esa-sceva/satcom-chunk-collection huggingface dataset:
# Edge Federated Optimization for Heterogeneous Data
## 2 Related Works
### Main Approaches to Handling Non-IID Data
#### 2.2.2 Algorithm-Driven Approach  
This subsection describes several major types of personalization methods, including conducting local fine-tuning, multi-task learning, and knowledge distillation [13]. The clients perform local fine-tuning to adjust the local models based on the global model from the server [7]. Note that FedAvg [23] is the basic form of local fine-tuning, which includes the procedures of finding a suitable initial shared model and combining local and global information. With meta-learning methods, a high-quality initial global model is developed with fine-tuning (e.g., Personalized FedAvg (Per-FedAvg) [5]). This method [5] achieves first-order optimality with convergence guarantees and better performance on heterogeneous data than FedAvg, but the approximate gradient of Per-FedAvg will significantly affect the results. An alternative to solving the personalization problem is to treat it as a multi-task learning problem [9], considering issues of communication cost, stragglers, and fault tolerance [10].  
Besides local fine-tuning and multi-task learning, knowledge distillation [12; 14; 15] is also a promising method for personalized federated learning. [PERSON] et al. [15] extend the concept of information transfer from large models to small ones [12] to knowledge distillation techniques. To deal with non-IID data, two types of strategies may be applied, namely federated transfer learning and domain adaptation. For federated transfer learning, [PERSON] et al. [31] propose a collaborative, robust learning method by uploading learnedfeatures instead of local models to implement local personalization. [PERSON] et al. [32] propose a framework called decentralized federated learning via mutual knowledge transfer (Def-KT), in which local clients exchange messages directly in a peer-to-peer manner without the participation of the cloud server. Another knowledge distillation strategy is domain adaptation, which emphasizes eliminating the differences between data shards between clients. [PERSON] et al. [33] propose a federated adversarial domain adaptation (FADA) algorithm that handles the domain shift problem with adversarial adaptation techniques. [PERSON] et al. [34] present the FedMD algorithm to transfer knowledge from a public dataset and enable clients to train their unique models on local data without privacy leakage risk.
## 2 Materials and Methods
### Landslide Inventory  
The position and extent of the landslide must be appropriately identified when creating the maps of landslide susceptibility. Landslide inventory is a critical component of all types of landslide zoning classification, including hazard, susceptibility, and risk zoning [42]. It has to deal with the time, location, sort, amount, and travel distance of land sliding in a specific area. Different methods are available for identifying landslides. Field observations, satellite photographs, book studies for details on previous landslides, and aerial photography are a few of them [43]. The landslide inventory map was produced using a combination of visual analysis of aerial imagery, field data, and satellite photography [14]. The whole data on landslides in India were made available by the Geological Survey of India (GSI). Resampled public data from the GSI were used to create the inventory map for the study area. One hundred and fourteen landslides have been found by this study using the available data (Figure 3). Most of the landslides recorded in this area have cliffs, highways, and banks of rivers. Erosion, steep terrain, and a lack of flora are often the causes of landslides along cliffsides.  
Figure 2: Graphical representation of overall methodology of landslide susceptibility mapping.
## 2 Experimental Study of Rotor Design  
The experimental study was performed using two different rotor designs, as shown in Figure 2.  
The first design (Figure 2a) is a Darrieus rotor with three asymmetrical blades secured between two horizontal parallel metal plates. The second design (Figure 2b) is a similar rotor, but with no horizontal plates, making it possible to compare the impact of the plates on the aerodynamic characteristics of the rotor.  
Figure 1: Aerodynamic forces acting on the wing profile.  
In the plated design (Figure 1(a)), the blades are secured between two horizontal metal plates, which provides additional rigidity to the system and can affect the air flow around the rotor. In the plateless design (Figure 1(b)), the blades are secured to the central shaft using extended 234.5 mm long studs, with the shaft itself mounted in the mast via a bearing assembly.  
