esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
As a transient electrochemical measurement, the EIS test is widely used in the choice of corrosion inhibitor [21]. However, the use of the EIS test is merely incommensurate with the long-term monitoring of steel bar corrosion in concrete. The long-term observation of a steel bar surface contributes to knowing the formation process of a passive film [22; 23]. Moreover, EIS and polarization curves in the chloride-containing solutions are occasionally affected by crevice corrosion; this phenomenon could reduce by the utilization of a non-stationary technique such as the CV test [24]. The CV test covers the overall process of steel bar corrosion including oxygen absorption and hydrogen evolutional corrosion in high alkaline solutions [25]. Nevertheless, the extensive potential range (test in the \(-1.5\)-\(1\) V) alters the specimen (steel bar surface) and changes the specimen before each test. In addition, cyclic voltammetry is suitable for transient measurements because electronic reactions that occur on steel bar surfaces do not achieve equilibrium conditions [26]. Interface complexity is considered to be a significant factor in the study of steel bar surface properties. For complex mortar, hydration products at the metal-hydrated cement paste interface may affect the onset of localized corrosion, complicating the analysis of the mechanism. Hence, the simplified simulated pore solution can be used in the mechanism investigations of passive film formation in well-controlled environment. In our previous research, a multi-component corrosion inhibitor was successfully prepared, and its inhibition properties and synergistic mechanisms were investigated [27]. Multi-component corrosion inhibitors are a new trend in the field of electromigration inhibitor research, which could combine the advantages of the quick absorption of the organic and the excellent long-term performance of the inorganic [11; 28]. However, whether the long-term performance of multi-component corrosion inhibitors could achieve the  
Figure 1: Functional groups in organic corrosion inhibitor [15]. inhibition level as traditional inhibitors (such as calcium nitrite) is still unclear, especially considering stability. Thus, the main purpose of this research was to compare the inhibition effect of various corrosion inhibitors by electrochemical measurements (open circuit potentials, EIS, and CV) and SEM over as long as 60 days. We also explored the mechanism of different corrosion inhibitors acting on steel bars when exposed to a simulated concrete pore solution with a high concentration of chlorides. Based on the experimental results, a protective film that can serve in alkaline environments over the long term was developed. According to the results of the experiments, a new concept of \"multi-functional protective film\" was proposed and provided a new insight into strengthening the microstructures of passive film.
# The Optimization of the Geometry of the Centrifugal Fan at Different Design Points
## 3 Results
### Study of Fan Operating Curves
#### 3.2.2 Operating Curves at Second Design Point
These results were achieved without increasing the level of noise emitted by the device. Researchers from Sherbrooke University in Canada [PERSON] et al. [5] utilized a fully automated cyclic process for the design of an eccentric centrifugal fan blade. The researchers noted that as restrictions and requirements for air quality and ventilation systems increase, it becomes challenging to find space for device improvements. Their geometry design process, using meta-modeling, allows for the optimization of up to seven different geometry parameters [36]. The optimization meta-model algorithm developed by the researchers improved blade efficiency from 2.35% to 5.71%. Optimization using \"OpenFOAM\" software (version is provided in Section 2) enhanced efficiency from 3.27% to 4.22%. The authors pointed out that the inclusion of a second group of parameters in the calculation algorithm led to a 4.1% improvement in efficiency compared to the introduction of the additional group with optimal geometry parameters. Work conducted by researchers from China introduced the process of optimizing fan blades by selecting an optimized blade profile ([PERSON] et al. [6]). The authors noted that in all fan optimization studies, there is a plethora of geometry parameter evaluations conducted, making the analysis resource-intensive due to the vast number of possibilities. For this reason, their study specifically focused on the blade geometry to optimize the arc-shaped profile. It was observed that the overall efficiency increased by 3.1%, and the airflow rate increased by 1.4 m\({}^{3}\)/min when comparing the optimized profile blade with the initial profile. The improvement in criteria was achieved by changing the angles of the starts and ends of the profile strings, with the greatest differences observed at the trailing edge of the profile, which was highly curved. From this review of scientific literature, it can be seen that the results of the experiments described in the article correlate with global practice, i.e., comparing the performance curves of the fans resulted in a 0.56-2.45% higher airflow rate for the fan optimized at the first DP compared to the maximum flow rate of the fan at the second DP in the absence of system static resistance. Reviewing the established operating characteristics at the same fan speeds, it was observed that the fan optimized at the first DP supported 1.4-4.5% higher static system pressure increases than the test object designed at the second DP.
