esa-sceva/satcom-chunk-collection huggingface dataset:
# Federated Learning-Based Security Attack Detection for Multi-Controller Software-Defined Networks
## 5 Experimental Results  
### Evaluation of Feature Selection  
To detect malicious traffic flow in the SDN data plane precisely, feature selection becomes a prerequisite in deep learning-based traffic classification for a huge set of features in the NSL-KDD dataset.  
Figure 6 depicts the feature importance analysis in detecting attack classes in the enriched NSL-KDD dataset before the feature selection. As shown in Figure 6, the statistical analysis reveals the feature importance in the dataset; however, to further efficiently and optimally select a set of salient features, the FedSec attempts to select the features using the enhanced MA-HS algorithm optimally. From the illustration of Figure 6, the effectiveness of the proposed feature selection algorithm with the standard assessment score is evaluated.  
Table 3 compares the performance variations of the proposed FedSec with enhanced MA-HS algorithm-based feature selection and without feature selection. Among the performance metrics of accuracy, specificity, precision, and recall, the proposed feature selection plays a significant role, depicted as the improvements in all the metrics against the proposed FedSec without feature selection. Table 4 illustrates the influence of various Reduced Features (RF) in the enriched NSL-KDD dataset for different percentages of training data samples as 40%, 60%, 70%, and 80%.  
The analysis of Table 4 shows that the optimal number of features only provides a better detection rate than a large number of entire features and a minimal set of features. This is because reducing the number of features from the NSL-KDD dataset leads to information losses, and retaining many features misleads the detection accuracy and increases the training time.
## 3 Results and Discussion
### Analysis of Volatile Compounds  
Thirty-four different volatile compounds were identified in the boiling water treatment and the thermo-ultrasonic treatment groups of fish broths. Aldehydes and alcohols are the main volatile substances in sea bass fish head broth, while others are less abundant in the broth. Aldehydes are obtained by oxidative degradation of fatty acids, and Table 1 shows that a variety of aldehydes were detected in the fish head broth prepared by both processing methods. The main aldehydes are hexanal, heptanal, octanal and nonanal, and the related studies have shown that low-carbon aldehydes contribute more to the flavor of fish soup. Hexanal has a raw, greasy and grassy aroma, heptanal has a fruity aroma, octanal has a sharp and powerful tallowy-waxy aroma, and nonanal has a strong greasy odor and a sweet orange smell ([PERSON] et al., 2017). For example, the content of hexanal in the fish broth of the thermo-ultrasonic treatment group was almost twice as much as that of the boiling water group, which suggests that ultrasound contributes to the formation of flavor in the fish broth. The increase in the concentration of hexanal with time was due to the continuous oxidation of fat. However, it decreased at 120 min of heating, probably due to the formation of micro-nanostructures in the broth emulsion system. Alcohols are produced by redox reactions between polyunsaturated fatty acids and carbonyl compounds, and 12 types of alcohols were detected in the fish head broth. It has been reported in the literature that 1-octen-3-ol contributes to the overall flavor of fish head broth, which has a mushroom and fermented smell to enhance the fat flavor of fish soup. Other substances were also detected in the fish head broth, including 2-pentylfuran, which is an important flavoring substance produced during the processing of fish. Pathways for the production of 2-pentylfuran in broth include the Maillard reaction, carbohydrate degradation and lipid oxidation. The 2-pentylfuran content was higher in the thermo-ultrasonic treatment groups due to higher fat extraction. Therefore, the cavitation effect of ultrasound caused more fat from the fish head to migrate into the broth, thus increasing the concentration of the main characteristically flavored substances of the broth. [PERSON] et al. (2017) found that the intensity of freshness, richness and saltiness of fish bone broth can be enhanced by ultrasonic-assisted cooking technology, and the intensity of sourness reduced. The content of flavor nucleotides as well as fresh amino acids in fish bone broth increased, and the flavor quality of salmon bone broth improved. Thermo-ultrasonic technology may contribute to better flavor development in fish broth. Figure 2: Heat map of the free amino acid content of fish broth (**A**) and the breakdown of the different types of free amino acids (**B**) by boiling water and thermo-ultrasonic treatment with different treatment times (0, 30, 60, 90, 120 min).
# TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
## 1 Introduction
In general, LSTM performs better than RNN and the Hidden Markov Model (HMM) [15]. [PERSON] et al. adopted LSTM in their research to predict sea-surface temperature (SST) and made a short-term prediction, including one day and three days, and long-term prediction, including weekly mean and monthly, mean [16]. [PERSON] et al. proposed a model that combines temporal and spatial information to predict future SST values. Its structure includes one fully connected LSTM layer and one convolution layer [17]. Marine data have the characteristics of wide distribution, a large amount of data, and many types. Effective use of cloud-computing resources benefit the development of large-scale high-performance-computing ocean data. [PERSON] et al. proposed a new segmentation method to isolate dark areas in SAR images. Cloud computing is used for scaling up the algorithms and providing communication between users [18]. The above research shows how their approach benefits from cloud resources. In this article, we put forward a TD-LSTM network to predict ocean-temperature changes. In TD-LSTM, we first analyze the temperature history of marine observation values, refer to their closeness, period, and trend, using the ocean-temperature fusion method based on time dependence to reconstruct the data sequence. According to the reconstructed LSTM model, we can predict the time series of ocean temperatures. This paper's main contributions include: (1) by analyzing the time dependence of ocean-temperature changes, the ocean-temperature fusion method is proposed based on time dependence; (2) putting forward an LSTM ocean-temperature prediction method based on time dependence; (3) extension based on a time-dependent LSTM ocean-temperature prediction method is applied to different depths; (4) verifying the validity of the TD-LSTM in different areas. The rest of this paper is organized as follows. In Section 2, we describe the problem formulation and long short-term memory networks in our paper. Section 3 puts forward an analysis method about temporal dependence and a method to construct TD-LSTM. Simulation results and performance evaluations are shown in Section 4. Section 5 presents our conclusions.
## 3 Mathematical Model
### Particle Movement  
Typically, the Euler-Lagrange method is used to predict gas flow in the suction port area of the pickup head. It is expedient to consider the gas phase as a continuum by solving the Reynolds-averaged Navier-Stokes equations described above, whereas the solid phase was calculated by observing the particles through a constant liquid field. In the process of absorbing dust particles, the solid phase is present in a low-volume fraction; therefore, the gas-solid flow is assumed to be a liquefied phase flow. The discrete phase model (DPM) calculates the trajectory of a particular particle by integrating the force balance in the particle. The equilibrium equation of the forces based on [PERSON]'s second law can be described as follows:  
\[m_{p}\frac{dU_{p}}{dt}=F_{D}+F_{g}+F_{s} \tag{7}\]  
The trajectory of motion of an individual particle can be realized by integrating the particle force balance, where \(m_{p}\) is the particle mass, \(U_{p}\) is the velocity of the particle, \(F_{d}\) is the drag force, \(F_{g}\) is the gravitational force, and \(F_{s}\) is the lift force.  
The drag force can be written in terms of the drag coefficient \(C_{D}\) as  
\[F_{D}=\frac{18\mu}{\rho_{p}d_{p}^{2}}\frac{C_{D}Re_{r}}{24}m_{p}\big{(}U-U_{p }\big{)} \tag{8}\]  
where the drag coefficient can be obtained from [35; 36; 37]  
\[C_{D}=(2.25 Re^{-0.31}+0.36 Re^{0.06})^{3.45} \tag{9}\]  
The deposition force \(F_{g}\) generated by gravity can be expressed as [38]  
\[F_{g}=\frac{\pi}{6}d^{3}(\rho_{d}-\rho_{\alpha})g \tag{10}\]where \(d\) is the particle diameter, \(\rho_{d}\) is the particle density (kg/m\({}^{3}\)), \(\rho_{a}\) is the gas density (kg/m\({}^{3}\)), and \(g\) is the gravitational acceleration (m/s\({}^{2}\)).  
Particle lifting force \(F_{s}\) can be written as [17; 19]  
\[F_{s}=\frac{2 Kv^{0.5}\rho d_{ij}}{\rho_{p}d_{p}(d_{lk}d_{kl})^{0.25}}\big{(}U-U _{p}\big{)} \tag{11}\]  
where \(\rho_{p}\) and \(d_{p}\) are the density and diameter of the particle, respectively, \(U\) is the air velocity, \(v\) is the kinematic velocity, \(k\) is the turbulence kinetic energy, \(d_{ij}\) is the deformation tensor, and the constant is \(K=2.594\)[39; 40].  
