esa-sceva/satcom-chunk-collection huggingface dataset:
# Metal Mobility in Afforested Sites of an Abandoned Zn-Pb Ore Mining Area
## 1 Introduction  
Metal mining and smelting have played a very important role in economies since ancient times. A side effect of this exploitation would be the pollution which often accompanies it. Usually, it is associated with the release of a portion of excavated metals into the environment, both during ore exploitation and also long after the cessation of mining activity [1; 2; 3]. Particularly, metals stored in waste heaps can be dispersed into the surrounding soils and surface waters for a very long time afterwards due to erosion, weathering and leaching processes [4; 5]. For these reasons, heaps of waste material constitute a serious environmental problem in regions throughout the world which have witnessed the historical exploitation and processing of metal ores [6; 7; 8; 9].  
Afforestation is the most common reclamation method of lands degraded by mining, particularly in previously forested regions [10]. Nonetheless, at abandoned historical mines where landform alignment, the placement of overburden and/or soil capping have not been undertaken, afforestation usually results from natural succession [11]. Establishing forest cover leads to the modification of the soil profile [12], elevated amounts of organic detritus and concentrations of dissolved organic matter [13; 14] as well as a drop in pH and the depletion of cations in the topsoil [15]. Subsequently,the production of organic matter and the associated drop in the pH of the topsoil favour the mobilization and transport of metals down soil profiles [16; 17]. For this reason, the mobility of elements in many forest soils was evidenced to be higher than in arable soils [18] with higher migration rates found in coniferous rather than deciduous forests [17; 19]. It suggests that metal remobilization rate in naturally aforested, highly metal-contaminated heaps differs from that observed in reclaimed areas and should be investigated more carefully.  
The current study focuses on metal mobility in spoil heaps of the Katarzyna mine in southern Poland which was abandoned at the beginning of the 20 th century. The Pb-Zn ore was extracted in shallow pits, no more than a dozen meters deep. They were located from a dozen to ca. hundred meters apart. In this area, remnants of more than 400 exploitation pits were found. Most of the area of the former mine was aforested naturally and the high content of heavy metals in plants suggest their high mobility [20]. In the profiles dug into the heaps found in deciduous and coniferous forests, we compared the content and speciation of the metals in the heap material with the material of the fossil soil horizons which are termed hereafter \"former soil\". The study aimed at the estimation of the metal remobilization rate based on metal distribution and soil properties in six vertical profiles in both forest types in the context of the potential danger posed to local groundwater.
# Evaluation of Metamorphic Testing for Edge Detection in MRI Brain Diagnostics
## 3 Methodology
### SSIM Based Output Comparison  
Image quality assessment is an important parameter of assessing the quality between two images. Usually, MSE (mean square error) and PSNR (peak signal-to-noise ratio) are used to assess the quality of images by giving absolute errors. However, these two measures are not normalized, and therefore, it is difficult to understand them. Recently,  
Figure 4: (a) Output of source test case E(Im); (b) output of follow-up test case E(C(Im)).  
Figure 5: (a) C(E(Im); (b) E(C(Im)).  
Figure 3: (a) Source test case; (b) follow-up test case.  
two new metrics, SSIM and FSIM (feature similarity index measure), have been developed to check the structure and feature similarity between two images [20]. We have used SSIM to compare the output of source and follow-up test cases because SSIM compares the image based on luminance, contrast, and structure, respectively. We are using a dataset of MRI brain images where correct identification of luminance, contrast and structure helps in the identification of edges and lesions. SSIM has also become a default measure in the field of image processing [19].  
As discussed earlier, if the value of SSIM is 0, then both images are different, but if the value of SSIM is 1, then the images are exactly similar. To check the satisfaction of MR, we have compared the outputs of all the source and follow-up test cases and set a threshold value of 0.95. If the value of SSIM is below this threshold values, then MR is in violation. Afterwards, FDR is calculated for each MR.
