esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 3 Proposed Methodology
### Cluster Formulation and CH selection with K-Means Sorted Fitness  
When it comes to the actual clustering and the selection of CHs, K-means sorted fitness (a variation on K-means density) is used. K-means density is utilized in conjunction with K-means for the first selection of CH/centroids. Input parameter for K-means computes the \"k\" ideal centroids placement depending on the neighborhood of data points, as seen in Figure 8. The K-means algorithm [30; 31] can swiftly arrive at an optimal solution due to the pre-computed centroids.  
There is a set value for \"k\" in K-means density. However, with FANETs, the nodes move around often, and the network topology varies. Node positions and transmission range determine how many clusters are needed in a given network configuration. When choosing the size and number of clusters in K-means sorted fitness, we consider the transmission range of CHs [32; 33]. The nodes' fitness values are supplied into K-means sorted fitness, which returns CHs and the members connected with them as output, and the flow of this process can be seen in Figure 8. By keeping the clustering function as basic as feasible, EECP-MFO aims to minimize the computing effort required. Before the selection of CH occurs, we try to make it more efficient as here, and we already had residual energy computed for all nodes. So, we try to evaluate the average residual energy first. Suppose a candidate node for CH has less than the average residual energy. In that case, it is skipped for the current round of the CH selection process. The fitness value is used to make decisions in this function [34]. To choose the best CHs, a more precise fitness value must be calculated first in this scenario. In the beginning, the fitness value is ordered in decreasing order for more usefulness. The node will take the CH position with the fitness values. All additional nodes within the transmission range of the selected CH will be designated as cluster members (CMs). The elected node for CH will not be considered as the remaining nodes. After that, the Selection process of CH is repeated until there are no more nodes left to be chosen. All nodes have assumed their respective roles, whether as cluster leaders or cluster members [35].
# Numerical Simulation of Subdam Settlement in Ash Disposal Based on CGSW Optimization
## 3 Modeling and Test Scheme Design
### Test Scheme Design  
The dynamic compaction method, displacement method, and composite foundation method are common methods of foundation treatment [40; 41; 42; 43]. The dynamic compaction method compacts the gravel soil, silt, and miscellaneous fill soil foundation with dynamic compaction instruments. The replacement method can be used to excavate all CGSW and replace it with fly ash. The composite foundation method utilizes a backfill mixture of breccia, gravel, and sand in the process of vibration compaction to form columns [44; 45; 46; 47; 48]. This method requires consideration of the fact that different diameters and spacing of columns could produce different stresses on the soil between them, resulting in the production of different settlement resistance effects [49; 50]. The management and investigation department prefers the composite foundation method; by filling the stone column to treat the gangue layer, the stone column can be not only conducive to the drainage of the upper stagnant water from the gangue body, it can also enhance the strength of the foundation.  
To find an economical and rational construction scheme, ten construction schemes were designed for comparison, one of which was the excavation backfill technique; the rest were stone column construction techniques. The orthogonal test theory was used to design the stone column scheme. Three measurements of the stone column diameter were selected: 0.5 m, 1 m, and 1.5 m, and three measurements of the center distance were chosen: 3 m, 6 m, and 9 m. All the stone columns were 15 m in length, which was sufficient to pass through the CGSW.  
In order to observe the settlement condition of the subdam, 79 monitoring points were set every 10 m, from the right shoulder to the left shoulder on the central axis of the subdam-XIV\({}^{\prime}\) crest (as illustrated in Figure 6--up). The displacement history of all the monitoring points and the local unbalanced stress ratio of the whole model were recorded. When the value of the local unbalanced stress ratio was less than \(10^{-5}\), the simulation ended, which indicated that the subdam settlement process had terminated. Therefore, the displacement of the monitoring points can represent the displacement of the corresponding position in the subdam crest.  
