esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast-RRT: A RRT-Based Optimal Path Finding Method
## 2 Background
### RRT and RRT*  
The RRT algorithm is the basis of the proposed Fast-RRT, while RRT* is an important method to find the approximate optimal path. Therefore, the RRT and RRT* algorithms are introduced in Algorithm 1 and Algorithm 2, respectively.  
The RRT is a single query search algorithm based on sampling, which can quickly find a feasible path. In the initialization phase, RRT builds a tree with the initial state \(x_{init}\) as the node. In each iteration, RRT first randomly samples a state \(x_{rand}\) in state space \(X_{free}\) and select the nearest vertex \(x_{near}\). Then, the RRT algorithm will generate \(x_{new}\) through the steering function, as shown in Figure 1. If the edge of \(\{x_{near},\ x_{new}\}\) is obstacle-free, then \(x_{new}\) will be added to the set of nodes, and \(\{x_{near},\ x_{new}\}\) will be added to the set of edges. If \(x_{new}\) is located in the target area \(X_{goal}\), the search is successful, and this result will be returned.  
The path found by RRT may be far away from the optimal path. RRT* overcomes this problem by introducing a rewire step. If the edge \(\{x_{near},x_{new}\}\) is obstacle-free, then nodes with a distance less than \(r\) around \(x_{new}\) will be calculated to determine the optimal parent node of \(x_{new}\). In addition, RRT* not only adds \(x_{new}\) to the tree, but also considers it as a replacement parent node for existing neighboring nodes. Therefore, as the sampling times approach infinity, RRT* continuously adjusts the random tree and finally finds an optimal  
Figure 1: Schematic illustration of the expansion steps of the RRT algorithm. The RRT algorithm performs random sampling in the state space to obtain \(q_{rand}\), and the vertex closest to \(q_{rand}\) is calculated as \(q_{near}\). Then, the random tree of RRT extends a certain distance from \(q_{near}\) to \(q_{rand}\) to obtain \(q_{near}\).  
path. However, the RRT* is time-consuming and thus is not suitable for applications that need to find an optimal path quickly.  
```
Input: \(x_{init}\), \(x_{goal}\) and \(Map\) T.init() for\(i=1\dots N\)do  Let \(x_{rand}\gets UniformSample(Map)\);  Let \(x_{near}\gets Nearest(Map)\);  Let \(E_{i}\gets Edge(x_{new},x_{near})\) If \(ObstacleFree(Map,E_{i})\)  T.addNode(\(x_{new}\))  T.addEdge(\(E_{i}\))  If \(x_{new}\in X_{goal}\) then  Success();  End End End
```  
**Algorithm 1** RRT  
```
Input: \(x_{init}\), \(x_{goal}\) and \(Map\) T.init() for\(i=1\dots N\)do  Let \(x_{rand}\gets UniformSample(Map)\);  Let \(x_{near}\gets Nearest(Map)\);  Let \(x_{new}\gets Seen(Map)\);  If \(ObstacleFree(Map,E_{i})\) \(X_{near}\gets NearC(T,x_{new})\); \(x_{min}\gets ChooseParent(X_{near},x_{near},x_{new})\); \(T.addNodeEdge(x_{min},x_{new})\); \(T.rewire()\);
```  
**Algorithm 2** RRT*
## 2 Materials and Methods
The volume fractions of the phases were obtained by analysis of SEM images taken on polished samples without etching. The crystalline nature of the W-TCP eutectic was identified by X-ray diffraction (XRD) (model RU 300, RIGAKU)working at 40 kV and 80 mA, using K\({}_{\alpha}\) radiation (1.5418 A). The scanning was carried out between 5\({}^{\circ}\) and 70\({}^{\circ}\) (2 Theta) in 0.03\({}^{\circ}\) steps, counting for 1 s per step. Indentation tests were performed to assess the Vickers Hardness of the samples (Matsuzawa, MXT70). The procedure was realized following the ASTM C1327-99 Standard. An indentation load of 4.9 N and a holding time of 15 s were used for the measurements, which were carried out in polished cross-sections for no less than 10 valid tests. Determination of the fracture toughness was evaluated based on