## 4 Numerical Examples  
In this section, we conduct numerical experiments to validate the effectiveness of our proposed dual-PIRNN FWI framework. To ensure robust evaluation and enhance data diversity, we implement multiple data augmentation strategies. These include (1) scale-decomposition approaches (time-scale and combined time-space-scale reduction), (2) initial model variations through different Gaussian smoothing levels, and (3) multi-source acquisition configuration with 11 equidistant sources and VSP observation system. Our experiments are organized as follows. First, we verify the accuracy of PIRNN forward modeling by examining the effects of PML boundaries and high-order staggered-grid finite-difference methods. Then, we evaluate the inversion performance on two velocity models: a three-layer homogeneous model and the Marmousi model. For each model, we analyze the results obtained using different scale-decomposition strategies and initial models. Finally, we analyze the GPU memory consumption of our network architecture.
## 5 Simulation Results of DP  
According to the actual mine road, the known working condition of the heavy-duty mining truck is shown in Figure 8. The general working state can be divided into four sections: 1 the a-b section is waiting for loading; 2 the b-c section is full-load climbing; 3 the c-d section is waiting for unloading; and 4 the final d-e section is no-load downhill. On this 10 km simulation road, the truck speed limit is 45 km/h and the maximum slope is 7%. The simulation step is set to 1 s, and the entire simulation time is 2000 s. The vehicle parameters are shown in Table 2.  
Figure 8: The known working condition information of the heavy-duty mining truck. (**a**) Road slope, (**b**) vehicle speed, and (**c**) load state (1: full-load, 0: no-load).  
Figure 7: The flow chart of optimizing AT shift strategy based on DP algorithm.  
Figure 9 shows the simulation results based on the DP algorithm optimization. After the adjustment of the AT, the engine speed and output torque are maintained in a relatively stable and efficient working range, \(1200\)\(\text{r}/\text{min}\leq\omega_{E}\leq 1550\)\(\text{r}/\text{min}\) and \(1400\) N\(\cdot\)m \(\leq T_{E}\leq 1900\) N\(\cdot\)m, as shown in Figure 9a,b. According to the difference in vehicle speed, throttle opening, vehicle load, and road slope, the optimized gear is selected properly in Figure 9c. The total number of shifts without shift cycle phenomenon, is 46 times, which is a reduction of 37.0%, compared to the traditional shift strategy, as shown in Table 3. Figure 9d shows the growth curve of vehicle fuel consumption, in which the final fuel consumption is 17.6 kg, with a decrease of 8.8%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Value** & **Unit** \\ \hline \(M_{0}\) & 30,000 & kg \\ \(M_{\text{max}}\) & 72,000 & kg \\ \(i_{FD}\) & 22.4 & \\ \(r_{w}\) & 0.97 & m \\ \(f_{\text{roll}}\) & 0.03 & \\ \(C_{D}\) & 0.8 & \\ \(S_{V}\) & 15 & m\({}^{3}\) \\ \(\eta_{g}\) & 0.95 & \\ \(\epsilon_{\text{max}}\) & 10.5\% & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 9: The simulation results of the optimized shift strategy based on the DP. (**a**) Engine speed, (**b**) engine torque, (**c**) the AT gear selected, and (**d**) vehicle fuel consumption.  
The engine operating points before and after optimization are compared in Figure 10. It can be seen that the DP optimized engine operating points are more concentrated in the high-efficiency range. Under the same engine output power, the optimized shift schedule can well avoid the inefficient range near \(\omega_{\rm E}=1100\) r/min, \(T_{\rm E}=1800\) N\(\cdot\)m and \(\omega_{E}=1750\) r/min, \(T_{\rm E}=1000\) N\(\cdot\)m, so as to achieve the best fuel economy.
