esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### The Factor Groups Geology, Dams, and Land Cover Are Most Important  
The metal concentrations depended on these F.G. in differing degrees. The most important F.G. was Geology. Geology was found to play an essential role in many other studies [41; 42; 43; 44]. There are several reasons for this: weathering rocks are an important source of different chemicals. They release these chemicals into soils and the hydrological system, providing a site-specific baseline content of metals as well as other elements that react with metals from different sources. Additionally, the abundance of mines as a source of metals can depend on geological factors. Geology also plays a vital role in developing landscape and soils, which influence hydrological pathways, affecting sediment transport and dissolved chemicals. The results of this study highlight the importance of chemical compounds in the surface geology of the basin, such as K\({}_{2}\)O, CaO, and SiO\({}_{2}\), as determining factors of potential metal pollution. The second most important F.G. was Dams. Dams and their associated reservoirs have been found in other studies to affect sediment and sediment chemistry [45; 46]. As potential matter and pollution sinks in the course of streams, they can significantly impact their discharge's water and sediment chemistry. Our results show that the construction type and average size of the reservoirs drainage areas affect potential metal pollution at a basin scale. The dam construction type is often associated with the reservoir size, the terrain, and other local conditions, which may explain the relationship between construction type and effect on metal concentration. Land Cover was the third most important F.G. Land cover is another well-documented factor of soil- and hydrochemistry [47; 48; 49]. Different land cover types are associated with different intensities of human impact and different hydrological processes. Especially agricultural land cover can be a source of many chemical compounds due to metal-containing agricultural chemicals and irrigation practices [50; 51]. Our results show that vegetation indices (NDVI and EVI) seem to be better indicators of metal pollution potential than percentages of individual land cover types. One reason for this may be that NDVI and EVI implicitly include information about vegetation health status and canopy coverage [52; 53]. These attributes of vegetation affect rock weathering, soil erosion, and especially transport processes in surface runoff [54; 55; 56].
# Data-Driven Modular Vehicle Scheduling in Scenic Areas
###### Abstract  
As tourism demand continues to grow and fluctuate, the problems of increasing empty capacity and high operating costs for tourist shuttle buses have become more acute. Modular vehicles, an emerging transport technology, offer flexible length adjustments and provide innovative solutions to address these challenges. This paper develops a data-driven method to address the problem of scheduling modular vehicles in scenic areas with dynamic passenger demand. The aim is to minimize operating costs and maximize vehicle utilization by exploiting the adjustable capacity of modular vehicles. This approach is applied to tourist shuttle scenarios, and a sensitivity analysis is conducted by varying parameters such as individual vehicle capacity and waiting penalties. Then, we investigate the optimization performance gap between the proposed model and the theoretical global optimum model. The results show that increasing vehicle capacity and varying penalties improve the performance of the data-driven model, and the optimization rate of this model can reach 70.2% of the theoretical optimum, quantifying the effectiveness of the model. The method proposed in this study can effectively reduce the operating cost of shuttle vehicles for scenic areas and meet the challenge of unpredictable passenger demand, which serves as a good reference for fleet management in scenic areas.  
Keywords:modular vehicles; data-driven; tourist shuttle transportation; scheduling optimization +
Footnote †: journal: Applied sciences
## 6 Conclusions and Future Research  
The accurate estimation and representation of quality properties is a key factor for the successful calculation of mineable coal reserves in multi-seam deposits. The usual approach for assessing the spatial distribution of these quality properties is by the use of non-statistical interpolation methods.  
In this research, the study of the quality properties of lignite in the South Field mine, Greece, has shown that it is possible to estimate LCV values by using CO\({}_{2}\) and ash content as auxiliary variables. Cokriging, which accounts for the spatial structure of the underlying phenomenon and also for some of the additional attributes, provided a better estimation of LCV compared to kriging, as shown by cross-validation of the results. Furthermore, PCA gives even better results because it takes into account the full set of the available attributes. The above results are significant for the mining industry, since a reduction to estimation uncertainty at no additional drilling cost could lead to critical savings in the cost of coal production.  
