esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulations
### Choosing Number of RBF in Hidden Layer  
Choosing the correct number of radial basis functions in the hidden layer is essential to obtain the best fit and avoid overfitting or underfitting. No noise was added to the data for the analysis in this section. Figure 3 shows how the accuracy of the inverse model changes with the number of rbfs in the hidden layer for rbf networks with and without Tikhonov regularization. The horizontal axis represents the number of rbfs in hidden layers, while the vertical axis represents the coefficient of determination \(R^{2}\) on the test set.  
Figure 2: Mesh used for solving the forward problem.  
For Figure 3a,c, the Gaussian width is given by Equation (14). If there is no regularization, the out-of-sample score decreases after a slight increase with increasing rbf. So, the model overfits large values of rbf, and we must use a smaller value of RBFs. Introducing Tikhonov regularization fixes this, and the out-of-sample score tracks the in-sample score. An almost-identical plot is given for rbf networks with fixed Gaussian width with and without Tikhonov regularization in Figure 3b,d, respectively. For the same \(b_{j}\) case, we found that the actual width value did not differ significantly when comparing all the rbf models. The effect of the width on performance is shown in [26].The changes in the mean squared error shown are small. Therefore, we use the simple choice of \(b_{j}=1\).  
Using the plots in Figure 3, we chose appropriate rbf numbers in each model's hidden layer. For variable- and fixed-Gaussian-width rbf networks without regularization, we choose 300 rbfs in the hidden layer. We use the same number of hidden layers for the method that adds differing levels of Gaussian noise to the data. We expect all regularizations to have the same effect as Tikhonov regularization, i.e., the out-of-sample error should track the in-sample error. Therefore, for all other regularization methods, we choose 1000 rbfs in the hidden layer for our network.
# Mechanical Performance of Curved Link-Slab of Simply Supported Bridge Beam
## 3 Numerical Analysis of CLS and FLS
### Finite Element Modeling  
The bridge substructure is of little significance in analyzing the mechanical performance of the link-slab structure. Therefore, the model bridge only establishes the FLS and CLS structures. The model numerically analyzes the mechanical performance of the link-slab under different load conditions. ABAQUS was used to model the single middle beam of the superstructure of the SSB bridge. The model was divided into a total of 91,173 elements.  
In the overall coordinate system of the model, the \(X\)-axis direction is the longitudinal bridge direction, and the \(Y\)-axis direction is the beam height direction. The \(Z\)-axis direction is the transverse bridge direction, Since the main objective of this paper was the concrete pavement layer of the link-slab, the main girder end of the link-slab was refined when the main girder was gridded so that the analysis of the structure of the link-slab could be more accurate. The mesh size was 0.4 m along the non-link-slab section, 0.05 m along the link-slab, 0.05 m in the cross-section of the main beam. The grid division results are shown in Figure 7. The concrete structure was simulated by a hexahedral linear reduction integral unit (C3D8R). The element type used for prestressed steel strands and steel bars was a two-node three-dimensional truss element (T3D2). The beam end support of the main beam was a plate rubber support. The support was simulated using a three-way grounding spring element.  
ABAQUS needs to give the corresponding intrinsic model for different materials to reflect the stress-strain relationship between various materials when performing finite element calculations. In this study, the C50 concrete plastic damage model was used for the CLS in the calculation model, and the values of the intrinsic model parameters are shown in Table 2. The ideal elastic-plastic principal structure model shown in Table 3 was used for the prestressed and common steel bars.  
Figure 6: Schematic of the CLS (unit: mm).  
Figure 7: Schematic of the main beam mesh.  
The bearing at the end of the main beam was a plate rubber bearing, and the bearing was simulated using a three-way grounded spring unit. The thrust stiffness of the bearing in the transverse and longitudinal directions was calculated as 1429 kN/m, and the vertical compressive stiffness was 457.6 MN/m.