The subsidence curve, while providing a visual representation of the differences in settlement at each monitoring point on the top of subdam-XIV, did not provide an evaluation criterion for us to determine which scheme produced a better inhomogeneity of subsidence values. This paper introduces standard deviation (SD), a statistical indicator that reflects the amount of variation or dispersion of a set of values [51]. It is calculated as follows:  
\[\sigma=\sqrt{\frac{\sum_{i=1}^{n}x_{i}-\overline{x}^{2}}{n-1}} \tag{6}\]  
where \(\sigma\) = standard deviation; \(x_{i}\) = subsidence value of the \(i\)th monitoring point; \(\overline{x}\) = average value of the parameter \(x\); and \(n\) = number of values of \(x\) (number of monitoring points).
## 2 Materials and Methods
### The Real-Aperture Radar (RAR)
#### 2.3.2 In-Field RAR Remote Monitoring  
For the purpose of this research, only the displacement on the east corner of the building, which corresponds to the transversal direction, was measured. To remotely monitor the structure, the RAR system was positioned on the other side of the south-eastern street at a distance of \(R_{0}=18.9\ m\) (\(D_{0}=25\ m\)) from the base of the structure. \(R_{0}\) and \(D_{0}\) were as defined in Figure 8. The inclination angle was \(\alpha=41^{\circ}\) above the horizontal. The RAR antennas were pointed to the upper part of the building (see Figures 7 and 8). The measurements lasted 36 min, with an output sampling frequency of 149 Hz.  
Figure 9 shows the Range bin profile of the RAR campaign of the \"_La Vina_\" building. According to the distances to the building, it was estimated that the bins Rbin38, Rbin39, Rbin40, Rbin41, Rbin42 and Rbin43 contain information on the structure. It can also be observed that the SNRs are the highest among the selected Rhins recorded.  
Once the structure is located within the range profile, we obtain the time history of displacements corresponding to the selected Rhins. Figure 10a shows the as-recorded signals of the five selected Rhins; Figure 10b displays a six-second (0.1 min) fragment. As part of the signal processing, the baseline was corrected. Vibrations outside the range of frequencies of interest were removed using a band-pass Butterworth filter. Figure 10c shows the processed signals. Finally, Figure 10d shows a six-second fragment.  
Figure 10: _Cont._  
Figure 9: Range bin profile of the “_La Vina_” building campaign. Dashed blue and red lines enclose the Rhins between Rbin38 and Rbin43.
# Enhanced Detection of Subway Insulator Defects Based on Improved YOLOv5
## 4 Experimental Results and Analysis
### Ablation Study on BL Function Parameters  
In Equation (3), the parameter \(\beta\) is utilized to mediate the balance between positive and negative samples by adjusting their respective proportions. A heightened \(\alpha\) augments the relative prevalence of positive samples. Conversely, the parameter \(\beta\) is employed to mitigate the loss attributed to undamaged insulator samples. Given the approximate 2:1 ratio of damaged to undamaged insulator samples in this investigation, \(\beta\) is assigned a value of 2. The parameter \(\gamma\) serves to curtail the loss associated with samples that are readily distinguishable--specifically, the undamaged insulator samples. An increase in \(\gamma\) diminishes the weighting accorded to these easily classifiable samples. This study elucidates the implications of modifications in the values of \(\alpha\), \(\beta\), and \(\gamma\) for the detection efficacy, as evidenced in Table 3 (under the conditions of \(\alpha=0.5\), \(\beta=1\), and \(\gamma=0\), the BL function reverts to the standard cross-entropy loss).  
The BL function optimizes the distribution of sample losses by fine-tuning the weights assigned to positive and negative samples, as well as those that are easy or challenging within the loss function. This strategy bolsters the model's proficiency in identifying challenging samples without compromising its capability to recognize simpler ones, thereby enhancing the accuracy of insulator defect detection. As indicated in Table 3, the parameters \(\alpha\), \(\beta\), and \(\gamma\) must be judiciously chosen in line with the sample distribution. Extremes in these values can negatively impact the model's detection accuracy. Given that challenging samples are comparatively scarce and elusive, an unduly low weight in the model diminishes the detection efficacy for these samples. In contrast, an excessively high weight can impede the accuracy in identifying numerous straightforward samples. The experimental data in Table 3 demonstrate that setting \(\alpha=0.75\), \(\beta=1.5\), and \(\gamma=1\) yields the ideal accuracy in insulator defect detection, marking a 5.8 pp enhancement relative to the baseline YOLOv5 model. This optimal ratio is incorporated into the subsequent algorithms discussed in this manuscript.
