esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Groups of Criteria**} & \multicolumn{8}{c}{**Scenario**} \\ \cline{2-11}  & \multicolumn{3}{c}{**SC 1**} & \multicolumn{3}{c}{**SC 2**} & \multicolumn{3}{c}{**SC 3**} \\ \hline Spatial & & 0.75 & & 0.25 & & 0.50 & \\ \hline Socio-economic & & 0.25 & & 0.75 & & 0.50 & \\ \hline \multicolumn{11}{c}{**Location Evaluation Results (Ranking of Locations)**} \\ \cline{2-11}  & \multicolumn{3}{c}{**SC 1**} & \multicolumn{3}{c}{**SC 2**} & \multicolumn{3}{c}{**SC 3**} \\ \cline{2-11}  & **L1** & **L2** & **L3** & **L4** & **L1** & **L2** & **L3** & **L4** & **L1** & **L2** & **L3** & **L4** \\ \cline{2-11}  & 31.75 & 30.75 & 32.75 & **34.5** & 31.25 & 32.25 & **42.25** & 41.5 & 31.5 & 31.5 & 37.5 & **38** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation results for the candidate locations according to different scenarios. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Evaluation Criteria**} & \multirow{2}{*}{**WC**} & \multicolumn{5}{c}{**Scores for Candidate Locations**} \\ \cline{3-6}  & & **L1** & **L2** & **L3** & **L4** \\ \hline Distance from protected natural areas & WC3 & 11.25 & 4.5 & 6.75 & 11.25 \\ \hline Distance from the nearest inhabited places and residential buildings for noise & WC3 & 11.25 & 11.25 & 11.25 & 11.25 \\ \hline Proximity to energy facilities for connecting the wind farm & WC3 & 2.25 & 4.5 & 9 & 9 \\ \hline Number of frosty days during the year & WC3 & 4.5 & 6.75 & 11.25 & 11.25 \\ \hline Engineering and geological characteristics of the soil & WC3 & 6.75 & 4.5 & 11.25 & 11.25 \\ \hline Relief features—terrain slopes & WC3 & 9 & 11.25 & 11.25 & 11.25 \\ \hline Local community’s acceptance of the location & WC3 & 11.25 & 11.25 & 11.25 & 11.25 \\ \hline Distance from water surfaces & WC2 & 7.5 & 6 & 6 & 6 \\ \hline Distance from protected immovable cultural assets & WC2 & 7.5 & 6 & 6 & 4.5 \\ \hline Distance from the nearest inhabited places and residential buildings for the effect of shadow flicker & WC2 & 3 & 7.5 & 6 & 7.5 \\ \hline Distance from the nearest inhabited places and residential buildings for the visual effect & WC2 & 3 & 7.5 & 3 & 4.5 \\ \hline Land purpose & WC2 & 6 & 6 & 7.5 & 7.5 \\ \hline Possibility of transportation & WC2 & 3 & 3 & 7.5 & 6 \\ \hline Distance from traffic infrastructure & WC1 & 5 & 4 & 5 & 5 \\ \hline Spatial organization of the land & WC1 & 4 & 5 & 5 & 5 \\ \hline Land ownership & WC1 & 4 & 4 & 5 & 5 \\ \hline Seismicity & WC1 & 2 & 1 & 3 & 2 \\ \hline Landscape—exposure of the location & WC1 & 3 & 1 & 1 & 1 \\ \hline Total scores & 104.2 & 105 & 127 & 130.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation results for the candidate locations according to weight categories. Although the results of the evaluation for the candidate locations do not highlight a significant difference between them in terms of point, they clearly indicate the reasons (advantages and disadvantages) for selecting the most suitable location and adequately simulate the process of selecting a location.
# PS-CARA: Context-Aware Resource Allocation Scheme for Mobile Public Safety Networks
## 6 Simulation Results and Discussions
### Users Receive Interference under the PS-CARA Scheme  
The performance of the proposed PS-CARA scheme is tested under the PS scenario. The PS-CARA scheme is verified for three possible cases: (1) PS-CARA: PS and Non-PS users without power control, (2) PS-CARA: with all users using power control, and (3) PS-CARA: PS UE without power control and Non-PS UE with power control. The reason for simulating these cases is to check the suitability of the PS-CARA scheme under different power control situations, and then to find the best suitable case.  
