esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Improved Radial Movement Optimization (IRMO) Algorithm
### Implementation of IRMO  
According to the above two-layered strip foundation multi-rigid block failure mechanism, for the determination of the critical sliding surface, only the bottom angle \(\theta\) and bottom edge \(B_{0}\) of the rigid block \(ABC\) and the angle variables \(\alpha_{i}\), \(\lambda_{i}\), and side length \(l_{i}\) of the \(i\)th rigid block can determine a unique critical sliding surface. A particular collection of variables for the foundation's critical sliding surface corresponds to a solution vector in matrix X. The IRMO algorithm could automatically search the critical sliding surface of the foundation and calculate the corresponding ultimate bearing capacity.  
Combined with the implementation process of the IRMO algorithm, each generation of particle swarm generates \(N\) particles in total, representing \(N\) critical sliding surfaces of the two-layered strip foundation. Equation (45) implements the application of the IRMO algorithm to the calculation of the ultimate bearing capacity of the two-layered strip foundation in the analysis of the ultimate bearing capacity of the two-layered strip foundation.  
\[X=\begin{bmatrix}\theta_{1,1}&\alpha_{1,2}^{1}&\lambda_{1,3}^{1}&\cdots&\alpha _{1,M-1}^{n}&\lambda_{1,M}^{n}\\ \theta_{2,1}&\alpha_{2,2}^{1}&\lambda_{2,3}^{1}&\cdots&\alpha_{2,3}^{1}& \lambda_{2,M}^{n}\\ \vdots&\vdots&\vdots&\ddots&\vdots&\vdots\\ \theta_{N,1}&\alpha_{N,2}^{1}&\lambda_{N,3}^{1}&\cdots&\alpha_{N,M-1}^{n}& \lambda_{N,M}^{n}\end{bmatrix} \tag{45}\]  
The variable in the matrix must adhere to the geometric restrictions in Equation (46) in order to guarantee that the critical sliding surface can be located within a tolerable range and that the ultimate bearing capacity of the two-layered strip foundation can be calculated:  
\[\left.\begin{array}{c}0<\theta<\frac{\pi}{2}\\ \theta+\sum\limits_{i=1}^{n}\alpha_{i}=\pi\\ \beta_{i+1}{<\alpha_{i}+\beta_{i}{<\pi}}\\ \alpha_{1}{+2\theta-\pi}<\lambda_{1}{<2\theta-2\varphi_{\max}}\\ \lambda_{i}{>0}\\ \alpha_{i}{+\alpha_{i-1}+\beta_{i-1}-\pi}<\lambda_{i}{<\alpha_{i-1}+\beta_{i-1 }-2\varphi_{\max}}\end{array}\right\} \tag{46}\]  
where \(\varphi_{\max}\) refers to the maximum value of the two-layered strip foundation soil layer parameters \(\varphi_{1}\) and \(\varphi_{2}\).  
According to the above basic variable matrix \(X\) and geometric constraints, all the codes and procedures were realized by the commercial software Matlab2021b. In the specific engineering example following, the basic parameters \(c_{1}\), \(c_{2}\), \(\varphi_{1}\), \(\varphi_{2}\), \(\gamma_{1}\), and \(\gamma_{2}\) of the two-layered soil and the parameters \(N\), \(M\), \(G\), max \(x_{j}\), min \(x_{j}\) of the IRMO algorithm are set in advance, so that the location of the critical slip surface of the two-layered foundation under vertical uniform load can be searched and the corresponding ultimate bearing capacity can be determined simultaneously.
## 3 Outer Loop Controller Design
### Obstacle Avoidance Mission  
The obstacle avoidance mission maintains a specified distance between a robot and an obstacle. Each robot is surrounded by a virtual sphere \(B_{i,\rho}=\left\{q_{i},q_{i}^{o}\in\mathbb{R}^{p}:\|q_{i}-q_{i}^{o}\|\leq d_{i}\right\}\), where \(q_{i}^{o}\) indicates the position of the current obstacle for the \(i\) th robot, and \(B_{i,\rho}\) indicates the sphere \(\sigma_{i,1d}=d_{i}\), where \(d_{i}\) is the minimum allowed safe distance between the \(i\) th robot and an obstacle [33].  