the both the mark and the crack lengths obtained from the indentation tests. Three-point flexural tests were performed in 1 mm diameter as-grown samples to assess the bending strength (Instron testing machine, model 5565). Loading span and crosshead speed were set at 10 mm and 30 \(\upmu\)m/min, respectively. Tests were realized at room temperature. To determine the mean values and standard deviation, five valid tests were performed. It was observed that samples showed a linear load-displacement trend. The maximal load obtained during the test allowed determining the flexural strength, according to the standard beam theory. For the estimation of the in vitro bioactivity of the MgO-MgSZ composite, the test proposed by [PERSON] was used. Discs were cut form the rod samples and maintained for four weeks in simulated body fluid (SBF). The ion concentration was observed to be nearly equal to that of the body plasma as a SBF environment (Na\({}^{+}\) 142.0, K\({}^{+}\) 5.0, Mg\({}^{2+}\) 1.5, Ca\({}^{2+}\) 2.5, Cl\({}^{-}\) 148.8, HCO\({}^{3-}\) 4.2, and PO\({}_{4}\)\({}^{2-}\) 1.0 mM), and the solution was buffered at pH 7.2. The temperature of the samples was kept at human body temperature, 37 \({}^{\circ}\)C, by using a stove (Memmert GmbH, model 100-800, Schwabach, Germany).
## 3 Result and Discussion  
### Data Preparation  
The mechanical data points from the compression test were recorded in units of 0.1 MPa, while AE data points are only recorded when the valid cracks are detected in the rock sample. For matching the two different types of dataset, a smooth spline approximation for 10 granite specimens was used. The AE parameters used in this study were AE amplitude, hit, count, rise time, absolute energy, and initiation frequency. These six parameters were converted to cumulative values and used as inputs for predicting the degree of rock damage. Figure 9 shows the scatter plots for the relationship between inputs and output. Although 10 granite specimens were extracted from the same borehole in RG-2 section, the curves for each input showed different trends. This phenomenon seems to be due to the difference of properties such as rock strength, stiffness, and Poisson's ratio (Table 1) and shows that predicting the degree of rock damage using only one or two AE parameters is a difficult problem.  
To prepare a dataset for ML-based models, the inputs values were normalized in the range from 0 to 1 for eliminating a scale effect. The entire dataset (5301 data points) was divided into training (80%) and testing (20%) set, randomly. Here, the training set was used for model training and hyper-parameter tuning, while the testing set was used for model evaluation [38].
# Analysis of Volatile Compounds in Coffee Prepared by Various Brewing and Roasting Methods
## 2 Materials and Methods  
### Chemical Reagents and Materials  
Green coffee beans (_Coffea arabica_, from Brazil) were bought from a commercial market in Seoul, Korea. The origin of green coffee beans was verified by the importers and experts. 2-Methylpyrazine, 2,5-dimethylpyrazine, 2,6-dimethylpyrazine, 2-ethyl-3-methylpyrazine, 2-ethyl-5-methylpyrazine, 3-ethyl-2,5-dimethylpyrazine, furfural, furfuryl acetate, furfuryl propionate, furfuryl alcohol, 1-furfurylpyrrole, 4-ethylguaiacol, 2-methoxy-4-vinylphenol, 5-methylfurfural, quinoxaline, and C7-C30 alkane standard were obtained from Sigma-Aldrich Chemical Co. (St. Louis, MO, USA). 2-Ethylpyrazine, 2,3-dimethylpyrazine, 1-methylpyrrole, guaiacol, and 2-acetylfuran were bought from Tokyo Chemical Industry Co., Ltd. (Tokyo, Japan). Divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS, 50-\(\mu\)m film thickness) solid-phase microextraction (SPME) fiber was purchased from Supelco, Inc. (Bellefonte, PA, USA).
