esa-sceva/satcom-chunk-collection huggingface dataset:
# Reconstruction and Measurement of Irregular Karst Caves Using BLST along the Shield Metro Line
## 4 Fine Measurement and Reconstruction of Complex Kast Caves
### Solution of Complex Cave Point Cloud Coordinates
\begin{array}{c}x_{0}\\ y_{0}\\ z_{0}\end{array} \right] \tag{21}\]  
Equations (20) and (21) include 7 parameters: 3 rotation parameters (\(\alpha,\beta,\gamma\)), 3 translation parameters (\(x_{0},y_{0},z_{0}\)), and a scale factor. The acquisition of the karst cave cloud coordinates can be regarded as a rigid process without any scale changes; thus, the scale factor \(\
u\) is 1 in the solution process. The rotation angles of the coordinate system \(o-xyz\) around the x, y, and z axes are defined as \(\alpha,\ \beta,\ \gamma\), respectively. The coordinate system \(o-xyz\) is translated along x, y, and z axes into \(o^{\prime}-x^{\prime}y^{\prime}z^{\prime}\). The rotation matrices around the \(x\), \(y\), and \(z\) axes are:  
\[R_{x}(\alpha)=\left[ \begin{array}{ccc}1&0&0\\ 0&\cos\alpha&\sin\alpha\\ 0&-\sin\alpha&\cos\alpha\end{array} \right] \tag{22}\]  
\[R_{y}(\beta)=\left[ \begin{array}{ccc}\cos\beta&0&\sin\beta\\ 0&1&0\\ -\sin\beta&0&\cos\beta\end{array} \right] \tag{23}\]  
\[R_{z}(\gamma)=\left[ \begin{array}{ccc}\cos\gamma&\sin\gamma&0\\ -\sin\gamma&\cos\gamma&0\\ 0&0&1\end{array} \right] \tag{24}\]  
Since \(R(\alpha,\beta,\gamma)=R_{x}(\alpha)\cdot R_{y}(\beta)\cdot R_{z}(\gamma)\), the following can be obtained:  
\[R(\alpha,\beta,\gamma)=\left[ \begin{array}{ccc}1&0&0\\ 0&\cos\alpha&\sin\alpha\\ 0&-\sin\alpha&\cos\alpha\end{array} \right]\left[ \begin{array}{ccc}\cos \beta&0&\sin\beta\\ 0&1&0\\ -\sin\beta&0&\cos\beta\end{array} \right]\left[ \begin{array}{ccc}\cos\gamma& \sin\gamma&0\\ -\sin\gamma&\cos\gamma&0\\ 0&0&1\end{array} \right] \tag{25}\]  
\[R(\alpha,\beta,\gamma)=\left[ \begin{array}{ccc}\cos\beta\cos\gamma&\cos \beta\sin\gamma&-\sin\beta\\ -\cos\alpha\sin\gamma+\sin\alpha\sin\beta\cos\gamma&\cos\alpha\cos\gamma+\sin \alpha\sin\beta\sin\gamma&\sin\alpha\cos\beta\\ \sin\alpha\sin\gamma+\cos\alpha\sin\beta\cos\gamma&-\sin\alpha\cos\gamma+\cos \alpha\sin\beta\sin\gamma&\cos\alpha\cos\beta\end{array} \right] \tag{26}\]  
During the scanning of the cave point clouds at two stations, the three-dimensional coordinates of the laser emission points are obtained; that is, the corresponding translation matrix \(T\) is known, and the rotation adjustment state \(R(\alpha,\beta,\gamma)\) of the laser emission probe and the corresponding coordinate system scanning results are also known. The point cloud coordinates in the coordinate system \(o^{\prime}-x^{\prime}y^{\prime}z^{\prime}\) can be calculated and obtained by Equation (20). Based on this, the point cloud data of several stations can be converted and spliced, and the multistation exploration and point cloud splicing of complex karst caves and large-scale karst caves can be realized.
## 3 Network Slicing Concepts
### Slice Provisioning  
The slice provisioning module cooperates with a corresponding lifecycle manager of a network service in order to setup and configure network paths, not only in the isolated instances of virtualized infrastructure, but alongside the different 5G domains. This includes wide area network paths, NFVI instances and, last but not least, radio components. In order to achieve this, it communicates with the corresponding component channels from the Virtual Infrastructure Manager (VIM), NFV Orchestrator (NFVO), and the Network Management System (NMS).