\(Re_{p}\) is the particle Reynolds number based on the relative velocity and is defined by the following equation:  
\[Re_{p}=\frac{\rho d_{p}\big{(}U_{p}-U\big{)}}{\mu} \tag{12}\]  
where \(U_{p}\) is the particle velocity, \(m_{p}\) is the particle mass, \(p_{p}\) is the particle density, and \(d_{p}\) is particle diameter.
## 1 Introduction
The lack of such research prompted the present authors to include climate as an element to be considered in this study, as it may affect the choice of the most suitable properties for any given radiant floor. This article enlarges the study performed in the previous research by [PERSON] et al. [29] by extending its scope from dwellings in Madrid, Spain, to include others in 28 cities in which there is a high likelihood of using the heating systems selected according to the Koppen-Geiger classification. The aim was to evaluate the possible effect of the thermal properties of wood, in combination with building characteristics and climate, on the thermal performance of radiant wooden flooring, and to compare it with a highly conductive natural stone floor. This research intends to answer the following questions:  
* Are woods with the lowest thermal conductivity efficient for use in radiant floors? The objective is to show whether the lowest conductivity woods can be used in radiant floor heating systems or, conversely, must be rejected based exclusively on that low value. Specifically, this research tried to determine whether woods with lowest thermal conductivity can meet comfort levels requirements with a low energy demand. * Should the flooring in radiant heating systems have a maximum thermal resistance value for good thermal performance? Is the standard thermal resistance limit of 0.15 m\({}^{2}\) K/W objectively justified? * Do the answers to the preceding questions depend on the climate and the construction characteristics of the buildings? To answer the above questions, 216 different dwellings in 28 cities were simulated with 61 radiant heat floorings (60 wood and one of low thermal resistance as a reference), resulting in a total combination of 368,928 cases. The thermal performance of the radiant floor heating systems was evaluated in terms of three parameters: (1) energy demand, (2) indoor thermal comfort, and (3) start-up lag time period.
## 1 Introduction
T-Scan 10 introduces new software features accessible through the expanded desktop Toolbar. These features allow doctors to import and export patient data, generate reports, attach notes and photos, and create MP4 files of the scan. Furthermore, clinicians can import intraoral digitally-impressed.stl files of a patient's arch and overlay them with T-Scan force data. These robust capabilities empower clinicians to efficiently diagnose and treat patients using objective and comprehensive force and timing data [12]. In terms of occlusal thickness, patients typically perceive occlusal registration strip thickness ranging from 12.5 to 100. For occlusal analysis, the foils used should be thinner than the patient's perception, with a thickness below 21. T-Scan sensors, on the other hand, have a thickness of 60. They consist of a coordinate system with 1500 sensitive receptor points made of conductive ink, which exhibit elastic deformation [13; 14]. It's important to note that the T-Scan system may have certain non-sensible areas due to its constructional features. The system's most sensitive area can measure forces from 0.1 kg to 2.1 kg, making it more suitable for recording within lower load ranges. While the system has some reproducibility disadvantages, it contributes to the quantitative evaluation of occlusal contacts for diagnosis and treatment purposes [15]. However, it is worth mentioning that the T-Scan system may record fewer occlusal contacts than are actually present [16]. The maximum clenching forces of the masticatory muscles recorded by the T-Scan system tend to be located in centric occlusion at the third molars in a complete dentition of 32 teeth and at the second molars in a dentition of 28 teeth [17]. Regarding retainers, thermoformed retainers have the same thickness in all directions, and preliminary contacts often occur, mainly in the distal area. In this study, we specifically investigated printed retainers with digitally distributed balanced contacts. The digital design allows for an even distribution of contacts during placement. The purpose of this study is to determine whether the force of the contacts is evenly distributed. The study examines the distribution of occlusal forces by quadrants during the placement of orthodontic retainers printed from a biocompatible material.