# Dynamic Pricing Method in the E-Commerce Industry Using Machine Learning
## 4 Results
Although their accuracy was slightly lower than the linear version, nonlinear SVMs could handle more complex and nonlinear relationships in the data better. The Naive Bayes classifier took third place with a score of 0.7654. Despite its simplicity and assumption of feature independence, this algorithm showed satisfactory effectiveness, which may have resulted from its ability to quickly process large amounts of data and its good generalization. The decision trees algorithm achieved an average accuracy of 0.7464, indicating moderate effectiveness in dynamic pricing. While decision trees are easy to interpret and can capture complex relationships between features, their tendency to overfit can limit their practical effectiveness. The k-nearest neighbor (KNN) algorithm showed the lowest average accuracy with a score of 0.6514. The low effectiveness of this algorithm may have been due to its susceptibility to noise in the data and its requirements for a large amount of memory and computational time, especially with the large datasets typical of e-commerce. To summarize, the results indicate that the linear SVM and nonlinear SVM are the most effective algorithms for dynamic pricing in the e-commerce industry, while Naive Bayes, despite its simplicity, can also be a useful tool. Decision trees and KNN show lower effectiveness, suggesting the need for further research and optimization of these algorithms or their possible replacement with more advanced methods in commercial applications. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Algorithm** & **Naive Bayes** & **Linear Support** & **Nonlinear Support** & **Decision Trees** & **K-nearest Neighbor Algorithm** \\ \hline Average accuracy from one hundred training processes & 0.7654 & 0.8692 & 0.8432 & 0.7464 & 0.6514 \\ \hline \hline \end{tabular} Source: own elaboration.  
\end{table} 
Table 6: Performance results of selected machine learning algorithms in dynamic pricing for the e-commerce industry.
# An Artificial Plant Community Algorithm for Collision-Free Multi-Robot Aggregation
## 4 The Collision-Free Multi-Robot Aggregation Problem
### Notation and Description  
The notations used in this article are described in Table 2.  
The CMAP can be defined as an undirected assignment graph \(G\)\((T,\)\(E)\). In the undirected graph, the set of vertices is a task set \(T=\{T_{0},\)\(T_{1},\cdots,T_{Tmax}\}\), while the set of edges is an assignment set \(E=\{E_{01},\)\(E_{02},\cdots,E_{n-1,n}\}\). \(T_{0}\) indicates the task depot, and each edge \(E_{ab}=(T_{a},T_{b})\in E\) is associated with a travel time \(k_{Ta,Tb}^{travel}\) from \(T_{a}\) to \(T_{b}\). In the aggregation process, different robots gather on the same task to perform different operations, where these different operations occur at different times or locations to avoid collisions when robots aggregate. The set of operations is \(O=\{O_{T1},\)\(O_{T2},\cdots,O_{Tmax}\}\), and each task \(T_{a}\) has a set of operations \(O_{Ta}=\{O_{T1},\)\(O_{Ta2},\cdots,O_{Tmax}\}\). Hence, each task can be divided into several operations and assigned to a set of robots \(R=\{R_{0},\)\(R_{1},\cdots,R_{Rmax}\}\) for aggregation and implementation. The robots are initially located at task \(T_{0}\), and each robot \(R_{i}\) has a load ability \(B_{Ri}\), which denotes the amount of demand that the robot can reduce per time unit. Additionally, each robot \(R_{i}\) has a battery capacity \(C_{Ri}\) for energy consumption in processing and travel. A collision-free multi-robot aggregation problem contains four elements \(\text{CMAP}=[T,O,R,E]\). For a set of solutions \(X=\{x_{0},\)\(x_{1},\cdots,x_{Tmax}\}\), there is a set of candidate solution \(x^{*}=\left\{x_{ab}^{Ri}\right\}\), where the robot selection bit \(x_{ab}^{Ri}=\)1 if \(R_{i}\) goes from \(T_{a}\) to \(T_{b}\) and 0 otherwise. In CMAP, the position of each task is fixed, and a swarm of robots can aggregate on a task to complete different operations separately. Different robots do not repeat the same operation. During the aggregation process, collisions between robots also need to be prevented. Figure 3: The robot state transition diagram.
