esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Path Planning for Robots with Improved A* Algorithm under Bidirectional JPS Strategy
## 3 Improved A* Algorithm
### Path Smoothing  
#### 3.3.1 Primary Smoothing  
The paths planned by the improved algorithm still exhibit issues such as having numerous redundant nodes and excessive turning points. Therefore, this paper employs a redundant point removal strategy to enhance path smoothness by reducing both the number of redundant nodes and excessive turning angles. The steps of the redundant point removal strategy, i.e., primary smoothing, are as follows:  
* Plan the optimal path using the improved algorithm and save information such as the path length and number of nodes;
* Create a collection of path nodes, \(R=\{N_{i}\}\), \(i\in[0,\,n]\), where \(N_{0}\) is the start node, \(N_{n}\) is the goal node, and \(N_{1}\),..., \(N_{n-1}\) are the intermediate nodes;  
Figure 10: Schematic diagram of bidirectional JPS algorithm.  
Determine whether the vectors \(N_{0}^{\rightarrow}N_{1}\) and \(N_{1}^{\rightarrow}N_{2}\) formed by points \(N_{0}\), \(N_{1}\), and \(N_{2}\) are parallel using the principle of vector parallelism. If they are parallel, the three points are collinear, and the redundant node \(N_{1}\) is removed. If they are not parallel, then the three points are not collinear, and there is no redundant node. If node \(N_{1}\) is removed, then continue to evaluate nodes \(N_{2}\), \(N_{3}\), and \(N_{4}\) to determine whether they are collinear. Otherwise, evaluate nodes \(N_{1}\), \(N_{2}\), and \(N_{3}\) for collinearity. Repeat this process until no more collinear nodes exist in the path. Create a new set \(P\) of path nodes and add the reserved nodes to set \(P\). Set \(P=\{p_{j}\},j\in[0,m]\), where \(p_{0}\) is the start node, \(p_{m}\) is the goal node, and \(p_{1}\),..., \(p_{m-1}\) are the intermediate nodes;
* Create the set \(K\) of nodes that the path must pass through. Add \(p_{0}\) to set \(K\) and consider \(p_{0}\) as the current point for evaluation. First, connect nodes \(p_{0}\) and \(p_{m}\) to determine whether the straight line segment \(p_{0}p_{m}\) passes through obstacles. If it does not, \(p_{0}p_{m}\) is considered a viable path, and one should add \(p_{m}\) to set \(K\). If \(p_{0}p_{m}\) is obstructed, connect \(p_{0}\) to \(p_{m-1}\) and continue the evaluation. If \(p_{0}p_{m-1}\) is clear, add \(p_{m-1}\) to set \(K\). If \(p_{0}p_{m-1}\) is obstructed, connect \(p_{0}\) to \(p_{m-2}\) and continue evaluating. Continue this process until the must-pass node \(p_{t1}\) is identified and added to set \(K\). Then, use \(p_{t1}\) as the current point and repeat the evaluation process. Repeat these steps until \(p_{m}\) is added to set \(K\);
* Connect the nodes extracted from set \(K\) sequentially; the resulting path is the path after one smoothing. The schematic diagram of the redundant node removal process is shown in Figure 11.
## 3 Inherent Optical Properties (IOPs)
### General Definitions
Then one finds, with \(x=\cos\Theta\)  
\[\beta(z)=2\pi\int_{0}^{\pi}\text{vsf}(z,\cos\Theta)\sin\Theta d\Theta=2\pi\int _{-1}^{1}\text{vsf}(z,x)dx\qquad[\text{m}^{-1}]. \tag{4}\]  
A normalized vsf, denoted by \(p(z,\cos\Theta)\) and referred to hereafter as the _scattering phase function_, may be defined as follows  
\[p(z,\cos\Theta)=4\pi\frac{\text{vsf}(z,\cos\Theta)}{\int_{4\pi}\text{vsf}(z, \cos\Theta)d\omega}=\frac{\text{vsf}(z,x)}{\frac{1}{2}\int_{-1}^{1}\text{vsf}( z,x)dx} \tag{5}\]  
so that  
\[\frac{1}{4\pi}\int_{4\pi}p(z,\cos\Theta)d\omega=\frac{1}{2}\int_{-1}^{1}p(z,x )dx=1. \tag{6}\]  
The scattering phase function has the following physical interpretation. Given that a scattering event has occurred, \(p(z,\cos\Theta)d\omega/4\pi\) is the probability that a light beam traveling in the direction \(\mathbf{\hat{\Omega}}^{\prime}\) is scattered into a cone of solid angle \(d\omega\) around the direction \(\mathbf{\hat{\Omega}}\). The scattering phase function \([p(z,\cos\Theta)]\) describes the angular distribution of the scattered light, while the scattering coefficient \(\beta(z)\) describes the total amount of scattered light integrated over all scattering directions. A convenient measure of the \"shape\" of the scattering phase function is the average over all scattering directions (weighted by \(p(z,\cos\Theta)\)) of the cosine of the scattering angle \(\Theta\), i.e.,  