## 1 Introduction
The study encompasses seven skin lesion types: melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AKIEC), benign keratosis (BKL), dermatofibroma (DF), and vasculopathy (VASC), as delineated in Figure 1. The principal objective of this framework is to augment the accuracy, precision, and resilience of multiclass skin lesion classification. Our main contributions to this endeavor are as follows:  
1. Data augmentation techniques are employed to address training data imbalances and generate a more representative set of skin lesion image samples. This methodology enables the model to glean robust representations of lesion features. 2. A novel local-global hierarchical attention mechanism is introduced to capture pivotal features across different abstraction levels. By amalgamating local and global features across multiple levels, the model harnesses both intricate and contextual information to amplify feature representation and diagnostic precision. This hierarchical attention mechanism furnishes adaptability in handling features across distinct abstraction levels, enabling the model to tackle complex diagnostic tasks adeptly. 3. This study proposes an encoder-decoder framework based on the Transformer model, which leverages its scalability. The model's representation capacity is bolstered by modulating the encoder and decoder layer counts alongside attention head numbers. Pre-training and fine-tuning strategies are additionally employed to refine the performance of skin lesion identification tasks. 4. Comparative experiments against state-of-the-art methodologies corroborate the superior performance of our proposed method in multiclass skin lesion classification. The experimental results demonstrate substantial enhancements in accuracy, precision, specificity, and F1 index, confirming the effectiveness of our method. The article is structured as follows: starting with the abstract and introduction, Section 2 provides a comprehensive literature review, offering a systematic overview of the current progress and status of related research. Subsequently, Section 3 elaborates on the proposed methodology, detailing the data sources and adopted data augmentation techniques. Furthermore, state-of-the-art models and methods are introduced and presented. In Section 4, this study presents the experimental results and conducts an extensive comparative analysis based on various indicators, including precision, accuracy, and F1 index. The experimental results are thoroughly interpreted to provide a comprehensive evaluation of the proposed method's performance. Finally, the conclusion summarizes the effectiveness and innovativeness of the proposed method.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Application Areas
#### 4.2.1 Emergency Department Applications  
Emergency Departments account for 25.8% of analyzed DES applications, reflecting their complex patient flows and critical need for resource optimization. [PERSON] et al. [37] demonstrated that DES models in EDs primarily focus on improving patient flow, optimizing resource allocation, refining triage processes, and enhancing capacity planning. Their study reported an average 25% reduction in waiting times through DES-guided process improvements, underscoring the impact of simulation-driven interventions in high-demand environments.
## 3 Results and Discussion
### Model Validation with Experimental Values  
The values of %\(Condens_{i1}\) related to vapour permeation-fractionated condensation were simulated based on the model explained in the modelling of fractionated condensation step (Section 2.5. using the input parameters: \(\kappa_{i,feat}\), \(T_{feat}\), \(p_{perm}\), \(Ii\), and \(n_{inert}\) and varying \(T_{1,condens}\). The red wine headspace model solution composition, including esters and ethyl phenols, is presented in Table 1A,B (Section 2.1.1). Figure 7 illustrates the predicted and experimental percentages of recovery for each compound, \(i\), in the first condenser. A good agreement between the experimental and simulated results of %\(Condens_{i1}\) was obtained. As described in Section 3.2, applying a \(T_{1,condens}\) (\({}^{\circ}\)C) of \(-25\)\({}^{\circ}\)C results in a reduction in the off-flavour concentration with the fractionated condensation.  
Even in the presence of noncondensable gases, this model offers a simple and efficient approach for the simulation of the percentage of condensation of each compound in each condenser at a given downstream pressure and condenser temperature. For some of the aromas studied, the values of %\(Condens_{i1}\) were slightly overestimated, which was explained by a lower condensation efficiency due to the inert gas stripping effect, which could not be predicted thermodynamically.