## 3 Results and Discussion
### Atmospheric Deposition
#### 3.2.3 PAHs
In atmospheric deposition, the heavier PAHs (i.e., over benzo[a]pyrene, B[a]P, molecular weight, 252.32 Da) are observed with higher frequencies than those detected in PM\({}_{2.5}\) and PM\({}_{10}\) fractions. The background station of Rocca Respamani records, on average, lower PAH rates than the other stations, with the exception of a single observation of benzo(b)fluoranthene in summer [68] which is very high compared to all other stations during all sampling periods. B[a]P was selected as a marker of the carcinogenic potencies of the six PAHs (i.e., Benzo[a]Anthracene, Benzo[b]Fluoranthene, Benzo[j]Fluoranthene, Indeno[1,2,3-cd] Pyrene and Dibenzo[ah]Anthracene) [22, 69, 70, 71, 72]. The comparison of the deposition rates for B[a]P in the four sites (Table 6) shows a substantial homogeneity of the annual average rates recorded in the Roccaccia and Allumiere sites which are, respectively, equal to 5.9 and 3.9 ng m\({}^{-2}\) d\({}^{-1}\), and significantly lower de-posimetric rates in the S. Agostino 2.3 ng m\({}^{-2}\) d\({}^{-1}\) and Rocca Respampani 2.2 ng m\({}^{-2}\) d\({}^{-1}\) stations, which constitutes the blank site. These annual average deposition rates are in line with those measured in remote/reference and urban Italian areas (see Table 7).
# Super-Resolution Multicomponent Joint-Interferometric Fabry-Perot-Based Technique
## 2 Principle Analysis of MJI-HI Spectral Super-Resolution Based on a FPI
### Spectral Mixing of MJI  
As shown in Figure 3a,b, the object spectrum is set as \(B(\
u)\) and its Fourier transform as \(I_{0}(\Delta)\), whereas \(L\) is the maximum OPD of the two-beam interferometer. When \(B(\
u)\) is modulated with a cosine transmission spectrum of frequency \(k\), that is, \(B(\
u)\cdot[1+\cos(2\pi k\
u)]/2\), the spectrum and its Fourier transform are as shown in Figure 3c,d.  
\[\begin{array}{ll}I_{k}(\Delta)&=I_{0}(\Delta)\otimes FT\Big{\{}\frac{1}{2}[1 +\cos(2\pi k\
u)]\Big{\}}\\ &=\frac{1}{2}I_{0}(\Delta)+\frac{1}{4}I_{0}(\Delta+k)+\frac{1}{4}I_{0}(\Delta -k)\end{array} \tag{6}\]  
It can be observed that the effect of a single cosine-modulated wave in the frequency domain is the duplication, translation, and superposition of the frequency spectrum \(I_{0}(\Delta)\); that is, the displacement component \(I_{0}(\Delta\pm k)/4\) is generated. In the present study, we refer to this spectral mixing. In this process, high-frequency information is encoded into the range of invertible calculations.  
Figure 3: (**a**) The object spectrum; (**b**) the frequency spectrum of the object spectrum; (**c**) the spectrum after cosine modulation; (**d**) the frequency spectrum of the spectrum after modulation; the red curve represents the displacement component.  
When \(T_{FPI}(v)\) is used as the modulation wave of spectrum \(B(v)\), the modulated spectrum is as shown in Figure 4a. \(T_{FPI}(v)\) can be decomposed into a fundamental wave and infinite number of higher harmonics. Each of these can be viewed as a separate cosine-modulated wave. From Equations (3) and (5), the Fourier transform of the modulated spectrum can be obtained as [28; 29; 30]:  
\[\begin{array}{ll}I(\Delta)&=I_{0}(\Delta)\otimes\text{FT}\biggl{[}\sum\limits \limits_{m=0}^{\infty}a_{m}\cos(m\cdot 4\pi ndv)\biggr{]}\\ &=a_{0}I_{0}(\Delta)+\frac{1}{2}\sum\limits_{m=1}^{\infty}a_{m}I_{0}(\Delta \pm m\cdot 2 nd)\end{array} \tag{7}\]  
Because \(a_{0}\) and \(a_{m}\) are related only to \(R\), they can be used as known quantities. Therefore, the effect of the FPI on the frequency spectrum \(I_{0}(\Delta)\) is to replicate \(I_{0}(\Delta)\) infinitely, translate them at equal intervals, and linearly superimpose them, as shown in Figure 4b.  
