esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Addressing this gap is advisable to establish how to maximize the effectiveness of any amendment in enhancing soil properties while minimizing the potential environmental impacts and resource inefficiency. An assessment of the amendments' effects on water retention capacity and the soil's air-to-water ratio can be conducted by estimating several parameters derived from the soil water retention curve and evaluating the soil physical quality (SPQ) [3; 4]. Capacitive-based parameters, such as macroporosity (\(P_{MAC}\)), air capacity (\(AC\)), relative field capacity (\(RFC\)), and plant available water capacity (_PAWC_), can provide valuable information about the effects of a treatment on specific soil pore classes, i.e., macroopes (drainable porosity) or meso-micropores (matrix porosity), which have direct effects on water flow and water redistribution process [4]. These parameters, together with the dry soil bulk density, \(BD\), have particular relevance in agronomic contexts, since they describe the overall soil strength, aeration, and ability to store and provide water to plant roots [22]. From a practical point of view, it appears necessary to establish the following, with reference to a given parameter: (i) if the amendment can actually serve the purpose of improving the SPQ; (ii) what is the minimum dose that makes physically good an otherwise poor soil; and (iii) what is the amount of amendment that should not be exceeded to avoid the risk that a good SPQ deteriorates to unacceptable levels [23]. To give an answer to these questions, it is necessary to compare the relationship expressing the amendment rate effect on the considered parameter with the optimal values of that parameter. All questions can be answered if the optimal conditions are defined in terms of the range of values. This is the case, for example, of the \(RFC\) for which the optimal conditions are \(0.6\leq RFC\leq 0.7\)[3]. Instead, only the first two questions can be answered if the optimal conditions are defined by a single value discriminating between a poor and a good SPQ. An example is the \(AC\), for which the optimal conditions for sandy-loam to clay-loam soils are defined by \(AC\geq 0.14\) cm\({}^{3}\)/cm\({}^{3}\)[3]. Likely, the possibility of giving an answer to all three questions regardless of the parameter could further broaden the practical interest of the SPQ investigations. The general purpose of this investigation was to compare the effects of three commercial organic amendments, that is compost, vermicompost, and biochar, on physical quality parameters of two coarse-textured soils with more than 70% sand. The specific objectives were as follows: (i) to review the available literature on the optimal values of the selected SPQ parameters (\(BD\), \(P_{MAC}\), \(AC\), \(PAWC\), and \(RFC\)) in order to suggest an optimal range of values for each parameter; (ii) to determine which soil amendment yielded the best response on coarse soils based on the considered SPQ parameters; and (iii) to verify if a single amendment rate could produce a good SPQ with reference to all considered parameters.
## 5 Numerical Results
Additionally, we discovered that when the number of antennas is increased from \(M=2\) to \(M=3\), the probability of a packet outage is greatly reduced because doing so enables the system to obtain greater diversity gain. Therefore, based on our simulation results, we can conclude that the HHT protocol is a preferable option for energy harvesting in wireless communication systems, as it achieves better system performance compared to the HDT protocol. Figure 7: The simulation results of the impact of energy harvesting time on system reliability.
## 2 Local Path Generation Based on Spatial Perception
### Dynamic Control Point Optimization  
The quasi-uniform cubic B-spline curve's six control points have been established, laying out an initial local obstacle avoidance path. Nonetheless, there's potential for this path segment to veer too close to obstacles, which might divert it from the optimal trajectory. The inherent characteristics of B-spline curves mean local control point modifications will not alter the overall curve. By incorporating road data, the positions of control points \(A_{2}\) and \(A_{3}\) are adjusted, framing the path planning challenge in mathematical terms. To maintain the safety of the intelligent driving sightseeing vehicle during obstacle avoidance, considerations are made for both the timeliness and the stability of the maneuver. The distance \(d_{obs}\) is used to determine whether the intelligent vehicle will collide with obstacles during the driving process. \(d_{obs}\) represents the minimum gap from the obstacle's center to the segment joining of \(A_{1}\) and \(A_{4}\), and gauges the risk of collision. The vehicle should adhere to the subsequent condition:  
\[d_{obs}\geqslant(W+0.5) \tag{6}\]  
where \(W\) denotes the vehicle's width. Accounting for the precision of the actual path-tracking algorithm, a buffer of 0.5 m is incorporated into the driving process. If \(d_{obs}\) does not adhere to Equation (6), local refinements of the B-spline curve's control points \(A_{2}\) and \(A_{3}\) become requisite. These control points are offset by a distance \(d_{4}\) along the B-spline curve in the positive normal direction. \(d_{4}\) is equal to half the vehicle's width with an added minor margin, facilitating local path optimization. The determination of \(d_{4}\) is equal to half the vehicle's width with an added minor margin, facilitating local path optimization. The determination of  
\[d_{4}=(W+0.5)-d_{obs} \tag{7}\]  
Figure 3: Illustration of B-spline curve control points.
