esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Heat Loss Reduction  
The heat losses in four HCEs and percentages of the reduced heat loss in NHCEs compared with the CHCE are presented in Figure 6. As explained in our previous study, three kinds of NHCEs, namely, NHCE with TCO film (NHCE-IRR-I), NHCE with ideal IR-reflector coating with a fixed cutoff wavelength of 2.5 \(\upmu\)m (NHCE-IRR-II), and NHCE with ideal IR-reflector coating with optimal cutoff wavelength (NHCE-IRR-III), possessed superior heat loss performances. In particular, NHCE-IRR-III has the lowest heat loss, and the percentage of heat loss reduction increases with the elevation of absorber temperature. In contrast to the NHCE-IRR-III, the role of the IR-reflector in the NHCE-IRR-II in reducing the heat loss gradually decreases with the growing absorber temperature. This is because, with the increase of absorber temperature, more emissive heat from the absorber tube occurs in the wavelength band below 2.5 \(\upmu\)m, but the IR-reflector with a fixed cutoff wavelength of 2.5 \(\upmu\)m cannot block this part of emissive heat, which results in the eventual escape of this part of emissive heat from the absorber tube to the surroundings. Due to not so satisfactory spectral selectivity parameters in real TCO film in comparison with the ideal IR-reflectors, the heat loss performance in NHCE-IRR-I is inferior to the other two kinds of NCHEs. However, the NHCE-IRR-I still has superior performance compared with the CHCE, and the heat loss performance gets better with the increasing absorber temperature.  
Figure 6: (**a**) Heat loss of four HCEs, (**b**) Percentage of the reduced heat loss of novel heat collecting elements (NHCEs) compared with conventional heat collecting elements (CHCEs). The ambient temperature and wind speed are set as 25 \({}^{\circ}\)C and 2.5 m s\({}^{-1}\).  
As shown in Figure 6b, the percentages of heat loss reduction in NHCE-IRR-I, NHCE-IRR-II, and NHCE-IRR-III reach 18.7%, 25.3%, and 43.8% at the absorber temperature of 600 \({}^{\circ}\)C, which demonstrate that the greatest potential of IR-reflector for the reduction of heat loss in HCE is 43.8% at this absorber temperature. Therefore, the application of IR-reflectors in the HCEs has great potential to improve the thermal performance.
## 2 Materials and Methods
### Design of Path Fitness Function  
In the iterative process of the genetic algorithm, numerous individuals will be generated. To select the optimal solution, it is necessary to design a reasonable fitness function to assess the performance of individual paths. The fitness function can evaluate the efficiency and applicability of each path. Considering the requirements of agricultural robots for paths, the evaluation factors proposed include the coverage rate, repetition rate, smoothness of the paths, and operation time. The path selected through the fitness function can not only maximize the efficiency of the task but also take into account the smoothness of the path and the task time. The designed fitness function is as follows:  
\[F(x)\ =\ w_{1}\cdot\frac{A_{h}\bigcap\{U_{i}S^{i}\}}{A_{h}}\ +\ w_{1}\cdot\frac{N_{r}}{N_{p}}\ +\ w_{3}\cdot\int_{0}^{t_{f}}\theta^{2}(t)dt\ +\ w_{4}\cdot N_{S} \tag{6}\]  
where, the \(A_{h}\bigcap\{U_{i}S^{i}\}/A_{h}\) is the coverage rate of the path, \(S^{i}\) is the \(i\)th path, the \(\{U_{i}\ S^{i}\}\) is the set of paths, and \(A_{h}\) is the total operation area. The \(N_{r}/N_{p}\) is the repetition rate of path, the \(N_{r}\) is the grids of the repeated operation, and the \(N_{p}\) is the completed operation grids of the whole field. The \(\int_{0}^{t_{f}}\theta^{2}(t)dt\) is the smoothness of the path, and the position of the rotation angle of the steering wheel of the robot is related to the smoothness of the path. The \(N_{s}\) is the number of turns. The \(w_{1}\)-\(w_{4}\) represents the weight coefficients of each evaluation indicator. Different types of target requirements can be met by adjusting the size of the weight coefficients.  
