esa-sceva/satcom-chunk-collection huggingface dataset:
# Biological Methods in Biodiesel Production and Their Environmental Impact
## 3 Production of Biodiesel by Biological Transesterification Process
The activity of enzymes gradually decreases, and the reaction time should be increased to maintain a proper degree of conversion. When the catalyst loses its activity, it must be replaced. Additionally, in batch reactors, a high shear stress caused by the stirrer leads to a disturbance in the work of the catalyst [63]. These disadvantages can be overcome with the use of a reactor with a porous basket. This solution also enables an easy way to separate the catalyst after the reaction. Research has been carried out on the selection of mixing methods, the type of agitators, and their rotational speed in order to optimize the reaction conditions, including mass transfer. The continuous bioreactor has some advantages in comparison with batch reactors, such as better control of process parameters, mass transfer, mixing of reagents, energy efficiency, and product separation (fewer unit operations), and better efficiency of the enzyme used (lower amounts of enzyme per volume in the reactor) [62]. Moreover, using the solutions in the bioreactor which enable stepwise addition of alcohols and continuous removal of glycerol, the conversion yield of transesterification can be improved [64; 65]. In the reactors, different solutions are tested which ensure intensification of the transesterification process, e.g., use of supercritical fluids, microwave irradiation, and ultrasound methods. The transesterification under supercritical conditions allows to conduct a reaction without a catalyst (alcohol is both a reactant and an acid catalyst) and affects the improvement of the phase solubility and mass transfer. The reaction needs high alcohol to oil molar ratios and high temperature and pressure conditions, which is the main barrier to overcome [66; 67]. For transesterification with microwave irradiation, the heat is provided by microwaves instead of the conventional method. The energy is delivered directly to the reactant, which results in more effective heat transfer, reduced energy consumption (compared with conventional heating methods), and an increase in catalyst activity. This method can reduce the time of the reaction, even to approximately 3-15 minutes [68; 69], which saves up to more than 44% of energy [70]. A similar effect as with the use of microwaves is achieved by supporting the transesterification process with ultrasound. Using this method can increase the yield of biodiesel by up to about 30% compared to conventional stirring, which is caused by the cavitation effect [71]. During cavitation, the asymmetric collapse of the bubbles occurs, which generates micro-turbulence and thus results in mass transfer increases. This eliminates the need of using mechanical stirring. Commercial use of ultrasound techniques has not yet found application, among others due to the lower biodiesel yield in bioreactors with a volume higher than 6 L [72].
# Quality Improvement of Green Saba Banana Flour Steamed Cake
## 3 Results and Discussion
### Cake Analysis  
#### 3.3.1 Specific Volume and Weight Loss  
Specific volume and weight loss were determined for the cakes (Figure 2). Specific volume is the indicator of the strength and extensibility of the food matrix, it is also a critical visual quality for the cake. Weight loss during the cooking of the batter is related to the gas escaping during steaming and it is a crucial parameter for the structural transformation of the cake [39]. Formation of cake structure primarily relies on the aeration and gas bubble stability during cooking [46]. The control sample presented a very compact structure with the lowest degree of expansion (Figure S1). Because of its extremely low batter viscosity (Figure 1), this sample failed to hold the gas bubbles within the food matrix while steaming. The results obtained suggest that the presence of both additives are important for the improvement of the cake volume; though the specific volume of the sample added with SPI alone (O0P10) achieved a similar specific volume to the standard (\(p<0.05\)), but the weight loss was still far higher than the desirable value. When SPI and Ovalette were incorporated in the formulation at an appropriate ratio, O35P15, the specific volume and weight loss were insignificantly different from the standard (\(p>0.05\)). The batter viscosity increments by addition of SPI and Ovalette (Figure 1) created a sufficient batter consistency that is crucial for retaining the air bubble formed during mixing and the CO\({}_{2}\) produced during steaming. However, the progressive addition of SPI was unfavorable because the batter density turned out to be too high to effectively incorporate the desirable amount of gas bubbles. As shown in Figure 2, supplementing 10% of SPI showed enhancement in the cake volume, but when 15% of SPI was used, the volume of the cakes was impaired instead. Apparently, the correct batter viscosity is crucial to ensure the successful aeration of the cake. [PERSON] et al. [29] found that an acceptable sponge cake can be obtained by using 20% SPI, but any further increase in SPI resulted in inferior cake quality. When used in bread batter, emulsifiers promoted the viscosity, which in turn improved the buoyancy of bubbles, preventing their coalescence and producing a uniform distribution [46]. The \(\alpha\)-gel phase of the emulsifier would form solid and elastic films at the oil-water interface, which encapsulated oil during air incorporation into cake batter, thus preventing foam destabilization [18; 47].  
