity can be minimised by implementing an additional parameter: the distance between heat source and thermal camera. The optimal distance between the heat source and the thermal camera is dependent on the movement speed of the heat source relative to the sample to be inspected and the starting depth of the defect measured from the top surface. A visual representation can be found in Figure 4. The optimal spacing between the heat source and the camera is the distance where the time needed for a sample section to travel from the heat source towards the camera equals the required time for the heat wave to travel through the sample, reflect on the defect and return to the surface. Equation (1) represents this constraint. The minimal distance is 50 mm and the maximum distance is 600 mm for this research.  
Figure 4: The distance between the heat source and the thermal camera has utter importance in maximising the temperature difference between a sound region and a defect region. The heat wave injected in the sample to be inspected needs an amount of time to reach the defect and to reflect towards the surface. This period is dependent on the depth of the defect and the material properties. A visualisation of the travelling heat wave in an optimised scenario can be seen in multiple steps starting at subfigure (**a**–**f**).  
In an optimised occasion, the time needed for the heat wave to propagate through the sample twice is equal to the time calculated by  
\[t_{h}=\frac{d_{heat-cam}}{v_{source}} \tag{1}\]  
where \(t_{h}\) represents the time needed for the heat wave to reach the defect and reflect to the surface, \(d_{heat-cam}\) the distance between the heat source and the camera and \(v_{source}\) the velocity of the heat source relative to the sample.
## 2 Theory and Method
### Geometry Model and Parameters  
To simulate the leaching process of the multi-step horizontal salt cavern, an idealized geometry model is assumed, as shown in Figure 4a. The initial geometry is composed of a reflux cavity and solution cavity. The boundary is the normal direction deformation boundary. The values of the formation and geometry parameters are adopted [32; 44; 45], as shown in Table 1. The depth of the formation is 1500 m. Thus, the temperature and pressure of the cavern is the same as that of the formation. The concentration of the initial brine in the salt cavern is saturated. The leaching duration of every stage is 50 days. Whole leaching includes 5 stages.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Sign** & **Value** & **Units** \\ \hline Diffusion coefficient & D & \(1.6\times 10^{-9}\) & m\({}^{2}\)/s \\ Molar mass of NaCl & M & 58.5 & g/mol \\ Density of NaCl & c\({}_{\text{s}}\) & 2165 & kg/m\({}^{3}\) \\ Saturated brine concentration & c\({}_{\text{sat}}\) & 318.4 & g/L \\ Viscosity of saturated brine & \(\mu_{0}\) & 0.001 & Pa-s \\ Density of saturated brine & \(\rho_{\text{sat}}\) & 1200 & kg/m\({}^{3}\) \\ Dissolving rate of salt rock & C\({}_{\text{dis}}\) & \(3.5\times 10^{-5}\) & m/s \\ \hline Depth of the formation & H & 1500 & m \\ T of the injection fluid & T\({}_{\text{in}}\) & 293.15 & K \\ Initial T of the salt wall & T\({}_{\text{wall}}\) & 333.15 & K \\ Pressure of salt formation & P\({}_{0}\) & 15 & MPa \\ Inner tube diameters & d\({}_{\text{in}}\) & 177.8 & mm \\ Outer tube diameters & d\({}_{\text{out}}\) & 244.5 & mm \\ Length of the solution cavity & Ls & 50 & m \\ Length of the reflux cavity & Lr & 50 & m \\ Injection flow rate & Q & 200 & m\({}^{3}\)/h \\ Leach time of every stage & T & 50 & day \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of the coupled model.  
Figure 4: Schematic diagram of geometry and meshing results.