# Analysis of Fire Resistance of Prestressed Concrete T-Beam Based on ABAQUS Numerical Simulation
## 2 Finite Element Model Building and Verification
### Finite Element Model Calibration Based on Existing Experimental Results  
In order to explore the reasonable cell size and verify the convergence of the calculation results of different cell sizes, the model is divided into three sizes of cells, as shown in Figure 3. The basic cell sizes of grid 1, grid 2, and grid 3 are 30 mm, 20 mm, and 10 mm, respectively. Model S2 in the literature [10] was used as a reference to verify that the finite  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mechanical Properties** & **Formula** & **References** \\ \hline Stress–strain principal structure relationship & \(\sigma_{p}(\sigma,T)=\begin{cases}E_{s}(T)&\epsilon\leq\varepsilon_{y}(T)\\ f_{y}(T)+E_{s}^{*}(T)(\epsilon-\varepsilon_{y}(T))&\epsilon>\varepsilon_{y}(T) \\ \end{cases}\) & ([PERSON] et al. [19]) \\ Tensile–  
element model is correct, and the deflection time curves for three cell sizes are shown in Figure 4. The calculation results in Figure 4 show that the deflection calculation errors for the three cell sizes are within 5%, and the deflection-time curves basically overlap, indicating that the chosen 20 mm cell size has met the convergence requirements.  
A comparison of the finite element model calculation results with the relevant experimental data in the literature [10] is shown in Figures 5 and 6. For the temperature field model, the temperature measurement points T2, B2, W2, and F2 in the model beam S2 in the test were selected, and the results of the finite element model analysis were compared with the temperature-time course curves in the literature, as shown in Figure 5. The time course curves of the measured points obtained from the finite element numerical simulation do not differ much from the measured points obtained in the test, and the overall agreement between them is good. This indicates that the selection of thermal parameters of materials and the definition of loads and boundary conditions in the model establishment process are correct and reasonable, and they apply to the fire resistance analysis of prestressed concrete T-beams.  
Figure 4: MID–span deflection time curves of three types of meshing.  
Figure 5: Temperature rise curves of measurement points.  
Figure 3: Three types of meshing details.  
For the calculation model of fire resistance analysis, the time course curves of the mid-span deflection of T-beam S2 in the test at a load level of 0.35 were selected for comparison and analysis with the corresponding deflection time course curves in the numerical simulation model, as shown in Figure 6. Figure 6 shows the overall agreement of the deflection time course curves obtained from the finite element numerical simulation is good. This indicates that the finite element numerical model simulation is effective and can be used to calculate and analyze the fire resistance of the prestressed concrete T-beam.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 2 Critical Research Agenda
### Inverters as VRE Interfaces with Grids
#### 2.2.1 Desynchronization  
Increasing proportions of VRE will change the underlying nature of a grid from synchronous to non-synchronous. The challenges in the use of VRE include a precise understanding of existing VRE inverters and their effects on the behavior of a VRE-dominated grid. Specifically, wind and solar PV generators are non-synchronously connected to the network via power electronics converters [38; 40]; this stands in contrast to synchronous generators. The converters decouple the inertial responses of rotating machines (wind turbines) from the network. Additionally, solar PV lacks an inherent inertial response, reducing the moments of inertia and thus increasing the rate of frequency change. Moreover, compared to synchronous machines, power converters have a limited capacity to supply transient currents during grid-side faults [41]; this means that protection relays and schemes must change. The decoupled (or near-absent) inertial responses and the limited fault currents exert major effects, for example, on grid frequency and voltage regulation, the dynamic responses during faults, black-start capability, and power quality, such as current harmonics. However, power electronics converters are eminently programmable; they can emulate a variety of dynamic behaviors, in contrast to the unprogrammable nature of synchronous generators. This opens the possibility to deliver 100% renewable electricity in a reliable and cost-effective manner.
