esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation  
In this section, in order to compare the conventional WMMSE [15] and deep power control (DPC) [13] schemes and our proposed DPC scheme in terms of SE and IF, we show their simulation results. We also show the IF results according to \(\lambda\) in the loss function in Equation (14) to find the maximum value of \(\lambda\) that satisfies the maximum IF in the training model.  
For simulation, we assume that \(D\) = 40, \(N\) = 10, \(P_{max}\) = 43 dBm, \(\beta=10^{-3.453}\), \(\alpha=3.8\), \(W\) = 10 MHz [21]. In addition, 100,000 and 20,000 channel samples are generated for the training and test data, respectively. The samples of the Nakagami-\(m\) fading channel power between transmitter \(i\) and receiver \(j\) are generated by \(|g_{i,j}|^{2}=\sum_{n=1}^{m}(R_{n}^{2}+I_{n}^{2})\), where \(R_{n}\) and \(I_{n}\) are independent Gaussian processes with zero mean and unit variance [17], and the channel samples for all of the links are independently generated. The batch size is set to 500 and the learning rate is set to 0.00005.
## 3 Construction of Local and Global Approximations
### Local Approximation on Singular Patch  
The purpose of NMM to introduce the mathematical cover lies in generating the weight functions. A better global approximation can be reached by selecting proper local approximations of the physical patches. Therefore, once we know the local behaviors of solution, we can exploit them in the construction the global approximation. Supposing we know the analytical solution near the discontinuity and the the analytical solution in the neighborhood of discontinuity is \(g_{l}(x)\), we can further use the property of partition of unit and the weight function \(s\), then the global approximation is defined below  
\[\phi^{*}=\prod_{l}(1-s_{l}(x))\sum_{ijk}w_{i}a_{ij}^{k}L_{ij}^{k}+\sum_{l}s_{l} (x)g_{l}(x) \tag{7}\]  
where \(s\) is the weight function and can be any function that less than or equal to 1. The value of \(s_{l}\) is more than zero at the \(l\)th singular patch, but is zero at other domains.  
For example, \(s\) can be selected as  
\[s_{l}=e^{-k(x-x_{l})^{2}} \tag{8}\]  
where \(k\) is a positive number, \(x_{l}\) is the position of the \(l\)th discontinuity.
## 5 The HE-BIA Maturity Model v2.0
### The Organization Part
#### 5.2.2 Program/Project Management  
The successful development of BIA solutions is an evolving and iterative endeavor. Hence, BIA systems are considered an organizational program (i.e., a collection of projects) rather than an individual project. The program/project management category includes three dimensions related to BIA project success factors, which are pivotal in the planning and managing the execution of the BIA program [46]. The Sponsorship dimension is instrumental in ensuring adequate resources for the development and improvement of the BIA solution. This dimension describes the level of support, ownership, and responsibility of top management towards the BIA solution. Maturity progression in this dimension includes the appointment of a business sponsor (a well-respected and influencer leader of the university, typically from the recry or top management), a BIA director, and an organizational unit dedicated to the development and management of the BIA solution. This unit is typically called a Business Intelligence Competence Center (BICC), with a cross-functional team, including members from IT and the \"business\" or academic side [47, 48].  
The Data governance dimension maps the level of adherence to data governance practices and methodologies to ensure the quality and accuracy of the data in the BIA solution. According to [49], there has been a growing interest and recognition of the importance of data governance in HE. Data governance refers to the exercise of authority and control over the management of data [50]. This includes the effective application of principles, policies, roles, responsibilities, use of data standards for data management (for modeling and data exchange) across the university.  
The final dimension in this category is Change management. This dimension maps the ability to manage changes within the context of the BIA solution. Change management is a crucial activity in DW/BI systems [5]. Any issue that impacts a BIA project's schedule, budget or scope should be considered a change. Industry experts advocate that a change management mechanism should be set in place to capture and document changes enabling better communication with the BIA users. Given the evolving nature of the BIA solutions, as previously discussed, there is a need for continuous support and development. Hence, change management is essential to manage the expectations of BIA users. This dimension captures this need to document and manage changes of the BIA solution; it does not cover organizational change management as in [51].