# An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
## 4 An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
### A Fast Constraint Evaluation Mechanism
#### 4.1.1 VTW Constraint  
In [24] the concept of Valid Observation Time Interval (VOTI) was proposed, which allows fast verification of the VTW constraint for insertion neighborhood in the context of satellite imaging scheduling. For each observation, its VOTI is a time interval during which at least one feasible observation start time is available without violating the VTW constraint of any other observations in the schedule.  
We follow the definition of the VOTI proposed by [24] and denote it for a window \(v\) as \([ES(v),LS(v)]\). \([ES(v),LS(v)]\) is the time interval during which the observation of \(v\) can start without violating the VTW constraint of any other observation in the schedule. For two consecutive windows \(v_{1}\) and \(v_{2}\) (VTW or DW), we define Transition \((v_{1},v_{2},t)\) as the transition time from \(v_{1}\) to \(v_{2}\) when starting maneuvering at \(t\). We define the violation in the VTW constraint of a schedule \(\sigma\) as \(TV(\sigma)\). Then, for an insertion where \(v_{2}\) is inserted after \(v_{1}\), the increased violation of VTW constraint, \(\triangle TV(\sigma)\), and the violation level at \(v_{2}\) are calculated as:  
\[\triangle TV(\sigma)=max(0,ES(v_{1})+d_{1}+Transition(v_{1},v_{2}, ES(v_{1}))-LS(v_{2})) \tag{11}\] \[TV(v_{2})=TV(v_{1})+\triangle TV(\sigma), \tag{12}\]  
where \(d_{1}\) is the observation duration of \(v_{1}\). In this way, we can validate the VTW constraint in constant time and calculate the violation in the case of infeasibility.  
For each pair of consecutive VTWs \(v_{1}\) and \(v_{2}\), the concatenation \(v_{1}\oplus v_{2}\) also requires the updating of \(ES(v_{2})\) and \(LS(v_{1})\). The rule is as follows:  
\[ES(v_{2}) = max(l_{2},ES(v_{1})+d_{1}+Transition(v_{1},v_{2},ES(v_{1}))) \tag{13}\] \[LS(v_{1}) = min(l_{1},LS(v_{2})-Transition^{-1}(v_{1},v_{2},LS(v_{2}))-d_{1}) \tag{14}\]  
where \(Transition^{-1}()\) is the reverse function of \(Transition()\). \(Transition^{-1}(v_{1},v_{2},LS(v_{2}))\) returns the transition start time from \(v_{1}\), which makes the arrival time at \(v_{2}\) equal to \(LS(v_{2})\).  
Each time a new VTW is inserted, it is necessary to update the value of \(ES(v)\) for each \(v\) before \(v_{1}\) and the value of \(LS(v)\) for each \(v\) after \(v_{2}\). Note that, if a VTW violation is related to a VTW, \(v\), then \([ES(v),LS(v)]\) is reduced to a number and \(ES(v)=LS(v)\).
## 4 Parametric Analysis
### Alignment Angle  
The effects of the alignment angle on the building response in the case of \(s_{1}=s_{2}=0\) are shown in Figure 9. As the alignment angle increases from \(0^{\circ}\) to \(90^{\circ}\), the maximum building settlement at the left end of the building decreases from \(16.1\) to \(15.2\) mm, while the minimum building settlement at the right end of the building increases from \(5.9\) to \(8.0\) mm (Figure 9a). For each alignment angle, the building settlements gradually decrease along the building axis. The rotation angles generally decrease with an increase in the alignment angle. The position where the maximum rotation angle occurs moves from \(y=5.0\) m to \(y=0\), and the rotation angle at \(y=20.0\) m is the minimum (Figure 9b). Along the building axis, the rotation angles first increase and then decrease when \(\alpha=0\), and gradually decrease in the other four cases. The increase in the alignment angle leads to the decreases in the maximum bending moments (including the maximum positive and negative bending moments), and causes the position of the maximum negative moment to move toward the tunnel face, as shown in Figure 9c. Similar characteristics can also be observed in Figure 9d, and the position of the maximum positive shear force is fixed at \(y=16.0\) m. On the condition \(\alpha=90^{\circ}\), the bending moments are all nonpositive and approximately symmetric with respect to the building centerline (\(y=10.0\) m). Meanwhile, the shear forces are approximately antisymmetric with respect to the building centerline.
