esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Effect of Water Content on Consumed Energy  
As the rapid drop of the penetration force must be related to the propagation of the main crack, it is necessary to establish the relationship between the energy release and the crack propagation. The consumed energy is introduced here, which, in the physical sense, is the energy consumed by the crack propagation and is calculated as follows:  
\[W=\int F(u)du\approx\sum_{i=1}^{n}F_{i}\Delta u_{i} \tag{7}\]  
where \(F_{i}\) is the indentation load of the _ith_ step, and \(u_{i}\) is the penetration depth of _ith_ step.  
The variation of the consumed energy with the water content as the cutter penetrated the granite specimen is shown in Figure 12b. As the water content increased, the consumed energy generally decreased. Through the comparison of the two indentations, it can be found that when the water content was 0, the energy required for the first indentation was 111.13 J and that required for the second indentation was 106.06 J. When the water content was increased to 0.43%, the required energy for the first indentation was 78.43 J and that required for the second indentation was only 39.26 J. This indicates that water can reduce the energy required to break a granite specimen, which is good for rock breakage. This phenomenon can be attributed to the softening effect of water on rocks, which reduces the effective stress in the rocks and weakens the reinforcing effect brought by the stress concentration in the rocks.
# Learning Attention-Aware Interactive Features for Fine-Grained Vegetable and Fruit Classification
## 4 Experiments
### Overall Performance Comparison  
We report a performance comparison in Table 3 and provide our analysis as follows:  
* AIF-Net presents the best performance in both top-1 and top-5 Acc on the VegFru292 set, outperforming the second-best, ResNet50, by 2.83% in top-1 and 0.627% in top-5. On Veg200, AIF-Net outperforms ResNet50 in top-1 Acc (89.154% vs. 88.195%) but slightly underperforms ResNet50 in top-5 Acc (98.045% vs. 98.187%). On Fru92, our AIF-Net outperforms the second-best API-Net by 1.14% in top-1 Acc, and also outperforms the second-best ResNet50 by 0.1% in top-5 Acc. It is observed that ResNet50, as a generic deep learning model, can achieve superior performance in this task, demonstrating its potential in FGVC. Additionally, the proposed AIF-Net presents its superior predictive power through interactive feature learning combined with a fusion of global and local attentive feature maps.
* Surprisingly, the SOTA methods (DCL, API-Net, and WS-DAN) that use ResNet50 as a backbone underperform ResNet50 in both top-1 and top-5 Acc on the VegFru292 set. Although API-Net demonstrated superior performance in other data sets [14], its performance in VegFru is sightly worse than its backbone network ResNet50, except on Fru92, where API-Net posts a top-1 Acc of 89.914%, with a 0.59% improvement over ResNet50. The results show that with interactive feature learning alone, the model does not present consistent performance improvement on the VegFru data set.
* The proposed AIF-Net, on the other hand, demonstrates a consistent improvement over both ResNet50 and API-Net, which means that a combination of an attentive feature aggregation and interactive feature learning can effectively push a model to learn subtle and fine-grained patterns from both local and global attentive feature maps, leading to consistent performance boost.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set** & **Number of Regions** & **Top-1 Acc** & **Top-5 Acc** \\ \hline \multirow{3}{*}{Veg200} & 2 & 87.132\% & 97.513\% \\  & 3 & 89.154\% & 98.045\% \\  & 4 & 88.019\% & 97.861\% \\ \hline \multirow{3}{*}{Fru92} & 2 & 90.629\% & 97.917\% \\  & 3 & 91.058\% & 98.809\% \\  & 4 & 90.681\% & 98.051\% \\ \hline \multirow{3}{*}{VegFru292} & 2 & 86.795\% & 97.513\% \\  & 3 & 90.832\% & 98.619\% \\ \cline{1-1}  & 4 & 88.019\% & 97.861\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: An evaluation of different numbers of informative regions.