The simulation results depict that the proposed PS-CARA scheme results in very low interference for case 2, where power control is applied by considering the location and deployment situation of PS and non-PS users. The results in Figure 7 clearly demonstrate that by applying PS-CARA with power control, around 70% of total users lie in between the low interference range of \(-100\) dBm to \(-80\) dBm, whereas for the PS-CARA without the power control scheme, only 3% of total users are in the specified low-interference range.  
Figure 6: User transmit power under the proposed PS-CARA scheme for various \((P_{0})\) and (\(\alpha\)).  
Figure 7: User received interference under the proposed PS-CARA scheme.  
Furthermore, we individually plotted the effect of interference on PS and non-PS users for the three cases described above. From the simulation results shown in Figure 8 we can clearly notice that the case 2 PS-CARA, with all users using power control, is the best possible solution to reduce the interference. Hence, based on these results, we conclude that PS-CARA with power control is the best possible option in the public safety scenario as it reduces the interference remarkably.
# An Application of Manifold Learning in Global Shape Descriptors
## 6 Conclusions  
With recent advances in imaging and data processing, shape analyses have found applications in various fields, from archaeology to bio-informatics. The current contribution utilizes Laplacian Eigenmap as a foundation, and introduces a simple, easy-to-implement, yet efficient global shape fingerprint. We have shown that the spectrum of LB operator that is discretized using exponential weighting scheme can efficiently represent a 3D shape, which can be used for object retrieval and classification. We have equipped our descriptor with an optional, straight-forward normalization method, which makes it suitable for applications in which scale-invariant and noise-resistant descriptors are preferred. The proposed shape fingerprint belongs to the category of spectral shape descriptors, which does not require training, as deep-learning based methods do. The proposed descriptor, as well as being used alone in geometry analysis problems, can be combined with learning-based methods to improve their performance. For example, in problems in which the number of samples is not enough, adding features extracted independently by a spectral method can prevent learning methods from over-fitting. Two possible extensions to our work could be investigating the applicability of the proposed descriptor in differentiating 3D objects with boundaries (e.g., 3D planes), and utilizing computed eigenvectors for graph partitioning. In the end, we conclude that manifold learning methods can be used to develop new spectral-based shape descriptors to learn the structure of manifolds despite the quality of sampled meshes.  
Conceptualization, F.S.B. and R.R.; methodology, F.S.B. and R.R.; software, F.S.B. and R.R.; formal analysis, F.S.B.; writing--original draft preparation, F.S.B. and R.R.; writing--review and editing, F.S.B., R.R., P.P., R.M.D. and Z.Y.; and visualization, F.S.B.  
This research received financial support from GE Healthcare through the UWM Catalyst Grant program, the Center for Predictive Computational Phenotyping supported by the National Institutes of Health Big Data to Knowledge (BD2K) Initiative under Award Number U54 AI117924, and the grant UL1 TR002373 from the Clinical and Translational Science Award (CTSA) program of the National Center for Advancing Translational Sciences, NIH.  
Our sincere thanks goes to [PERSON] and [PERSON] for their expertise and constructive comments. Any errors are our own and should not tarnish the reputation of these esteemed scientists.  
The authors declare no conflict of interest.
# Effects of Fineness and Dosage of Fly Ash on the Fracture Properties and Strength of Concrete
## 2 Research Significance  
Laboratory investigations have shown that when the fly ash particle size is reduced, its mechanical performance in concrete is improved. This study additionally confirms the comprehensive results that significant improvements in concrete strength and fracture toughness without loss in workability can be achieved with fine fly ashes, whose particle size passes through no. 250 and no. 325 sieves. Furthermore, the finer the fly ash is, the higher the enhancement of the strength, fracture energy, and critical stress intensity factor of the concrete will be.
# Research on the Trajectory and Operational Performance of Wheel Loader Automatic Shoveling
## 4 Loader Automatic Shoveling Operation Performance Analysis
### Full Bucket Rate Analysis  
The full bucket rate is one of the most critical evaluation standards for the automatic shoveling performance of wheel loaders. Any other work is futile if the full bucket rate is not up to standard. The shoveling method of manual control can only rely on the operator's experience to meet the full bucket rate. However, frequent operations and long working hours make it difficult to ensure that the full bucket rate can reach the standard. In the case of meeting the full bucket rate, other shoveling performances may not always meet the requirements. It is not easy to achieve consistency in shoveling performance parameters each time. Therefore, the automatic shoveling of the loader is very important, which can eliminate the dilemma of manual operation shoveling and avoid some human errors.  