\[g(z) =\langle\cos\Theta\rangle=\frac{1}{4\pi}\int_{4\pi}p(z,\cos\Theta )\,\cos\Theta\,d\omega\] \[=\frac{1}{2}\int_{0}^{\pi}p(z,\cos\Theta)\,\cos\Theta\,\sin\Theta d \Theta=\frac{1}{2}\int_{-1}^{1}p(z,x)xdx. \tag{7}\]The average cosine \(g(z)\) is called the _asymmetry factor_ of the scattering phase function. Equation (7) yields complete forward scattering if \(g=1\), complete backward scattering if \(g=-1\), and \(g=0\) if \(p(z,\cos\Theta)\) is symmetric about \(\Theta=90^{\circ}\). Thus, isotropic scattering also gives \(g=0\). Similarly, the probability of scattering into the backward hemisphere, is given by the backscattering ratio (or backscatter fraction) \(b\), defined as  
\[b(z)=\frac{1}{2}\int_{\pi/2}^{\pi}p(z,\cos\Theta)\ \sin\Theta\,d\Theta=\frac{1}{2} \int_{0}^{1}p(z,-x)dx. \tag{8}\]  
The scattering phase function \(p(z,\cos\Theta)\) depends on the refractive index as well as the size and shape of the scattering particles, and will thus depend on the physical situation and the practical application of interest. Two different scattering phase functions, which are useful in practical applications, are discussed below.
## Materials and Methods
### Data and Pre-Processing  
#### 2.2.1 Ohs-1 Imagery  
OHS-1 is the first satellite constellation that was built and operated by a privately listed company in China. The whole constellation consists of 34 satellites, including video, hyperspectral, high-resolution optical, and infrared satellites. The hyperspectral image of the constellation was used in this study. The Orbita hyperspectral satellites (OHS) were successfully launched on 26 April 2018. They were in a sun-synchronous orbit of 500 kilometers. The hyperspectral image contains \(5056\times 5056\) pixels, and the imaging spectrum ranges from 400 to 1000 nm. The spectral average of 400-1000 nm is divided into 32 spectral segments through a filter. The image has a spatial resolution of 10 meters and a spectral resolution of 2.5 nm. The OHS-1 data have the potential to be an important data source for tree species mapping, as they are available to the public at the official website of the Orbita Hyperspectral Satellite (www.obtdata.com; Zhuhai Orbita Aerospace Science & Technology Inc., Zhuhai, China).  
The OHS-1 image was recorded under cloudless conditions over the site in the middle of the growing season on 19 September 2018 (orbit altitude: 520 km, solar elevation angles: 50.53\({}^{\circ}\), lateral angular: \(-2.194^{\circ}\)). The study sites were subset into the forest farm centers and their surrounding regions with spatial extents of \(387\times 404\) pixels. The pre-processing of the hyperspectral images was performed using the image processing system ENVI (Environment for Visualizing Images; Exelis Visual Information Solutions Inc. Boulder, CO, USA). Firstly, according to the digital elevation model (DEM) [28], orthorectification was executed for the OHS-1 image. Then a relative radiometric normalization was applied to the images based on the mean-standard deviation normalization algorithm [4]. Finally, the FLAASH model was used in atmospheric correction, and the parameters of FLAASH were selected based on the acquisition time and location for the imagery and other ancillary data that were provided by Zhuhai Orbita Aerospace Science & Technology Inc. in Zhuhai, China. The DN values of the OHS-1 images were transferred into the top of atmosphere (TOA) radiance with given spectral response functions by the ENVI software [27].  
Figure 1: The location of the study site in the Changbai Mountain, the RGB composition (670 nm, 566 nm, and 480 nm) of the hyperspectral image, and the distribution of the samples data.
## 2 Experimental
### Experimental Setup and Procedure  
The experimental configuration intended for Hg\({}^{0}\) adsorption is illustrated in Figure 1. The entire experimental system consists of a mercury generation system, an electric tube furnace reactor, an online mercury detector, an online data acquisition system, and an exhaust gas absorption device.  