Figure 2: Specific volume (mL/g) and weight loss (%) of different formulations of Saba banana flour cake. Histogram with different alphabet (a–f) indicates significant difference (\(p<0.05\)).
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**ProRoot MTA**} & \multicolumn{4}{c}{**RetroMTA**} & \multicolumn{2}{c}{**X\({}^{2}\)**} & \multicolumn{1}{c}{\(p\)**} \\ \cline{2-11}
**60 Days** & **1** & **2** & **3** & **4** & **1** & **2** & **3** & **4** & & \\ \hline Continuity & 10 & - & 60 & 30 & 20 & - & 20 & 60 & 11.33 & 0.01 \\ Morphology & 20 & - & 60 & 20 & 60 & 10 & - & 30 & 2.077 & 0.557 \\ Thickness & - & - & 90 & 10 & - & - & 10 & 90 & 1.25 & 0.264 \\ Localisation & 50 & - & - & 50 & 50 & 40 & - & 10 & 2.019 & 0.364 \\ Intensity & 90 & 10 & - & - & 50 & 20 & 30 & - & 3.533 & 0.171 \\ Extension & 20 & 80 & - & - & 60 & 30 & 10 & - & 1.143 & 0.565 \\ General state & 50 & 50 & - & - & 70 & 30 & - & - & 0.267 & 0.606 \\ Giant cells & 100 & - & - & - & 100 & - & - & - & - & 1 \\ Particles & 10 & - & 70 & 20 & - & - & 30 & 70 & 2.277 & 0.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Percentage of scores (%) attributed to each group in each criterion of hard tissue bridge after 60 days for both cement types: ProRoot MTA and RetroMTA. Figure 1: Hard tissue bridge (arrow) and inflammatory cells (star) demonstrated in relation to ProRoot MTA and RetroMTA at 30 days and 60 days (haematoxylin and eosin, original magnification \(\times\)10). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**ProRoot MTA**} & \multicolumn{4}{c}{**RetroMTA**} & \multicolumn{2}{c}{**X\({}^{2}\)**} & \multicolumn{1}{c}{\(p\)**} \\ \cline{2-11}
**30 Days** & **1** & **2** & **3** & **4** & **1** & **2** & **3** & **4** & & \\ \hline Continuity & 10 & - & 60 & 30 & 10 & 60 & 20 & 10 & 9.0 & 0.029 \\ Morphology & 20 & - & 60 & 20 & 80 & - & 10 & 10 & 7.503 & 0.023 \\ Thickness & - & - & 90 & 10 & - & - & 10 & 90 & & <0.001 \\ Localisation & 50 & - & - & 50 & 30 & 50 & 10 & 10 & 9.167 & 0.027 \\ Intensity & 90 & 10 & - & - & 100 & - & - & - & - & 1.0 \\ Extension & 20 & 80 & - & - & 80 & 20 & - & - & 7.2 & 0.007 \\ General state & 50 & 50 & - & - & 100 & - & - & - & - & 0.033 \\ Giant cells & 100 & - & - & - & 100 & - & - & - & - & 1 \\ Particles & 10 & - & 70 & 20 & 40 & 30 & 20 & 10 & 7.911 & 0.048 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Percentage of scores (%) attributed to each group in each criterion of hard tissue bridge after 30 days for both cement types: ProRoot MTA and RetroMTA. The pulpal inflammatory response with the criteria (intensity, extension, and general state) no significance could be observed 30 days after the treatment for the parameter intensity (\(p\) = 1.00), while RetroMTA performed better in terms of extension (\(p\) = 0.007) and general state (\(p\) = 0.033). Pulpal findings including the parameters (giant cells and particles) performed only significantly different for the parameter particles when both direct capping materials were compared after 30 days, in favour of RetroMTA (\(p\) = 0.048). After 60 days, both direct capping materials Pro Root and RetroMTA performed similar regarding inflammatory response of the pulp and pulpal findings.