# A 3D Vision-Based Weld Seam Extraction Method for Arbitrarily Placed Spatial Planar Weldments
## 5 Conclusions  
This paper introduces a global weld seam extraction method tailored for arbitrarily placed spatial planar weldments. The key contributions and outcomes of this research endeavor are summarized as follows:  
1. By introducing point cloud registration with multiple frames of point clouds captured from a weldment, a three-dimensional reconstruction of the weldment is carried out without knowing the position and posture of the weldment. Due to the universality of this method, it may be applied to most scenarios that require a complete 3D digital twin model.  
Figure 22: The WSE results: valid weld seams are marked with thick red lines and invalid weld seams are marked with thin blue lines.  
2. We propose an improved RANSAC algorithm designed to execute consecutive plane segmentation with remarkable accuracy. This algorithm is adept at handling the plane segmentation of complex spatial planar weldments, a task challenging for conventional similar algorithms. As demonstrated by the experiments, the average errors are reduced by 90.3% to 99.8% over the traditional RANSAC algorithm, and the standard deviations are reduced by 64.8% to 97.0%. This method can also be used in other processing techniques that require obtaining planar features through point clouds.
3. Additionally, we present a weld seam extraction (WSE) algorithm for multiple weld seam extraction, which calculates all the edges from the segmented planes and selects valid weld seams with vertex validation and edge convexity validation utilizing the half-edge data structure. The proposed methodologies offer an elegant solution for automatically and accurately extracting weld seams from complex weldments without precise fixing. Nevertheless, our approach still has limitations and areas that deserve extension.
1. This research uses a single-axis turntable to expand the 3D camera's FOV. When the workpiece shape is particularly complex, there could still be geometric features that are difficult to capture. To further achieve the 3D reconstruction capability, we consider introducing multi-axis turntables as a replacement. The calibration of multi-axis turntables will become a research focus.
2. The WSE method proposed in this paper approximates the weld seams as the intersection lines of adjacent planes in the workpiece. For the case where welding grooves are to be concerned, further microscopic analysis and modeling of the grooves is required before it is used for robot welding path planning. Also, due to the poor performance of RANSAC (even the improved version proposed in the paper) in identifying narrow and long planes, this method only applies to plate structures with a thickness of more than 2 mm.
3. To improve the method's scope of application, our future investigations will center on weldments featuring free surfaces.  
Not applicable.  
The authors declare no conflicts of interest.
###### Abstract  
Federated learning has emerged as a promising approach for collaborative model training across distributed devices. Federated learning faces challenges such as Non-Independent and Identically Distributed (non-IID) data and communication challenges. This study aims to provide in-depth knowledge in the federated learning environment by identifying the most used techniques for overcoming non-IID data challenges and techniques that provide communication-efficient solutions in federated learning. The study highlights the most used non-IID data types, learning models, and datasets in federated learning. A systematic mapping study was performed using six digital libraries, and 193 studies were identified and analyzed after the inclusion and exclusion criteria were applied. We identified that enhancing the aggregation method and clustering are the most widely used techniques for non-IID data problems (used in 18% and 16% of the selected studies), and a quantization technique was the most common technique in studies that provide communication-efficient solutions in federated learning (used in 27% and 15% of the selected studies). Additionally, our work shows that label distribution skew is the most used case to simulate a non-IID environment, specifically, the quantity label imbalance. The supervised learning model CNN model is the most commonly used learning model, and the image datasets MNIST and Cifar-10 are the most widely used datasets when evaluating the proposed approaches. Furthermore, we believe the research community needs to consider the client's limited resources and the importance of their updates when addressing non-IID and communication challenges to prevent the loss of valuable and unique information. The outcome of this systematic study will benefit federated learning users, researchers, and providers.  