## 3 Results
### Robustness of the LAI Products Uncertainty  
The magnitude of the reported EO LAI uncertainty is typically smaller than the bias to in situ estimates (Figure 4), here shown as the ratio of uncertainty and reported bias (\(r\)), and this result is consistent across forests (Figure S5, Table 2). GEOV3 300 m has the smallest proportion of EO uncertainty estimates below the observed bias (86%) while MODIS 500m LAI and GEOV2 1 km have 87% and 91% of uncertainty estimates below the reported bias. The median ratio indicated similar average skill between both GEOV3 300 m (0.26) and GEOV2 1 km LAI products (0.28) and MODIS 500 m LAI products (0.21) (Figure 4, Table 2).  
Figure 3: Regression between the field LAI and the satellite retrieved LAI. Each point represents the monthly LAI field measurement against the satellite LAI retrieval. The blue solid lines represent the regression between the field LAI against the satellite LAI for all the sites combined on a monthly scale. Point colors and shapes represent different sites.
## 2 The Proposed Function Composition  
### Definition  
Garasym et al. [66] proposed a PRNG based on coupled one-dimensional chaotic maps. They claim that a robust PRNG can be designed by coupling the tent and logistic maps, and the number sequences produced by that PRNG can achieve excellent pseudorandom properties and uniform statistical distribution. So, they conclude that their PRNG is suitable for chaos-based cryptography applications. Garasym et al. [66] based their proposal on the idea of combining the characteristics of the tent and logistics maps to achieve a new map with improved properties, through the combination of various network topologies. They proposed it because both logistic and tent maps have never been used in cryptography as they have weak security. Then, based on the review of the network topologies of 1-D chaotic maps presented by Garasym et al. [66], we propose a function composition (FC) from sine function and skew tent map (STM).  
Thus, we define STM using the linear functions \(\sigma_{1}(\mu,\alpha,y)=\frac{y}{\mu}\) and \(\sigma_{2}(\mu,\alpha,y)=\frac{\alpha-y}{\alpha-\mu}\) according to Equation (1).  
\[\sigma(\mu,\alpha,y)=\begin{cases}\alpha\,\sigma_{1}(\mu,\alpha,y)&0<y\leq\mu \\ \alpha\,\sigma_{2}(\mu,\alpha,y)&\mu<y<\alpha\end{cases}, \tag{1}\]  
The iterated version of the STM is given by Equation (2),  
\[y_{n}=\sigma^{n}(\mu,\alpha,y_{0})=\begin{cases}\alpha\,\sigma_{1}(\mu,\alpha, y_{n-1})&0<y_{n-1}\leq\mu\\ \alpha\,\sigma_{2}(\mu,\alpha,y_{n-1})&\mu<y_{n-1}<\alpha\end{cases}. \tag{2}\]  
Then, when the function \(g(x)=sin(\pi x)\) is applied in a conjugate form to \(\sigma_{1}(\mu,\alpha,y)\) and \(\sigma_{2}(\mu,\alpha,y)\), in such a way that \(\tau_{1}(\cdot)=g\circ\sigma_{1}(\cdot)=g[\sigma_{1}(\mu,\alpha,y)]\) and \(\tau_{2}(\cdot)=g\circ\sigma_{2}(\cdot)=g[\sigma_{2}(\mu,\alpha,y)]\), we define the FC according to Equation (3).  
\[\tau(\mu,\alpha,x)=\begin{cases}\alpha\,sin\left[\pi\frac{x}{\mu}\right]&0<x \leq\mu\\ \alpha\,sin\left[\pi\frac{\alpha-x}{\alpha-\mu}\right]&\mu<x<\alpha\end{cases}, \tag{3}\]  
The iterated version of the FC is given by Equation (4),  
\[x_{n}=\tau^{n}(\mu,\alpha,x_{0})=\begin{cases}\alpha\,sin\left[\pi\frac{x_{n- 1}}{\mu}\right]&0<x_{n-1}\leq\mu\\ \alpha\,sin\left[\pi\frac{\alpha-x_{n-1}}{\alpha-\mu}\right]&\mu<x_{n-1}< \alpha\end{cases}, \tag{4}\]  
In both cases, \(n\) = 0, 1, 2,... represents the iteration step, \(y_{0}\) and \(x_{0}\in(0,\alpha)\) are the initial conditions of the chaotic maps, \(y_{n}\) and \(x_{n}\) are the number produced by the iteration \(n\) of each chaotic map, \(\mu\in(0,\alpha)\) and \(\alpha\in\mathbb{R}^{+}\) are the control parameters of the chaotic map, and \(\tau^{n}(\mu,\alpha,x_{0})\) represents Equation (3) applied \(n\) times on \(x_{0}\) using \(\mu\) and \(\alpha\).  