The obstacle avoidance task function and the error of the task function are represented as  
\[\sigma_{i,1}=\max\{\|q_{i}-q_{i}^{o}\|,d_{i}\}\]  
\[\widetilde{\sigma}_{i,1}=\sigma_{i,1d}-\sigma_{i,1} \tag{3}\]  
Furthermore, we denote \(J_{1}=[J_{1,1},\ldots J_{n,1}]\in\mathbb{R}^{1\times pn}\), \(\dot{q}_{i,1}=J_{i,1}^{\dagger}\lambda_{i,1}\widetilde{\sigma}_{i,1}=\lambda_ {i,1}\big{(}\max\{d_{i}-\|q_{i}-q_{i}^{o}\|,0\}\big{)}\dot{p}_{i}\), where \(J_{i,1}=\dot{p}_{i}^{T}\) is the Jacobian matrix, a unity vector pointing at the nearest obstacle is \(\dot{p}_{i}=\big{(}q_{i}-q_{i}^{o}\big{)}/\|q_{i}-q_{i}^{o}\|\)\(\lambda_{i,1}>0\) is a state-dependent gain to be defined in the next section, and \(J_{i,1}^{\dagger}=J_{i,1}^{T}\). Note that \(\widetilde{\sigma}_{i,1}=\sigma_{i,1d}-\sigma_{i,1}\geq 0\) only if the robot is close enough to the obstacle, and \(\widetilde{\sigma}_{i,1}=0\) when the mission is inactive. This task is built individually for each robot and not as an aggregate task function.
# A Review of Parallel Heterogeneous Computing Algorithms in Power Systems
## 4 Conclusions  
In this paper, we presented a review of PHC techniques in power systems applications, such as power flow analysis, transient stability, contingency analysis, and smart grids, among others. The review covered more than 200 works where technologies, such as CPU clusters, FPGA, hybrid CPU-GPU platforms running on on-premise and cloud environments, have been used to improve modern power system planning, operation, studies, visualization, and analysis.  
Power flow analysis is the application where PHC techniques have been most used, followed by transient stability and contingency analysis. Regarding power flow analysis, different technologies have been adopted to accelerate the tasks of the power flow algorithm, such as building the admittance matrix, or--regarding the Newton-Raphson method--computing the Jacobian matrix and updating the state variables. On the other hand, applications, such as electricity market analysis, small-signal analysis, security constrained-economic dispatch, and hydrothermal scheduling, are the situations where PHC techniques are not frequently used since they are the applications with the lowest number of references.  
The review is organized by application and two groups of technologies: the first group corresponds to works that use GPU, and the second group refers to works where CPU clusters, FPGA, and fog and cloud computing are used. This classification shows that GPU is the most-used PHC technique in power system studies and analysis since works that use this technology are more than half of all the articles presented by the review. GPU stands as the current tendency since 80% of the articles published during the last five years correspond to GPU implementations.  
This tendency shows that researchers are taking advantage of the GPU architecture designed to exploit data parallelism using the SIMD parallel computing approach. The development of toolkits and frameworks to program GPU instructions in non-graphic programming languages has helped to expand the role of the GPU in power system applications. Finally, the wide number of applications that have used PHC techniques present PHC as a proper alternative for studying and analyzing modern power systems, due to the flexibility and adaptability of these techniques.
