esa-sceva/satcom-chunk-collection huggingface dataset:
# Utility Analysis about Log Data Anomaly Detection Based on Federated Learning
## 3 Proposed Federated Learning Model
### Anomaly Detection Based on CNN1D  
Figure 3 displays the architecture of the CNN1D model to be applied to the proposed federated learning. In this study, we trained a single CNN1D model with the following architecture: an embedding layer with dimensions of 50 \(\times\) 16, followed by a single CNN1D layer with a filter size of 5 \(\times\) 128, and a dense output layer with two units for normal or anomaly prediction tasks. Each input-encoded log depends on the dataset. First, an encoded learning matrix is input into the embedding layer. Log sequences grouped in identifier order by applying the session window method are encoded using the TF-IDF method. For example, log sequences grouped into [09\(a\)53393, 3\(d\)91_fa_85, 09\(a\)53393,...] are encoded in a vector set of [\(-4.723555488\times 10^{-12}\), \(-1.57451829\times 10^{-12}\),...] and then entered in the embedding layer. After calculating the features embedded in a single convolutional layer of size 5 \(\times\) 128, the rectified linear unit activation function is applied to prevent gradient loss for nonlinear conversion, and the reduced matrix is input into the fully connected (FC) layer by removing the low-correlation feature in the max-pooling layer. In addition, a 0.3% dropout layer was added to prevent overfitting before inputting it into the output layer. Finally, after mapping the matrix input through the FC layer in one dimension, the softmax layer is applied to output the classification result to classify the anomaly or normal data based on the learned weight. The equation is provided in (4), and the model parameter settings for each layer are listed in Table 1.  
\[Softmax_{i}=\frac{e^{a_{i}}}{\sum\limits_{k=1}^{T}e^{a_{k}}} \tag{4}\]  
Figure 2: Framework of federated learning process.  
where \(a_{i}\) represents the log sequence input to the softmax layer, and \(T\) is the total length of the log sequence.
## 3 Results
### Sintering Characterization  
Figure 2 shows the relative densities of La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) ceramics synthesized by the two different methods, as a function of sintering temperature. It is clear from the above figure that at approximately 1400 \({}^{\circ}\)C, a higher relative density value of 93% was obtained for the combustion method samples, whereas in the case of the sample synthesized by the SSR method, the densification started at the same temperature and a relative density value of 90% was not achieved until 1500 \({}^{\circ}\)C. In contrast, the La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) powders synthesized by the combustion exhibit better sinterability, due to finer particle morphology and high sintering activity. The above-mentioned results were also supported by the microstructural variations observed on the sintered samples.  
Figure 1: (**a**) XRD patterns and SEM images of La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) powders synthesized by (**b**) the combustion method and (**c**) the conventional solid state reaction (SSR) method, respectively.  
Figure 3 shows SEM micrographs of fracture surfaces of La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) ceramics synthesized by the combustion and SSR methods as a function of the sintering temperature. It can be seen that a close-grained microstructure was obtained for the combustion method sample sintered at 1400 \({}^{\circ}\)C in Figure 3a. While the SSR sample sintered at the same temperature displayed a porous microstructure with small grain size in Figure 3b. Despite the compact microstructure, Figure 3c showed many small holes on the surface of grains of the ceramics made by the high temperature SSR method and sintered at 1500 \({}^{\circ}\)C, likely due to volatilization of chromium oxide in higher temperature in air. It is well known that under oxidizing conditions at high temperature, the gaseous chromium oxides are easily produced, which hold back sintering of La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) and lead to small holes on the microstructure, which is moreover likely to impair transport of electronic carriers of the materials.  
Compared with that synthesized by the SSR method, the combustion method exhibited good sinterability and markedly lowered the temperature to density La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\), due to finer particle morphology and higher sintering activity. Furthermore, the decrease of sintering temperature optimized the microstructure of the ceramic and would improve physical properties.