Since this test was carried out according to the independently planned trajectory, other parameters and the test conditions were consistent, so the calculation of the full bucket rate only needed to be calculated according to the relevant parameters of the shoveling trajectory. The formula for calculating the full bucket rate is as follows:  
\[E=\frac{V_{1}}{V_{2}}\times 100\% \tag{2}\]  
where \(E\) is the full bucket rate; \(V_{1}\) is the volume of material in the bucket; and \(V_{2}\) is the rated volume of the bucket.  
Since the rated bucket volume and the bucket's width in Equation (2) are known, the bucket is shown in Figure 10. Therefore, the full bucket rate can be obtained by finding \(V_{1}\). According to the planning principle of the operating trajectory, the volume of material  
Figure 9: Parallel shoveling trajectory.  
in the bucket is equal to the volume of material between the stockpile's surface and the shoveling trajectory. Thus, the theoretical volume of material in the bucket is:  
\[V_{1}^{\prime}=BS \tag{3}\]  
where \(V_{1}{}^{\prime}\) is theoretically the volume of the material in the bucket; \(B\) is the bucket width; \(S\) is the shovel cross-sectional area.  
The theoretical full bucket rate calculated from the shoveling trajectory is:  
\[E=\frac{V_{1}^{\prime}}{V_{2}}\times 100\%=\frac{BS}{3}\times 100\%=\frac{2.9 \times 1.05}{3}\times 100\% \tag{4}\]  
In general, it is impossible to achieve a 100% full bucket rate due to operating resistance, material particle size, and the loader's performance, which means that the true \(V_{1}\) is not equal to the \(BS\), so the specific shoveled volume in the bucket is calculated based on the porosity, the density, and the specific shoveled weight in the bucket.  
\[V_{1}^{\prime\prime}=\frac{G(1+N)}{\rho} \tag{5}\]  
Therefore, the full bucket rate is:  
\[E^{\prime}=\frac{V_{1}^{\prime\prime}}{V_{2}}=\frac{G(1+N)}{3\rho} \tag{6}\]  
where \(V_{1}{}^{\prime\prime}\) is the real volume of the material in the bucket; \(N\) is the porosity of the gravel; \(G\) is the weight of the material in the bucket; and \(\rho\) is the density of the material.
## 3 Three-Dimensional Laser Scanning Technology and Application Scheme  
### Fundamentals of 3D Laser Scanning  
Three-dimensional laser scanning technology can capture the geometric information of the object from the light reflected from the object's surface. During the measurement, the instrument actively acquires and stores the point cloud data of the tunnel entity, mainly including the 3D coordinates of the scanned entity, laser reflection intensity information, color information, and rich image information. The use of 3D laser scanning technology can actively, non-contact, and quickly acquire massive spatial point cloud data to obtain a 3D solid model of the tunnel space, as well as real-time, dynamic, and omnidirectional deformation characteristics of the tunnel section.  
The scanning ranging of the tunnel section by the scanner is roughly divided into two processes. First, the scanner calculates the distance S between the two points by calculating the travel time of the laser light between the emission point (point O in Figure 7) and the object reflection point (point M in Figure 7) through the built-in laser rangefinder (as shown in the following formula [23]).  
\[S=\frac{C\times t}{2} \tag{1}\]  
where \(s\) is the distance between the laser emission point of the scanner and the reflection point of the object (m), \(c\) is the speed of the laser (m/s), and \(t\) is the time (s) that the laser has elapsed from emission to reception.  
Secondly, the scanner simultaneously records the horizontal rotation angle \(\theta\) and vertical rotation angle \(\omega\) of the instrument in the process of rang  
Figure 6: Construction steps in the tunnel.  