communication-efficient; federated learning; non-IID data; systematic mapping study
## 3 Results and Discussion
### Time Effect  
According to Figures 6 and 7, by increasing the processing time (duration under pressure/heating) from 15 min to 45 min and 75 min, the thermoelectric properties (specifically the electrical conductivity and the Seebeck coefficient) improve only slightly. Additionally, minor changes can be observed in the thermal conductivity as a function of the processing time. Based on the aforementioned findings, the processing time of 15 min seems to be sufficient for achieving good thermoelectric properties. Moreover, an increase in the processing time seems to be beneficial for composite samples with 30 wt.% BST.  
Table 4 summarizes the optimum thermoelectric properties recorded at 130 \({}^{\circ}\)C for the PANI-BST composite samples with 20 wt.% and 30 wt.% BST content.
## 3 Results and Discussion
### Transmission Electron Microscopy  
Transmission electron microscopy was used to evaluate the quality of the dispersion and orientation of nanofillers in different types of processes (i.e., compression molding and extrusion). In Figure 7, TEM micrographs of compression molded samples are presented. An evident good dispersion of graphene and carbon nanotubes can be observed with no detectable aggregates. In addition, both graphene and carbon nanotubes seemed to be randomly oriented in the ABS matrix. In the ABS/GNP nanocomposite the presence of wrinkled graphene flakes can be observed, while in ABS/CNT the nanotubes entangled in interconnected structures can be detected.  
The surface of nanocomposite filaments was investigated in two directions (i.e., perpendicular and parallel) to flow polymer direction in order to determine the orientation of nanofillers. Similar to the compression molding process, nanofillers without visible agglomerated structures were observed (see Figures 8 and 9). Figure 8 presents the ABS/GNP nanocomposite filament, and graphene flakes were oriented along polymer flow in the extrusion direction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Samples** & **0.1 Hz** & **1 Hz** & **10 Hz** & **100 Hz** & **Average** \\ \hline ABS & 83.1 & 130.7 & 122.5 & 90.4 & 107 \(\pm\) 23 \\ ABS/GNP & 11.5 & 16.9 & 45.3 & 47.8 & 30 \(\pm\) 19 \\ ABS/CNT & 71.6 & 60.5 & 66.5 & 61.7 & 65 \(\pm\) 5 \\ \hline \end{tabular}
\end{table}
Table 1: Activation energy (E\({}_{\text{act}}\)) in kJ/mol at various frequencies at temperature 250 and 280 \({}^{\circ}\)C.  
Figure 7: TEM micrographs of compression molded (CM) nanocomposites: (**a**,**b**) ABS/GNP and (**c**,**d**) ABS/CNT.  
On the other hand, nanotubes in the ABS matrix were randomly oriented from the extrusion process (see Figure 9b). Additional TEM images of the composites are given in the Supplementary Materials and they better illustrate the orientation and random distribution of the particles in the ABS/GNP and ABS/CNT composites, respectively (see Figure S1).
## 5 Simulation Results and Discussions  
In this section, we provide the simulation results to illustrate the performances of the proposed iterative combinatorial auction algorithm for relay selection (CARS). Besides, we give the necessary analysis for the results. A single cellular cell environment is considered. Without loss of generality, we consider that UEs within a D2D cluster are randomly distributed in the cell. Furthermore, mobility during the data sharing process is not considered in the simulations. The results are averaged over 1000 realizations. Main simulation parameters are listed in Table 1, which are inspired by [8].
# Detection of Impact Damage on PVA-ECC Beam Using Infrared Thermography
## 4 Results
### Temperature Variation Analysis  
To provide quantitative analysis of the results, the thermal contrast variation profile was analyzed. The computed profile perpendicular to the extension direction of the crack along the x axis was shown in Figure 8, which revealed that the temperature variation gradually increased in the same profile line from the 6 th to the 9 th impact. It can be interpreted as the impact-induced cracks stretching and broadening as the impact continues. The thermal contrast was always the highest when it comes to the cracks. In this figure, we can easily find that the crack is near the 210 pixel on the x axis, where the temperature was higher than that of the nearby areas. We can also locate the position and even quantify the width of the crack from this figure. The width of the crack after the 9 th impact was the most obvious. In Figure 8, the length of the highest temperature section was also the widest. These results agreed well with the actual crack width.  