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 2 Origin, Characteristics, and Properties of Heavy Metals and Metalloids  
The term \"heavy metals\" is used to refer to metals and metalloids present in the environment and having a density higher than 5000 kg\(\cdot\)m\({}^{-3}\) and an atomic mass higher than 20. Based on this definition, 51 elements of the periodic table are considered heavy metals/metalloids [13]. Their mobility and bioavailability in soil are due to their chemical characteristics and those of soil. The pH, surface properties of the adsorbents, presence of cations, and anions affect the interactions between soil components and metals/metalloids [14]. The most common heavy metals and metalloids in the environment are chromium, manganese, nickel, copper, zinc, cadmium, lead, and arsenic. Chromium, copper, zinc, cadmium, lead, mercury, and arsenic are the most toxic [15]. Usually, they found in the weathering of underlying bedrock, and they are present as ones (sulfides of Pb, Co, Fe, As, Pb, Zn, Ag and Ni, and oxides of Se, Al, Mn, and Sb). In soils, normally, sulfides of arsenic, mercury, lead, cadmium naturally occur together with sulfides of copper (chalcopyrite, CuFeS\({}_{2}\)) and iron (pyrite, FeS\({}_{2}\)) [3]. The environmental problem is mainly due to the anthropological activities that cause an increase in heavy metal and metalloid concentrations, especially in the refining and mining of ores, pesticide applications, fertilizer industries, and solid wastes [16]. The anthropogenic sources of metals and metalloids can be dived into five groups, as shown in Figure 1.  
Heavy metals and metalloids are used in different sectors, increasing market demand and world production. Copper, selenium, zinc, iron, vanadium, and manganese, in trace amounts, are essential for various biological processes, such as in respiration systems, biosynthesis of complex compounds, nervous system, regulation, and functioning of enzymes. Iron, zinc, tin, lead, copper, and tungsten have an important role in electronic devices, especially in the realization of semiconductors [17]. In Table 1, the characteristics of the most toxic heavy metals and metalloids are summarized [18,19].
# _Beta vulgaris_ subsp. _maritima_: A Valuable Food with High Added Health Benefits
## 3 Results and Discussion
### In Vitro Study of Anti-Diabetic Activities of B. vulgaris subsp. maritima Leaves' Extract  
The ability of \(\alpha\)-amylase and \(\alpha\)-glucosidase to inhibit complex carbohydrates' hydrolysis during digestion, such as starch, was investigated as a strategy for controlling post-prandial plasma glucose levels by retarding or avoiding complex carbohydrates' hydrolysis during digestion, resulting in lower glucose absorption [42]. Acarbose is an antidiabetic drug that works by inhibiting the enzymes \(\alpha\)-amylase and \(\alpha\)-glucosidase. Although this medication is effective at lowering blood glucose levels, long-term use can cause liver toxicity and uncomfortable gastrointestinal symptoms [43]. As a result, there is a demand for new \(\alpha\)-amylase and \(\alpha\)-glucosidase inhibitors derived from natural sources, especially plants, that do not have any negative or unwanted side effects in diabetic patients. _Beta vulgaris_ is one of the most medicinal plants that could be employed by diabetics [44]. The inhibitory effects of the methanolic extract of _B. vulgaris_ subsp. _maritima_ leaves against the enzymes \(\alpha\)-glucosidase and \(\alpha\)-amylase were also investigated in our investigation. The results are shown in Table 4 and Figure 2. Both \(\alpha\)-glucosidase and \(\alpha\)-amylase inhibition had a moderate effect on _B. vulgaris_ subsp. _maritima_ leaves. The inhibitory effect of \(\alpha\)-amylase was 25.93% at 3 mg/mL, while the inhibitory concentration of acarbose was 92.43% at 1 mg/mL and the percentage of \(\alpha\)-glucosidase inhibition was 63.28% at 2.22 mg/mL compared to the acarbose, which showed 58.71 inhibition percent at 0.33 mg/mL (Figure 2A,B). The presence of some molecules such as saponin may explain the potential of _B. vulgaris_ subsp. _maritima_ to inhibit enzymes related to diabetes disease [34]. Indeed, the flavonoid content of _B. vulgaris_ subsp. _maritima_ extract may be responsible for its enzyme inhibitory capacity. Flavonoids inhibit glucose transporters. For example, quercetin has been shown to have anti-diabetic properties by inhibiting the intestinal glucose transporter GLUT2 [45]. Another complementary mechanism could be flavonoid-induced inhibition of \(\alpha\)-amylase and \(\alpha\)-glucosidase activity. For example, two flavonoid glycosides isolated from Salsola kali that have _B. vulgaris_ leaves and seeds, such as vitexin, isovitexin, orientin, and isorientin,have been found to inhibit \(\alpha\)-glucosidase and may be the most likely cause of the enzyme inhibitory activity shown in diabetes patients [46; 47].  