Figure 1 shows the behavior of the STM and the FC according to Equations (1) and (3), respectively. It worth noting that when \(\mu\) = 0.5\(\alpha\) the STM is symmetric. In this case, each chaotic system is applied to the interval (0, \(\alpha\)), \(\alpha\) = 3.0, and \(\mu\) = 0.0, 0.25\(\alpha\), 0.50\(\alpha\), 0.75\(\alpha\), \(\alpha\).
## 1 Introduction  
Federated Learning (FL) [1] has emerged as a powerful approach for training machine learning models on decentralized data without compromising data privacy. It allows multiple clients to collaboratively train a shared global model while keeping their data locally. This distributed learning paradigm has gained significant attention and has been applied to various domains, including healthcare, finance, and the Internet of Things (IoT).  
The primary objective of federated learning is to improve communication efficiency and ensure uniform training times among clients; however, the heterogeneity of data and systems in federated learning challenges client selection and training processes. Selecting appropriate clients to participate in training becomes crucial to achieving accurate and efficient model updates.  
In this context, the Oort algorithm has been proposed as a client selection method that considers the heterogeneity of data and systems. However, implementing the Oort algorithm (detailed in Section 3.3) revealed temporal discrepancies in training and communication, leading to inefficient federated learning.  
We propose integrating the Multi-Branch Network (MBN) into the existing Oort architecture to address this issue and enhance communication efficiency and training uniformity. The MBN construction is inspired by BranchyNet [2] and Triplewins [3], where additional branch classifiers are incorporated at equidistant points within a given neural network. This modification allows for model averaging and improved performance without needing multiple convolutional layers in each branch.  
Furthermore, we introduce a Model Distributor component to allocate different model branches to clients based on their computational capabilities and communication bandwidth. This clustering-based approach ensures that clients receive models tailored to their specific training requirements, optimizing the effectiveness and efficiency of the federated learning process.  
This work presents the construction of the MBN and the Model Distributor in detail. We evaluate the performance of our proposed method on benchmark datasets, including CIFAR-10 and FEMNIST, using the ResNet34 model and the MBN. We measure the training time to achieve specific accuracy levels and the number of rounds required to reach the desired performance. Additionally, we analyze the uniformity of the training process and investigate the impact of different hyperparameters.  
The rest of this paper is organized as follows: Section 2 provides background information on federated learning and MBN. Section 3 explores the related work in federated learning. Section 4 describes the construction of the MBN and the Model Distributor. Section 5 presents the experimental results and discusses the findings. Finally, Section 6 concludes the thesis and outlines potential directions for future research.
## 3 Result and Analysis
### Effect of Erosion under Different Conditions
#### 3.2.1 Effect of Particle Size on Corrosion  
The sand particle diameter is an important factor affecting the erosion wear of the spillway caused by sand-laden water flow. In this paper, the effects of sediment with different particle sizes \(d\) of 0.005 mm, 0.01 mm, 0.015 mm, 0.02 mm, 0.025 mm, 0.03 mm, 0.035 mm and 0.04 mm on the erosion amount of spillway are simulated. In consideration of the impact of sand particle size on spillway erosion, the sediment concentration in water is 3.2 kg/m\({}^{3}\), the water depth before setting the spillway is 6.5 m, the opening degree of the gate is 1 m, and the flow velocity is 5 m/s. The simulation process is that the multiphase flow model is opened after the water flow is stable, and the erosion cloud images of the bottom of the spillway and the gate leaf five seconds later are shown in Figures 6 and 7. Obviously, when only sediment diameter changes, the position of maximum erosion at the bottom of the spillway remains unchanged, which is in front of the ground sill (the position of the ground sill is indicated in Figure 6), and the erosion position is mainly concentrated around the ground sill. With the increase in sediment diameter, the erosion of gate leaf tends to be evenly distributed.  