Thus far, by using the FPI to load high-frequency information in the spectrum into these modulated waves, each cosine-modulated wave achieves one spectral mixing. Moreover, the frequency spectrum produces displacement components \(a_{m}I_{0}(\Delta\pm m\cdot 2 nd)/2\). These displacement components move the otherwise unobservable high-frequency information to within \(L\) and superimpose it onto the original spectrum \(I_{0}(\Delta)\). Through the unmixing inversion calculation, an interferogram with a wider OPD range is obtained, thereby improving the spectral resolution.
## 1 Introduction
After introducing the idea underlines for rotation of path and its adjustment against various parameters, we proposed a rotational-based scheme that accordingly selects forwarder nodes in its routing path by ROBINA. We used Path Adjustment and Path Loss, called PA-ROBINA and PL-ROBINA, respectively for the adjustment and path loss mechanism. The PA-ROBINA is basically used for adjustment of the path based on the provided information by relay nodes and I\({}_{\text{AFN}}\). As in IoUT, the nodes can only be remotely accessed, and unmanned effort is required for this environment to make the path consistent and reliable for acoustic IoT devices. We used the path loss function to detect the void nodes and rotate the path with the following ROBINA, called PL-ROBINA. The ROBINA and its co-variants work with I\({}_{\text{AFN}}\), which is the core mechanism of internodes adjustment. The criteria to choose the I\({}_{\text{AFN}}\) was provided, while the mathematical model was also produced to select the optimal I\({}_{\text{AFN}}\) with the help of Urick's Model and Thorp's Formula. The contributions can be summarized as follows:  
* We presented the ROBINA for inter-node modifications in all possible directions following path rotations until all nodes are designated for communication and are within the radius of the rotated path. * We also analyzed the relay nodes quantity that is selected under the criteria of path rotation and adjusted its position by PA-ROBINA. It also uses the I\({}_{\text{AFN}}\) function and Parity bit-based flags to choose nodes that are closest to the destinations. * We opted for and reformulated the ambient noise-reduction solution using the Urick's Model for acoustic signals, and absorption loss factor by Thorp's Formula, that modeled continuous Power Spectral Density (PSD) and Colored Gaussian Statistics (CGS). All these work for PL-ROBINA. * We evaluated the first rotational orbit-based proposed scheme's performance by comparing it with state-of-the-art benchmark schemes under different parameters of transmission loss, throughput, number of dead nodes, and etc. The rest of the work is organized as follows. We reviewed state-of-the-art literature in Section 2 and provided the problem formulation in Section 3. Furthermore, Sections 4 and 5 present the proposed schemes and their sub-sections. The path loss channel model is described in Section 6. Finally, the simulation results and conclusion are provided in Section 7 and Section 8, respectively.
# The Optimization of UAV-Assisted Downlink Transmission Based on RSMA
## 6 Conclusions  
This paper has investigated the application of UAVs as base stations in emergency communication scenarios. With their flexible deployment and excellent line-of-sight communication capabilities, UAVs can provide better QoS compared to ground-based stations. To enhance system access capacity and transmission rates, this study adopted RSMA as the access mechanism for UAV base stations and focused on optimizing the downlink rate of the RSMA system through joint UAV deployment strategies.  
First, the downlink system was modeled, and the optimization parameters were divided into three categories: UAV positioning parameters, user group parameters, and RSMA parameters. The original optimization problem was decomposed into three subproblems, each solved using a ternary iterative approach. For the UAV positioning optimization subproblem, a solution algorithm combining the ALM and APSA was proposed. Moreover, the AFSA was enhanced into the MD-AFSA algorithm, incorporating dynamic step size, vision, and memory strategies. For the optimization of user group and RSMA parameters, a detailed analysis was performed, followed by the design of a modified MD-AFSA algorithm under linear equality constraints--LEC-MD-AFSA. The effectiveness of both MD-AFSA and LEC-MD-AFSA algorithms was validated through simulations, and the performance of the downlink RSMA system was evaluated.  
Simulation results showed that the MD-AFSA and LEC-MD-AFSA algorithms can reduce errors by more than 85% compared to the original algorithm, with error reductions exceeding 99% across multiple test functions. Additionally, in the downlink system, the RSMA scheme improved system throughput by 13.30% compared to NOMA.  