Figure 1: The effects of _B. vulgaris_ subsp. _martima leaves_ extract (0.125–3 mg/mL) and aminoguanidine (1 mg/mL) on (**A**) the level of protein thiol group, (**B**) the level of fructosamine, and (**C**) the percentage of AGE inhibition in BSA/Trutose system. Each value represents the mean of three replicates. Bars represent the standard error. Different letters indicate significant differences among treatments at \(p<0.05\).
## 3 Results and Discussion
### Scenario of Products in RA  
In a standard SC warehouse, a manual method of sorting the received products and identifying the proper location takes almost one-quarter of the total time and operational effort to store products [33]. The case of products in RA is different when utilising the AOSF framework and the _6-Feature Strategy_ of AOSR, which recommends business process re-engineering (BPR) based on a proactive approach of sensing ASNs and ADNs, to maintain a minimum possible number of products in the RA by utilising the prior knowledge of upcoming products. The results are shown in Figures 4 and 5 for System State 0 and System State 1, respectively.  
A difference between the two standard approaches is slightly noticeable in Figure 4, particularly after the third hour, because the zoning with fewest logic has taken more time than the zoning with FIFO logic to sort and identify a proper space for the products, and has, thus, detained more products in the RA. In the case of the AOSR 2.0 strategy, there are very few or no products in the RA because of its proactive approach that allows the AOSR planner agent to make plans before products arrive to ensure the RA is clear for the auto-identification of upcoming products [26].  
The difference between the two standard approaches remains unnoticeable in System State 1, as shown in Figure 5, up until the fifth hour. Before then, there is more space available in the warehouse, so both approaches take less time and effort to optimise the available space. Therefore, there is almost the same number of products in RA in both cases. However, when the same products start repeating themselves in upcoming ASNs and ADNs after the fifth hour, the zoning with fewest logic takes more products to RA to identify the available space than the zoning with FIFO logic. In this scenario, the AOSR 2.0 recommended strategy takes the lead and provides up to a 148% decrease in the number of products in RA by incorporating its cognitive and integrative approach to support warehouse activities with its proactive utilisation of its slotting and re-slotting capabilities.
# Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter
## 3 The Proposed Method  
The proposed method consists of three parts; (i) tracking failure detection algorithm, (ii) re-tracking algorithm, and (iii) motion vector analysis algorithm, based on KCF method as shown in Figure 1. Tracking failure detection and motion vector analysis algorithms are performed in every frame, and re-tracking algorithm is performed only when the tracking failure is detected. In this section, we discuss about proposed method in detail.
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 3 Results and Discussion
### Validation versus Results from a Cantilever Beam Connected to a \(R-L\) Shunt Circuit  
The developed finite-element model is verified with the results of [24]. For comparison reasons, a concentrated mass of 4.2 g at the tip of the beam is added to model the magnet used in the non-contact electromagnetic driving system. The dynamic analysis of the beam is performed, and the first three flexural modes are presented in Table 4. Comparing the results from the present FE model with that of the finite-element computations and experiments obtained in [24], an excellent agreement is obtained.  
In addition, the effect of an \(R-L\) shunt circuit connected in series with the upper and lower piezoelectric patch, as presented in [24], is studied next. Using the values of \(R=7900\Omega\) and \(L=21.8H\), the driving point FRF modulus at the tip of the beam (i.e., the response at the tip for an excitation at the same point) from the present finite-element formulation as well as from the formulation of [24] is shown in Figure 5. Again, an excellent agreement is obtained between the two formulations.  