The performance test was carried out using a 0.4 g sample. Hg\({}^{0}\) vapour (\(\approx\)200 \(\upmu\)g/m\({}^{3}\)) was generated through a mercury permeation tube placed in a U-shaped quartz tube. The temperature of the water bath was maintained at 50 \({}^{\circ}\)C. The total flow rate of the flue gas was set to 600 mL/min, with 5% O\({}_{2}\) 500 ppm NO (when used), 800 ppm SO\({}_{2}\) (when used), and N\({}_{2}\) contained as the balanced gas. Both inlet and outlet Hg\({}^{0}\) concentrations were  
Figure 1: Schematic diagram of the experimental setup.  
monitored by an online mercury analyzer RA-915M (LUMEX Ltd., St. Petersburg, Russia). Hg\({}^{0}\) removal efficiency was calculated using the following equation [33]:  
\[\eta=\frac{C_{\text{in}}-C_{\text{out}}}{C_{\text{in}}}\times 100\% \tag{1}\]  
where \(\eta\) represents the Hg\({}^{0}\) removal efficiency (%), while \(C_{\text{in}}\) and \(C_{\text{out}}\) (\(\mu\)g/m\({}^{3}\)) refer to the inlet and outlet mercury concentrations.  
Hg\({}^{0}\) uptake capacity q (\(\mu\)g/g) was determined for the sample using Equation (2) [33], where M represents the mass (g) of samples, \(f\) indicates the total flow rate (mL/min) of flue gas, and t denotes the adsorption time (min) required for Hg\({}^{0}\) capture. Each test lasted for 90 min to calculate Hg\({}^{0}\) removal efficiency and Hg\({}^{0}\) adsorption capacity.  
\[q_{t}=\frac{1}{M}\int_{t_{1}}^{t_{2}}(C_{\text{in}}-C_{\text{out}})\;fdt \tag{2}\]  
The pseudo-first-order model and pseudo-second-order model were applied to analyze the process of Hg\({}^{0}\) capture for the estimate of Hg\({}^{0}\) adsorption capacity. The pseudo-first-order model is expressed as follows [34; 33]:  
\[q_{t}=q_{e}\times\left(1-e^{-k_{1}t}\right) \tag{3}\]  
The pseudo-second-order model is expressed as the following equation [34]:  
\[q_{t}=\frac{q_{e}^{2}\cdot k_{2}\cdot t}{1+q_{e}\cdot k_{2}\cdot t} \tag{4}\]  
where q\({}_{t}\) and q\({}_{e}\) represent the level of mercury adsorption capacity at time t (\(\mu\)g/g) and the equilibrium point, respectively, while k\({}_{1}\) and k\({}_{2}\) refer to the rate constant of pseudo-first-order-model and pseudo-second-order-model, respectively.  
Recyclability of the adsorbents was carried out in the above-mentioned fixed-bed micro-reactor. Briefly, after the adsorption process, the spent Co4 Fe1-ZSM-5 was treated in certain flue gases at different temperatures for 2 h. Then, the sample was cooled down to the desired temperature point for the next Hg\({}^{0}\) removal performance evaluation cycle.
## 3 Results
### Offsets Measurement

Table 2: Transformation of coordinates between TLS and Total Station point clouds (accuracy estimation). Table 3 shows the coordinates of the control points measured by Total Station after their transformation to a single coordinate system. These points were the reference for measured point clouds using terrestrial laser scanning. The accuracy of the control points was 2.4 mm, while the individual scans in the area including the boat (Figure 6) fitted in with an accuracy within 3-10 mm, which was within the limits of the point cloud density, with a distance of the scanned points from the scanner up to 20 m. This result was considered satisfactory. Based on the point cloud processed in this way, the coordinates of the DGNSS receivers and the offsets of the measuring devices were determined on it with an average error of 6 mm (Figure 6). The approach used is satisfactory compared to traditionally used Total Station survey methods. In the approach presented here, as well as the measuring offsets, a TLS point cloud was used while maintaining the accuracy characteristic of tachymetric measurements of 1-10 mm.