The geometric parameters of both rotor designs are given in Table 1, which indicates the main dimensions of the wind wheel, mast, and blades.  
Experimental studies of aerodynamic characteristics were performed in the T-1-M wind tunnel shown in Figure 3; the experimental setup diagram is presented in Figure 4[29].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Wind wheel diameter & 450 mm \\ \multicolumn{1}{c}{} & 350 mm \\ \multicolumn{1}{c}{} & Number of blades \\ \multicolumn{1}{c}{} & 3 \\ \multicolumn{1}{c}{} & 260 mm \\ \multicolumn{1}{c}{} & 158 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main dimensions of the wind wheel.  
Figure 3: General view of the wind tunnel T-1-M [29].  
Figure 2: Experimental model of a wind turbine with three asymmetric blades.  
The wind tunnel operates by creating a controlled air flow that passes through the wind turbine model being tested, installed in the wind tunnel test section, which had a test section size of 0.5 m and a length of 0.8 m. The air flow velocity created by the fan was measured by a digital anemometer and varied from 3 m/s to 15 m/s. The rotation frequency of the wind turbine model was measured using an infrared optical tachometer, which recorded the passage of marks on the wind wheel.  
To determine the torque of the wind wheel, a dynamometer method was used. One end of a nylon thread was attached to the model shaft, and the other to the dynamometer, fixed on the platform.
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 2 Related Works
WSNs coverage optimization can be categorized into two types based on the number of optimization objectives: single-objective optimization and multi-objective optimization. Multi-objective optimization problems can further be classified into two major categories. One category is linear weighting, where the idea is to integrate the solutions to multiple objectives into a single objective for optimization. For example, to achieve the goal of minimizing energy consumption and sensor node overlap area, [PERSON] et al. [24]used a Multi-Objective Differential Evolution Algorithm (MODEA) to optimize the distribution of sensor nodes in regular and irregular regions. Different weights were assigned to each objective, transforming the multi-objective optimization into a single optimization problem. The other category is Pareto-based, where direct optimization of multiple objectives is performed. [PERSON] et al. [25], for instance, proposed a multi-objective deployment strategy that considers sensor node positions and network cost. They then used a multi-objective evolutionary algorithm to search for global optimal solutions. [PERSON] et al. [26] proposed the use of the multi-objective evolutionary algorithm NSGA-II to address the multi-objective problem of coverage and connectivity. They linearly programmed the multi-objective problem and introduced chromosome crossover and mutation methods to enhance the algorithm's performance. In order to increase coverage while reducing node energy consumption, [PERSON] et al. [27] utilized the MOEA/D-I and MOEA/D-II algorithms to jointly optimize network coverage, energy consumption rate, and energy balance rate. [PERSON] et al. [28] aimed to optimize network coverage, energy consumption rate, and energy balance rate, and they integrated these three optimization objectives into a single objective using linear weighting. They then employed an improved Whale Algorithm to optimize this single objective function.
## 4 Discussion
### Considerations on the Automated Approach  
Several researchers have used TF methods to characterize iEEG signals in epilepsy. They have found that the visual inspection of these images provides more information than traditional time series-based inspection. The study attempts to fully automate the visual inspection process by leveraging deep learning-based image inspection techniques. Even though this method reduces human effort, there are certain limitations associated with using this technique. Primarily, the deep learning method requires the conversion of the iEEG signals into TF images as input. These images require more space and additional computational effort to load, process, and save. This means that additional efforts need to be taken for memory management, computational resource management, and garbage collection. Further, this method might be difficult to implement in long-term EEG recordings due to the above-mentioned complexities.