# Progress in Construction Robot Path-Planning Algorithms: Review
## 4 Local Path-Planning Algorithms
### Deep Reinforcement Learning Algorithm
#### 4.4.1 Q-Learning  
Q-learning is a model-free reinforcement learning algorithm that is widely applied in the field of construction robotics. The algorithm aims to determine the optimal policy by learning the state-action value function (Q function). The Q function represents the expected return of taking a certain action in a particular state. The learning process mainly relies on the Bellman equation, which provides the theoretical basis for updating Q values. Through continuous exploration and exploitation of the environment, Q-learning can find the optimal policy within a limited time, thereby maximizing cumulative rewards. The algorithm has many advantages, such as simplicity, model independence, and wide applicability. However, its poor scalability, slow convergence, and the challenge of balancing exploration and exploitation have always constrained its development. Scholars have made many efforts to address these issues. [PERSON] proposed the Double Q-learning method to address the slow convergence rate, which involves two independent Q-value estimations. Double Q-learning can more accurately estimate state values, thereby increasing convergence speed and learning efficiency [91]. [PERSON] used the Flower Pollination Algorithm (FPA) to improve the initialization of Q-learning, accelerating its convergence [92], and also utilized a modified version of Q-learning, the Improved Q-learning (IQL), to speed up machine learning [93]. [PERSON] introduced an enhanced version of the Q-learning algorithm, referred to as optimized Q-learning (O-QL). This refined approach incorporates a novel technique for initializing the Q-table, which plays a crucial role in setting the foundation for the learning process. Additionally, it presents a fresh strategy for selecting actions, aiming to improve decision-making during the learning phase. Furthermore, a redefined reward function is implemented to better align with the specific objectives of the task at hand. To optimize learning rate adjustments, the Root Mean Square Propagation method is utilized, ensuring that updates to the learning parameters are both effective and efficient. Collectively, these modifications lead to a significant acceleration in the learning process, enhancing the overall efficiency of path planning [94]. [PERSON] introduced a dynamic rewards factor to Q-learning for improving the efficiency of path planning and the capacity of obstacle avoidance [95]. [PERSON] used artificial potential fields to prevent random exploration to avoid the slow convergence of Q-learning and provide basic information about the environment for mobile robots. [PERSON] also proposed a dynamic reward factor to accelerate algorithm convergence [96].
## 3 Case Study
### Modeling Preparation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Input**} & \multicolumn{6}{c}{**Statistics**} \\ \cline{2-7}  & \multicolumn{3}{c}{**Training Datasets**} & \multicolumn{3}{c}{**Test Datasets**} \\ \cline{2-7}  & **Average** & **Standard** & **Median** & **Average** & **Standard** & **Median** \\  & **Value** & **Deviation** & **Median** & **Value** & **Deviation** & **Median** \\ \hline Concrete age (d) & 59.75 & 34.64 & 59.75 & 59.72 & 34.63 & 59.50 \\ Initial temperature (\({}^{\circ}\)C) & 20.85 & 1.96 & 20.90 & 20.77 & 2.02 & 20.90 \\ Cooling inlet water & 14.60 & 5.46 & 14.35 & 14.10 & 5.62 & 13.94 \\ temperature (\({}^{\circ}\)C) & & & & & & \\ Cooling water flow rate & 6.24 & 8.85 & 6.13 & 6.36 & 8.88 & 6.41 \\ (L/min) & 22.97 & 5.87 & 23.27 & 22.93 & 5.89 & 23.25 \\ Ambient temperature (\({}^{\circ}\)C) & 36.66 & 2.68 & 35.00 & 36.94 & 2.70 & 35.00 \\ Concrete grade & 2.95 & 0.26 & 3.00 & 3.01 & 0.55 & 3.00 \\ Height of the block (m) & 13.02 & 5.73 & 12.35 & 12.54 & 5.40 & 12.10 \\ Intermittent time of the & & & & & & \\ block (d) & 254.59 & 521.36 & 0.00 & 239.87 & 502.47 & 0.00 \\ Heat dissipation area of the & & & & & & \\ block (m\({}^{2}\)) & 254.59 & 521.36 & 0.00 & 239.87 & 502.47 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of the dataset. Comparing the sensitivity of multiple \(\beta\) values within the range of 0-1 to the model's performance, the model showed the optimal accuracy when \(\beta\) was 0.1. Therefore, in this article, \(\beta\) was set to 0.1. During the KKT condition test, the time required for a single test was approximately 1 s, while the average time for the model update computation was approximately 48 s.  