# Hot Air and Microwave Combined Drying of Potato Monitored by Infrared Thermography
###### Abstract  
Hot air drying (HAD) at temperatures below the spontaneous evaporation temperature could be combined with microwave (MW) radiation as a thermal energy source in order to reduce the drying time. A photon flux in the microwave range interacts with dipolar molecules (water) through orientation and induction, producing electrical energy storage and thermal energy accumulation and generating an increase in the internal energy of food. The different mechanisms involved in water transport could change when the microwave penetration depth exceeds the sample characteristic dimension of mass transport. The aim of this paper is to determine the effect of MW in the combined HAD-MW drying of raw potato in order to obtain the real driving forces and mechanisms involved in the water transport, with the purpose of optimizing the MW power used. For this purpose, combined drying was carried out on potato samples (0, 4 and 6 W/g). The sample surface temperature was monitored by infrared thermography, and the sample mass was measured continuously through a precision balance. In parallel with continuous drying, another drying treatment was performed at different times (20, 40, 60, 90, 120, 180, 420 min) and conditions (0, 4 and 6 W/g) to analyze the dielectric properties, mass, moisture, volume and water activity. The results show that it is possible to monitor combined drying by infrared thermography, and it can be concluded that the convection heating is mostly transformed into surface water evaporation, with negligible thermal conduction from the surface, and microwave radiation is mostly transformed into an increase in the potato's internal energy.  
drying; hot air drying; microwave drying; infrared thermography; water transport; combined drying +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
To determine the transmission coefficients of CEBs with different percentages of earth, WHA, and SBA, a straightforward acoustic test apparatus was constructed by utilizing water pipes to create a long acoustic waveguide. It was established that the blocks' measured acoustic transmission coefficients were sensitive enough to pick up on the small percentage fluctuations in their composition. This made it possible to comprehend the impact that each binder and its quantity had on the acoustical properties. It was determined that the two binders exhibited different behaviors. While SBA reduced the transmission coefficient and porosity (from 0.42 to 0.29 when the SBA was increased from 0% to 11%) and increased airflow resistivity (from 11.0 \(\times\) 10\({}^{3}\) pa\(\cdot\)s\(\cdot\)m\({}^{-2}\) to 22.2 \(\times\) 10\({}^{3}\) p\(\cdot\)s\(\cdot\)m\({}^{-2}\)), the WHA was shown to increase the transmission coefficient and porosity (from 0.4 to 0.7 when the WHA was increased from 0% to 15%) and reduce airflow resistivity (from 11.8 \(\times\) 10\({}^{3}\) pa\(\cdot\)s\(\cdot\)m\({}^{-2}\) to 6.0 \(\times\) 10\({}^{3}\) p\(\cdot\)s\(\cdot\)m\({}^{-2}\)). The WHA and SBA-bound CEBs investigated in this study were found to be generally porous and, therefore, ideal for the construction of porous walls. The lowering of the transmission coefficient by SBA is highly desired for building houses that are located in noisy places, such as those close to airports or railway lines. However, the mechanical properties, which equally play a crucial role in the confection of building materials, were not considered for these CEBs. Studies therefore need to be conducted on the mechanical properties of the blocks.  
Conceptualization, N.O.O. and A.B.A.; methodology, N.O.O.; software, N.O.O.; validation, N.O.O.; formal analysis, N.O.O. and A.B.A.; investigation, N.O.O. and A.B.A.; resources; N.O.O. and A.B.A.; data curation, N.O.O.; writing--original draft preparation, N.O.O. and A.B.A.; writing--review and editing, N.O.O. and A.B.A. All authors have read and agreed to the published version of the manuscript.  
This research work was funded by Institutional Fund Projects under grant no. (IFPIP: 1142-130-1443). The authors gratefully acknowledge technical and financial support provided by the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflict of interest.  
Figure 8: Cost function against airflow resistivity for all the eight compressed earth block samples.
## 1 Introduction
Against this background, we affirm that our proposed SAC dialog system is a resourceful application capable of contributing immense benefits to both the end-users and  
Figure 1: A simple network structure on how SAC connects prospective FBC patients to the healthcare provider. the healthcare domain responsible for handling FBC incidences. Such benefits include but are not limited to the following:  
* Benefit to users: SAC's Q&A assessment raises the awareness of FBC ailment and encourages self examination of the breasts during dialog in the comfort of the users' valued privacy, which will stimulate them to communicate freely and sincerely about their FBC symptoms. In congruence to this regard, an earlier study by [PERSON] et al. [15] revealed that patients are more comfortable responding honestly to computer-administered assessments than that of human physicians due to the \"intimate\" nature of information usually required in such conversational contexts. On the other hand, SAC has the ability to promptly schedule medical appointment for the users scored with positive FBC symptoms in-order to expedite timely FBC diagnosis and early medical treatment. * Benefit to hospitals: SAC offers the potential benefit of controlling undue and inundating influx of patients in FBC healthcare departments, most of whom may schedule FBC medical appointments while having unrelated symptoms (i.e., FBC false negative cases). According to [PERSON] et al. [14], dialog systems can allow physicians to spend more time in treating patients who need the most consultation. On this account, the healthcare sector of many countries are beginning to utilize the digital services of chatbots for patients' disease prescreening assessment and triage in-order to reduce stress on health providers [16]. Therefore, by adopting SAC, hospitals can drastically minimize wastage of time, money, and other resource costs, and more importantly, doctors and other healthcare personnel will be enabled to work more productively to achieve effective patient-centric care service delivery. The remainder of this paper is structured as follows: Section 2 covers a review of related works, and thereafter, in Section 3, we present the model description comprising of the adopted concepts used in implementing the SAC model. Section 4 contains brief details about the experiment and the corresponding result outcomes, respectively. Finally, Section 5 summarizes the work and provides a conclusion.