The results clearly illustrate the high accuracy and reliability of the present FE formulation for frequency response analysis of piezoelectric composite beams, and, therefore, this numerical analysis can be readily integrated efficiently with the PSO algorithm to provide optimal solutions to the optimization problems.
## 3 Results and Discussion
### Single-Factor Analysis of Bond Strength
#### 3.3.1 Effect of Bonding Length on Bond Strength  
In this test, the bonding lengths were controlled by different lengths of PVC tubes. The effect of the bonding length on bond strength in different concrete types was investigated with the center pull-out tests. The damage forms of the specimens were not the same when rebar pull-off damage occurred, so the ultimate load at that point was not the true ultimate bond load. It only represented the ultimate load at the time of pulling off the rebar, whereas the actual ultimate load would be greater than the measured value. Therefore, when selecting specimens for comparison, specimens where rebar pull-off damage occurred were avoided.  
As shown in Figure 5, the effects of bonding lengths on the bond strength of rebar with different concrete types were all relatively obvious, which was also reflected in the specimens with different rebar diameters. For the three groups of specimens with different concrete types and rebar diameters, the bond strengths decreased nearly linearly with decreasing bonding lengths, as shown in Figure 6. When the bonding lengths of the specimens were in a wide range of 60-100 mm, the ultimate bond strength (\(\tau_{u}\)) decreased by an average of 13.91-16.72% for every 20 mm increase in bonding length. Therefore, in this range, increasing the bonding length had a negative effect on the bond strength between rebar and concrete.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Group**} & **Bonding Length** & **Rebar Diameter** & **Ultimate Bond Stress** & \\  & **(mm)** & **(mm)** & **(MPa)** & **Damage Form** \\ \hline  
plitting damage \\
100-16-OC-2 & 100 & 16 & 13.66 & Pull-out damage \\
100-16-OC-3 & 100 & 16 & 14.08 & Pull-out damage \\ \hline \hline \end{tabular}
\end{table}
Table 7: Cont.  
The reason for this may be that the bond stresses were not uniformly distributed in the direction of the rebar. The bond stresses were zero at the loading and free ends. From the loading end to the free end, the bond stress rose rapidly from zero to a peak at 20% of the bonding length, after which the bond stress gradually decreased. The bond strength varied unevenly along the direction of the rebar because of the concrete in front of the ribbed rebar. Therefore, this indicates that as the bonding length increased, the bond force increased and the bond strength decreased.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 2 Materials and Methods
### Training and Test Samples  
The LULC classes were defined from the Lesotho Land Cover Atlas developed by the Food and Agriculture Organization (FAO) of the United Nations [30]. The training and test points used to classify all the satellite imageries were collected from the agricultural field (AF), bare soil (BS), built-up (BU), grassland (GL), waterbody (WB) and woody vegetation (WV) classes. The points were collected with the help of aerial photographs, panchromatic images, and Google historical maps. To test the performance of the classification model, the sample points for the multitemporal images were divided into 70% (training) and 30% (test) for the classification of all the imageries (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mission** & **Sensor** & **Date** & **Resolution** & **Path/Row** \\ \hline Landsat-5 & TM & 29 March 1988 & \\ Landsat-5 & TM & 11 March 1993 & \\ Landsat-5 & TM & 5 February 1998 & \\ Landsat-5 & TM & 20 December 2003 & 30 m & 170/080 \\ Landsat-7 & TM & 20 March 2008 & \\ Landsat-8 & OLI & 15 December 2013 & \\ Landsat-8 & OLI & 14 January 2019 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of Landsat imagery used in this study.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**1988**} & \multicolumn{3}{c}{**1993**} & \multicolumn{3}{c}{**1998**} & \multicolumn{3}{c}{**2003**} & \multicolumn{3}{c}{**2008**} & \multicolumn{3}{c}{**2013**} & \multicolumn{3}{c}{**2019**} \\  & & **TR** & **TS** & **TR** & **TS** & **TR** & **TS** & **TR** & **TS** & **TR** & **TS** & **TR** & **TS** \\ \hline \multirow{6}{*}{
\begin{tabular}{c} **SVM** \\ **SVM