In consideration of further improving estimation results, especially when accounting for distant model locations, the additional use of related attributes like geological properties or relative positions and proportions of lithofacies might be of prominence. Therefore, a future step of the research would be to consider the above parameters through the adoption of current geostatistical simulation methods (e.g., Plurigaussian simulation) in order to estimate the quality parameters and tonnage of coal deposits.  
Conceptualization, D.S., C.R., and K.M.; methodology, D.S., C.R., and K.M.; validation, D.S., C.R., K.M., and F.P.; formal analysis, D.S., C.R., F.P., N.P., and K.M.; data curation, D.S., C.R., F.P., and K.M.; writing--original draft preparation, D.S., C.R., N.P., and K.M.; writing--review and editing, D.S., C.R., F.P., N.P., and K.M.; visualization, D.S., C.R., F.P., N.P., and K.M.; supervision, D.S., C.R., and K.M.; project administration, D.S., C.R., and K.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 JTAG Exploitations
### Attacks on Android OS  
Fault injection is a well known term in the software industry for when the robustness of software is tested by injecting various code paths and analyzing behavior of the code. In software, there are mainly two types of methods, namely compile time injection and run time injections, and various tools that utilize these methods are available to date. These techniques are predominantly used in the software field to test the flow of codes/algorithms. However, [PERSON] et al. [22; 23] combined this software field with a hardware interface (JTAG) to exploit the recent Linux Android OS 6.0.1 (Marshmallow). As we know, OS contains two major spaces for organizing basic OS functionalities, i.e., user space and kernel space. Usually, kernel space access is given to authorized administrators, developers or certified applications to use/manage system resources. To do this, the OS hides all details of processes/applications to normal users to keep security intact. Here, the authors used the JTAG tool to get access of internal memory and injecting the faults. This experiment have successfully changed modes from normal access (restricted) to root access (unrestricted) which is known as a privilege escalation attack. This kind of attack can put systems at a huge risk of sharing sensitive data and, moreover, can corrupt the whole system.  
In Android OS, /proc/sys/kernel/kptr_restrict provides a security feature which hides the addresses of kernel functions. The kernel function sys_setresuid provides the attribute which denotes whether the application contains root privilege or not. The Author modified this bit to get root privilege to an unauthorized application. In this experiment, the authors used a 32-bit ARM Cortex A9 processor which had Android OS version 6.0.1 Marshmallow along with Android debug tool (ADB) (command line tool), and Lauterbach Trace32 JTAG tool. This experiment is performed with three main steps: (1) find the address of sys_setresuid; (2) modify this system call; and (3) change root privilege from user space. The first two steps are carried out with the JTAG probe. %pK is kernel specifier which hides kernel pointer. If an attacker changes this %pK specifier with %p, then the system starts to display its symbol pointer regardless of its privileges. A non-privileged user can now ask for addresses with the command:  
$cat /proc/kallsyms | grep setsreuid  
This system call address is easily traceable in the memory footprint. Now the attacker can modify assembly code as per his wish. In the third step, root privilege is assigned from user space through ADB command line by executing simple C program. This is achievable because ADB command line provides a new terminal window with root identity. In this whole experiment, JTAG plays a crucial role since it can see internals of memory directly. If the attacker is skilled and knows about system internals, then even the latest smartphone's security can be breached.
## 5 The New Solution Procedure  
The main steps of the new solution algorithm are described briefly as \"PRE \(\xrightarrow{\text{Procedure}}\) POST\", where \"PRE\" denotes an input of the \"Procedure\". Then, \"POST\" is the result of the \"Procedure\" for the input \"PRE\".  
The parametrized DAE-multiple shooting method algorithm:  