## 4 Results and Analysis
### Influence of Solution Cavities Characteristics on the Wellbore Stability
#### 4.3.2 Influence of Solution Cavities Quantity on Wellbore Stability  
Figures 23 and 24 illustrate the variations in stress and displacement distribution around the wellbore for varying solution cavity quantities (\(N\)). Notably, these figures illustrate a consistent trend: as solution cavities quantity (\(N\)) increases, both stress and displacement around the wellbore gradually rise. For example, when \(N=1\), the stress and displacement values at point A are measured as 165.42 MPa and 2.78 mm, respectively. However, with an increase in \(N\) to 16, the stress and displacement at point A increase to 178.62 MPa and 2.92 mm, respectively. This marks a notable increase of 7.98% in stress and 5.03% in displacement compared to the previous values.  
Figure 23: Stress nephogram around the wellbore for different solution cavities quantities: (**a**) \(N=1\); (**b**) \(N=4\); (**c**) \(N=8\); (**d**) \(N=16\).  
Figure 22: Curve between the equivalent drilling fluid densities of collapse pressure and solution cavities diameter.  
This trend can be attributed to the fact that an increase in the quantity of solution cavities near the wellbore can compromise the integrity of the rock, significantly reducing the stability of the surrounding rock mass. Consequently, stress and displacement around the wellbore, particularly in proximity to the solution cavities, increase as \(N\) grows.  
Figure 25 presents the relationship between the equivalent drilling fluid densities of collapse pressure (\(\rho_{\rm c}\)) and the number of solution cavities (\(N\)). Figure 25 reveals an approximately linear increase in \(\rho_{\rm c}\) with the growth of \(N\). For instance, when \(N=1\), \(\rho_{\rm c}\) is measured at 1.18 g/cm\({}^{3}\), whereas for \(N=16\), \(\rho_{\rm c}\) increases to 1.31 g/cm\({}^{3}\). This represents a notable increase of 11.02% compared to the previous value. This trend indicates that wellbore stability would significantly decrease with an increase in the number of solution cavities. This decrease in stability is attributed to the compromised integrity of the rock surrounding the wellbore, as mentioned above.
# Numerical Estimation of Bending in Holographic Volume Gratings by Means of RCWA and Deep Learning
## 2 Theory  
In this section, we present the theoretical models and methodologies applied in this work. These include the development of a framework that incorporates bending into the RCWA for accurately modelling nonplanar grating structures. Furthermore, we outline the theoretical basis for the deep learning approach employed to predict bending behavior based on the RCWA-SM model.
## 4 Applications of AI in Modern Communication Networks
### Network Optimization
#### 4.1.1 Bandwidth Management  
AI-driven models predict network traffic in real-time, enabling dynamic bandwidth allocation and efficient spectrum usage [96]. RL algorithms, for example, can optimize the use of frequency spectrum [97] by adapting to varying traffic demands, minimizing congestion, and improving overall network performance [98; 99]. RL is particularly suitable for dynamic environments where the network traffic is highly variable, as it enables continuous learning and adjustment based on real-time data.  
As shown in Figure 4, the AI model, based on RL, dynamically adjusts bandwidth allocation across different stages, reacting to network traffic fluctuations in real-time. RL is particularly suitable here because it continuously learns from ongoing network conditions and improves bandwidth allocation strategies over time. This ability to adapt in real-time is essential for maintaining network performance, especially in scenarios with variable traffic patterns. In comparison, traditional allocation methods are more rigid, with slower responses to traffic changes, making them less effective in optimizing network performance during dynamic conditions.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Latency (ms)** & **AAE (\%)** & **Energy Consumption (J)** & **Response Time (ms)** \\ \hline Deep Learning & 10 & 2.1 & 50 & 15 \\ \hline Reinforcement Learning & 12 & 2.5 & 60 & 18 \\ \hline Federated Learning & 15 & 2.8 & 45 & 20 \\ \hline Rule-Based Methods & 25 & 5.0 & 30 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance metrics of AI models in network applications.