# Graphs Regularized Robust Matrix Factorization and Its Application on Student Grade Prediction
## 2 Related Works
### Student Grade Prediction (SGP)  
In current higher education in university, the teachers provide a \"one-size-fits-all\" curriculum, while the students enroll many courses to obtain academic credit. To graduate on time, the student expects to know which course he/she can pass with high score/grade, while the teacher expects to know which student has a risk of failure in his/her course. Hence, the problem of predicting the student grade at a course is significant to improve the educational outcomes.  
Generally speaking, the grade of one student at a target course can be inferred by his/her learning records, including historical grades in enrolled courses, academic behaviors and his/her background [31; 32]. In this paper, we make the following assumption--the grade can be determined by the latent features of student and course, where those features can be derived from the data of students and courses. We explicitly define the task of SGP as follows:  
Figure 1: The proposed workflow of student grade prediction using GRMF.  
**Problem 1 (Student Grade Prediction):** Let \(g(s,c)\) be the grade of student \(s\) at course \(c\). Denote by \(\mathbf{u}_{s}\) the feature of student \(s\) and \(\mathbf{v}_{c}\) the feature of course \(c\). Given the grade matrix \(\mathbf{M}\), SGP aims to seek the mapping \(\mathcal{H}(\mathbf{u}_{s},\mathbf{v}_{c})\), such that \(g(s,c)=\mathcal{H}(\mathbf{u}_{s},\mathbf{v}_{c})\) for all grades in \(\mathbf{M}\).  
To solve Problem 1, we should extract \(\mathbf{u}\) and \(\mathbf{v}\) and design a mapping using the given data matrix \(\mathbf{M}\). Most research designs or learns the features by using the background information [33; 34], such as student age and credit time, or the student grades on all finished courses. Since both of them are helpful, in this paper, we combine both information for SGP through developing the MF [26].
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**SOS**} & \multicolumn{6}{c}{**EOS**} \\ \cline{2-13}
**Parameters** & \multicolumn{3}{c}{**SIF**} & \multicolumn{3}{c}{**EVI**} & \multicolumn{3}{c}{**SIF**} & \multicolumn{3}{c}{**EVI**} & \multicolumn{3}{c}{**EVI**} \\ \cline{2-13}  & **s** & **r** & **Cr** & **s** & **r** & **Cr** & **s** & **r** & **Cr** & **s** & **r** & **Cr** \\ \hline Temperature-limitation & 150.23 & 0.73 & 62.00\% & 92.55 & 0.62 & 38.00\% & \(-\)67.31 & \(-\)0.57 & 68.50\% & \(-\)31.07 & \(-\)0.26 & 31.50\% \\ Water-limitation & 97.10 & 0.55 & 90.00\% & 10.62 & 0.07 & 10.00\% & \(-\)70.82 & \(-\)0.54 & 80.00\% & \(-\)17.52 & 0.14 & 20.00\% \\ Radiation-limitation & 197.98 & 0.87 & 53.65\% & 166.89 & 0.80 & 46.35\% & \(-\)183.13 & \(-\)0.90 & 49.35\% & \(-\)191.74 & \(-\)0.88 & 50.65\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The relationships between phenology from SIF or EVI and climatic limitation indices. s: slope of the linear regression between phenology from SIF or EVI and climatic limitation indices. r: correlation coefficient of the linear regression. Cr: contribution ratio of phenology from SIF or EVI to the difference of phenology derived from SIF and EVI under climatic limitations. addition, the radiation-limiting area in this study was distributed in northern China, where snow cover existed in autumn and winter, which may introduce the undesired errors of EOS extracting from reflectance-based EVI [42]. Although the GOSIF product was generated using remote sensing data from the MODIS and meteorological reanalysis data as inputs to the predictive SIF model, which may increase the correlation of SIF and climatic factors in a time series, this correlation from data sources will be offset in the spatial statistics adopted in this study. In addition, we employed SIF and EVI to extract phenology from photosynthesis and greenness perspectives, respectively. Other proxies, such as Chlorophyll/Carotenoid Index [43], Normalized Difference Vegetation Index, can be further analyzed in future studies to investigate the unique characteristics of each proxy on remote sensing derived phenology. The relationship between vegetation phenology and multiple climatic factors instead of one dominant climatic limitation index needs to be analyzed further to explore whether and how the impacts of climatic interactions on vegetation dynamics. Moreover, we focused on natural vegetated areas in China as a target, as it provides a natural laboratory with a wide variation of ecosystems and climate types. Further research could be expanded to the hemisphere or global scale to evaluate our findings.