1. Define the index-1 DAE constraints in the form (1).
2. For the system (1), find the values \(n_{y},n_{z},n_{u}\in\mathcal{N}_{+}\).
3. Define the number \(N\in\mathcal{N}_{+}\), which denotes the number of considered subintervals: \[\begin{array}{ccccc}\text{EQs:}&\dot{y}^{i}(t^{i})&=&f^{i}(y^{i}(t^{i}),z^ {i}(t^{i}),u^{i}(t^{i}),p,t^{i})\\ &0&=&g^{i}(y^{i}(t^{i}),z^{i}(t^{i}),u^{i}(t^{i}),p,t^{i})\\ \text{TI:}&t^{i}&=&[t^{i}_{0}\quad t^{i}_{f}]\\ \text{IC:}&\dot{y}^{i}(t^{i}_{0})&=&x_{y^{i}}\\ \text{CIC:}&0&=&g^{i}(x_{y^{i}},z^{i}(t^{i}_{0 and, in particular:  
\[\widetilde{\mathcal{J}}(y(t),z(t),\widetilde{u}(P_{u},t),t)=\phi(y^{N}(t_{f}^{N}),z^{N}(t_{f}^{N}),t_{f}^{N})+\sum_{i=1}^{N}\int_{t_{0}^{i}}^{t_{f}^{i}}\psi(y^{ i}(t^{i}),z^{i}(t^{i}),\widetilde{u}^{i}(P_{u^{i}},t^{i}),t^{i}) \tag{51}\]  
where \(t^{i}\in[t_{0}^{i}\quad t_{f}^{i}]\) and \(i=1,\ldots,N\).
9. The nonlinear optimization task: The objective function (50) is optimized subject to the modified DAE constraints (48) and (49). Then, the vector of the consisted initial conditions is needed to perform the numerical computations correctly. Additionally, the continuity and similarity constraints are considered. The new nonlinear optimization task is formulated by the indicated components. The numerical continuous optimization procedure, which co-operates with the outer DAE solver, can be used to design an efficient sequential optimization algorithm.
10. The trajectories \(y(t)\), \(z(t)\), and \(u(t)\), where: \[y(t)=\left[\begin{array}{ccc}y_{1}^{1}(t^{1})&\cdots&y_{1}^{N}(t^{N})\\ \vdots&\ddots&\vdots\\ y_{u_{y}}^{1}(t^{1})&\cdots&y_{n_{y}}^{N}(t^{N})\end{array}\right]\] (52)  
\[z(t)=\left[\begin{array}{ccc}z_{1}^{1}(t^{1})&\cdots&z_{1}^{N}(t^{N})\\ \vdots&\ddots&\vdots\\ z_{n_{z}}^{1}(t^{1})&\cdots&z_{n_{z}}^{N}(t^{N})\end{array}\right] \tag{53}\]  
\[\widetilde{u}(P_{u};t)=\left[\begin{array}{ccc}\tilde{u}_{1}^{1}(P_{u^{i}}; t^{1})&\cdots&\tilde{u}_{1}^{N}(P_{u^{N}};t^{N})\\ \vdots&\ddots&\vdots\\ \tilde{u}_{n_{y}}^{1}(P_{u^{i}};t)&\cdots&\tilde{u}_{n_{y}}^{N}(P_{u^{N}};t^{ N})\end{array}\right] \tag{54}\]  
as well as the appropriate matrices \(P_{\tilde{f}^{i}}\), \(P_{\tilde{g}^{i}}\), \(P_{u^{i}}\) and the vector of the initial conditions \(x_{\psi^{i}}\) for \(i=1,\ldots,N\) are the solutions obtained by the new optimization algorithm. Moreover, it is suggested to save the final value of the similarity constraint vector. This information can be used to improve the similarity between the original and modified DAE constraints by a modification of the subinterval number \(N\).
# Abrasive Surface Finishing on SLM 316L Parts Fabricated with Recycled Powder
## 3 Results and Discussion
### 2D Surface Roughness
#### 3.1.2 Grouped Surfaces  
For further inspection into the characteristics of each type of surface, the results were divided into three groups being the vertical sides noted with A, B, C, D (Ra, Rz), the angled sides right below A, B, C, D (RaZk, Rzzk), and the top side (RaTop, RzTop). These roughness values were averaged and reported separately according to the scanning strategies in the following Figures 7 and 8, corresponding to the data in Tables 3 and 4.  
Figure 7: 2D surface roughness results grouped according to the types of surfaces for the meander strategy: (**a**) Ra, (**b**) Rz.  
One of the obvious traits from the two figures is that the surface roughness of the angled sides (RaZk, RzZk), in as-built specimens no. 1, is almost double the other two (Ra, Rz and RaTop, RzTop). This is because of the \"staircase\" effect caused by one layer not being wholly supported by preceding layers. In contrast, as the vertical sides and the top side are built with layers that are fully supported, their surface roughness is remarkably better.  