## 2 Materials and Methods
### Magnetic Levitation Force
The magnetic norm and contour for two different magnet positions are shown in Figure 4a,b. The color scale refers to of the magnetic field density in (T). Each magnet yields a spatial distribution of the magnetic flux lines. These plots are useful for visualizing where the magnetic flux is stronger and how fast it decreases with distace from the magnet. Figure 4: Magnetic flux density norm of the 2 DOF harvester: with two separated magnets (**a**) and two magnets together (**b**). Figure 3: Magnetic forces between the moving and fixed magnets \(F_{B,A_{1}B_{2}}\) and \(F_{B,A_{2}B_{2}}\) (**a**), and the magnetic restoring force \(F_{A_{1}A_{2}}\) between two moving magnets (**b**). The blue line marks the mathematical model, the blue dots represent the experimental results, and the black line with crosses denotes the FEM results. With two separated magnets, the magnetic flux is spread over the width of the structure, giving a weaker yet more homogeneous field. For simplicity, in the FEM model, the effect of rotation was omitted. For the magnetic levitation harvester, the magnetic forces (called magneto-elastic) are crucial and decisive for the definition of resonant frequency. Magnetic force models are defined using the polynomial curve fitting approach with numerical and experimental findings. The mathematical model has been calculated using the MATLAB Curve Fitting Toolbox. It offers extensive fitting capability, including evaluating the goodness of fit using residuals and prediction bounds. The magnetic forces between the moving and fixed magnets \(F_{B_{1}A_{1}B_{2}}(x)\) and \(F_{B_{1}A_{2}B_{2}}(x)\) were fitted to the experimental data, and a mathematical polynomial model was proposed,  
\[F_{B_{1}A_{1}B_{2}}(x)=F_{B_{1}A_{2}B_{2}}(x)=p_{1}x^{6}+p_{2}x^{5}+p_{3}x^{4}+ p_{4}x^{3}+p_{5}x^{2}+p_{6}x+p7, \tag{1}\]  
where \(x\) is the moving magnet's displacement (\(x_{1}\) or \(x_{2}\)) and \(p_{1}\)-\(p_{7}\) are the coefficients obtained from the curve-fitting technique. The magnetic force between the magnets \(A_{1}\) and \(A_{2}\) can be described by  
\[F_{A_{1}A_{2}}(x_{1},x_{2})=1+k_{11}(x_{1}-x_{2}-0.1)+k_{22}(x_{1}-x_{2}-0.1)^ {5}, \tag{2}\]  
where \(k_{11}\) and \(k_{22}\) are the linear and nonlinear stiffness coefficients. All coefficients are listed in Table 2. The fit goodness of the proposed models is described by the R-square parameter, having a value of about 0.97, which indicates that a greater proportion of variance is accounted for by the model.
## 2 Results and Discussion
### Examples
#### 2.2.1 Plant samples
Based on the results of performed analysis, the network graph provides us with some rough ideas that the genotype difference affects mostly on energy-providing pathways, especially the TCA cycle and its intermediates and precursors. Since mitochondria is the major cellular organelle in which all these reactions occur, a logical hypothesis could be generated from our data mining results, for example that mitochondria biochemistry may be affected in _Pinus taeda_ L. with such genotypes.
## 5 Experimental Simulation and Result Analysis
### Simulation Results and Analysis
#### 5.2.1 Solution Results for Dataset N  
By subtracting the power parameter sequence of the final assembly sequence obtained from the painting production line sequence, one can determine which vehicle bodies have been moved. The difference between the input and output sequences is shown in Figure 7. Based on the greedy strategy state transition algorithm, the calculation of the average number of iterations is illustrated in Figure 8. It can be observed that when the average number of iterations reaches 34, the score of the optimization plan will have already approached a stable state. At this point, the average computation time required is 19.07 s, and the optimal average score result for the multi-objective optimization model is 23.46.  
Figure 8: Iteration plot for the state transition algorithm based on the greedy strategy for dataset N.  
Figure 7: Difference plot between input and output sequences for dataset N.  