In fitness function (1), the dimensions of coverage and repetition rate functions are between [0-1]. However, the dimensions of the path smoothness function are relatively large, so data normalization is required for comparison or comprehensive evaluation. There are three common normalization methods: the maximum normalization method, the standard score normalization method, and the median normalization method [18]. Due to the fixed range of the front wheel steering angle for agricultural robots (\(-40^{\circ}\)-\(40^{\circ}\)), the maximum normalization method is adopted as follows:  
\[\text{J}_{norm}\ =\ (J\ -\ J_{min}\ )/(J_{max}\ -\ J_{min}\ ) \tag{7}\]  
where, \(J_{min}\) and \(J_{max}\) are the minimum and maximum values of the path smoothness within the predetermined evaluation range, respectively. In order to generate a safe, efficient, and trackable path that satisfies the kinematic constraints of the robot, a cubic B-spline curve [21] is used to smooth the path. The curvature \(k\) of each path point in the turning  
The algorithm is executed once each time the robot performs a task, meaning the algorithm runs once per task. The sampling accuracy of the path points is set to 0.01 m.
# Numerical Simulation on Dynamic Response of Drilling Parameters in Loaded Rock Mass
## 3 Establishment of Numerical Model  
### Drilling Model Establishment  
The drill bit model with a diameter of \(\Phi\) = 28 mm was used in the drilling process and was established in Solidworks v.2022 software and then imported into Abaqus/CAE v.2023 for component transformation. The subsequent numerical model was established in ABAQUS as shown in Figure 3. In view of the irregular shape of the drill bit, it is defined as a rigid body. In the model construction, the C3D4 tetrahedral element with relatively simple calculation and strong rigidity is selected, and the total number of grid nodes is 7402. According to the principle of Saint-Venant, in order to weaken the influence of the rock boundary on the calculation results, the diameter of the rock model should be at least 5 times the diameter of the drill bit. Therefore, this paper takes the rock model as a cube with a side length of 300 mm, and the rock part adopts the 8-node reduced integral unit (C3D8R) sweep grid division technology. The axis algorithm divides the rock mass into loose grid units around the dense center as shown in Figure 3b, which saves calculation time and ensures calculation accuracy. The total number of nodes is 5,232,000. The simulation process in this paper is mainly the whole process of the bit breaking the rock. In order to increase the analysis accuracy, the model is solved by the explicit analysis module Abaqus/Explicit.  
In the process of simulating the drilling process of the drill bit, the full degree of freedom of the rock is constrained to ensure that the pose of the rock during the drilling process remains unchanged as shown in Figure 3c. The addition of constraints to the drill bit so that the drill bit will not move and bend in addition to the drilling direction is shown in Figure 3d. The parameters such as the thrust of the drill bit, the revolution speed, and the stress state of the rock during the drilling process are changed, respectively, and the dynamic response characteristics of the coal mine roadway under different drilling parameters and different confining pressures are studied.  
Figure 3: Numerical drilling model diagram. (**a**) Drilling model; (**b**) mesh subdivision; (**c**) rock load setting; (**d**) drill constraint; (**e**) profile of rock drilling process.
# Innovative Tool to Determine Radiative Heat Transfer Inside Spherical Segments
## Appendix A  
In the particular situation of analyzing a curved space made by three surfaces, by virtue of the third postulate presented we may need to supplement the radiative balance by considering the possible internal reflections between the said surfaces. Such an issue has been addressed by the author with the ensuing deductions [13; 31]:  
The final balance of energy is based on the expression:  
\[\mathrm{E_{tot}=E_{dir}+E_{ref}} \tag{1}\]  
In it, E\({}_{dir}\) stands for the ratio of directly emitted energy and E\({}_{ref}\) represents the amount of reflected energy. Adding both magnitudes, we find the total balance of radiated energy E\({}_{tot}\).  
When more than two surfaces appear in the process of reflection, it is customary to arrange the set of necessary equations into an array. Two types of square matrices are instrumental, they are named F\({}_{r}\) and F\({}_{d}\)[3; 31], the number of rows and columns coincides with the surfaces involved in the calculation:  
\[\mathrm{F_{d}=\begin{pmatrix}0&\mathrm{F_{12}}\mathrm{\rho_{2}}&\mathrm{F_{13 }}\mathrm{\rho_{3}}\\ \mathrm{F_{21}}\mathrm{\rho_{1}}&0&\mathrm{F_{23}}\mathrm{\rho_{3}}\\ \mathrm{F_{31}}\mathrm{\rho_{1}}&\mathrm{F_{32}}\mathrm{\rho_{2}}&\mathrm{F_{ 33}}\mathrm{\rho_{3}}\end{pmatrix}} \tag{2}\]  
\[\mathrm{F_{r}=\begin{pmatrix}1&-\mathrm{F_{12}}\mathrm{\rho_{2}}&-\mathrm{F_{13 }}\mathrm{\rho_{3}}\\ -\mathrm{F_{21}}\mathrm{\rho_{1}}&1&-\mathrm{F_{23}}\mathrm{\rho_{3}}\\ -\mathrm{F_{31}}\mathrm{\rho_{1}}&-\mathrm{F_{32}}\mathrm{\rho_{2}}&1\end{pmatrix}} \tag{3}\]  
Factors Fij have been previously found, they give the energy emission exchanged by the surfaces under consideration. In this case, \(\mathrm{\rho_{i}}\) stands for the ratio of reflection associated with a given surface i [3; 13].  