## 6 Sensitivity Analysis of Cloud Droplet Effective Radius Retrievals
[PERSON] and [PERSON] [52] have demonstrated that the LWP retrievals with new inverse model can be improved by 10 g\(\cdot\)m\({}^{-2}\) by choosing three frequencies--two frequencies sensitive to water presence in the 20 to 30 GHz band [53, 54] and one frequency around 50 GHz sensitive to the vertical profile of temperature [55, 56]. However, [PERSON] and [PERSON] [57] pointed out that when the 50.8 GHz channel is combined with lower two channels, the algorithm accuracy depends very much on the cloud model used. Therefore, though some improvement may be expected with the MP-3000A system, we limit our analysis to the conservative estimate. Instrument Radiometric uncertainties due to the sensor, data acquisitions, _etc._ are significantly smaller and are not considered in the error budgets. Based on the considerations by [PERSON] _et al._[15], dynamic changes in the cloud droplet effective radii are around 30% making these measurements quite challenging. On the other hand, ground-based methods make some global assumptions including the  
Figure 9: 2D distributions (left panel) and respective 1D distributions integrated over the CODs (right panel) for atmospheric variability (**a**,**b**), surface albedo variability (**c**,**d**), and convolved variability (**e**,**f**) of both variable parameters. requirements of single layer clouds and horizontal homogeneity (_i.e._, no broken clouds). The lack of broken clouds can be seen by imposing a buffer of one hour to the times when the lidar captures broken portion while coincident GOES data can assist in identifying single layer cases. Only when all these assets are combined, we can quantitatively explore the changes in Aerosol Cloud Index (ACI).
## 4 Algorithm Design
### Judgment of Network Congestion  
Assuming there are two services, each with one path, as shown in Figure 1 with corresponding red and blue colors, if both of these service paths pass through a certain satellite node, then this satellite node becomes a hotspot. Calculate the total traffic at the hotspot, and if the total traffic exceeds the threshold, congestion is considered to have occurred. Regarding the threshold setting, we typically set it to 1.5 times the highest traffic volume in the service paths. The judgment of network congestion is depicted in Algorithm 1.  
Figure 1: Satellite network architecture.  
```
Input:\(path1\), \(path2\), threshold Output:\(Congestion\)
1 Find the shortest path from Source1 to Target1 by traditional Dijkstra algorithm, taking the geographical distance as the weight -> (\(path1,length1\)) ;
2 Find the shortest path from Source2 to Target2 by traditional Dijkstra algorithm, taking the geographical distance as the weight -> (\(path2,length2\)) ;
3 Obtain the traffic volume for \(path1\) -> \(path1\_volume\) ;
4 Obtain the traffic volume for \(path2\) -> \(path2\_volume\) ;
5\(threshold\) = \(max\{path1\_volume,path2\_volume\}\ast 1.5\) ;
6 if\(path1\) and \(path2\) have a common node then
7\(hotspot\) = the common node ;
8\(hotspot\_volume\) = \(path1\_volume+path2\_volume\) ;
9 if\(hotspot\_volume\) > thresholdthen
10\(Congestion\) = True ;
11
12 else
13\(Congestion\) = False ;
14
15 return\(Congestion\);
```  
**Algorithm 1**Judgment of network congestion
## 3 Results and Discussion
### Health Risk Analysis  
PAHs are carcinogenic substances, which makes it essential to assess the cancer risk associated with inhaling these compounds adsorbed onto PM\({}_{10}\) in the atmosphere. This risk is estimated using the toxic equivalent factor for benzo(a)pyrene. According to WHO (2000) [9], the equivalent concentration of BaP that causes an excessive lifetime cancer risk of 1/10,000 is approximately 1.2 ng/m\({}^{3}\). The equivalent concentration of carcinogenic PAHs in the air was 3.54 ng BaP\({}_{\text{eq}}\)/m\({}^{3}\) (Table 1). The average concentration of BaP was 0.90 ng/m\({}^{3}\). The main contributor to the total carcinogenic activity of the PAH mixture was BBF (31.5%), followed by BaP (5.5%), IP (18.2%), BJF (12.9%), BKF (8.5%), BaA (2.5%), and DBahA (1.0%). BaP\({}_{\text{TEQ}}\) for station 6, where the concentrations of individual PAHs were highest, showed that the winter value (7.75 ng/m\({}^{3}\)) was the highest, followed by spring (2.85 ng/m\({}^{3}\)), autumn (2.46 ng/m\({}^{3}\)), and summer (0.50 ng/m\({}^{3}\)). These values were higher and comparable to other urban atmospheres in Europe. In Naples, Italy, this parameter ranged from 0.48 to 1.06 ng/m\({}^{3}\)[29], while in Dettenhausen, Germany, it was at 2.7 ng/m\({}^{3}\)[30]. In contrast, [PERSON] et al. (2022) [7] reported lower BaP\({}_{\text{eq}}\) values, averaging at 0.18 ng BaP\({}_{\text{eq}}\)/m\({}^{3}\).  