Additionally, the ISVR, BP, and LSTM models were introduced for performance comparison. Two comparison strategies were employed: (1) comparing the unoptimized BP, LSTM, and ISVR models and (2) comparing the unoptimized ISVR model with the SSA-ISVR model optimized via SSA. The BP and LSTM models utilized well-established modeling mechanisms, where network weights were automatically optimized and can obtain relatively optimal parameters during training through backpropagation. ISVR and BP hyperparameter tuning were performed through cross-validation, while the LSTM model was determined using an early stopping strategy. All model configurations and training parameters are summarized in Table 3.
###### Abstract
However, most research focused on crack initiation and propagation as well as DSIF, and was less concerned about studying crack behaviour in a complete process, including its initiation, propagation and arrest, and even its stop. Under impact loading, the mechanical behaviour of fracturing specimen presents a complicated state, as material physical parameters change significantly with loading rates [24; 25]. Sometimes the tensile strength of material increases by 6-8 times, even exceeding to nearly an order of magnitude [26]. With well-established computational techniques, combined with the actual experiment, an experimental-numerical method is introduced into the research of crack fracture mechanisms and crack propagation. Therefore, the physical experimental results could be validated, and crack extension could be well predicted through computational techniques. A finite difference code (AUTODYN) has demonstrated great superiority in solving multifarious problems characterized by both geometric nonlinearity and material non-linearity, performing elegantly in investigating crack propagation in the complete process [27; 28; 29; 30]. In this paper, an improved single cleavage semi-circle specimen was applied. Meanwhile, impact experiments were carried out to investigate the rock crack propagation phenomenon with the validity of the configuration verified. Moreover, the effect of loading rate on crack propagation behaviour was considered as a significant factor. In order to study the crack propagation velocity, the crack propagation gauge (CPG) test system was employed. Then, a suitable numerical simulation model was established by using a tensile crack softening law [31]. Some comparative studies between experiments and simulations were carried out to verify the modelling approach and to check its level of accuracy. At last, an accurate numerical simulation with ABAQUS finite element code figured out the crack propagation DSIF of the whole process [32]. Therefore, the present paper demonstrates a method to determine the crack propagation dynamic stress intensity factor in the whole process, which providesthe reference data for practical projects to deploy a rational strategy for engineering design and hazard mitigation. Notably, the loading rates' effect on crack propagation and DSIF has significant meaning for engineering applications attempting to deal with the problems related to loading impact change. Moreover, the investigation of the crack propagation behaviour and the influence of holes on the extending crack illustrates the physical mechanism of a practical phenomenon in engineering projects. It also provides some insights into the crack propagation process, as well as proposing an innovative crack arrest technique to take advantage of this with regards to tunnelling and deep earth engineering. The experimental and numerical simulation investigation method, which can be utilized to measure the mechanical parameters of brittle material, can be applied in civil engineering for further research.
# GPS + BDS Network Real-Time Differential Positioning Using a Position Domain Estimation Method
## 3 Results
### Static Experiment
#### 3.1.1 Data Set I
For both PD and RD, the percentage of position errors in each threshold has a higher value when using GPS + BDS solution instead of GPS-only solution. In GPS + BDS solution, the percentages of the position errors falling within \(\pm\)1 m in the N, E and U components are 96.4%, 99.4% and 89.4% for RD, and 99.4%, 100.0% and 96.7% for PD.
# Detection of Azo Dyes in Curry Powder Using a 1064-nm Dispersive Point-Scan Raman System
## 2 Materials and Methods
### Acquisition of Spectral Image  
Each sample was held immobile in the two-stage moving platform. An exposure time of 1 s and laser power of 120 mW was used for collection of spectral signal. Figure 1 shows the process of acquiring the spectral image by the point-scan method. The process is a row-wise scan. After collecting Raman spectrum from a point, the sample is moved horizontally (X-axis) with a 0.25 mm increment to the next point. After completing the Raman spectral measurement from the first row, the sample is moved vertically (Y-axis) to the next row with a 0.25 mm increment. The process was repeated to collect Raman spectra across the 25 mm \(\times\) 25 mm surface area of the sample. The Raman spectra were accumulated spatially to construct a hyperspectral image of the sample. The final sample image was a 100 \(\times\) 100 \(\times\) 512 hyperspectral cube with 10,000 spatial pixels, each of 512 spectral wavenumbers. One hyperspectral cube was constructed for each mixture sample. Prior to acquisition of Raman spectra, a dark current spectrum was acquired with the laser off and a cap covering the probe. The dark current spectrum was subtracted from the Raman spectrum at each pixel during spectral measurement.