Notice, in the array defined as Fd (Equation (2)), the last coefficient of the diagonal is non-zero since the Fii factors or self-factors for the coincidental sub-indexes have definite values for curved surfaces such as the sphere, unlike cuboids or planar faces [2].  
Once we are able to obtain the value of the elements in the matrices (2) and (3), we can find different formulas (Equations (4)-(6)) to link the direct radiation with that given by reflection [1].  
\[\mathrm{F_{r}E_{ref}=F_{d}\ E_{dir}} \tag{4}\]  
\[\mathrm{F_{rd}}\ =\mathrm{F_{r}^{-1}F_{d}} \tag{5}\]  
\[\mathrm{E_{ref}}\ =\mathrm{F_{rd}E_{dir}} \tag{6}\]  
In this manner, the issue of radiative energy interchanges in the void for curved spaces is eventually settled.
## 3 Data and Methodology
### Estimation of the \(V_{\text{s30}}\)
#### 3.5.2 V\({}_{s30}\) from Vs Models  
The inversion of the dispersion curves allows retrieval of the Vs models. The averaged V\({}_{s30}\) can be calculated from the Vs models using the following equation [55]:  
\[\text{V}_{s30}=\frac{30}{\sum_{i=1}^{N}\frac{Hi}{Vi}} \tag{8}\]  
\(H_{i}\) is the thickness and \(V_{i}\) is the shear-wave velocity of the layer \(i\).  
The obtained V\({}_{s30}\) values were then spatially meshed using the Kriging method [56], with a linear transformation. A map of V\({}_{s30}\) variation was obtained for each city. As for the classification of the sites, it was completed according to the NEHRP site classification [57] (Table 2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Title 1** & **Title 2** & **Title 3** \\ \hline Vs \(>\) 1500. & Hard rock & A \\
760 \(<\) Vs \(\leq\) 1500 & Rock & B \\
360 \(<\) Vs \(\leq\) 760 & Very dense soil and soft rock & C \\
180 \(<\) Vs \(\leq\) 360 & Stiff soil & D \\ Vs \(\leq\) 180 & Soft soil & E \\ \hline \hline \end{tabular}
\end{table}
Table 2: NEHRP soil classification as a function of the average shear-wave velocity to 30 m depth [57].  
Figure 5: Results of the inversion of Rayleigh waves dispersion curves. The left panels for each site represent the dispersion curves. The right panels represent the Vs models. The black line corresponds to the best fit model, the dark grey represents models with minimum misfit + 10%. All the tested models are in light grey. The misfit is shown at the middle top of each site.
# A Multilevel Adaptive Path-Planning Model in Off-Road Environments
## 3 Construction of the Environment Model  
### Contributing Factors to Trafficability  
#### 3.1.1 Influence of Surface Features on Trafficability  
Surface features refer to natural or artificial features on the ground surface, which have varied impacts on trafficability. A vehicle that can drive smoothly on highway will slow down or even get trapped on a sandy road. It is usually difficult to quantify the impacts of surface features on the vehicle speed. To achieve semi-quantitative measurements, we introduced the speed influence coefficient of surface features with reference to the definition of the speed influence coefficient in the literature [31; 32]. The coefficient we proposed is the ratio of average driving speed on a certain surface feature to average speed on concrete pavements; the value of the coefficient is within the range of [0, 1], which is used to assign value to the generalized barrier grid map. Table 1 shows the values of the speed influence coefficient of surface feature.