## 1 Introduction
provided a comprehensive overview of technical flexibility assessment of multi-energy systems and distributed multi-energy systems, with a focus on their potential to provide support to a low-carbon grid [28]. These studies described flexibility of power systems, gas network and multi-energy systems and provided good enlightement for further research on flexibility assessment of multi-energy systems. While, a systematic methodology to assess flexibility of integrated power-natural gas-heating energy systems with P2G and gas storage has not been given. With regards to the significant benefit of P2G on the integrated power-natural gas energy systems in terms of reducing renewable curtailment and providing additional gas supply for gas network [16; 17; 24], P2G will increase flexibility of the integrated power-natural gas energy systems. In particular, P2G generates methane (synthetic natural gas, SNG) through electrolysis and methanation [16; 17; 24] and replenishes linepack of pipelines, raises gas pressure and strengthens the ability to deal with high ramp caused by gas demand/heating demand particularly in winter. In addition, gas storage affects flexibility directly, which can absorb excess gas when a lot of SNG is injected into a natural gas network and can supply gas when gas demand or heating demand increases. Moreover, sufficient natural gas can guarantee power output of gas turbines and improve flexibility of power systems. Therefore, it is of importance to analyze flexibility of integrated power-natural gas-heating energy systems with P2G and gas storage. This work here developed a framework for evaluating the flexibility in integrated power-natural gas-heating energy systems with P2G and gas storage based on a multi-objective economic/environmental optimal operation strategy. Both linepack of pipelines and volume of gas storage are modelled as the sources of system flexibility and their capability are evaluated. More specifically, at first, output of renewable generations, output of thermal units, gas flow of P2G, volume of gas storage, linepack of each pipeline and gas pressure of each gas node are obtained using the proposed optimal operation strategy. Then the flexibility of the integrated power-natural gas-heating energy systems can be evaluated using the proposed flexibility assessment model. Case studies are carried out on a hybrid IEEE 39-bus power system and Belgian 20-node gas system [9; 16; 23] in a period of 24 h to investigate effects of P2G on operation results, cost, emissions, wind curtailment and flexibility. Optimal Operation Model of Integrated Power-Natural Gas-Heating Energy Systems with P2G and Gas Storage  
In the integrated power-natural gas-heating energy systems, heating supplied by gas affects gas demand and gas ramp. P2G and gas turbines coupled with power system and natural gas system are flexibility providers. P2G is both the gas source and power load. Similarly, gas turbines are power sources and gas load. In addition, the flexibility analysis framework relies on operation of the integrated power-natural gas-heat energy systems.
## 4 Numerical Results
### Simulation Results and Analysis  
The results of the distribution system planning model presented in Section 3.1 are based on the stochastic objective function of all three scenarios of the joint restoration scheme presented in Section 3.2. As the scenario with the highest probability, scenario 2 is used to present the results and analysis of the second stage of the model. The faults in scenario 2 are on branches 16-17, 17-18, 21-22, 6-26, 9-15, and 22-12. The model's response to these faults for Case 0 is shown in Figure 6. Acting as the grid forming generator, the gas turbine generator GT4 forms microgrid MG4 energizing the loads on nodes 18 and 26-33. While GT4 acts as the master generator in MG4, the PV generator PV1 acts as a slave generator providing active power when available and reactive power support to improve the voltage of the system.
## 3 System Design
### Multicast Tree Generation Procedure  
Figure 6 shows the multicast routing architecture of CCSM which consists of control plane and forwarding plane. Control plane is responsible for gathering network topology, constructing multicast delivery tree, generating and distributing multicast routing information. The core functional entity is MMC which may be multiple. The different MMCs are in charge of different MSIs. Forwarding plane is responsible for packets forwarding in access network and core network, which mainly consists of multicast member, AR, ASR and CR. Once packets traverses the boundary between access network and core network, ASR will perform the mappings of AID-RID and MSI-MGI.  
Figure 5: Member join/leave procedure.  
Figure 6: Multicast routing architecture of CCSM.  
The fundamental of multicast routing is to set up the SPT which represents the forwarding paths of multicast from root to all ASRs that have joined the group. The calculation of SPT is executed in MMC, and needs full core network topology and elected roots as preconditions. In the beginning of CCSM, topology acquiring and roots election need to be done, and they are performed once in a fixed time to ensure the topology and roots election are up to date.  
Figure 7 shows the main function flow of MMC which contains four procedure: (1) topology gather; (2) Root election; (3) multicast routing calculation; (4) multicast routing information distribution.