Additionally, as previously drawn, 2 min of sandblasting can significantly reduce the surface roughness from specimens no. 1 to specimens no. 3. Nevertheless, for better surface finishing, tumbling, or a combination of the two processes should be employed at the cost of treatment time. Specifically, Ra value below 1 \(\upmu\)m can already be achieved from specimens no. 6 for meander and specimen no. 5 for stripes.
## 3 Practical Implementation  
### Unit Cell Geometry  
The geometry of the proposed pixel is shown in Figure 2. An elliptically shaped metallic patch with a cross-shaped aperture is printed on top of a grounded dielectric of AD-1000 material, with a relative permittivity of \(\varepsilon_{r}=10.2\) and a thickness of \(h=1.27\) mm. The unit cell dimension is \(u=3.14\) mm. The minor axis of the elliptical metallic patch (\(e_{a}\)) is oriented towards the \(x\)-axis and the major axis (\(e_{b}\)) towards the \(y\)-axis in the rectangular reference system. Inside the metallic patch, there is an asymmetric cross-shaped aperture. The main axes of this cross aperture are also aligned with the rectangular reference, i.e., they are aligned with the ellipse axes. The length and width of the cross are denoted as \(c_{a}\), \(w_{a}\) and \(c_{b}\), \(w_{b}\) towards the \(x\) and \(y\) axis, respectively. Since the main applications of the MTSs composed with such subwavelength elements are in the microwave regime, the metal is treated as a perfect electric conductor, and dielectric losses are neglected.  
A full wave eigenmode analysis for the supported SWs has been carried out. For this purpose, periodic boundary conditions are imposed in the \(x\) and \(y\) axis with \(k_{x}u\) and \(k_{y}u\) phasing, respectively. The imposed phasing corresponds to a mode impinging on the cell towards the \(\phi\) angle, which is evanescent in the \(z\) direction. Thus, it meets: \(k_{x}{}^{2}+k_{y}{}^{2}=\beta_{i}{}^{2}>k_{0}{}^{2}\).  
Figure 1: A rotationally symmetric metasurface is excited by a magnetic dipole oriented towards the \(y\)-axis: TM and TE SWs propagate radially on the surface phase synchronized. \(\left|E\rho\right|\) follows a \(\cos(\varphi)\) function while \(\left|E\varphi\right|\) follows a \(\sin(\varphi)\) function.  
Figure 2: Unit cell geometry composed by a metallic elliptical patch with asymmetric cross-shaped aperture in the center, printed on a grounded substrate. Dimension \(u=3.14\) mm, AD-1000 dielectric with relative permittivity \(\varepsilon_{r}=10.2\) and thickness \(h=1.27\) mm.
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 5 Numerical Results and Analysis
### Convergence Analysis
The learning rate:  
As shown in Figure 8, when the learning rate is 3 \(\times\) 10\({}^{-5}\), RIS-TD3 peaks at round 140 with a peak value of 3174.04; when the learning rate is 15 \(\times\) 10\({}^{-5}\), the algorithm peaks at round 90 with a peak pick higher than that of the former. This indicates that the learning rate has significant influence on the optimal value, and when the learning rate is small, the proposed algorithm obtains a smaller optimal value. Figure 5: Effect of different hidden layer sizes on the algorithm performance with \(N=36\), \(\gamma=0.99\). Figure 6: Effect of different exploration step lengths on the performance of the algorithm with \(N=36\), \(\gamma=0.99\) and Hidden_dim = 128. Figure 8: Effect of different learning rates on algorithm performance; \(N=36\), Explore_steps = 5000, \(\gamma=0.99\), and Hidden_dim = 128. Figure 7: Effect of different discount rates on algorithm performance with \(N=36\), Explore_steps = 5000 and Hidden_dim = 128.