## 4 Chemical Methods for Synthesis of Magnetic Ferrite Nanoparticles
### Microemulsion Synthesis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Synthesis Method** & **Sizees, Shapes, and Control Level** & **Advantages** & **Disadvantages** & **Ref.** \\ \hline \multirow{3}{*}{Coprecipitation} & \multirow{3}{*}{5-50 nm, spherical, moderate} & Simple process, environmentally benign & Wide particle size distribution of synthesized NPs. Generated & [70,158,222] \\  & & & & wastewater with a high basic pH & \\ \hline \multirow{3}{*}{Thermal decomposition} & \multirow{3}{*}{5-25 nm, spherical, cubic, octahedral, convolves, multibranches, tetrahedrons, very high} & High-quality monodispersed NPs & High reaction temperature requirement, requirement, complicated procedure & [157,158,160,161,162,223,224,225,226] \\ \cline{2-5}  & & Simple and rapid process, energy efficiency, cost-effectiveness, controllable stoichiometry and crystallite size & High energy demand & [70,172,181,227,228] \\ \hline \multirow{3}{*}{Sol-gel} & \multirow{3}{*}{5-30; 10-100, spherical, porous, faceted, high} & Reproducibility, products with high surface-to-volume ratio, good stoichiometric control, narrow size distribution of NPs & Heating requirement & [158,171,172,175,178,180,181,183,229] \\ \hline \multirow{3}{*}{Polyol} & \multirow{3}{*}{10-100, spherical, porous, faceted, high} & Lower temperatures are needed compared to the combustion method & & [188,191,193,195,196,230] \\ \hline \multirow{3}{*}{Hydrothermal} & \multirow{3}{*}{10-200 nm, spherical, rod-like, plate-like, high} & High yield of products, cost-effectiveness, excellent particle crystallinity, controllable size and good morphology & Slow kinetics due to the lower reaction temperature & [157–163,224–226,231–234] \\ \hline \multirow{3}{*}{Microemulsion} & \multirow{3}{*}{5-50 nm, spherical, high} & Very fine and monodispersed NPs, economic method, environmentally benign & Usage of large amounts of solvent, uncontrollable effects of the remaining surfactants & [213,216,218,220,221,235] \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of chemical synthesis methods for magnetic ferrite nanoparticles.
# Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method
## 4 Experiments and Results
### Test and Evaluation
#### 4.3.2 Performance Study in Fall Detection  
Among the six typical activities, walking, sitting, standing, picking up things, drinking, and falling, falling is the most harmful to the elderly. Older adults may suffer severe injuries after falling, or even endanger their life. For this reason, recognizing falls was critical in the HAR field. We separated the other five daily activities from falls and used binary classification to recognize the fall.  
As can be seen from Figure 11, our proposed method had a significant advantage in detection accuracy, with a fall detection of 96% and non-fall detection of 95.5% compared with the other three methods. It can also be seen that method 2 was slightly higher than method 1 in the accuracy of fall detection, but there was no difference in identifying normal activities. The reason might be that the improved VGG16 model used in method 2 can better identify differences between falls and other activities. The performance of method 3 was the worst, mainly because the spectrograms processed by the traditional PCA algorithm  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Accuracy/\%** & **Precision** & **Recall** & **F1-Score** &
\begin{tabular}{c} **Training** \\ **Time** \\ **(epoch/s)** \\ \end{tabular} \\ \hline Random & 84.75 & 0.86 & 0.85 & 0.85 & 16.49 \\ \hline SVM & 74.46 & 0.76 & 0.74 & 0.70 & 13.76 \\ \hline KNN & 90.85 & 0.91 & 0.91 & 0.91 & 14.28 \\ \hline Bi-LSTM & 83.53 & 0.87 & 0.84 & 0.85 & 23.17 \\ \hline Proposed & 96.34 & 0.96 & 0.96 & 0.96 & 10.88 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different methods with the proposed method.  
not only reduced the dimension of the samples but also discarded a large amount of information, resulting in method 3 having difficulty identifying the fall. The proposed method achieved the best performance in fall detection, mainly because the improved PAC algorithm enhanced the values of the retained dimension while reducing the redundant dimension, thereby improving the training speed and recognition of the spectrograms.