## 5 Results and Discussion
### Packet Drop Ratio Analysis  
Table 5 shows the comparative analysis of the packet drop ratio of the four existing methods with the proposed approach. Figure 6 represents the packet drop rate with respect to the different numbers of nodes. In contrast to existing methods, the MHSEER protocol uses a beam heuristic approach to construct a residual energy-based weighted function, the number of hops to the BS, and the connection integrity criteria. Such an approach allows for the choice of the most reliable and energy-efficient nodes for packet transmission, and the packet drop ratio can be calculated by Equation (9).  
\[Packet\ drop\ ratio=\frac{Received\ packets}{Total\ packets} \tag{9}\]  
Figure 6 depicts the minimum packet drop ratio in the proposed approach. As shown, if there are 50 nodes, it is 4.8% as compared to the existing approaches. If there are 250 nodes, it is 30% for Sechrust-RPL; 21.27% for HBEER; 15% for SEHR; 9.54% for SEAMHR; and 8.24% for the proposed approach, which is the minimum that is required.  
Figure 5: Throughput.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. of Nodes** & **Sectrust-RPL** & **HBEER** & **SEHR** & **SEAMHR** & **Proposed** \\ \hline
50 & 22.51 & 16.22 & 10.11 & 5 & 4.8 \\ \hline
100 & 23.63 & 16.32 & 12 & 6.21 & 5.10 \\ \hline
150 & 24.94 & 17.41 & 13.24 & 7.12 & 6.56 \\ \hline
200 & 25.66 & 19.71 & 13.32 & 7.34 & 6.85 \\ \hline
250 & 30 & 21.27 & 15 & 9.54 & 8.24 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis of proposed packet drop ratio (%) with state-of-art approaches.
# Real-Time Statistical Measurement of Wideband Signals Based on Software Defined Radio Technology
###### Abstract  
The increase in channel bandwidth and peak-to-average power ratio (PAPR) of modern communication standards poses a serious challenge to performing channel power (CP) and complementary cumulative distribution function (CCDF) measurements in real-time using standard measurement solutions based on spectrum analyzers (SA). Recently, Software Defined Radio (SDR) technology has become a viable alternative to the conventional real-time spectrum monitoring approach based on SA due to its reduced cost. Therefore, in this paper, we propose a novel, innovative, agile and cost-effective solution to enable both CP and CCDF measurements on a state-of-the-art SDR platform. The proposed solution exploits the ability of the SDR equipment to access signal samples in the time domain and defines both CP and CCDF-type measurements. The two measurement functions are software implemented in GNU Radio by designing customized blocks and integrated into a graphical user interface. The proposed system was first tested and parameterized in a controlled environment using emitted signals specific to the IEEE 802.11 ax family of wireless local area networks. After parameterization, the SDR-based system was used for over-the-air measurements of signals emitted in the 4G+, 5G and 802.11 ax communication standards. By performing the measurement campaign, we have demonstrated the capabilities of the measurement system in performing real-time measurements on broadband channels (up to 160 MHz for IEEE 802.11 ax). Altogether, we have proved the usability of CP and CCDF measurement functions in providing valuable insights into the power distribution characteristics of signals emitted by the latest communication standards. By exploiting the versatility of SDR technology, we have enabled a cost-effective solution for advanced real-time statistical measurements of modern broadband signals.  
software defined radio; spectrum analyzer; channel power; complementary cumulative distribution function; IEEE 802.11 ax; LTE-A; 5G; real-time spectrum monitoring +
Footnote †: journal: Electronics Electronics
## 3 Motivation and Problem Statement
### Merging Replies and Requests  
Before going into details of connector behavior specification, we describe the specific issues the connector behavior specification method should cover. In the example scenario shown in Figure 1, let us assume that the connector behavior is designed so that when object \(a\) makes a request, the request is sent to all connected providers. Additionally, let us assume that the interface used to specify the contract of the ports is _Xlfc_. Let us also assume that the interface declares an operation \(xOp\), which returns an integer value.  
When object \(a\) makes a request, the request will be sent to all three providers \(b\), \(c\), and \(d\). Providers will handle the request and reply with an integer value. There will be three different replies from the providers, but the original requester object \(a\) expects only one reply. We can modify the design of object \(a\), as shown in Figure 3, so it expects three distinct replies from three separate ports, but doing so will reduce its reusability. The reduction in reusability is caused by having to modify the design of object \(a\) if we add another provider.  
Without modifying the requester, the connector behavior can perform one of the operations described in Figure 4 to solve the problem caused by multiple replies. In the first strategy shown in Figure 4a, the connector behavior discards replies from all providers and sends a default reply to the requester. In the second strategy shown in Figure 4b, the connector behavior chooses the reply from one of the providers. In the third strategy shown in Figure 4c, the connector behavior uses the replies from all providers to calculate a _merged reply_ and sends this merged reply to the requester.  