## 3 Multi-Physical Coupled Fields Simulation Analyses
### Visualized Analysis  
To further investigate the aerodynamic mechanism and elucidate the phenomena observed in Section 3.2, a comprehensive comparative analysis was conducted on the flow characteristics of the airfoil-based flutter piezoelectric energy harvester system under a constant incoming flow velocity of 8 m/s. Figures 9 and 10 illustrate the pressure distributions and velocity profiles under four mounting angles during one vibration cycle.  
For the case of the 0\({}^{\circ}\) mounting angle (Figures 9a and 10a), the flow field exhibits symmetric characteristics. A low-pressure region coupled with high-velocity flow alternately develops near the leading edge of the upper and lower surfaces at maximum and minimum angles of attack, respectively, corresponding to peak aerodynamic force generation. In contrast, at a 3\({}^{\circ}\) mounting angle (Figures 9b and 10b), the flow field demonstrates significant asymmetry, with more pronounced low-pressure regions and higher flow velocities at the maximum angle of attack, while these effects are substantially weakened during the downward motion. This asymmetric behavior aligns with the kinematic data in Figure 6, where the energy harvester achieves an 8.4\({}^{\circ}\) maximum angle of attack during  
Figure 9: Pressure distribution diagram with the installation angle: (**a**) 0\({}^{\circ}\) installation angle; (**b**) 3\({}^{\circ}\) installation angle; (**c**) 6\({}^{\circ}\)installation angle; (**d**) 9\({}^{\circ}\) installation angle.  
upward motion but only 2.2\({}^{\circ}\) during downward motion, resulting in predominant energy harvesting during the upward phase.  
At higher mounting angles of 6\({}^{\circ}\) and 9\({}^{\circ}\) (Figure 9c,d and Figure 10c,d), the system experiences a local stall during its motion cycle, with notable flow separation at maximum angle of attack. This phenomenon is particularly severe at a 9\({}^{\circ}\) mounting angle, where flow separation persists even as the angle of attack decreases toward 0\({}^{\circ}\), leading to reduced displacement and voltage output capabilities compared to the 6\({}^{\circ}\) configuration. However, comparative analysis of Figure 9a-d reveals that increasing mounting angles generate greater lift at 0\({}^{\circ}\) angle of attack, providing enhanced excitation for flutter initiation, which explains the observed decrease in critical velocity with increasing mounting angle as shown in Figure 5.
## 2 Materials and Methods
### Specification of the Numerical Analysis
The spatial independence analysis is developed under the same conditions and for the same geometry as the domain independence analysis. Five discretizations known as meshes are built, with the same structure, but with a number of partitions according to the degree of refinement of each one. Meshing was done in _ANSYS Meshing_ software. The static body that simulates the far fluid field has a structured mesh with only quadrilateral elements, while the mobile body that simulates the field near the rotor has an unstructured mesh with predominant quadrilateral elements and some triangular elements to achieve greater adaptability to geometry (Figure 5 left). The mesh adjacent to the rotor walls is refined and has a perpendicular layer structure (inflation) that allows a better prediction of the boundary layer (Figure 5 right). The results obtained in this analysis are shown in Figure 6 and allow estimating the averaged \(C_{T}\) for each mesh. It is observed that the variation of the averaged \(C_{T}\) is minimal when compared with the corresponding value of the finest mesh, thus generating an indicator of asymptotic convergence of 1.0122 according to the [PERSON]'s extrapolation (Table 3). When using the \(k-\omega\)\(SST\) turbulence model, it is recommended that the value of \(y^{+}\) be less than unity to ensure proper predictions in the flow near the walls [32; 36]. For this reason, the fourth mesh is selected, which also requires a much shorter simulation time than the finest mesh (Table 3). \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Domain** & **1** & **2** & **3** & **4** & **5** \\ \hline Blockage ratio \((D/W)\) & 0.4 & 0.2 & 0.1 & 0.05 & 0.025 \\ Number of elements & 73,804 & 99,408 & 201,821 & 611,476 & 2,250,093 \\ Averaged \(C_{T}\) & 0.5393 & 0.3192 & 0.2393 & 0.2048 & 0.1950 \\ Deviation & 176.63\% & 63.73\% & 22.74\% & 5.03\% & \\ Simulation time [h] & 3.12 & 4.20 & 8.53 & 25.83 & 95.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Domain independence test results. Figure 5: General structure of the mesh (**left**) and details of the mesh near the profile walls (**right**). Starting from the fourth meshing, the temporal discretization independence analysis is carried out in the same way, under the same conditions, and for the same geometry as the meshing independence analysis. Five time discretizations are taken, generated by dividing the period of the rotor's revolution into a number of elements or _time steps_. The results obtained in this analysis are shown in Figure 7 and allow estimating the average value of \(C_{T}\) for each temporal discretization (Table 4). It is evident that the variation of the averaged \(C_{T}\) decreases as the partition tightens, getting closer and closer to a convergence value that is supported by an asymptotic convergence indicator of 1.0281 for Richardson's