## 3 Synthetic Test
### Inversion Results for Time-Lapse
#### 3.3.2 Results and Discussion  
In the objective function, \(\lambda\) controls the smoothness of the model in spatial terms, with a larger value resulting in a smoother inverted model. On the other hand, \(\beta\) governs the degree of temporal variation in the model. A comparative analysis of the inversion results of Test 1 and Test 2 reveals that the time-lapse term enhances the inversion accuracy.  
Figure 8: Synthetic data testing results for the time-lapse electric field inversion (The red and blue boxes represent the theoretical locations; (**a**) Nx = 60, Ny = 60, Nz = 42; (**b**) Nx = 62, Ny = 62, Nz = 44).  
A larger \(\beta\) value suppresses changes in the model at adjacent time steps, yielding more similar inversion results at each time step. Conversely, a smaller \(\beta\) value imposes fewer constraints on the model at each time step, allowing for more liberal changes in the model at adjacent time steps. The inclusion of the time-lapse term in the results of Test 2 not only improves the accuracy of recovering the low-resistivity anomaly block but also reduces false anomalies around the blue box. Temporal constraints holds great significance for monitoring purposes as they not only enhance the ability to identify anomalies but also enable a more efficient comparative analysis of the monitoring results.  
The time-lapse calculation represents the disparity between model parameters, while the weight in inversion aims to minimize the discrepancy between the models. Thus, even when the data are heavily contaminated with noise, accurate results can still be obtained through the process of inversion.
# Approximation Method for Stress-Strain Using Metamodel Parameter Updating
###### Abstract
This is called the inverse method or finite element model updating (FEMU) and it approximates the FE simulation result with the actual test result by updating the parameter values of the constitutive model applied to the FE simulation [33; 34; 35; 36]. The simulation results are compared with the tensile test data, and the load-displacement curve is the measurement data required to determine the conformity between the simulation and actual test results [10]. The methods for updating the factors of the inverse method can be classified into the direct search method and the metamodel-based simulation optimization (MBSO). Both methods aim to improve the accuracy of the FE simulation by updating the factors of the constitutive equation. MBSO is a method that uses design of experiment (DOE), response surface (RS), and global optimization techniques, and can reduce trial-and-error and derive statistically significant factor values. Leveraging MBSO with the inverse method can take advantage of the ability to solve optimization problems with fewer resources. However, MBSO has a problem in that the performance of the search algorithm deteriorates as the number of factors in the constitutive equation increases. DOE, RS, and global optimization techniques can be modified to improve the performance of navigation algorithms, but filtering is more important to minimize the number of factors that do not affect results. The inverse method that utilizes MBSO also has such a problem. Inappropriate constitutive equation use, excessive factor number setting, and improper variable initialization method consume a considerable amount of resources in the inverse method utilizing MBSO. In this paper, we propose a method of adjusting the constitutive model coefficient to a scale factor to reduce the resources consumed by the inverse method. To verify the validity of the inverse method presented in this paper, we performed a typical method that did not utilize the scale factor and a proposed method that used the scaling factor. Then, the performance of each method was compared and analyzed in three stages: pre-process, FE simulation, and inverse method. The proposed method in this study was applied to the constitutive model of the power function, exponential function, and combination function [37; 38; 39; 40; 41]. When the combination function was used, an engineering stress-strain curve with strong nonlinearity could be estimated [42; 43]. However, it was difficult to express nonlinear properties in the exponential and polynomial models. These results were represented by the engineering stress-strain curve and the root mean square error (RMSE) value. We expect that initializing the combination constitutive model and setting the inverse method parameters using the proposed method will improve the efficiency and accuracy of the analysis.
## 3 Results
### Cytotoxicityicity  
To investigate the cytotoxicity of our samples, we tested them on human keratinocyte cell lines (HaCaT). The skin is an organ that is continually self-renewing and participates actively in host defenses [49]. Epidermal cells consist mostly of keratinocytes (about 95%). These cells have an important role as a barrier layer of the epidermis as well as in the initiation and perpetuation of skin inflammatory and immunological responses [50].  