## 2 Materials and Methods
### Modeling Methods
#### 2.3.3 Linear Regression  
Linear regression is a statistical analysis method that uses regression analysis in mathematical statistics to determine the interdependent quantitative relationship between two or more variables. It has a very wide range of applications. Since there is a linear relationship between the selected variables, multiple linear regression models can be used. The expression for this is \(y=\omega^{T}X+e\). In the formula, \(\omega^{T}\) is the regression coefficient matrix, and the number of regression coefficients in the matrix is equal to the number of independent variable X. Where e is the error, e is normally distributed and the mean is 0, which is \(e\sim N\big{(}0,\sigma^{2}\big{)}\). In regression analysis, if there is only one independent variable and one dependent variable, and the relationship between the independent variable and the dependent variable can be approximated by a straight line, then this regression analysis is called unary linear regression. If there are two or more independent variables in the regression analysis, and there is a linear relationship between the dependent variable and the independent variable, then this regression analysis is called multiple linear regression. The effect of the linear regression model mainly depends on the data themselves, rather than the ability to improve the model by adjusting parameters. As long as the linear connection of the data is strong, the least square method can be used to establish a linear regression model with fine effect.
## 2 Materials and Methods
### Model Development and Software Availability  
Four different machine learning models (ANN, ELM, ANFIS, and SVM) were developed and compared for the purpose of simulating the daily carbon fluxes in five different ecosystems. In order to achieve a reasonable comparison, not only among different carbon fluxes but also among different ecosystems, the same environmental variables (\(T_{a}\), \(T_{s}\), \(R_{n}\), and \(R_{h}\)), which have relatively strong correlation with each carbon flux, were used as inputs to develop the proposed models. At the five sites, daily available data during the period of six years are divided into three parts. The first four years dataset was utilized for training, the fifth-year dataset was used for validation with the intention of preventing over-training or over-fitting of the training dataset, and the final year dataset was employed for testing. Before the training of each applied model, the inputs and outputs were normalized to a range between 0 and 1.  
In developing aforementioned ANN and ANFIS models, MATLAB software (version 8.2, The Mathworks, Inc., Natick, MA, USA) and its toolboxes, including Neural Network Toolbox 8.1 and Fuzzy Logic Toolbox 2.2.18, were utilized. In addition, the LIBSVM package written by [PERSON] and [PERSON] [70] was used to develop the SVM model, which is one of the most popular packages. It can be downloaded from [[https://www.csie.ntu.edu.tw/~cjlin/libsvm/](https://www.csie.ntu.edu.tw/~cjlin/libsvm/)]([https://www.csie.ntu.edu.tw/~cjlin/libsvm/](https://www.csie.ntu.edu.tw/~cjlin/libsvm/)). The ELM model was developed based on the software package written by [PERSON] et al. [54], which can be downloaded from [[http://www.ntu.edu.sg/home/egbhuang/](http://www.ntu.edu.sg/home/egbhuang/)]([http://www.ntu.edu.sg/home/egbhuang/](http://www.ntu.edu.sg/home/egbhuang/)).
## 3 Results
### Characteristics of the Spectral Features of Maize Seeds
Figure 4: **(a)** The training accuracies of model developed only using the spectral feature and model developed using integration of the spectral and image features; **(b)** The test accuracies of model developed only using the spectral feature and model developed using the integration of the spectral and image features. Classification Results Using the Integration of Spectral and Image Features Combined with the Feature Transformation/Reduction Method  
After the 11 optimal wavelengths were selected by SPA, a total of 159 features, including 11 spectral, 55 first-order statistics (5 \(\times\) 11), 88 second-order statistics (8 \(\times\) 11), and 5 morphologic features, were finally obtained. When these 159 features were used as the input of the LS-SVM, the classifier had the risk of classification accuracy reduction resulting from the dimension disaster caused by feature redundancy and noise contained in the features. Therefore, PCA and MDS were used to achieve feature transformation and eliminate feature redundancy. The 60 principal components obtained by PCA or MDS were employed for developing the LS-SVM classification model. The classification results for the two feature transformation/reduction methods are presented in Table 1. The LS-SVM models using PCA achieved 99.87% and 92.65% average classification accuracies for the training set and test set, respectively, while the LS-SVM model using MDS obtained 99.85% average training accuracy and 92.19% average test accuracy. The test accuracies for both PCA and MDS were significantly better than that of the non-transformed/reduced model with 83.68% although their average training accuracies were almost the same. The non-transformed/reduced model yielded above 90% test accuracy for 4 varieties (Gumang178, Jizaojnixiangnuo, Yudan998, and Zhendan958), between 80% and 90% for 7 varieties (BNA07, Jinnuowang, Jinsaitian, Jingketian195, Liangu16, Mongda108, and Sida205), and between 70% and 80% for 6 varieties (SNN12, Bositian8, Huayu11, Wannuo11, Xiangtiannianyumi, and Zhongnongdatian413). The model using PCA achieved between 80% and 90% test accuracy for 3 varieties (Bositian8, Jingketian195, and Sida205) and above 90% for the remaining 14 varieties. The model using MDS obtained between 80% and 90% test accuracy for 4 varieties (Bositian8, Jingketian195, Sida205, and Zhongnongdatian413) and above 90% for the other varieties. The model using PCA and MDS significantly improved the classification accuracies for 12 varieties, with improvements of 7.43%-26.09% and 6.09%-27.38% over those of the non-transformed/reduced model. Figure 5: The 11 optimal wavebands selected by the successive projections algorithm (SPA) for one random run.