###### Abstract  
The Helium Cooled Pebble Bed (HCPB) breeding blanket, being developed by the Karlsruhe Institute of Technology (KIT) and its partners is one of the two driver blanket candidates to be selected for the European demonstration fusion power plant (EU DEMO). The in-box Loss of Coolant Accident (LOCA) is a postulated initiating event of the breeding blanket (BB) that must be accounted within the design basis. In this paper, the BB cap region is analyzed for its ability to withstand an in-box LOCA event. Initially, an assessment is performed using conventional elastic design codes for nuclear pressure vessels. However, it is thought that the elastic rules are not 'equipped' to assess the material damage modes which are essentially inelastic. Therefore, a non-linear inelastic analysis is further performed to better understand the damage in the material. Two predominant inelastic failure modes are thought to be relevant and addressed: _exhaustion of ductility_ and _plastic flow localization_. While the design of HCPB BB has been predominantly based on the elastic design-by-analysis studies, results from the present study show that the elastic rules may be overly conservative for the given material and loading and could lead to inefficient designs. To our knowledge, this study is the first attempt to investigate the structural integrity of the European DEMO blankets under in-box LOCA conditions using the inelastic methods.  
EU DEMO; breeding blanket; LOCA; thermomechanical assessment; design-by-analysis; exhaustion of ductility; plastic flow localization; Eurofer97
# CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
## 1 Introduction
CGFL utilizes data generation techniques to tackle the challenges posed by class imbalance and distribution disparities in client data. First, CGFL conducts high probability sampling for categories that have limited client data. Then, a data generator is used to produce virtual data based on the global data distribution information learned by the global model. By combining virtual data with local data, we can create class-balanced datasets for local model training. This approach improves the performance of each local model and reduces the disparity between them, ultimately leading to the development of a high-performance global model. CGFL has implemented a robust federated aggregation method to address the issue of privacy leakage caused by clients generating data from other clients using global model parameters. This paper makes the following contributions:  
* We propose a novel form of data generation called CVAE-GAN, combining CVAE and Generative Adversarial Network(GAN) to construct class-balanced datasets.This method addresses the challenge of imbalanced data distribution in Federated Learning by generating synthetic data, significantly improving the generalization ability of the model, particularly in small sample detection scenarios. This data generation method enhances classification accuracy under class imbalance and small sample conditions, providing an innovative solution to improve the performance of intrusion detection systems in industrial control networks. * We address the privacy limitations of traditional Federated Learning aggregation methods by adopting a geometric median aggregation approach instead of the commonly used weighted averaging. This method prevents clients from inferring other clients' data via the global model during data generation, significantly enhancing privacy protection in Federated Learning, particularly in multi-client scenarios. * Experimental results on datasets such as NSL-KDD, CIC-IDS2017, and CSE-CIC-IDS2018 demonstrate the effectiveness of CGFL in improving model accuracy and stability, particularly in scenarios with highly imbalanced data. These results highlight CGFL's superiority over existing Federated Learning methods, particularly in environments where client class distributions are severely skewed. The rest of this paper is organized as follows: In Section 2 we explore related work and provide an overview of the existing literature that is relevant to our study. Section 3 details our recommended methodology. We describe our model and loss function in detail. We will present our performance experiments and evaluations in Section 4. Finally, Section 5 offers conclusions.
###### Abstract
ESDD represents the amount of sodium chloride (NaCl) that, when dissolved in demineralized water, gives the same volume conductivity as that of the natural deposit removed from a given surface of the insulator divided by the area of this surface, generally expressed in mg/cm\({}^{2}\). Hence, according to the definition of ESDD given by IEC/TS 60815-1 [4], this parameter indicates a fictitious equivalent mass corresponding to the amount of equivalent NaCl that gives the same conductivity as the sample. Therefore, in this paper, the term SDD (salt deposit density) is also used to indicate the real mass of the conductive inorganic ionic fraction (characterized by means of ion chromatography) present in the deposit. The real chemical composition of ESDD will be shown in Section 3.2. The IEC 60815 also calls for an estimate of non-soluble deposit density (NSDD) in the accumulated pollution. If the ESDD contribution to the flashover mechanism is known, the NSDD role is still quite unclear. NSDD represents the amount of non-soluble residue removed from a given surface of the insulator divided by the area of this surface, generally expressed in mg/cm\({}^{2}\)[4]. According to this, site pollution severity (SPS) is defined as the maximum value of ESDD/NSDD recorded over an appropriate period of time. Consequently, the SPS class classifies the pollution severity at a site, from very light to very heavy, as a function of the SPS. However, despite the aforementioned guidelines and the scientific literature on aerosol pollution-insulator interaction, up to now, to the authors' knowledge, a full investigation of the aerosol chemical properties, phase transitions, and electrical effects on insulators is lacking. For this purpose, the present paper aims at experimentally analyzing the chemical composition of both ESDD and NSDD in Italy together with the consequent DRH and CRH values and relationship with the conductance of the aerosol deposit on insulators to identify the possible criticalities relating to flashover events in Italy.