## 5 Discussion  
Although the three samples discussed in this paper all have reasonable detector performance, analysis on the Sb3583 n-CBIRD and the Sb3587 pn-CBIRD device results shows that in both samples, the \(n\)-type absorber thickness exceeds the hole diffusion length, resulting in sub-optimal QE. Discussions of additional (V)LWIR CBIRD devices, as well as results on FPAs fabricated from pn-CBIRDs, can be found in [47, 48]. In particular, a comparison between a pair of n-CBIRD and pn-CBIRD samples with shorter cutoff wavelengths was presented; the hole diffusion length issue appears to be less problematic in these samples than for the Sb3583/Sb3587 pair discussed here. Comparison between a pair of VLWIR pn-CBIRD and p-CBIRD devices are also presented. Interested readers can find more details in these references. Overall, we find that the CBIRD architecture can accommodate (V)LWIR \(p\)-type T2 SLS absorbers for QE enhancement over the nBn or XBn, while suppressing surface leakage currents due to the inverted sidewall surface of the \(p\)-type absorbers. The inverted surfaces of exposed \(p\)-type absorbers of the p-CIBRD and the pn-CBIRD still require good passivation to suppress surface \(p\)-\(n\) junction-related dark currents. On the other hand, (V)LWIR devices that contain only \(n\)-type absorber, such as the nBn or n-CBIRD, have better dark current characteristics. (V)LWIR InAs/InAsSb T2 SLS CBIRD FPAs are now being developed or deployed for NASA Earth Science applications such as sustainable land imaging [51] and hyperspectral imaging [52, 53].  
Conceptualization, D.Z.T.; investigation, D.Z.T., A.S., A.K., S.B.R., A.M.F., C.J.H. and B.J.P.; writing--original draft preparation, D.Z.T.; writing--review and editing, D.Z.T. and A.S.; supervision, D.Z.T. and S.D.G.; funding acquisition, D.Z.T. and S.D.G. All authors have read and agreed to the published version of the manuscript.  
The research described in this publication was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80 NM0018D0004).  
Not applicable.  
The data that support the findings of this study are available within the article.  
The authors wish to thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON].  
The authors declare no conflict of interest.
# A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks
## 5 Simulation Analysis
### Scenario  
The goal is to compare GCMR against ST, PIM-SSM and BIER in terms of stretch, memory size, communication cost, and transmission cost as defined in Section 3.1. Therefore, we developed an ad-hoc simulator written in c in order to be able to execute, in reasonable time, instances of the four protocols in large-scale network topologies.  
The simulations are performed using four CAIDA maps of the Internet topology comprising 16k, 26k, 36k, and 46k nodes as of January 2004, August 2008, January 2011 and November 2013 ([[https://api.asrank.caida.org/v2/docs](https://api.asrank.caida.org/v2/docs)]([https://api.asrank.caida.org/v2/docs](https://api.asrank.caida.org/v2/docs)) accessed on 15 September 2021), respectively. The scenario executed simulates the construction of the multicast routing path for a leaf node set of increasing size from 400 to 4000 nodes. Each execution is performed 20 times by considering 20 different random selections of the source and the leaf nodes.  
It is worth mentioning that the stretch and the transmission cost of BIER strongly depend on the configuration of its subsets. In fact, each subset can contain 256 nodes at maximum; since we setup MDT ranging from 400 to 4000 leaf nodes, one single BIER sub-domain is not able to contain all leaf nodes. Thus, to be fair, we consider two opposite cases: (i) BIER (best) considers the best possible situation where the minimum number of subsets is required to allocate all leaf nodes and hence the minimum number of both edges in the separate MDTs and packet transmissions are needed; and (ii) BIER (worse) considers the worse case where the maximum number of subsets is required.