To assess the health risk for humans, it was assumed that both adults and children are exposed to airborne PAHs through the inhalation of PM\({}_{10}\). The estimates were made considering LADD and the corresponding ILCR. The LADD for children ranged from 0.34\(\cdot\)10\({}^{-7}\) to 17.70\(\cdot\)10\({}^{-6}\), while for adults it ranged from 1.2\(\cdot\)10\({}^{-8}\) to 6.3\(\cdot\)10\({}^{-7}\). The ILCR for children ranged from 7.34\(\cdot\)10\({}^{-7}\) to 7.79\(\cdot\)10\({}^{-6}\), while for adults it ranged from 0.3\(\cdot\)10\({}^{-8}\) to 3.04\(\cdot\)10\({}^{-6}\). The acceptable ILCR was not exceeded for either children or adults. According to US EPA guidelines, the acceptable ILCR range typically lies between 1 \(\times\) 10\({}^{-6}\) and 1 \(\times\) 10\({}^{-4}\), indicating that the cancer risk associated with exposure to PAHs bound to PM\({}_{10}\) is within acceptable levels for the studied population [16]. The highest values were recorded at station 6, where during the winter season, this parameter was over 15 times higher compared to the summer season.
## 6 Results and Analysis
### Resilience  
During secure communication between the patient and doctor, devices from different regions communicate using intermediate devices. The chance of compromised devices in the path exists. The probability \(\ Pr_{\beta}\) that an intermediate node is compromised is given in Equation (1), where \(N\) is the number of devices in the network and \(\beta\) is number of devices compromised by adversary. The term \(N-2\) represents that sender and receiver are considered uncompromised, whereas \(N-3\) means to exclude one more intermediate device which is a direct neighbor of the sender.  
\[\ Pr_{\beta}=\ 1-\left(\begin{array}{cc}N-\ 3\\ \beta\end{array}\right)/\left(\begin{array}{cc}N-\ 2\\ \beta\end{array}\right)=\ \frac{\beta}{N-2} \tag{1}\]  
Figure 8 elucidates the scenario for measuring the impact of compromising intermediate devices by calculating the probability \(\ Pr_{\beta}\\) when the number of devices in the network varies from 100 to 500. In the case of 300 devices, the probability is 0.0167, 0.033, 0.050, and 0.067 for \(\beta=5,\ 10,\ 15,\) and 20 compromised devices, respectively. If an intermediate device is compromised, then data and security credentials stored in that device are exposed. Moreover, the probability \(\ Pr_{\omega}\) that a particular  
Figure 6: Computation cost at user side.  
Figure 7: Computation cost at server side.  
session key and its related credentials are compromised is given in Equation (2), where \(\omega\) credentials are compromised out of a total of M devices in the network.  
\[Pr_{\omega}=1-\left(\begin{array}{c}M-1\\ \omega-1\end{array}\right)/\left(\begin{array}{c}M\\ \omega\end{array}\right)=\ \frac{\omega}{M} \tag{2}\]  
Figure 9 shows that different security credentials stored in devices can be revealed to intruders when the devices are compromised. We have considered the scenario where the number of devices is varied from 100 to 500, and the number of compromised nodes is varied from 5 to 25, respectively. In the case of 400 devices in the network where 20 of them are compromised, the number of compromised bytes are 15.62 KB, 18.75 KB, 43.12 KB, and 63.12 KB for EAKA, AAS, TPKE, and A-TIMS respectively. Our proposed SAPS method dominates and achieves better resilience against node capture attack by revealing only 8.98 KB.