From Figure 7, it can be seen that the difference between the input and output sequences appears in the form of 31 peaks, indicating the number of times the painting production line sequence was moved while meeting the requirements of the final assembly production workshop. The detailed vehicle movement process is shown in Table 1. In the table, \"1\(\rightarrow\)4\" indicates that the position of vehicle body number 1 in the original sequence has been moved to the position of vehicle body number 4 in the new sequence, \"6\(\rightarrow\)15\" indicates that the position of vehicle body number 6 in the original sequence has been moved to the position of vehicle body number 15 in the new sequence, and so on.
## 4 Results
### Healing Inspection  
The results of the UPV and permeability tests are presented in this section to highlight the impact of reservoir systems on the crack healing. In Figure 7 the UPV measurements are reported and the data are split into two groups: Figure 7a presents UPV values for partially and fully healed beams (B2-B4 and B11-B12) and Figure 7b illustrates the results for unhealed beams (B7-B10 and B13-B15).  
It is shown that the UPV through the crack after healing was almost fully recovered (black compared to the intact light blue points series) for all  
Figure 6: Ultimate force, toughness and compliance recovery for the healed (Beam 4), the partially healed (Beam 11) and the unhealed beam (Beam 9). The load-crack opening curves of these samples are reported in Figure 5.  
Figure 7: UPV measurements for (**a**) partially and fully healed (B2–B4 and B11–B12) and (**b**) unhealed beams (B7–B10 and B13–B15). The UPV is not measured at the re-healing phase for the unhealed series.  
present (Beams 2, 3, 4, 11 and 12). The UPV recovered by 95% on average and increased by 126% relative to the damaged state (dark blue dot series).  
On the contrary and as expected, the UPV recovery was not evident in the unheaded series (Beams 5-10 and 14-15). Indicatively, in none of the cases, the UPV did reach values higher than 4000 m/s after cracking. The UPV results demonstrate the potential of the technique to track the crack refilling, agent polymerization and mechanical restoration after healing.  
It should be reported that the UPV was also measured after the second round of healing and no recovery of UPV was evident (red series), another indication that repeatable healing cannot be reached with this beam's design.  
The water permeability test results validate the crack tightness by tracking the water transport rates [21]. As reported in Figure 8, the permeability coefficient reached very high values in the unheaded case (black series), whereas low values were obtained for partially healed beams (red series).  
It should be noted that both Figures 7 and 8 report on inspection relative to the crack size at the end of the first loading cycle as obtained by DIC. This way the authors wish to communicate the critical role of crack opening on healing feasibility. The UPV inspection accuracy was proven by the fact that the velocity remained high relative to the intact state for small-size cracks (see dark blue dots series) and significantly dropped as the cracks were open up to 550 m. Interestingly, after healing, all cracks, regardless of their size, appeared well-sealed and recovered in Figure 7a. Thus, it is shown that the healing provided by vascular networks can be effective at any crack size.  
On the contrary, no link between the crack width and the water permeability could be acquired. Previous studies reported on the fact that healing can be achieved, although the crack is partially sealed [22]. The crack plane roughness and spatial topography appeared to control the water flow and as a result, cracks of identical size appeared to have different permeability coefficients [23].