# Landslide Displacement Prediction Method Based on GA-Elman Model
## 3 Application Research and Method Comparison  
### Overview of the Study Area  
The Jianshanying landslide is located in the northwest of Faer Town, Shuicheng County, Liupanshui City, Guizhou Province, China. It is located in the western part of the Guizhou Plateau. The topography is up and down with strong cutting, and it belongs to the low-medium-low mountain landform formed by structural erosion. The landslide is high in the southeast and low in the northwest. The landslide boundary presents an irregular semicircle shape on the plane, including Jianshanying steep cliff and its lower gentle platform area. The front elevation range of the unstable slope deformation area is 1040-1120 m and the rear elevation range is 1380-1502 m, with a height difference of about 483 m. The rear wall of the slope is nearly steep and the front slope is about 63\({}^{\circ}\). A total of five sets of monitoring equipment were deployed in the landslide, numbered EI19001-EI19005, respectively. The LoRa + 4 G method was used to collect the field crack displacement sensor data. As the gateway node, EI19001 is responsible for monitoring the rainfall in the landslide area. It is also responsible for obtaining the crack sensor monitoring  
Figure 2: Flowchart of GA-Elman model.  
data of other terminal nodes on the site. EI19002 EI19005 are the terminal nodes responsible for monitoring the changes of four key surface cracks in the landslide area. The schematic diagram of monitoring equipment deployment is shown in Figure 3.
# Multi-User AF Relay Networks with Power Allocation and Transfer: A Joint Approach
## 3 Proposed Power Allocation and Transfer Algorithm
### Subproblem Solution  
The values of power allocation and transfer at the \(i^{th}\) time slot for the \(S_{k}\) node and node \(R\) can be computed iteratively using the Karush-Kuhn-Tucker (K.K.T.) conditions. The \(\tilde{P}_{k,i}^{(t+1)^{*}}\), \(\tilde{W}_{r,i}^{(t+1)^{*}}\), and \(\delta_{k,i}^{(t+1)^{*}}\) are obtained by taking the partial derivative with respect to \(\tilde{P}_{k,i}\), \(\tilde{W}_{r,i}\), and \(\delta_{k,i}\) and by equating the result to zero. The detailed derivation which is straight forward is omitted here.
## 3 Results and Discussion
### Advanced Energy Management Systems and Control Architectures
#### 3.1.1 Architectures for Energy Management: Centralized and Distributed Approaches  
Initial research concentrated on centralized control architectures [12; 13], where decision-making processes were conducted from a central control center. However, the proliferation of DERs and the increasing demand for resilience have driven a shift toward distributed control models [14; 15], which support local, real-time decision-making and improve the system's responsiveness to disturbances.  
Several studies have proposed hierarchical control schemes to efficiently manage the different operational layers of microgrids [24; 26]. These architectures combine centralized supervisory control with local and field-level subsystems, optimizing network stability and economic performance [25; 50]. The literature also highlights the importance of effective control mechanisms to mitigate the uncertainty caused by the variability of RES [41; 51; 52].  
Despite these advancements, interoperability challenges among devices from different manufacturers and the absence of standardized communication protocols remain unresolved barriers [19; 53]. Several studies emphasize the need to develop common frameworks that facilitate the integration of heterogeneous technologies [20; 54; 55]. In this context, the implementation of intelligent agents and multi-agent systems has been identified as an effective solution for distributed management, enhancing system adaptability to unforeseen events and operational contingencies [56; 57].