## 2 SnAgCu-Based Solders  
### Microstructure  
Adding trace La\({}_{2}\)O\({}_{3}\) nanoparticles to SAC-305 solder could provide a large number of nucleation sites to refine the microstructure of the composite solder. However, when the content exceeded 0.1 wt.%, the nanoparticles would aggregate together, resulting in the formation of cracks and voids, as shown in Figure 1[23].  
In the reflow process, a brittle interfacial layer is rapidly formed at the interface between the molten solder and the substrate; meanwhile, the overgrowth of intermetallic compounds (IMCs) will result in lower connecting strength of solder joints [24; 25]. After multiple reflow, the coarsening of the Cu\({}_{6}\)Sn\({}_{5}\) layer occurs and the Cu\({}_{3}\)Sn layer is formed at the interface between the Cu\({}_{6}\)Sn\({}_{5}\) layer and the substrate, which decreases the reliability of the joints. [PERSON] et al. [26] found that adding Zn nanoparticles could decrease the growth rate of Cu\({}_{6}\)Sn\({}_{5}\) and suppress the formation of Cu\({}_{3}\)Sn. Research [27] found that the grain size of Cu\({}_{6}\)Sn\({}_{5}\) IMC increased with the increasing reflow time, and TiO\({}_{2}\) nanoparticles could effectively suppress the growth of the IMC layer by preventing the diffusion of Cu and Sn atoms.  
Considering the effect of secondary phase particles on the migration of grain boundary during grain growth, the total restraining force acting on the boundary can be expressed as follows:  
\[\mathrm{F}=\frac{3f\gamma_{b}}{2r} \tag{1}\]  
where \(f\) is the volume fraction of secondary phase particles at grain boundary, \(\gamma_{b}\) is the interface energy per unit area of grain boundary, and the \(r\) is the radius of secondary phase particles. It is obvious that F is inversely proportional to \(r\). Therefore, the \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticle with smaller size have a better refining effect on \(\beta\)-Sn phase in SAC105-Fe\({}_{2}\)O\({}_{3}\) composite solder, as shown in Figure 2[28]. On account of high surface energy, the doped CNTs were easily adsorbed on the surface of \(\beta\)-Sn and IMCs, which significantly refined the microstructure of the composite solder by inhibiting the migration of grain boundaries [29].  
Figure 1: Field emission scanning electron microscope (FESEM) image of SnAgCu–nano La\({}_{2}\)O\({}_{3}\) composite solder: (**a**) 0% La\({}_{2}\)O\({}_{3}\); (**b**) 0.01% La\({}_{2}\)O\({}_{3}\); (**c**) 0.03% La\({}_{2}\)O\({}_{3}\); (**d**) 0.05% La\({}_{2}\)O\({}_{3}\); (**e**) 0.1% La\({}_{2}\)O\({}_{3}\); (**f**) high-resolution image of (e) [23].