## 4 Experiment and Result Analysis
### Discussion  
#### 4.7.1 Computational Complexity Analysis  
In the proposed method, the optimization of scout-task assignment needs to optimize task allocation and time coordination between UAVs, which is the part with high computational complexity of our proposed method. Therefore, analyzing the computational complexity of this part will aid further improvement.  
In Algorithm 2, the most time-consuming process is to use line 24 to optimize the plan using GA. The computational complexity of GA can be expressed as \(O(\pi_{p}\times\pi_{i})\), where \(\pi_{p}\) is the population size and \(\pi_{i}\) is the number of iterations. For each plan generated by the GA, its fitness will be calculated through line 17-21 of Algorithm 2. Among them, line 20 uses Algorithm 1 for time collaborative optimization. Let the number of UAVs in the plan be \(N\). The worst case of the while loop of Algorithm 1 will iterate \(N-1\) times, and each loop needs to calculate \(N\times(N-1)\) time alignment reward gains according to Equation (12), so the complexity of Algorithm 1 is about \(O(N^{3})\). Then line 21 of Algorithm 2 uses Equation (9) to calculate the fitness of the plan, in which the start time between any two UAVs needs to be compared to evaluate the task threat, and thus the complexity is about \(O(N^{2})\).  
Therefore, the overall computational complexity of Algorithm 2 is about \(O(\pi_{p}\times\pi_{i}\times(N^{3}+N^{2}))\approx O\big{(}\pi_{p}\times\pi_{i} \times N^{3}\big{)}\). Among them, the settings of \(\pi_{p}\) and \(\pi_{i}\) not only affect the calculation cost, but also affect the quality of the optimization results. In this paper, these two parameters are simply linearly mapped from the number of allocation combinations, and before deployment, it is necessary to further study the setting strategy of these parameters to compromise between the calculation cost and the optimization quality.  
\begin{table}
\begin{tabular}{l c c c c} \hline \multirow{2}{*}{**Deterrence Maneuver Type**} & \multicolumn{2}{c}{**Enable Time Collaboration**} & \multicolumn{2}{c}{**Disable Time Collaboration**} \\ \cline{2-5}  & **Scout Risk** & **Cap. Coverage** & **Scout Risk** & **Cap. Coverage** \\ \hline Enable deterrence maneuver & 0.238 \(\pm\) 0.065 & 153.1 \(\pm\) 61.3 & 0.311 \(\pm\) 0.090 & 166.6 \(\pm\) 63.9 \\ Disable deterrence maneuver & 0.234 \(\pm\) 0.048 & 131.5 \(\pm\) 33.7 & 0.311 \(\pm\) 0.091 & 116.7 \(\pm\) 31.5 \\ \hline \end{tabular}
\end{table}
Table 7: The scout risk and strike capability coverage compared with no time coordination or deterrence maneuver. Each result is the mean and standard deviation of 10 simulation scenarios.
## 3 Methodology
### Particle Filter  
The particle filter is a sequential Monte Carlo technique that is used to solve state estimation problems ([PERSON], 1966). The basic idea is to represent the required posterior density function by a set of random samples with associated weights and then compute a new generation of samples based on these samples and weights. The construction of a discrete system dynamic model is mainly expressed by two equations and can be written as:  
\[x_{k}=F(x_{k-1})+w_{k}, \tag{12}\] \[z_{k}=H(x_{k-1})+v_{k}, \tag{13}\]  
where \(x_{k}\) is the system's state at time \(k\), \(F(\cdot)\) is the system's model, \(x_{k-1}\) is the system's state at the previous time, \(z_{k}\) is the measurement state, \(H(\cdot)\) is the measurement or observation model, and both \(w_{k}\) and \(v_{k}\) are noise. This paper used the autoregressive model (AR) to estimate the wind speed. The AR parameters were determined by using the previous iteration estimate information of the wind speed in an iteration particle filter system. The state-space representation of the AR model can be expressed by:  