To compare the temperature variations for different locations in the warm-up and cool-down periods, the temperatures over time in the defective area and nearby intact area after the 9 th impact were computed, as shown in Figure 9. The temperatures show an overall increasing trend, and a sharp increase was observed near the 24 th min. The possible explanation is that the defective area is filled with air and slag, which have different density, heat capacity and heat conductivity. Therefore, it shows the higher temperature curve in the warm-up and cool-down periods. In summary, the temperature on the defective area was higher than that on the intact area.  
The occurrence and evolution of impact-induced crack was monitored in real time by detecting the thermal responses under external heat stimulus. For both the warm-up period with the cool-down period, temperature variation analysis showed an abrupt temperature change that corresponded to the crack or damage. Under the same heat stimulus, the maximum of temperature variation depends on both the depth and area of the subsurface defects. Therefore, temperature variation analysis using the data from the infrared thermography has the capability to determine the occurrence of an impact-induced damage.  
Figure 8: Thermal contrast profile perpendicular to the extension of the crack.
## 4 Application
### Clinical Application
#### 4.1.7 Burn care  
[PERSON] et al. [PERSON] et al. (2019) introduced that PPG imaging can be used to identify the correct depth of burn excision as there was significantly less blood circulation in burnt tissue. Consequently, it can help surgeons by giving an idea of where and how much to resect. Thus, iPPG helps to improve burn care as well.
## 2 Results and Discussion
### Profiling of Onion and Lemon Secondary Metabolites via UPLC-QTOFMS and Multivariate Data Analysis
#### 2.2.1. Phenolic Acids  
As precursors for most phenolic metabolites, phenolic acids are commonly reported in metabolite profiling studies of functional foods, often conjugated with sugars as in the case of _C. limon_. Phenolic acids are also known to contribute, among others, to the flavor/taste of foods. In this study, two phenolic acid glycosides were found to be predominant in lemon fruit extracts, namely 6-O-Feruloyl-hexoside (peak 6, \(m/z\) 341.087 \(\mathrm{[M-H]^{-}}\) and citrusin E (peak 24, \(m/z\) 353.0872 \(\mathrm{[M-H]^{-}}\)) preferentially ionized in negative-ionization mode.
# Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China
## 5 Construction of Collapsibility Evaluation Method
### Calculation of the Moisture Content, Void Ratio, and Saturation Indicators  
In addition to the dry density, the other indicators in the evaluation method included the moisture content, void ratio, and saturation. Based on the indoor geotechnical tests described in Section 3.3, we obtained the water content and specific gravity values by collecting disturbed soil samples. Then, by substituting the moisture content, dry density, and specific gravity into Equations (10) and (11), we obtained the void ratio (\(e\)) and saturation (\(S_{r}\)); the results are listed in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Pit** & **Void Ratio** & **Saturation** & **Test Pit** & **Void Ratio** & **Saturation** \\
**Number** & **Void Ratio** & **(\%)** & **Number** & **Void Ratio** & **(\%)** \\ \hline JSZH 1-1 & 0.749 & 12 & JSZH 5-1 & 0.751 & 15 \\ JSZH 1-2 & 0.759 & 14 & JSZH 5-2 & 0.727 & 15 \\ JSZH 2-1 & 0.728 & 14 & JSZH 5-3 & 0.680 & 17 \\ JSZH 2-2 & 0.703 & 15 & JSZH 6-1 & 0.699 & 21 \\ JSZH 3-1 & 0.697 & 13 & JSZH 6-2 & 0.687 & 21 \\ JSZH 3-2 & 0.684 & 13 & JSZH 6-3 & 0.663 & 21 \\ JSZH 4-1 & 0.723 & 16 & JSZH 6-4 & 0.664 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Void ratio and saturation in each test pit.