A similar problem can exist for merging requests when there are multiple requesters, as shown in Figure 5. For this scenario, requests received from objects \(a\), \(b\), and \(c\) can be merged into a single request and sent to the provider object \(d\). Alternatively, the connector can choose one of the requests and forward it to the provider, discarding other requests.  
As a result, the two examples demonstrate the need for merging replies for cases involving multiple providers and the need for merging requests for cases involving multiple requesters. In the next section, we describe how to specify connector behaviors that also include the merge functionality.  
Figure 3: Modifying object \(a\) in Figure 1 to receive replies from multiple ports.
# PC5-Based Cellular-V2X Evolution and Deployment
## 5 PC5-Based C-V2X Deployment in Taiwan
20\({}^{\circ}\): Compare Threshold_  
```
if\(|Head_{n}-Head_{h}|<20^{\circ}\)then  the target pedestrian is heading in the same direction as the host vehicle, no collision warning is sent elseif\(|Head_{n}-Head_{h}|>160^{\circ}\)then  the target pedestrian is heading in the conflict direction with the host vehicle, collision warning is sent elseif\(20^{\circ}<|Head_{n}-Head_{h}|<160^{\circ}\)then  the target pedestrian is crossing on the left side of the host vehicle, collision warning is sent elseif\(-160^{\circ}<|Head_{n}-Head_{h}|<-20^{\circ}\)then  the target pedestrian is crossing on the right side of the host vehicle, collision warning is sent endif
```  
**Algorithm 1**  
**Solution3: Automated Driving System Integration**  
We have designed and implemented the system integration between PC5-based C-V2X and automated driving system, shown below in Figure 8:  
1. RSU receives information from traffic light controllers or AI servers. Then, it broadcasts this information by using a predefined message format in its coverage field. 2. OBU receives broadcasting messages via PC5-based C-V2X communication. 3. OBU is connected with IPC (Industrial PC) of the autonomous driving system via TCP/IP. OBU receives GSNN (Global Navigation System Satellite) and CAN (Controller Area Network) messages from the vehicle. 4. OBU uses advanced internal algorithms to judge if the situation is dangerous or not. Then a warning message will be sent accordingly to the IPC of the autonomous driving system. 5. Finally, C-V2X technology is integrated with the automated driving system as expected. Based on the above solutions that were discussed, the following chapters will introduce which projects are implemented. Figure 8: C-V2X Automated Driving System Integration. Figure 6: C-V2X SPAT Application System Architecture. Figure 7: PC5-based C-V2X VRUCW.
###### Abstract  
The inhibition of the alpha-amylase digestive enzyme impedes starch digestion by blocking access to the active site of the enzyme, thereby playing a role in the prevention of obesity and type 2 diabetes. Plant-derived alpha-amylase inhibitors (\(\alpha\)Als) are promising nonpharmacological alternatives for the prevention of these diseases. Alpha-amylase inhibitor-1 (\(\alpha\)AI-1) present in common bean (_Phasolus vulgaris_) is derived from a precursor protein. In this study, the effect of digestion on the digestibility, immune reactivity, and bioactivity of \(\alpha\)AI-1 was assessed from four varieties of Hungarian common bean (_Phasolus vulgaris_), with special regard to the precursor protein. For this purpose, \(\alpha\)AI-1 was tested in both matrix (native flour and cooked flour) and purified forms under in vitro and acute rat in vivo digestion experiments. The effect of digestion on \(\alpha\)AI-1s was monitored by lab-on-a-chip (LOC) electrophoresis, SDS-PAGE/immunoblot, and inhibitory activity analyses by native PAGE. After both in vitro and in vivo digestion, we established that \(\alpha\)AI-1 was not degraded even after 60 min gastric digestion and showed immune-reactive properties as well. Although the activity of the purified \(\alpha\)AI-1 was lost, that of \(\alpha\)AI-1 in the flour matrix (noncooked and cooked) was retained in the stomach. Presumably, in the beans, \(\alpha\)AI-1 polypeptides became active due to the pepsin digestion of the precursor protein. The latter samples were also tested in vivo in the small intestine and their resistance and immune reactivity were observed, but \(\alpha\)AI-1 did not show activity, as \(\alpha\)AI-1 polypeptides were probably complexed by pancreatic amylases. From these results, we can assume that the \(\alpha\)AI-1-rich bean protein preparation can affect the carbohydrate metabolism; thus, it could be a promising ingredient for weight loss purposes.  
Bean alpha-amylase inhibitor; pre-protein; bioactivity; digestibility +
Footnote †: journal: applied sciences