## 2 Fiber Design  
Our proposed composite PCF has a tellurite glass cladding with three rings of air holes in a hexagonal lattice, surrounding an As\({}_{2}\)S\({}_{3}\)-core in the center of the fiber. We use [PERSON]'s equation [7] to find the amount of linear refractive index of materials in different excitation wavelengths,  
\[n^{2}(\lambda)=1+\sum_{j=1}^{k}\frac{A_{j}\lambda^{2}}{\lambda^{2}-B_{j}^{2}} \tag{1}\]  
where \(A_{j}\) and \(B_{j}\) are [PERSON]'s coefficients, \(\lambda\) is the excitation wavelength in micrometers and \(n(\lambda)\) is the wavelength-dependent linear refractive index of material [7]. The [PERSON]'s coefficients for As\({}_{2}\)S\({}_{3}\) are \(A_{1}=1.8983678\), \(A_{2}=1.9222979\), \(A_{3}=0.8765134\), \(A_{4}=0.1188704\), \(A_{5}=0.9569903\), and \(B_{1}=0.15\), \(B_{2}=0.25\), \(B_{3}=0.35\), \(B_{4}=0.45\) and \(B_{5}=27.3861\) (\(\mu\)m\({}^{-2}\)) [13]. Therefore, one can find the refractive index of As\({}_{2}\)S\({}_{3}\) as a function of wavelength using the following equation,  
\[n^{2}=1+\frac{1.8983678\lambda^{2}}{\lambda^{2}-0.0225}+\frac{1.9222979\lambda ^{2}}{\lambda^{2}-0.0625}+\frac{0.8765134\lambda^{2}}{\lambda^{2}-0.1225}+ \frac{0.1188704\lambda^{2}}{\lambda^{2}-0.2025}+\frac{0.9569903\lambda^{2}}{ \lambda^{2}-750}. \tag{2}\]  
The composition of the tellurite glass is considered to be 76.5 TeO\({}_{2}\)-6 Bi\({}_{2}\)O\({}_{3}\)-11.5 Li\({}_{2}\)O-6 ZnO (mol%), which is the same as that of the tellurite glass in [13; 14]. The [PERSON]'s coefficients for this tellurite glass are \(A_{1}=1.67189\), \(A_{2}=1.34862\), \(A_{3}=0.62186\), and \(B_{1}=0.0216\), \(B_{2}=0.23971\) and \(B_{3}=6.8356\) (\(\mu\)m\({}^{-2}\)) [13]. Similar to As\({}_{2}\)S\({}_{3}\), one can find the refractive index of tellurite as a function of wavelength using the following equation  
\[n^{2}=1+\frac{1.67189\lambda^{2}}{\lambda^{2}-0.00046656}+\frac{1.34862 \lambda^{2}}{\lambda^{2}-0.0574608841}+\frac{0.62186\lambda^{2}}{\lambda^{2}- 46.72542736}. \tag{3}\]  
The material dispersion of As\({}_{2}\)S\({}_{3}\) and tellurite glass can be easily calculated using the dispersion formula, \(D_{material}=-\frac{\lambda}{c}\frac{d^{2}n_{material}}{d\lambda^{2}}\)[20]. The linear refractive index and material dispersion of As\({}_{2}\)S\({}_{3}\) and tellurite from visible (500 nm) to mid-infrared (3000 nm) are shown in Figure 1a,b as a function of wavelength.  
The design parameters of the circular-core composite PCF (C-PCF) are the hole diameter \(d\), hole pitch \(\Lambda\), and core diameter \(d_{c}\). In the case of elliptical core composite PCF (E-PCF), instead of core diameter we have two new parameters: width \(a\), and height \(b\) of the ellipse. The cross sections of the proposed PCFs are shown in Figure 2.  
Figure 1: Linear refractive index and material dispersion of (**a**) As\({}_{2}\)S\({}_{3}\) and (**b**) tellurite glass as a function of wavelength. The solid blue curve and left vertical axis represent refractive index values, and dotted red curve and right vertical axis represent material dispersion.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 5 Results and Comparison
### Performance Comparison
#### 5.3.2 Comparison on Virtex 5 Platform  
The proposed two-stage pipelined design showcases significant optimization compared to the architecture presented in [23]. It requires 42.7% fewer resources while achieving an impressive 4.49-times higher throughput/area. This reduction in resource utilization demonstrates the efficiency of our design in maximizing performance while minimizing hardware requirements. Similarly, the non-pipelined design in this article surpasses the architecture in [23] by achieving a higher throughput/area while demanding 1.65-times fewer FPGA slices. This substantial reduction in hardware resource usage showcases the efficiency of our design in optimizing resource utilization.  