## 3 Parameter Modeling and Error Analysis of the INS and GNSS Systems
### GNSS Error Analysis and Modeling
#### 3.2.2 Ionospheric Delay Error  
When the satellite signal reaches the ionosphere, the propagation path and propagation velocity of the signal will be affected by the ionosphere and change accordingly. In this case, if we still use the time measured by the receiver to calculate the geometric distance between the satellite and the receiver, there will be a large deviation, which is the so-called ionospheric delay error [29]. The approximate model is as follows:  
\[I\propto\left[1-(\frac{R\cos\theta_{nt}^{as}}{R+h_{i}})^{2}\right]^{-\frac{1} {2}}, \tag{30}\]  
where \(\theta_{nt}^{as}\) is the elevation angle of the satellite, \(R\) is the average radius of the earth, and \(h_{i}\) is the average ionospheric altitude.
# Clustering of a Health Dataset Using Diagnosis Co-Occurrences
## 3 Results
### Selection of the Clustering Solution  
All approaches were applied to cluster the diagnoses of the health dataset of the ED and the quality indicator MR was computed. Figure 1 illustrates the approaches' HAC-cooc Rule (a), HAC-cooc Rule (b) and HAC Jaccard with the MR criterion as a function of the number of clusters. The plot shows increasing MR values with the decreasing number of clusters. While these former approaches performed well, the HAC-RR and MCA HAC approaches (for all cluster linkage) were inappropriate, because they did not identify any patterns: they produced a single cluster catching all singleton block clusters at each agglomeration. Particularly, the shared variance explained by the first two components produced by the MCA approach was only 1.87%, which is very low (compared to the minimum of 1.32%) and indicates a very poor ability to find clusters. The HAC Jaccard approach succeeded in producing an adequate clustering distribution, but with poor performance regarding the indicator of the present study, with a mean MR of 0.496 for solutions from 5 to 100 clusters. Even though the MR was 1.53, 3.82 and 4.53 for four, three and two clusters, respectively, the RRintra was too low with a value of 0.47 for the three solutions. Using the Jaccard coefficient directly as the rule for the HAC-cooc approach performed even worse, with MR values between 0.1 and 0.31 with the optimum number of clusters being 103. The HAC Yule approach provided good clustering results with better quality performance for solutions between 130 and 70 clusters with an MR ranging from 0.44 to 1.47. Although it performed less well than our proposed approaches, the HAC Yule approach still yielded good quality solutions, with an MR between 0.98 and 1.53 for solutions with RRintra \(>1\) with an optimal result at 47 clusters, and a maximal value of 1.58 at 2 clusters but RRintra = 0.45. For both the direct (rule (a)) and indirect strategies (rules (b)), the HAC-cooc approach had the best overall quality performances compared to the state-of-the art algorithms. Rule (a) was initially better for solutions with 45 to 115 clusters. However, for solutions with less than 45 clusters, rule (b) showed a sudden rise and thus rules (a) and (b) produced similar clustering solutions in terms of MR, which varied between 1.6 and 1.82 for solutions between 45 and 13 clusters. With rule (a), the best solution was with 17 clusters, an MR of 1.77 based on an RRintra of 1.5 and RRinter of 0.82. Rule (b) produced a better solution
## 3 Problem Description and Model Formulation
### Demand Model  
In the real world, demand fluctuations may be influenced by various factors, such as seasonal trends and brand preferences. This study examines how the trade-off between price and freshness influences customer purchasing behavior and its implications for optimal pricing and inventory policies. To keep the model simple and tractable, this study uses a multinomial logit model for demand. The multinomial logit (MNL) model [26] is a popular choice for demand modeling in the literature. In the model shown in Equation (1), the demand ratio \(\lambda_{j,t}(p_{j,t})\) for product \(i_{j,t}\) with price \(p_{j,t}\) relative to all fresh products on the shelf is obtained.  