# Acute Effects of Whole-Body Vibration on Gait Kinematics in Individuals with Parkinson's Disease
###### Abstract  
**Background:** Whole-body vibration (WBV) favors central integration and elaboration of proprioceptive stimuli, enhancing gait performance in individuals with Parkinson's disease (PD). However, the effect of WBV on spatiotemporal gait kinematics in PD has been neglecting so far. This study aims to examine how exposure to WBV could influence kinematic parameters in PD. **Methods:** Gait kinematic parameters of 26 mild-stage PD participants (age: \(66.7\pm 1.63\) years) were measured using BTS G-Walk sensor during a 10 m walk test under three conditions--WBV, half squat without vibration (HS), and control condition (CC)--in a crossover randomized design. **Results:** Walking time was significantly slower (\(p<0.01\)) in CC compared to WBV and HS, while no significant differences were observed between WBV and HS. Right leg propulsion was significantly lower in CC compared to HS (\(p<0.01\)), with no significant differences between CC and WBV. Left leg propulsion was significantly lower in CC and WBV compared to HS (\(p<0.01\) and \(p<0.05\), respectively). Pelvic tilt was significantly lower (\(p<0.05\)) in CC compared to WBV and HS, but no significant difference was observed between WBV and HS. Cadence was significantly lower (\(p<0.05\)) in CC and WBV than HS. **Conclusions:** WBV shows promising effects on functional mobility and postural control in PD, with HS offering greater benefits. Exercise modalities should be carefully selected to enhance different gait parameters.  
**Keywords:** motor symptoms; gait performance; vibration stimuli; isometric exercise
## 4 Experimental Research on the Multi-DOF Seat Damping Suspension
### Test Results  
Since a vehicle damping seat mainly works at medium and low frequencies, the test excitation frequencies were set to 1 Hz, 1.5 Hz, 2 Hz, 2.1 Hz, 2.2 Hz, 2.3 Hz, 2.4 Hz, 2.5 Hz, 2.6 Hz, 2.7 Hz, 2.8 Hz, 2.9 Hz, 3 Hz, 3.5 Hz, 4 Hz, 4.5 Hz, and 5 Hz, and each excitation test used 0 A current, 1 A current, 3 A current, and 5 A current. During the test, the acceleration values at the center of the upper and lower platforms were simultaneously measured, and the vibration transmission rate was calculated as the test result. The seat platform was tested in the vertical, horizontal, and lateral directions. The test results are shown in Figure 18.  
Figure 18 shows the multi-dimensional vibration-damping seat suspension test results for the 0 A, 1 A, 3 A, and 5 A currents. Figure 18a-c show the test results of the vertical, horizontal, and roll directions. As seen from Figure 18, with an increase in the excitation frequency, the vibration-damping seat's vibration transmission rate in the three directions initially increased, and then decreased. When the vibrational excitation was more than 4 Hz, the vibration transmission rate in the three directions was less than 1, indicating that the vibration-damping seat entered the vibration-damping area, and could realize vibration damping in the three directions.  
Figure 17: Vibration test system of the multi-dimensional damping seat test platform. (**a**) 6-DOF vibration test bench; (**b**) vibration test bench control system.  
In the resonance region of 1-3 Hz, the vibration transmission rate in three directions was greater than 1, indicating that vibration amplification occurred. After adding the fixed current, the vibration amplification amplitude gradually decreased, due to an increase in damping. After the current increased to 5 A, the vibration transmission rate in the resonance region was close to 1. This result shows that the current applied to the MRF damper effectively improved the low-frequency vibration response in all directions. However, it was also found that increasing the MRF damper's current in the high-frequency damping region increased the vibration transmission rate, and reduced the damping effect. Therefore, further research on semi-active vibration control is necessary to obtain better damping effects.