\[\begin{bmatrix}x_{k}^{i}\\ \varphi_{1}\\ \varphi_{2}\\ \varphi_{3}\\ x_{k-1}^{i}\\ x_{k-2}^{i}\\ x_{k-3}^{i}\end{bmatrix}=\begin{bmatrix}\varphi_{1}&0&0&0&\varphi_{2}& \varphi_{3}&0\\ 0&1&0&0&0&0&0\\ 0&0&1&0&0&0&0\\ 0&0&0&1&0&0&0\\ 1&0&0&0&0&0&0\\ 0&0&0&0&1&0&0\\ 0&0&0&0&0&1&0\\ 0&0&0&0&0&1&0\end{bmatrix}\begin{bmatrix}x_{k-1}^{i}\\ \varphi_{1}\\ \varphi_{2}\\ \varphi_{3}\\ x_{k-2}^{i}\\ x_{k-3}^{i}\end{bmatrix}+w_{k}, \tag{14}\]  
where \(i\) is the particle number and \(k\) is the sample time. \(\varphi_{1}\), \(\varphi_{2}\), and \(\varphi_{3}\) are the autoregressive parameters. The measurement model was described in the previous section. The predicted distribution of \(x_{k}\) is given by \(p(x_{k}\mid z_{1:k-1})\), which can be expressed by:  
\[p(x_{k}\mid z_{1:k-1})=\int p(x_{k}\mid x_{1:k-1})p(x_{k}\mid z_{1:k-1})dx_{k- 1}. \tag{15}\]For each time step, the normalization weight (\(\sum w_{k}^{i}=1\)) and the posterior probability density distribution function can be approximated as follows:  
\[p(x_{1:k}\mid z_{1:k})\approx\sum_{i=1}^{N}w_{k}^{i}\delta(x_{1:k}-x_{1:k}^{i}), \tag{16}\]  
where:  
\[w_{k}^{i}\propto\frac{p(x_{k}^{i}\mid z_{1:k})}{q(x_{k}^{i}\mid z_{1:k})}, \tag{17}\]  
\[p(x_{k}\mid z_{1:k})=\frac{p(z_{k}\mid x_{k},z_{1:k-1})p(x_{k}\mid z_{1:k-1})}{ p(z_{k}\mid z_{1:k-1})}, \tag{18}\]  
\[q(x_{k}\mid z_{1:k})=q(x_{k}\mid x_{k},z_{1:k-1})q(x_{k-1}\mid z_{1:k-1}), \tag{19}\]  
and \(q(\cdot)\) is the proposal density. Afterwards, the weights are normalized in order to determine the importance of the sample based on the resampling technique. The final step is to generate a new generation of particles that are close to the measurement values. The particle filter process is illustrated in Figure 4.
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
### Link  
The hybrid beamforming techniques are attractive in downlink links as well as in uplink.
## 2 Methods and Materials
### Fuzzy C-Means (FCM)  
FCM is a data clustering method. This technique groups a data set into N clusters with every data point. The fuzzy methods determine which data belong to which clusters in a certain degree; they often provide better results than definite methods [30]. FCM clustering is an unsupervised technique successfully applied to feature analysis, clustering, and image segmentation, and it is the most popular fuzzy method [31]. The FCM algorithm classifies the image by grouping similar data points in the feature space into clusters if the image is represented in various feature spaces. The FCM algorithm assigns pixels to each category using fuzzy memberships. Every pixel is assigned to each category partially using fuzzy memberships. Let \(\textbf{X}=(x_{1},x_{2},\ldots,x_{N})\) denote a feature space with \(N\) points to be partitioned into \(M\) clusters, where \(x_{j}\) represents a multispectral (feature) data point. The algorithm is an iterative optimization that minimizes the cost function defined as follows:  
\[J=\sum_{j=1}^{N}\sum_{i=1}^{M}u_{ij}^{m}\parallel x_{j}-c_{i}\parallel^{2} \tag{5}\]  
where \(\textbf{u}_{ij}(\in[0,\ 1])\) indicates the degree that the sample \(x_{j}\) belongs to the cluster center \(\textbf{c}_{i}\) and \(\sum_{i=1}^{M}u_{ij}=1\); the \(\parallel\cdot\parallel\) is a norm metric. The fuzziness factor m is used to adjust the weighting effect of membership values.  
The fuzziness of the resulting partition increases with \(m\). In this study, \(m=2\) was used. \(\textbf{c}_{i}\) is the ith cluster center and it can be calculated as  
\[c_{i}=\frac{\sum_{j=1}^{N}u_{ij}^{m}x_{j}}{\sum_{j=1}^{N}u_{ij}^{m}} \tag{6}\]  
\[u_{ij}=\frac{1}{\sum_{k=1}^{M}\left(\frac{\parallel x_{j}-c_{i}\parallel}{ \parallel x_{j}-c_{k}\parallel}\right)^{\frac{2}{m-1}}}\ \ \ m\
eq 1 \tag{7}\]  
Starting with an initial guess for each cluster center, the FCM will converge to a solution for \(\textbf{c}_{i}\) representing the local cost function minimum. The iteration is stopped when the maximum difference between two cluster centers at two successive iterations is less than a threshold (=0.00001). FCM was implemented using Matlab, version 2017b (9.3.0.713579).