Additionally, our two-stage pipeline architecture exhibits a higher clock frequency and utilizes 1.87 times more resources compared to the architecture in [25]. Despite the increased resource utilization, our design significantly outperforms it in terms of throughput/area, indicating the superior efficiency and performance.  
When comparing the proposed architecture to the non-pipelined architecture presented in [29], significant optimizations are achieved. The proposed architecture showcases a reduction of 14.7% in hardware resource utilization, operating at 1.71-times lower frequency. Moreover, it achieves a notable improvement of 1.17-times higher throughput/area. These enhancements highlight the effectiveness of the proposed hybrid algorithm in terms of resource efficiency and overall performance. Furthermore, in the comparison with the two-stage pipelined architecture presented in [29], the proposed approach exhibits additional improvements. The hardware resource utilization is reduced by 3.3%, while the operating frequency is increased by 1.73%. Notably, the proposed two-stage pipelined architecture achieves a 1.32-times higher throughput/area, indicating a significant boost in computational efficiency and performance compared to the architecture proposed in [29].  
The proposed non-pipelined design showcases superior performance compared to the first solution presented in [39]. It achieves a clock frequency that is 2.35 times lower but compensates with a 1.74-times higher throughput/area. Moreover, it utilizes 74.9% fewer hardware resources. When compared to the second solution in [39], our design outperforms it by employing 42.4% fewer resources while achieving a similar clock frequency and a negligible difference in throughput/area. The proposed two-stage pipelined architecture outshines the first solution in [39]. It utilizes 71.5% fewer resources while achieving a 1.7-times higher throughput/area, albeit at a lower clock frequency. On the other hand, the second solution in [39] achieves a clock frequency that is 1.33 times higher while utilizing 34.7% fewer slices. However, it exhibits a 1.91-times lower throughput/area when compared to our proposed two-stage pipeline architecture.
# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 4 Slant Wet Delay Simulations  
In general, most current studies used the radiosondes or the reanalysis products to evaluate the tomographic results. However, the differences of water information between GNSS and radiosonde or reanalysis products may affect the evaluation besides the tomographic technique itself. In this work, the reanalysis products are utilized to simulate the GNSS slant wet delays as inputs in tomography, and the tomographic results are evaluated by comparing with reanalysis products to exclude the influence of differences between inputs and references.
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 4 System Model
### Data Packet Forwarding Mechanism in ERPMSA-Uiwsn  
The following steps are taken to transmit the data packet towards its destination point. First, when a node becomes a source node it will broadcast neighbor request (NR) in its range and gets the Acknowledgment (ACK) in response from neighbor nodes.  
* After receiving the ACK packet, the source node updates its neighbor table and sorts out the master and slaves.
* The source node sends a Control packet in its surroundings, containing the IDs of Masters of each PFN region, which turn the slave nodes to the idle state while keeping the masters active.
* The data packet is sent which is only received by the master nodes while the holding time is assigned based on their priority. In this way, masters nodes of the region PFZ1 get a higher priority than the region PFZ2.
* Before forwarding data, master must check for the energy hole scenario. In case of an energy hole, the 2 nd Master takes charge and forwards the data packet. If the 2 nd Master also encounters the same situation, it hands over the responsibility to its subordinate master, i.e., the 3 rd master. If it does not find a way out, the packet is dropped. This increases the chances of the data packet getting its destination most probably.  
Figure 6: (**a**) Packet duplication (**b**) Master-Range extension to end data packet duplication.  
If there is no energy hole then the master beating the clock first will forward the data to the forwarder in its range in the same fashion, while the other master drops the packet after hearing the control packet from the forwarding master.  
The whole procedure is well elaborated in the sequence diagram in Figure 7.