## 3 Our Proposed Method
### LTRSH Model Building
#### 3.1.3 Microstructure Image Production  
After local texture pattern detection, we use eight neighbors (shown in Figure 6), representing eight orientations, to produce a microstructure image using the CS-LTP map as a mask with the color space smoothing in CIE L*a*b. A microstructure image can characterize the local texture information and contour information by the color distribution with a near-uniform texture.  
To remove insignificant details and better describe the near-uniform texture information, we start from the original point (0, 0), (1, 0), (0, 1) and (1, 1), and move in a 3\(\times\)3  block from left to right and top to bottom throughout the CS-LTP map \(T\) to detect structural elements with the image color smoothing in CIE L*a*b to produce the microstructure image \(f_{i}\),(\(i\in\{1,2,3,\)\(4\})\). The procedure for \(f_{1}\) can be observed in Figure 7.  
Figure 6: Eight structural elements used in the microstructure image.  
Figure 7: Illustration of producing a microstructure image \(f_{1}\).  
According to Figure 7, when the starting block (0, 0) is centered at point 24 in the CS-LTP map \(T\), only the right point among its eight neighbors is the same as point 24. Meanwhile, we locate the same location on the image color smoothing in CIE L*a*b. Then, we obtain 2 and 3 for the correspondent points and assign the other seven points with \(-1\) for the correspondent points in microstructure image \(f_{1}\), because the other seven correspondent points in the CS-LTP map \(T\)  are different from point 24. Then, we move the block to the next three point centers at point 28 to obtain the same point 3 and point 4 in microstructure image \(f_{1}\). The remainder can be performed in the same manner, with the block moving from left to right and then down to produce the microstructure image \(f_{1}\).  
After we start from the original points (1, 0), (0, 1) and (1, 1) in the same way of gaining the microstructure image f\({}_{1}\), we obtain the microstructure images \(f_{2}\),\(f_{3}\), and \(f_{4}\). Next, we determine the overall maximum of the images \(f_{i}\) (\(i\in\{1,2,3,4\}\)) to obtain the final microstructure image \(f\), as illustrated in Figure 8. In the final microstructure image \(f(f(x,y)\in\{-1,0,1,...,G-1\})\), the point values \(f(x,y)\) represent the category of color following the original image color smoothing, which are combined together to characterize the local region texture in detail and contour information, except for the value \(-1\).
## 5 Experimentation, Results, and Analysis
### Analysis
#### 5.4.3 Visual Explanation Using Grad-CAM  
To provide an interpretable visual justification for the generated captions, we employ Grad-CAM to visualize the spatial regions in the image that influenced the model's decision. This analysis is crucial to understand whether the DCAT framework focuses on semantically relevant parts of the image, especially in a low-resource language generation context.  
Figure 4 presents the Grad-CAM heatmaps for three sample images and their corresponding generated Tamil captions:
## 2 A New Software Reliability Model
### Rayleigh Fault Detection Rate Function Model  
The general MVF of an NHPP SRM that considers the uncertainty of operating environments is obtained via a differential equation as follows [28]:  
\[\frac{d\ m(t)}{dt}=\eta[b(t)][N-m(t)],\]  
where \(m(t)\) is the MVF, \(b(t)\) is the fault detection rate function, \(N\) is the expected number of faults that exist in the software before testing, and \(\eta\) is a random variable that represents the uncertainty of the system fault detection rate in the operating environment and has the following probability density function:  
\[m(t)=\int_{\eta}N\Big{(}1-e^{-\eta\int_{0}^{t}b(s)ds}\Big{)}dg(\eta).\]  
The following MVF is obtained from a differential equation, as expressed in Equation (3). The random variable \(\eta\) has a generalized probability density function with two parameters \(\alpha\geq 0\) and \(\beta\geq 0\), where the initial condition is \(m(0)=0\)[29]:  
\[m(t)=N\Bigg{(}1-\frac{\beta}{\beta+\int_{0}^{t}b(s)ds}\Bigg{)}^{\alpha}, \tag{3}\]  
where \(b(t)\) is the fault detection rate per fault per unit time \(t\).  