\[\lambda_{j,t}(p_{j,t})=\frac{e^{-b_{j}p_{j,t}}}{1+\sum_{k=1}^{m}e^{-b_{k}p_{k, t}}} \tag{1}\]  
The parameter \(b_{j}\) represents price sensitivity, indicating how responsive customers are to price changes. A higher \(b_{j}\) results in larger changes in the demand ratio with price fluctuations, while a lower \(b_{j}\) leads to smaller changes in the demand ratio with price fluctuations. Next, the total demand \(d_{t}\) is distributed probabilistically to each product using Equation (2).  
\[d_{j,t}(p_{j,t})=d_{t}\lambda_{j,t}(p_{j,t}) \tag{2}\]  
The inventory state is updated at the next time \(t+1\) following Equation (3).  
\[i_{j,t+1}=\{i_{j,t}-d_{j,t}(p_{j,t})\}^{+} \tag{3}\]  
where \(\{x\}^{+}\) represents \(\max\{x,0\}\).  
As shown in Equation (4), unmet demand is determined.  
\[d_{t}^{\prime}=\sum_{k=1}^{m-1}\{d_{k,t}(p_{k,t})-i_{k,t}\}^{+} \tag{4}\]  
As shown in Equation (5), sales are determined.  
\[g_{t}=\sum_{k=1}^{m-1}p_{k,t}(i_{k,t}-i_{k,t+1}) \tag{5}\]  
One of the key challenges in perishable inventory management is to make decisions under the uncertainty of demand. Demand forecasting is a well-known method to predict future demand based on historical data. While accurate forecasts can enhance inventory management, this study takes a different approach. Instead of focusing on forecasting, we model the decision problem under demand uncertainty as an MDP. By computing the state values within the MDP, we derive the optimal policy for perishable inventory management without explicitly forecasting demand.
## 3 Results
### DFT20 and DFT40 Values for Various Binders and Aggregates of Different Sizes and Polishing Cycles  
The COF values were measured using DFT at speeds of 20 and 40 km/h across multiple polishing cycles, including initial (0 cycles), 30 K, 70 K, and terminal (140 K) cycles. This was accomplished for CB and Rhy based on their performance in BPT results for three different aggregate sizes: HFST, medium, and coarse. Among the binders, epoxy resin, PG88-16, CRS-2P, PG82-22 (PM), and PG88-16 (PM) showed better performance in the BPT values before and after polishing and were therefore selected for this evaluation. Figure 7 shows that the initial COF values at 20 and 40 km/h were similar across all binders for the HFST gradation, with CB having higher values than Rhy. After 30 K polishing cycles, the epoxy resin maintained a higher COF for CB compared with other binders, while PG82-22 (PM) showed the least decline among the asphalt-based binders over time. For Rhy, PG88-16 performed similarly to epoxy at 30 K cycles but declined after 70 K and 140 K cycles, with epoxy performing best at 140 K. The CRS-2P emulsion experienced a sharp drop in COF after 30 K cycles due to bleeding as well as aggregate and binder loss during the testing period, which exposed the substrate. As a result, further testing at 70 K and 140 K cycles was not conducted for this binder. For the medium gradation with CB, epoxy resin showed the smallest COF decline, while PG88-16 (PM) performed well after 30 K cycles, and PG82-22 (PM) had the highest COF after 70 K cycles. For Rhy, PG82-22 (PM) led at 30 K, but the epoxy resin had the highest COF at 140 K cycles. In the coarse gradation, CB had higher COF than Rhy, with asphalt-based binders like PG88-16 showing better friction retention (19% reduction) compared with epoxy (23%). For Rhy, PG88-22 (PM) recorded the highest COF at 140 K cycles, with a 38% reduction, outperforming epoxy's 77% drop. Larger aggregates improved friction, making PG and modified binders strong alternatives to epoxy resin due to their better durability and reduced aggregate loss during polishing cycles. CRS-2P followed a similar pattern after 30 K polishing cycles, with significant bleeding and aggregate loss.