## 3 Results and Discussion
### Bubble Growth and Departure Regimes
Subregion 2b, observed at a potential approaching 3.2 V, is characterized by a single coalesced bubble detaching from the surface, encompassing most detachable bubbles present before coalescence. In this subregion, most bubbles reach a diameter equal to the electrode diameter at the time of detachment. Within this subregion, bubbles can be identified and tracked, as presented in Figure 9:5 Departure frequency is presented for applied potentials from 3 V upwards. \"Region 3\", defined at applied potentials of 3.2 V and above, involves bubble coalescence leading to the formation of larger bubbles that detach more predictably. The departure frequency can be reliably extracted from the potentiostat measurement data, as the electrical current density exhibits pronounced drops corresponding to bubble detachment events. For Region 3, the departure frequency is presented for all applied potentials, as it can be readily extracted via high-speed video recordings as well as from the electrical current measurements. The limitation of bubble tracking using high-speed video is that applied magnification does not enable observation of the entire bubble at detachment due to its large diameter. In Table 1, an overview of the discussed regions is presented along with departure diameters and frequency ranges, presented in previous sections. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Region** & **Applied Potential (V)** & **Bubble Behavior** & **Diameter at Detachment (\(\mu\)m)** & **Departure Frequency (Hz)** \\ \hline \multirow{2}{*}{1} & \(\geq\)2.0 & No coalescence; departure of individual bubbles & \(\leq\)50 & N/A \\ \hline \multirow{3}{*}{2a} & \(\geq\)2.6 & Partial coalescence: multiple coalesced bubbles & \multirow{3}{*}{50–60} & \multirow{3}{*}{N/A} \\  & \(<\)2.8 & & & \\ \cline{1-1}  & (subregion a) & & & \\ \hline \multirow{3}{*}{2b} & \(\geq\)2.8 & Predominantly single coalesced bubbles & \multirow{3}{*}{70–200} & \multirow{3}{*}{8–25} \\  & (subregion b) & & & \\ \hline \multirow{3}{*}{3} & \(\geq\)3.2 & Departure of single coalesced bubbles & \(\geq\)450 & \multirow{3}{*}{0.5–5.5} \\  & \(<\)3.5 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of bubble behavior, diameter at detachment, and departure frequency for the identified voltage regions.
###### Abstract
[36] conducted the first investigation, where the lever arm is embedded into the conventional tuned mass damper (LTMD). However, a detailed theoretical investigation was not performed in their work. [PERSON] et al. [37] made a further effort. It was found that the LTMD is superior to the conventional TMD in terms of the spring static stretch and the optimal damping requirement. This feature makes the LTMD more favourable to engineers. [PERSON] et al. [38] conceived a new type of LTMD, where two concentrated masses are attached to the lever two endpoints. Likewise, this new device exhibits better control performance than the conventional TMD. In addition to the one LTMD configuration, a multiple LTMDs configuration strategy was also adopted by many researchers [39]. In addition to those passive damping devices, the lever mechanism is also introduced into the active or semi-active dampers and achieved excellent performance. [PERSON] et al. [13; 14; 40] included this lever mechanism into active dampers. The results show that the lever enhanced active damper can significantly improve the damping performance for the ground motion-excited structure response. [PERSON] et al. [15] developed a novel lever-based semi-active damper, in which a movable concentrated mass is employed and achieved desirable results. The mentioned investigations implies that this classic lever mechanism can enhance the performance of the original dampers using an appropriate design. In consideration of this promising result, a novel tuned lever inerter-like mass damper (TLIMD) was developed in our previous study [22], and its nonlinear dynamic properties (i.e., the large-angle situation) under a harmonic excitation were systematically discussed. However, the TLIMD optimization scheme and its control performance assessment under practical conditions (i.e., the small-angle situation, the structural inherent damping and also the stochastic load-induced structure vibrations) are still lacking. To remedy this issue, a set of closed-form formulae for the TLIMD optimal design is developed (using the classic \(H_{2}\) norm optimization) in the present study under the stochastic load-induced structure vibration condition. The structural inherent damping contribution is accounted for by adopting a simplification procedure for the primary damped system. Thus, more generalized formulae for the TLIMD optimization are developed. Three critical parameters (including the damper mass ratio, the lever length ratio and the host structural damping) that dominate the TLIMD optimal performance are revealed from a theoretical perspective. The accuracy of the proposed formulae is verified by the comparison results with the ones obtained by the numerical optimization technique (here using the particle swarm optimization). The seismic analysis is conducted to evaluate its control performance and robustness property. The results show the optimized TLIMD (by the proposed formulae) is significantly superior compared to conventional inerter-based dampers.