The relationship between the maximum erosion amount and sediment diameter is shown in Figure 8. When the particle diameter varies from 0.005 mm to 0.04 mm, the relationship between sand particle diameter and erosion amount is not monotonic: The erosion amount increases with the increase in particle size when the sediment diameter is 0.005 mm-0.015 mm, and decreases with the decrease in particle size when the sediment diameter is 0.015 mm-0.04 mm.  
Figure 5: Finite element model of fluid domain in middle gate chamber and corresponding middle gate section.  
This result is inconsistent with the experimental results of [PERSON] [9], whose research results show that under the condition of consistent velocity, the particles with large particle sizes will produce greater erosion amounts on the erosion surface. However, when considering the effect of water flow, particle size will affect the carrying capacity of water flow on particles, so that sediment particles with different particle sizes can obtain different velocities under the condition of the same flow velocity. According to the velocity distribution of particle flow in Figure 9, when the particle diameter is 0.008 mm, the maximum velocity of particle flow is 15.1 m/s, while when the particle diameter is 0.04 mm, the maximum velocity of particle flow is only 10.8 m/s. When the sand particle diameter is larger, the mass of a single sand particle is also larger, and the carrying effect of water flow on sand particles is significantly reduced, leading to a lower velocity of particle flow. Therefore, when the sand particle size increases to a certain range, the erosion amount will decrease with the increase in the sand particle size.
## 2 Materials and Methods
### Meteorological Measurements and Soil Moisture  
Meteorological data were obtained from the 21-m-high microclimate observation tower installed in the study area (installed in 2011, and the distance to the study plot is about 400 m). In-situ data from the microclimate observation tower were used for analysis, including precipitation (TE525 MM, Texas Electronics Inc., Dallas, TX, USA), air temperature (HMP155A, Vaisala, Helsinki, Finland), relative humidity (HMP155A, Vaisala, Helsinki, Finland), solar radiation (CNR4, Kipp&Zonen, Delft, The Netherlands), etc. A net radiation sensor (CNR4) was installed at 1.5 m height in an open spot outside the monitored forest stand. All other sensors for meteorological data were installed within the stand. All data were collected every 5 s, and 30 min averages were recorded in a data logger (CR1000, Campbell Scientific Inc., Logan, UT, USA).  
The vapor pressure deficit (VPD, kPa) is a comprehensive factor of air temperature (T\({}_{\mathrm{a}}\), \({}^{\circ}\)C) and relative humidity (RH, %), calculated using Equation (2) [41]:  
\[\mathrm{VPD}=0.611\times\mathrm{Exp}\left(\frac{17.502\mathrm{T}_{\mathrm{a}} }{240.97+\mathrm{T}_{\mathrm{a}}}\right)\times(1-\mathrm{RH}) \tag{2}\]  
The REW was calculated as the ratio of the actual extractable water to the maximum extractable water [10] according to Formula (3). Soil moisture sensors (CS616-L50, Campbell Scientific, Utah, USA) were installed at soil depth intervals of 0-5 cm, 5-10 cm, 10-20 cm, 20-40 cm, and 40-80 cm to monitor the volumetric soil moisture (VSM) in the root zone (0-80 cm soil layer) from May to September. Data were collected every 30 min by a data logger (CR1000). The VSM of the 0-80 cm soil layer is the mean of the observed VSM in different soil layers, shown in Equation (3):  
\[\mathrm{REW}=\frac{\mathrm{VSM}-\mathrm{VSM}_{\mathrm{m}}}{\mathrm{VSM}_{ \mathrm{FC}}-\mathrm{VSM}_{\mathrm{m}}} \tag{3}\]  
where VSM (%) is the volumetric soil moisture, VSM\({}_{\mathrm{FC}}\) (%) is the soil field capacity, which had a mean value of 43.2%, obtained by the cutting ring method in this study, and VSM\({}_{\mathrm{m}}\) (%) is the wilting point with a value of 5.75% (the volumetric soil moisture when white birch wilts) [42].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Hydrological Years** & **Annual Precipitation (mm)** &
\begin{tabular}{c} **Percentage Difference from** \\ **the Multi-Year Average** \\ \end{tabular} \\ \hline
2015 & Wet year & 445 & +18.5\% \\
2017 & Normal year & 394 & +5\% \\
2018 & Dry year & 223 & \(-40.7\%\) \\
2021 & Wetter year & 561 & +49.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Division of hydrological years.