## 3 Results
### Factors Influencing Vegetation Growth Changes in the Mining Area  
#### 3.3.1 Correlation Between Temperature, Air Precipitation, and Vegetation Growth  
Precipitation and air temperature are important climate factors that determine the growth and distribution of vegetation. Based on meteorological data collected from 1999 to 2023, a Pearson correlation analysis [53] was conducted to examine the relationship between seasonal average precipitation, temperature, and kNDVI in the Huoijituo mining area (Figure 14). The analysis shows that kNDVI exhibits similar seasonal patterns with seasonal average precipitation and temperature. Specifically, both precipitation and temperature reach their lowest levels in winter, increase in spring, peak in summer, and decrease in autumn. This pattern results in high correlations between kNDVI and seasonal average precipitation (r = 0.81, \(p\) < 0.01) as well as seasonal average temperature (r = 0.65, \(p\) < 0.01). From 1999 to 2023, both precipitation and kNDVI showed an upward trend (linear regression slopes of 0.0048 and 0.00071, respectively), while temperature remained relatively stable (linear regression slope of \(-\)0.0092). Additionally, kNDVI and summer average precipitation were significantly higher than those in spring (\(p\) < 0.01), but there was no significant difference in average temperature between these two seasons. Overall, the increasing trend in precipitation suggests that it is the most influential climatic factor for the peak value of kNDVI, playing a crucial role in vegetation growth in the Huoijitu mining area.
## 3 Experimental Methodology
### Shock and Swept-Frequency Experiments of Metal Lattice-Polyurea Parallel Composite Structure  
#### 3.2.1 Experimental Setup  
To evaluate the damping enhancement effect of the parallel polyurea damping structure on the metal lattice vibration isolation system, an experimental setup was designed based on the metal lattice-polyurea parallel composite structural model shown in Figure 1b. Shock and swept-frequency tests were then conducted on the metal lattice-polyurea parallel device using the TBS-800 shaking table (Suzhou Su Shi Testing Group Co., Ltd., Suzhou, China). The experimental setup is shown in Figure 5, where the load mass and baseplate are made of 304 stainless steel, with the load mass \(m=56.54\) kg.  
The two body-centered cube with balls (BCC-B) metal lattice structures, fabricated using selective laser melting (SLM) with Ti-6 Al-4V powder, and the polyurea damping  
Figure 4: Force–displacement curve of viscoelastic material under dynamic cyclic loading.  
Figure 5: Experimental setup of the metal lattice–polyurea parallel composite structure.  
structures were symmetrically arranged and rigidly connected between the load mass and baseplates using screws. Figure 6 shows the BCC-B metal lattice structure model and sample. The BCC-B metal lattice structure consists of a 3 \(\times\) 3 \(\times\) 3 BCC-B lattice unit cell and two integrated cover plates at the top and bottom. The unit cell has a side length (\(L\)) of 15 mm, a beam diameter (\(d\)) of 1.5 mm, a ball diameter (\(b\)) of 2.5 mm, and a cover plate thickness (\(t\)) of 2 mm. The stiffness and damping coefficients under shock and vibration excitations were calculated through shock and swept-frequency experiments, respectively. Polyurea samples were designed based on the research results of their dynamic mechanical properties.  
The shock excitation and swept-frequency signals were provided by the shaking table, with the vibration signals detected by acceleration sensors (Model B06A00, Shandong LNS Intelligent Technology Co., Ltd., Dezhou, China) whose factory calibration certificate specified measurement range (\(\pm\)50 g), sensitivity (10.469 mV/(m/s\({}^{2}\))), frequency response (\(\pm\)5% within 1 Hz to 8 k Hz), and resolution (0.1 mg rms). The shaking table's output signal was monitored and controlled by sensor (#1), the shaking table's output was detected by sensor (#2), and the load response signal was measured by sensor (#3). The output signals from the shaking table and load response signals were acquired using the high-precision data acquisition device INV3018 (China Orient Institute of Noise & Vibration, Beijing, China) and processed with DASP-V11 software.