Figure 7: Schematic diagram of monitoring point coordinate calculation.  
relative coordinates of the monitoring point as shown in the following figure according to Formula (2) [23].  
\[\left\{\begin{array}{l}X_{M}=S\cos\omega\cos\theta\\ Y_{M}=S\cos\omega\sin\theta\\ Z_{M}=S\sin\omega\end{array}\right. \tag{2}\]
# Data-Driven Modular Vehicle Scheduling in Scenic Areas
## 3 Problem Statement and Solution Method
### M3: Theoretical Global Optimal Model  
The policy in M3 is a predictive model with full knowledge of passenger numbers and penalties across the entire period, representing a globally optimal scheduling policy that exists only in theory. In terms of implementation, solving M3 can be viewed as a specialized and simplified case of M2. After obtaining the solution for M2, we record the passenger numbers, penalties, and other parameters used by M2 at each moment and input them into M3. Figure 4 shows the schematic of M3. In Figure 4, passenger demand at each moment is assumed to be identical to that in M2, meaning the demand at all moments is known to achieve a globally optimal solution. Unlike M2, which makes decisions incrementally, M3 seeks the optimal solution by minimizing total costs on a global scale.  
The formulation of M3 is given below, and the specific parameter definitions can be found in Table 3.  
\[\text{min}_{1}=\sum_{t^{\prime}=1}^{\mathcal{T}_{2}}[x_{t^{\prime}}(k_{1}+k_{0}(v _{t^{\prime}}-1)+(1-x_{t^{\prime}})p_{t^{\prime}}N_{t^{\prime}}] \tag{19}\]  
subject to  
\[v_{t^{\prime}}\geq\frac{n_{t^{\prime}}+N_{t^{\prime}}}{c}x_{t^{\prime}},\forall t \in\{1,\cdots,\mathcal{T}_{2}\} \tag{20}\]  
\[v_{t^{\prime}}\leq Mx_{t^{\prime}},\forall t^{\prime}\in\{1,\cdots,\mathcal{T }_{2}\} \tag{21}\]  
\[N_{t^{\prime}}=(N_{t^{\prime}-1}+n_{t^{\prime}-1})(1-x_{t^{\prime}-1}),\forall t \in\{2,\cdots,\mathcal{T}_{2}\} \tag{22}\]  
\[N_{1}=0 \tag{23}\]  
\[x_{\mathcal{T}_{2}}\geq\mathbb{I}\big{(}(n_{\mathcal{T}_{2}}+N_{\mathcal{T}_{ 2}}\geq 1)\big{)} \tag{24}\]  
\[x_{t^{\prime}}\in\{0,1\},\forall t^{\prime}\in\{1,2,\cdots,\mathcal{T}_{2}\} \tag{25}\]  
\[v_{t^{\prime}}\in\mathbb{N}_{0},\forall t^{\prime}\in\{1,2,\cdots,\mathcal{T} _{2}\}. \tag{26}\]  
Function (19) defines the objective function of M3. Since M3 assumes that the number of future passengers is fully known, there is no need to set up hypothetical future scenarios. Instead, the model can directly compute the value that minimizes total cost over the \(T_{2}\) cycle, where \(t^{\prime}\in\{1,\cdots,\mathcal{T}_{2}\}\). The meanings of Constraints (20) and (21) align with those of Constraints (7) and (8). Constraint (22) states that for \(t^{\prime}>2\), if a fleet is not dispatched at the time \(t^{\prime}-1\), then the unmet passenger demand at the time \(t^{\prime}\) equals the sum of unmet demand at \(t^{\prime}-1\) and newly generated demand at the time \(t^{\prime}\). Otherwise, the unmet demand is zero. Constraint (23) specifies that unmet passenger demand at \(t^{\prime}=1\) is zero. The meaning and linearization of Constraint (24) are consistent with Constraint (13). Constraints (25) and (26) define the threshold values for the variables. The pseudocode of the algorithm for solving M3 is outlined as Algorithm 3.  
Figure 4: The schematic diagram of M3.
## 3 Results and Discussion
### Antibacterial Activity  
The antibacterial activity of the AuNPs was studied against gram-positive (_B. subtilis_ and _S. aureus_) and gram-negative (_E. coli_) using the well diffusion method (Figure 7). The highest antibacterial activity was observed against _E. coli_ (24 \(\pm\) 0.67 nm), followed in order by _S. aureus_ (16 \(\pm\) 0.91 nm) and _B. subtilis_ (12 \(\pm\) 0.13 nm). However, a leaf extract of _E. prostrata_ did not show any significant antibacterial activity, whereas the AuNPs showed maximum bactericidal activity. AuNPs possess well-developed surface chemistry, chemical stability, and a large surface-to-volume ratio, due to which more drug molecules get adsorbed on their surfaces via electrostatic attraction between the amino group of drugs and nanoparticles. The gold nanoparticles surrounded by some drug moieties now act as a single group against the microbial organisms, thereby increasing the microbial activity [44]. AuNPs may initially get anchored to bacterial cell wall and interact with the peptidoglycan layer, thereby causing the breakage of bonds and entering inside the cell to cause perforations at the exterior [45]. This result reveals that the AuNPs synthesized here have effective antibacterial activity, and they contribute the most to the antibacterial capability of AuNPs.