## 4 Theoretical Error Analysis
### Projection Accuracy
#### 4.1.2 Errors in Propagation Delay  
Consider the model for the clocks of the sensor \(S\) and the anchor \(A\):  
\[f_{S}(t)=t\ f_{A}(t)=at+b\]  
where \(a\) is the skew, \(b\) is the offset, and \(t\) is the global reference time. When the detector sensor node \(S\) and the anchor node \(A\) exchange timestamps, with \(T_{S}=f_{S}(t_{send}),T_{R}=f_{A}(t_{send}+t_{prop})\), the corresponding error in propagation delay can be calculated as:  
\[\Delta t_{prop}=\ t_{prop}-(T_{R}-T_{S}) \tag{13}\]
## 2 Materials and Methods
### In-Situ Gaseous \(H_{2}\) Setup Design and Testing
#### 2.2.2 In-Situ H\({}_{2}\) Test Procedure  
In this research, the newly designed test setup was used to perform SSRT tests on the base and weld metal specimens. The tests consist of a constant cross-head displacement rate of 1.5 mm/h until fracture occurs, which equals a strain rate of approximately \(10^{-5}\)/s for a smooth specimen. Fracture is defined as the point where the applied load drops by 20% from its maximum between two measurements, which were taken once per second. In case a test is performed in hydrogen gas, a pre-charging step is included in the test procedure. The system is twice flushed with nitrogen gas and evacuated in order to remove any impurities in the system. Then it is pressurised up to the test pressure, which is held for 10 min while the pressure is monitored. If the pressure does not drop, e.g., the volume is not leaking, the system is evacuated once more and filled with H\({}_{2}\) up to the testing  
Figure 4: **(a) Overview of the specimen and adapters inside the tensile tester. (b) Cross-section of the area outlined in red in (a) displaying the connection between the specimen and the bottom adapter.**  
pressure. This pressure is maintained overnight to allow for sufficient hydrogen diffusion into the steel to reach near-equilibrium values. This is discussed further in Section 2.3.1.  
Three groups of specimens were subjected to SSRT tests in this work. BM X60 samples without a notch, BM samples with a notch and specimens including the girth weld and a notch in the weld zone (WM samples). All groups were tested under 100 bang \(\mathrm{N_{2}}\) pressure and 100 bang \(\mathrm{H_{2}}\) pressure. The smooth (un-notched) BM samples were also tested under lab environment without applied pressure of any gas to evaluate the influence of the hoop stress created by the internal pressure. Notched BM specimens were compared to smooth BM specimens to evaluate the general influence of a blunt notch and to validate the expectation that a blunt notch does not substantially affect the tensile properties of the specimen. All WM specimens were notched to enforce fracture in the weld zone. These specimens were also tested at 30 and 70 bang \(\mathrm{H_{2}}\) pressures in order to record the influence of varying pressures. This is summarised in Table 2. All combinations of specimen type and environment were tested at least threefold to verify repeatability.
## 3 Simulation Verification
### Analysis of Results  
As can be seen from the simulation diagram, both APF algorithm and A* + DWA algorithm do not complete the path planning from the starting point to the target, so only the improved fusion algorithm of this paper is analyzed for its results, and the results are shown in Table 3.  
Based on the above two sets of working condition experiments, it can be seen that when random U-shaped obstacles appear on the global path, the fusion algorithm of A* + DWA as well as the traditional APF algorithm are unable to perform avoidance and are caught in the localized oscillations of the U-shaped obstacles.  
In this paper, the fusion algorithm inflates the U-shaped obstacle ellipsoidal and relies on the virtual target point provided by the ellipse to attract the vehicle for avoidance; at the same time, when dynamic obstacles appear around and the vehicle model passes through the narrow channel, the fusion algorithm establishes an envelope circle model of the vehicle, which is regarded as a whole and can prevent the outer profile from touching the obstacle, compared with A* + DWA's fusion algorithm in Case 2. In contrast, the A* + DWA fusion algorithm in Case 2 bypasses the first U-shaped obstacle, but its planned path touches the boundary of the obstacle. As can be seen from Table 3, the minimum distance between the obstacle and the center of the rear axle of the vehicle is 1.79 m, 1.82 m, and 1.66 m, respectively, which are all higher than the minimum distance of 1.60 m set in this paper, which in turn proves that the method can improve the safety of the path.  