But fresh keratinocytes require supplementary growth factors to survive and proliferate in vitro, and once induced to differentiate, they rapidly die and do not allow long-term investigation of differentiation signals [51].  
To minimize these problems, the spontaneously immortalized human keratinocyte cell line HaCaT from adult skin has been proposed as a model for the study of keratinocyte functions. HaCaT is a nontumorigenic monoclonal cell line adapted to long-term growth without feed-layer or supplemented growth factors [51]. Figure 7 shows the viability of HaCaT cells after being treated with neat PU as the control sample and the CPD/PU and C\({}_{60}\)/PU composite film samples.  
The control sample did not show any toxicity under any concentration and without blue light irradiation. The CPD/PU composite film sample did not show any toxicity at concentrations of 12.5, 25 or 50% and showed severe toxicity at a concentration of 100%. Similar results were obtained for C\({}_{60}\)/PU composite film samples. The C\({}_{60}\)/PU composite film sample did not show any toxicity at concentrations of 12.5 or 25%, whereas they showed mild toxicity at a concentration of 50% and severe toxicity at a concentration of 100%. All experiments were performed in the dark without irradiation.  
Figure 7: Cytotoxicityicity of PU (control), CPD/PU and C\({}_{60}\)/PU composite film samples without blue light irradiation determined as a percentage of viable HaCaT cells. The dashed lines highlight the limits of viability according to EN ISO 10993-5: viability >80 corresponds to no cytotoxicity, >60–80 mild cytotoxicity, >40–60 moderate toxicity and <40 severe toxicity.
## 2 Materials and Methods  
### Materials  
In this work a calendered bleached paper (Advantage MG White High Gloss, Mondi Group, Addlestone, UK) was used as reference paper. According to technical sheet, it is obtained from a long-fiber sulphate pulp, with a grammage of 35 g/m\({}^{2}\), a thickness of 50 \(\upmu\)m, a tensile strength of 3.5 and 1.5 kN/m, a tear resistance of 280 and 420 mN (respectively in machine and cross direction). Commercial polyethylene-coated paper and fluorinated paper were used as commercial references. Polyethylene coated paper (CCM Soc. Coop. Group, Modena, Italy) has a thickness of 73 \(\upmu\)m and a grammage of 54 g/m\({}^{2}\), 9 g/m\({}^{2}\) of which represent the polymeric coating. Fluorinated paper (ACL, Azienda Cartaria Lombarda s.p.a., Malagnino, Italy) is superficially treated with fluorinated compounds, to grant an elevated grease resistance, and with epichlorohydrin, a polyamide polymer which cross-links during paper drying, partially hindering water penetration. Chloroform was used because of the good solubility of PHBV, PCL and PEG [24] in this solvent and to avoid paper modification during the coating. Only few solvents are suitable candidates for PHB, of which chloroform is one of the most compatible and most commonly used [24; 25]. The good solubility of PCL in chloroform has been demonstrated in previous studies [26; 27; 28].  
Coating paper solutions were prepared dissolving in chloroform two biopolymers: poly(-3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) and polycaprolactone (PCL). In the first case, the product used was PHI 003 (NaturePlast\({}^{\circledR}\), Ifs, France). It is 94% biobased according to ASTM D6866 standard and industrially compostable according to ASTM D6400 standard. The product is a powder with a melt flow rate of 15-30 g/10 min (tested at 190 \({}^{\circ}\)C and with 2.16 Kg). PCL (Sigma-Aldrich, Mw \(\sim\)80.000) has a melt flow rate in the range from 2.01 to 4.03 (tested at 160 \({}^{\circ}\)C and with 5.00 Kg) and a water content lower than 0.5%; melting point at 60 \({}^{\circ}\)C and relative density of 1.145 g/mL at 25 \({}^{\circ}\)C. As a plasticizer, in the second phase of the study, polyethylene glycol (PEG) 200 (Mw \(\sim\)190-210; Fluka Analytical) was used.