# Evaluation of Eccentric Compressive Behavior Between Active and Passive Preloading in CFST
## 1 Introduction
### Active Preload  
If a preload is intentionally imposed on a CFST column as a specific design method to improve the performance of the column, the preload is called an active preload [8]. Typically, an active preload appears in a design to improve the interaction between the steel tube and the concrete, prevent the concrete from drying out and de-hollowing, and ensure that the steel tube and concrete can always work together well. [PERSON] et al. [19; 20] also put forward the same concept of \"active preloading\", i.e., the use of hydraulic methods to actively apply radial pre-pressure to CFST columns. It was found that an active preloaded CFST column had a higher compressive bending capacity than an ordinary CFST column. [PERSON] et al. [21] also adopted this method to confine the concrete with a steel tube in three directions to improve the mechanical properties of a CFST column. It was also concluded that the hydraulic pre-compression increased the stiffness of the column and delayed cracking in the concrete. [PERSON] et al. [22] developed a novel pre-stressed CFST column (Figure 3a). The key technique was to create a circumferential pretension by tightening the high-strength bolts on the exterior of the steel tube and increasing the friction force between the inner side of the steel and the concrete by planting bars. [PERSON] [23] proposed a method for strengthening concrete with a steel jacket. Two semi-circular steel tubes were bolted together to restrain the concrete, and radial pre-compression was applied to the concrete via the bolts (Figure 3b). The experimental results of the axially compressed specimens showed that when the active circumferential pre-strain of the steel tube was 0-0.2 times the steel yield strain, the peak load increased. [PERSON] et al. [24] used self-stressing concrete to produce active pre-stress. The results indicated that the CFST column's deformation coordination  
Figure 2: Comparisons of load-deformation relationships for CFST columns for different preloading schemes [11].  
performance was improved, but its ultimate bearing capacity with axial compression was not improved. [PERSON] [25] proposed a novel method for applying active initial stress by first thermally expanding the steel tube, then filling in the concrete, and finally using the cooling back of the steel to produce initial extruded pre-stress on the concrete. The results of the axial compression tests on short CFST columns showed that the concrete was always in an effective three-way stress state and its ductility was better than that of an ordinary CFST. [PERSON] et al. [26] found that thermal preloading technology is very helpful in improving the ductility of CFST. Overall, the above investigations into the performance of actively preloaded CFSTs suggested that deliberately applied preloads in design could be beneficial for the mechanical performance of CFSTs under compression.
###### Abstract  
The elastic full-waveform inversion (EFWI) method efficiently utilizes the amplitude, phase, and travel time information present in multi-component seismic recordings to create detailed parameter models of subsurface structures. Within full-waveform inversion (FWI), accurate source wavelet estimation significantly impacts both the convergence and final result quality. The source wavelet, serving as the initial condition for the wave equation's forward modeling algorithm, directly influences the matching degree between observed and synthetic data. This study introduces a novel method for estimating the source wavelet utilizing cross-correlation norm elastic waveform inversion (CNEWI) and outlines the EFWI algorithm flow based on this CNEWI source wavelet inversion. The CNEWI method estimates the source wavelet by employing normalized cross-correlation processing on near-offset direct waves, thereby reducing the susceptibility to strong amplitude interference such as bad traces and surface wave residuals. The proposed CNEWI method exhibits a superior computational efficiency compared to conventional L2-norm waveform inversion for source wavelet estimation. Numerical experiments, including in ideal scenarios, with seismic data with bad traces, and with multi-component data, validate the advantages of the proposed method in both source wavelet estimation and EFWI compared to the traditional inversion method.  
elastic full-waveform inversion; source wavelet; cross-correlation norm 2023