And the average steering angles under the three sets of working conditions are 12.57\({}^{\circ}\), 7.98\({}^{\circ}\), and 8.28\({}^{\circ}\), respectively, and the maximum steering angle does not exceed the set value of 40\({}^{\circ}\). For the problem that the algorithm is prone to sharp corners of the path due to uneven joint forces while avoiding obstacles, the fusion algorithm in this paper can well smooth the path in real time. As can be seen from Table 3, the average curvature change  
Figure 37: A map of the distances between the path points and the nearest obstacles.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Obstacle** & **Map Size** & **Path** & **Average** & **Rate of Change** & **Standard** & **Minimum** \\
**Rate P/\%** & **Length/m** & **Steering** & **of Average** & **Deviation of** & **Distance from** \\
**Angle/\({}^{\circ}\)** & **Curvature** & **Curvature** & **Curvature** & **Obstacles/m** \\ \hline
29.50 & 50 \(\times\) 50 & 67.21 & 12.57 & 0.248 & 0.159 & 1.79 \\ \hline
31.10 & 60 \(\times\) 60 & 88.13 & 7.98 & 0.162 & 0.158 & 1.82 \\ \hline
30.78 & 70 \(\times\) 70 & 99.74 & 8.28 & 0.169 & 0.158 & 1.67 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation data results of the fusion algorithm in this paper.  
rates under the two conditions are 0.248, 0.162, and 0.169, respectively, and the standard deviation of curvature is about 0.16, which indicates that the overall path curvature change is relatively smooth, and the path smoothing effect is good.
## 1 Introduction  
In the rhizosphere, many microorganisms including bacteria and fungi are abundantly present, most often organized in micro-colonies, and grow by consuming various nutrients secreted by the plant roots. Some of these microorganisms benefit the plant both directly and indirectly, leading to the stimulation of plant growth. Apart from inducing growth promotion, these beneficial microbes suppress disease-causing microbes and accelerate nutrient availability and assimilation. Thus, in the quest to improve soil fertility and crop yield and to reduce the negative impacts of chemical fertilizers on the environment, there is a need to exploit these microbes for continued beneficial agricultural purposes. Beneficial microbes have been applied to a wide range of agricultural species for the purpose of growth enhancement, including increased seed emergence, plant weight, crop yield and disease control [1; 2; 3; 4]. Several reports confirm the efficacy of this technology and their suitability as components in sustainable agricultural practices.  
Research on Plant Growth Promoting Rhizobacteria (PGPR) and Plant Growth Promoting Fungi (PGPF) has been increasing at a fast pace since the term PGPR was first used by [PERSON] and coworkers in the late 1970s [1]. The various mechanisms of action of these microbes in bringing beneficial effects of growth promotion, disease suppression, induced systemic resistance (ISR), _etc._, to the plants have been reported. Beneficial microbes enhance the nutrient status of host plants by biological N\({}_{2}\) fixation, increasing the availability of nutrients in the rhizosphere, increasing root surface area, and various combinations of these activities [5]. The vast majority of studies that investigate the mode of action of these plant growth promoting microorganisms have focused on their influences on plant growth regulating substances [2; 3; 6; 7].  
Fungi produce various mixtures of gas-phase, carbon-based compounds called volatile organic compounds (VOCs) that due to their small size are able to diffuse through the atmosphere and soils. Several fungal VOCs have been identified to occur as either simple hydrocarbons, heterocycles, aldehydes, ketones, alcohols, phenols, thioalcohols, thioesters, benzene derivatives or cyclohexanes [8; 9]. [PERSON] _et al_. [10] first reported that the microbial VOCs 2,3-butanediol and acetoin promoted the growth of _Arabidopsis thaliana_. PGPF-derived VOC mediated growth promotion in plants was first reported by [PERSON] _et al_. [11]. VOCs of certain PGPFs including the biocontrol fungus _Trichoderma_ have been shown to enhance plant growth [7; 11]. Here, we report the plant growth promoting potential of the PGPF _Cladosporium cladosporioides_ CL-1. This isolate was originally obtained from the rhizosphere of red pepper and has been proven earlier to induce growth promotion in paprika, tobacco and _A. thaliana_. Some of volatiles produced by CL-1 were identified in this study using solid phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC-MS).