## 3 Methodology
### Reliability Updating Procedure for Series Systems Using System-Level Data  
For products amenable to whole-machine life testing, system-level test data directly provide crucial life information. During actual product operation, substantial service life data accumulate, reflecting the product's actual reliability within its operational environment. Incorporating these system-level life data to update the reliability assessment derived from component-level data can significantly improve the accuracy of the system reliability assessment.  
Suppose that \(n\) zero-failure data \(t_{1},t_{2},\cdots,t_{n}\) are obtained from a system field test or actual operation. Since all components constituting the series system have not failed at these censored moments, zero-failure data are available for all components. This dataset can be combined with the original component life data \((\alpha_{i},T_{i})\) to form a new set of component zero-failure data \(\big{(}\alpha_{i},T_{i}+\sum_{i=1}^{n}t_{i}^{\alpha_{i}}\big{)}\), thereby enriching the component-level life information. Subsequently, the series system reliability can be reassessed using the method in Section 3.1 to obtain more accurate LCLs of the series system reliability and reliable life. For specific calculations, it is sufficient to directly replace the GTT \(T_{i}\) with \(T^{\prime}{}_{i}=T_{i}+\sum_{i=1}^{n}t_{i}^{\alpha_{i}}\) for each component in Section 3.1, \(i=1,2,\cdots,m\).  
By combining Equation (8) with the new component zero-failure data \((\alpha_{i},T^{\prime}{}_{i})\), the LCL of system reliability \(R(t)\) can be updated from \(R_{\text{L},\gamma}(t)\) to \(R^{\prime}_{\text{L},\gamma}(t)\), that is  
\[R^{\prime}_{\text{L},\gamma}(t)=\exp\Bigl{\{}-\chi_{\gamma}^{2}(2)/2\text{min }\biggl{(}\frac{T_{1}^{\prime}}{t^{\alpha_{1}}},\cdots,\frac{T_{m}^{\prime}}{ t^{\alpha_{m}}}\biggr{)}\,\Bigr{\}}, \tag{25}\]  
Figure 2: Simplification of system reliability model when identical components are present.  
According to Equation (25) and the equivalence principle of the confidence limit curves, the LCL of the system's reliable life \(t_{R}\) can be updated from \(t_{\text{RL},\gamma}\) to \(t^{\prime}_{\text{RL},\gamma}\), that is  
\[R=\exp\left\{-\chi_{\gamma}^{2}(2)/2\text{min}\left[\frac{T_{1}^{\prime}}{\left( t^{\prime}_{\text{RL},\gamma}\right)^{\kappa_{1}}},\cdots,\frac{T_{m}^{\prime}}{ \left(t^{\prime}_{\text{RL},\gamma}\right)^{\kappa_{m}}}\right]\right\} \tag{26}\]  
Figure 3 illustrates the framework of the system reliability assessment and update algorithm. This algorithm offers several advantages. First, it efficiently integrates multi-source lifetime information, such as test data and operation data of components and the system, providing sufficient data support for accurate system reliability assessment. Second, by simplifying the system reliability model, the framework can handle cases where the system contains identical components. Finally, the algorithm is based on the GTTs of all components and directly derives closed expressions of the LCLs for system reliability and reliable life, which is convenient for engineering applications.