## 5 Experimental Data Analysis
### Total Station Data Analysis  
A total of 56 measurement points were arranged on the model's surface. Among these, 44 points were distributed across four rows placed above the opening cut, with each row containing 11 measurement points. The points in the first row are labeled from left to right as a0, a1, a2,..., a10; the second row as b0, b1, b2,..., b10; etc., with the fourth row labeled as d0, d1, d2,..., d10. Additionally, there are two rows of measurement points along both sides of the opening cut, comprising 12 points in total. The first row, from left to right, is labeled e1, e2, e3, e9, e10, e11, and the second row is labeled f0, f1, f2, f9, f10, f11. The total station data collected before loading were plotted as a scatter plot, as shown in Figure 13.
###### Abstract  
Background: The aim of this study was to evaluate the effect of Photobiomodulation Therapy (PBMT) on the eruption rate of impacted maxillary canines after surgery, using a split-mouth design. Methods: A 16-year-old patient was referred to the Department of Oral and Maxillo-Facial Sciences of \"Sapienza\" University of Rome. Clinical examination revealed the persistence of maxillary primary canines and the absence of a palpable canine bump. CBCT scans confirmed that both upper permanent canines were impacted in the palatal osteemucosal region and showed a mesial inclination. Conventional surgery with cold blade was performed for both impacted canines, and PBMT was applied on element 2.3 for three sessions after surgery. Since this is a case report, no statistical analysis was performed. Results: The eruption movement of the two impacted canines was evaluated 16 weeks after surgery. The extent of the spontaneous eruption movement of element 1.3 was 0.21 mm, while element 2.3, which underwent three sessions of PBMT following surgery, moved 3.16 mm. Conclusions: The element that underwent PBMT appears to respond positively in terms of spontaneous eruption following disinclusion surgery compared to the contralateral element that was not stimulated with the laser. These findings suggest that photobiomodulation therapy could be effective in accelerating the eruption of palatally impacted canines following surgery, avoiding the need to use orthodontic traction. Future randomized controlled clinical trials are needed to demonstrate these preliminary results.  
impacted tooth; diode lasers; laser biostimulation; photobiomodulation therapy; LLLT
## 3. Designed Scheme
### Image Protection
#### 3.1.1 Watermarking Generation for Reconstruction and Authentication Purposes  
The original image has three channels according to its color space. Consequently, Pseudocode 1 is applied on each one of the channels, generating three watermarks for reconstruction and three for authentication. The watermarks used for reconstruction were designated as \(wr\), \(wg\) and \(wb\), and the ones utilized for authentication were named \(autr\), \(auto\) and \(auth\) for the channels R, G and B, correspondingly.  
```
Input: Image to be processed Ih \([A,B]=size(Ih)\) \(iReference=imageResize(Ih,0.25)\to image subsampling\) \(iReference=bitAND(iReference,248)\to replace3 LSBs by 0\) \(recoveryW=[]\to recovery watermark\) \(autentW=[]\to authentication watermark\) For\(i=1\)to\(A/4\)do For\(j=1\)to\(B/4\)do \(tmpW=get5 MSB\big{(}iReference_{ij}\big{)}\to extract5\)\(MSBts\) \(recoveryW=concat(recoveryW,tmpW)\to concatenation process\) \(aut=XOR(XOR(tmpW_{1},tmpW_{2}),XOR(tmpW_{3},tmpW_{4}))\) \(aut=XOR(aut\,,tmpW_{5})\) \(autTW=concat(autTW,aut)\to concatenation process\) Endfor Endfor Output: Recovery watermark recoveryW; Authentication watermark autentW
```  
**Pseudocode 1** Recovery and authentication watermark generation  
Once the watermarks are obtained, a subsampled process with a factor of 0.25 is performed to significantly reduce the number of bits representing each channel of the original image. When 5 MSB of each pixel are extracted, a total number of \(5 AB/16\) bits are available for reconstruction watermark embedding, and \(AB/16\) bits can be used for authentication watermarks. The authentication bit generation process consists of applying the bitwise XOR operation in the 5 MSBs, where a single bit is generated for each pixel block of size 4 \(\times\) 4.