Future research could focus on optimizing RSMA systems in more complex channel conditions to further enhance system throughput. Moreover, practical implementation of the proposed algorithms on hardware platforms could be considered for future validation and development.  
Conceptualization, L.H.; methodology, L.H. and D.Q.; software, L.H., D.Q. and J.Z. ([PERSON]); validation, D.Q., [PERSON] ([PERSON]) and J.Z. ([PERSON]); investigation, L.H. and J.Z. ([PERSON]); writing--review and editing, [PERSON] ([PERSON]) and [PERSON] ([PERSON]) All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data are not available.  
The authors declare no conflicts of interest.
# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 5 Weed Detection Using Remote Sensing Technique
### Image Data Collection
#### 5.1.1 RGB Sensor  
The RGB sensor is the most widely used and widely available commercial camera. Because of their promise in delivering high-quality images and low-cost operational needs, their possible applications have been the focus of most research for many years [42; 43]. These sensors are increasingly employed in machine learning algorithms for object recognition, diseases, phenology, and other applications.  
These are typical steps to acquire RGB images captured by UAV remote sensing: (1) pre-flight planning, (2) flight and image acquisition and (3) post-processing and indices or dataset extrapolation [44]. However, when preparing the images for machine learning algorithms, the processing steps are different depending on the research's objective [45; 46; 47]. The advantage of using this sensor is that radiometric and atmospheric calibration are not required, unlike multispectral and hyperspectral images [41]. Therefore, noises from electromagnetic radiation (EMR) can be ignored.
# Boundary Matching and Interior Connectivity-Based Cluster Validity Analysis
## 2 Related Work
### Typical Clustering Algorithms
#### 2.1.2 DBSCAN Algorithm  
In comparison to the C-means, DBSCAN has two parameters: a neighborhood radius of any point and the number of points within the neighborhood. However, in practice, the two parameters have to be turned to a density measure, which usually is their rate and is used to find the density differences and distributed characteristics of all points. In terms of a density threshold, DBSCAN distinguishes core points and border ones from all points. If the density of a point is higher than \(\varepsilon\), then this point is called a core point; or else a border point.  
DBSCAN starts with an arbitrary point \(p\) in \(X\). If it is a core point, then assign all the points which are density-reachable from \(p\) with \(\varepsilon\)to the same cluster. If \(p\) is not a core point, then \(p\) is temporarily assigned to noise. Afterward, DBSCAN deals with the next point until all the points in \(X\) have been visited.  
The density threshold \(\varepsilon\) in DBSCAN is a crucial parameter, and significantly affect the clustering results. As an example, a dataset with 176 points is evaluated (see Figure 1). Figure 1 shows the dataset is clustered into 5, 2, and 1 cluster with different values of \(\varepsilon\), where points in different clusters are marked with different signs. It is clear that the number of clusters decreases as \(\varepsilon\) increases. Therefore, if \(\varepsilon\) is incorrectly chosen, then the number of clusters cannot be determined. Although Ordering Points to Identify the Clustering Structure (OPTICS) algorithm [29] can be applied to solve the value of \(\varepsilon\), it neither provides clustering results explicitly nor is applied in high-dimensional data space.  
Figure 1: The number of clusters varies as \(\varepsilon\) increases: (**a**) five clusters; (**b**) two clusters; (**c**) one cluster.
## 2 Dam Sliding Model
### New Sliding Safety Factor  
In Figure 1, a dam-foundation system having a partially fracture-damaged condition at the dam base interface is depicted. Assuming that the fractured area exists as shown in Figure 1b, the total normal force against the external force is the sum of the normal forces at the undamaged and the fractured areas. However, since the normal force distribution is not uniform, as shown in Figure 1c, the normal force cannot be simply calculated in consideration of the ratio of the fractured area [20; 21]. In Figure 1c, it is shown that the magnitude of the vertical force at the toe is larger than the vertical force at the heel, and the distribution varies with time. When a dam is subjected to real earthquakes, the actual distribution of the vertical force will be more irregular.  