In this study, we consider a fault detection rate function of the Rayleigh distribution, \(b(t)\), which is expressed as follows:  
\[b(t)=\frac{f(t)}{1-F(t)}=\frac{2a^{2}te^{-(at)^{2}}}{1-\Big{(}1-e^{-(at)^{2}} \Big{)}}=2a^{2}t\,,\ a>0.\]  
This yields a new NHPP SRM that considers the uncertainty of the environment, \(m(t)\). By substituting \(b(t)\) into Equation (3), this model can be used to determine the expected number of software failures detected by time \(t\) as follows:  
\[m(t)=N\Bigg{(}1-\frac{\beta}{\beta+(at)^{2}}\Bigg{)}^{\alpha}.\]
# Remote Sensing for Irrigation of Horticultural Crops
## 4 Approaches to Precision Irrigation
For many field and horticultural crops with short and dense canopies, control of transpiration may be expected to be minimal until stomata close, and canopy conductance decreases markedly. Another crucial point is the ability of some crops to limit their leaf water potential, preventing it from falling below a critical level (isohydric behavior). [PERSON] et al. [113] have reported that daytime leaf water status (or water potential) was maintained relatively constant as long as the potential transpiration exceeded the hydraulic threshold. This means that water potential of the leaves remained relatively constant during the day and during periods of minor-to-moderate water stress events, regardless of soil water status, because of active stomatal control. The assessment of the isohydric crop response is important for crop-water-use predictions and requirements, and photosynthetic productivity under uneven irrigation conditions. In contrast, many species show unisohydric behavior with leaf water potential relatively coupled to the environment. The different behavior of isohydric and unisohydric plants may affect Remote Sensing measurements based on thermography. Two crops, one isohydric and the other unisohydric, both held in non-limiting soil water conditions, would be expected to return different values of Crop Water Stress Index (CWSI). Since both crops are well-watered, the differing behaviors of isohydric and unisohydric crops may affect irrigation scheduling if appropriate corrections are not applied to each species (e.g., by using different thermal index threshold values). According to [PERSON] et al [114], several methodologies are available for estimating crop water use and water use efficiency. [PERSON] [115] developed a theoretical approach to precision irrigation based on remote sensing, considered a powerful, practical and cost-effective tool for obtaining comparable yield, crop quality, and water and nutrient use efficiencies profitably and sustainably. In the field of irrigation, five major approaches to irrigation management using remote sensing were reported (Table 3). The five approaches can be grouped into three major categories related to ET estimation (point 1 and 4); infrared thermography (point 2 and 3) and crop characteristics (point 5). Many papers report \"new methods\" to calculate precision irrigation based on remote sensing, combining, simplifying or integrating the methods described above.
# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
## 2 Overview of mmWave HetNets
### Characteristics of 5G Networks and Devices
#### 2.1.3 Networks
Several standards have already been considered in various commercial wireless systems, such as IEEE 802.15.3c for indoor wireless personal area networks (WPAN) [17] and IEEE 802.11 ad for wireless local area networks (WLAN) [18]. Although the mmWave network can provide sufficient bandwidths and gigabit-level communication services, such as high definition television (HDTV), ultra-high definition video (UHDV), and virtual reality, it also faces several challenges compared with other existing communication systems. The main challenges are summarized as follows: (1) **High propagation loss**: Due to the small wavelength of mmWave signals, their diffractions are quite weak. Therefore, the strength of received signal power might be very weak due to NLOS transmission. (2) **Sensitivity to blockage**: Certain materials such as concrete walls, furniture, human bodies, or even foliage will cause severe penetration loss and consequently degrade the signal quality. (3) **High hardware complexity**: Traditional analog-to-digital converters (ADCs) in microwave transceivers should be redesigned in order to fulfill the directional pencil-beam transmission of mmWave transceivers, leading to high hardware complexity.