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 4 Navigation Integration Enhanced by the ANN
### Hybrid Methods
#### 4.2.3 Measurement Reconfiguration Based on NN
The ANFIS is used to characterize the relationship between INS- and GPS-derived navigation data in order to precisely simulate vehicle dynamics and predict its location during GPS failures. It combines the advantages of fuzzy reasoning with neuro-computations. The intended ANFIS technology operates in predicting mode to determine location during periods of GPS signal interruption by shifting periodically depending on the INS velocity and azimuth. The AKF can benefit from a navigation technique that uses a wavelet neural network (WNN) with random forest regression (RFR) to design the RFR-WNN to forecast INS errors during GPS failures. To increase forecast accuracy, a novel MLP network prediction model that establishes the relationship between INS information and GPS position increments was presented. The KF is enhanced by the addition of an adjusted factor to reduce the impact of the complexity environment and random mistakes on the filtering precision. AKF is used to repair INS inaccuracies. When GPS is functioning properly, a high-precision prediction model is built using RFR-WNN, and when GPS is malfunctioning, RFR-WNN provides the necessary observations for an AKF update. RFR is used to optimize the single WNN, which can increase generalizability and prediction accuracy to address the issue that the absence of comprehensive training data makes the single neural network model susceptible to classifier, unpredictability, and low prediction accuracy.
## 1 Introduction  
Alternative energy has become a topical subject in many countries in recent years, with solar energy emerging as a promising energy source since the radiant energy of the sun is inexhaustible. In recent years, this aspect of the development of the smart grid is booming [1]. Photovoltaic (PV) panels for solar power generation can convert solar radiant energy directly into electric energy. At present, the PV power system has not reached high conversion efficiency, requires high cost, and the power benefit varies with climatic conditions and ambient temperature. Therefore, how to maximize the power output of PV power system and reduce power waste has become an important topic.  
Many Maximum Power Point Tracking (MPPT) technologies have been used in PV power systems in the literature [2], such as the voltage feedback method [3], the power feedback method, the incremental conductance method (INC) [4], and Perturbation and Observation (P&O) [5,6]. Otherwise, a hybrid MPPT technique proposed by [PERSON] _et al._[7] was applied in the MPPT of PV power system since it is neither only distributed on the PV modules of the PV array nor only centralized at the input of the inverter. The optimal scheme was used to estimate the optimal values of PV module voltages and the input inverter voltage. Therefore, the high speed of tracking of MPPT was obtained by this scheme. The tracking speed of general MPPT methods is determined by the step size, and continuous MPPT during steady-state response results in steady-state power oscillation, thereby causing power loss and wearing of electronic modules.  
Most previous studies used decreasing steps to reduce the steady-state oscillations. However, the sampling time cannot be infinitely smaller due to the limits of physical elements in the real physical system. Therefore, how to reduce the steady-state oscillations in cases where the step sizes of MPPT cannot be reduced is an important issue.  
Our previous study used fractional order incremental conductance method as the algorithm implement for MPPT [8]. This method has improved the transient tracking speed, but the oscillation during steady-state response is not improved completely. In order to avoid the PV system having power oscillation near the MPP, this study combines the fractional order chaos synchronization dynamic error detector with the MPPT control of fractional order incremental conductance method proposed in [8]. The proposed MPPT controller is combined with a voltage detector algorithm [9,10]. The voltage from the PV panels is captured and calculated by MPPT controller. The result is extracted to the chaos synchronization dynamic error detector to adjust the PWM signal to control the PWM duty ratio of DC/DC converter [11], as the framework of the overall PV power system.  
The remainder of this study is organized as follows. Section 2 describes the existing problems and research motives; Section 3 presents the MPPT design and illustrates the voltage detector design; Section 4 discusses the simulation and experimental results. Finally, conclusions are drawn in Section 5.