The engineering guideline of FERC [20] states that the cohesion of the fractured concrete-rock foundation interface should be assumed to be zero. Also, CDA [22] specifies that the cohesion is assumed to be considered within an undamaged concrete structure. Hence, it is reasonable that the cohesive strength in SSF evaluation vanishes to zero at the damaged area where the fracture is severely developed, which is already applied in Equation (2).  
While the ratio of the damaged area to the total area, \(\eta\), is given as a fixed value in Equation (2), for realistic evaluation of the sliding stability of a concrete dam, it is more reasonable that \(\eta\) varies with time as the fracture progresses along the dam-foundation  
Figure 1: Dam–foundation interface in the partially fractured condition: (**a**) a schematic dam–foundation model; (**b**) partially fractured dam–foundation interface; (**c**) conceptual normal force distribution along the fractured and undamaged areas.  
interface. Considering those observations, a modified formulation of SSF is suggested in this study as:  
\[\text{SSF}=\frac{(1-\eta(t))cA+N_{u}\Big{(}t\tan\!\phi_{i}+N_{d}\Big{(}t\Big{)}\tan \!\phi_{f}}{V(t)} \tag{3}\]  
where \(N_{u}\) is the vertical force at the undamaged area, \(N_{d}\) is the vertical force at the fractured area. In Equation (3), it is noted that \(\eta\), \(N_{u}\), and \(N_{d}\), as well as \(V\), are functions of time. It is also noted that the two vertical forces, \(N_{u}\) and \(N_{d}\) in Equation (3), are computed by a nonlinear finite element analysis considering the nonlinearity of sliding-contact problems, described in Section 2.3, while those vertical forces are inaccurately assumed as the linear proportions of the area, \((1-\eta)N\) and \(\eta N\), respectively, in Equation (2). To evaluate the suggested SSF, a contact-sliding model for the dam-foundation interface is necessary for the computation of the fractured area and corresponding vertical forces, which is explained in the following section.
## 1 Introduction  
Breast cancer is the fifth most significant cause of cancer-related deaths worldwide and is a significant health concern among women [1, 2]. The early diagnosis and treatment of breast cancer are crucial for reducing mortality [3, 4]. Mammography screening can reduce breast cancer mortality by 15-20% [5]. Thus, X-ray mammography is the optimal choice for the screening and diagnosis of breast cancer.  
During X-ray mammography, the glandular tissue in the breast receives a significant dose of absorbed radiation. In 2007, the International Commission on Radiological Protection (ICRP) almost doubled the previously identified breast tissue weighting factor based on epidemiological studies, showing an increase in fetal breast cancer following exposure to ionizing radiation [6, 7]. Therefore, the breast is among the most radiosensitive organs in the body, along with the colon and bone mirrors.  
In diagnostic radiology, organizations such as ICRP emphasize setting and using diagnostic reference levels [6, 7, 8, 9, 10]. As defined by the ICRP, DRLs are a type of investigation level used as a tool to help determine radiation dose optimization [8]. Significant variations in dose levels highlight the need for DRL benchmarking in clinical practice. There was also a need to identify hospitals that performed below average inpatient radiation exposure, thus necessitating optimization measures. The development of this concept has motivatedseveral authors worldwide to propose DRLs in diagnostic and interventional radiology, including mammography [11; 12; 13; 14; 15; 16]. Significant improvements in patient doses have been reported due to the establishment and implementation of DRLs [17].  
In 2017, the Sudan government passed an act [18] that established the Sudanese Nuclear & Radiological Regulatory Authority (SNRRA) as an independent body that oversees all activities related to the peaceful applications of nuclear radiation. The SNRRA mandate comprises guidelines, DRLs in medical imaging, and dose constraints for different exposure scenarios. Several dose surveys have been conducted in diagnostic radiology for optimization [19; 20; 21; 22]. Despite these efforts, mammography was left behind, mainly because there are few mammography units across the country, which is a source of concern. As of 2018, there were only 12 units across the country, serving 35 million people. Given the lack of mammography-related radiation dose data available from Sudan and Africa at large, it is not surprising that this is the first nationwide survey to be conducted in Sudan.  
Through the present study, we aimed to determine the average glandular dose (AGD) of radiation to promote dose optimization and acquire baseline data that will eventually help set national DRLs (NDRLs) for future dose optimization.