## 4 The Role of Routing Protocols to Resilience in Self-Configuring WSNs
### Fundamentals of AODV, DSR and MPH Routing Protocols
This is a very important feature that helps to deal with the dynamic conditions of the wireless environment, such as loss of links, damaged nodes, battery exhaustion, _etc._ Besides, a proper neighbor update is especially important during a jamming attack, because this action has the effect of isolating the affected area and modifying the network topology, for instance re-routing the traffic using the rest of the network; this is true even if the attacker keeps moving. Moreover, since in the MPH protocol, the neighbor discovery packets are transmitted periodically, when the jamming attack finishes, the node can be discovered again, and it starts to behave as a normal node. This feature, intrinsic to MPH, is a proactive way to update the neighbor tables and provides to the network some resilience capabilities; this is not the case for other routing protocols, such as AODV and DSR: they may need special countermeasures against jamming attacks. In MPH, in order to assign a score to the steadiness of a neighbor, a persistence variable was defined, which is related to the number of times a neighbor node responds to the discovery packets; when a node replies to the discovery packet, it receives a maximum persistence value, and for each time the neighbor does not respond, then it will be decreased by one. If the persistence variable reaches zero, then it is concluded that the node is not responding, and it is deleted from the neighbor table. The MPH protocol mainly uses three packets: the \(ND\) (network discovery), the \(NDR\) (neighbor discovery response) and \(NDRACK\) (neighbor discovery response ACK) packets. At initial state, if the node is the coordinator, then we set its hierarchy to the maximum value; otherwise, we simply set it to zero. Then, each node sends periodically \(ND\) packets to initiate the discovering and updating of its neighbors. When a node receives an \(ND\) packet, then it answers with an \(NDR\) packet, which includes its hierarchy information. The node that receives an \(NDR\) packet will reply with an \(NDRACK\) acknowledgment packet that also includes its hierarchy level. When an \(NDR\) or \(NDRACK\) packet arrives, the updating of the hierarchy for a node except the coordinator is summarized as shown below:
For example, active regeneration events, different ambient temperatures, and different fuels such as hydrotreated vegetable oil (HVO) and CNG have received high attention over the past few years [47; 48; 49; 50; 51]. We are not aware of any other study assessing PEMS for heavy-duty vehicles. A plausible explanation is that there are no \"validation\" test requirements as in the light vehicle regulation. For light-duty vehicles, the PEMS can be compared with the laboratory-grade equipment on the chassis dynamometer--the so-called \"validation\". For heavy duty, such provisions are not foreseen during the type approval of the engine. Based on this background, our aim is to assess SPN PEMS with various heavy-duty vehicles in different conditions. Although the focus is on the future 10 nm systems, resultswith the 23 nm systems are also given. Compared to previous PEMS assessment studies with light-duty [43] and heavy-duty vehicles [22], we extend with more engines and boundary conditions. The study covers conditions from \(-7\) to \(35\)\({}^{\circ}\)C for CNG and diesel vehicles with market (B7) or HVO fuel operating in real-world driving conditions. The results of this study give uncertainty estimations of the particle number measurements.
## 1 Introduction
By contrast, few studies have found a significant correlation between global asymmetry in jumping tests and COD performance. Studying female soccer players, [PERSON] et al. [21] found a significant positive correlation between inter-limb asymmetry in single-leg depth jump height and performance in 505 COD test on left (\(r\) = 0.66, \(p\) < 0.01) and right (\(r\) = 0.52, \(p\) < 0.05) side. Another study that reported a relationship between asymmetry and performance was of [PERSON] et al. [22], that explained 63% (\(p\) < 0.001) of variance of COD performance with leg stiffness and height asymmetry during vertical depth jump. The reason for inconsistent findings could lay in discrepancies among populations, methods of asymmetry calculation, and COD tests, as well as in low asymmetry values, that are not large enough to influence performance. Taking that into consideration, [PERSON] et al. [23] found that explosive strength parameters like rate of torque development (RTD) are more sensitive to detect inter-limb asymmetries compared to maximal strength outcomes like peak force or peak torque during maximal voluntary contractions (MVC), which were used in previous research. Also, local inter-limb asymmetries were assessed only for the knee joint, while proximal regions of hip and trunk were overlooked. Moreover, asymmetries in range of motion were not previously researched in relation to performance. A substantial portion of asymmetry studies was done on amateur athletes and soccer players, which does not give enough insight into the functioning of elite athletes and other team sports, such as basketball. Basketball is characterized by many high intensity changes of direction (COD) [24], indicating that COD ability plays a critical role in basketball performance. Therefore, the aim of this study is two-fold: (a) to profile elite basketball players in different local strength and range of motion asymmetries of hip and trunk region, and global power asymmetries in horizontal and vertical jumping and (b) to quantify the relationship of those asymmetries with COD performance. We hypothesized that these asymmetries could predict COD performance.
# Origin and Function of Structural Diversity in the Plant Specialized Metabolome
## 4 Decoration and Conjugation in Specialized Metabolism
### High Molecular Weight Conjugates  
Glucosylation and acylation allow for the concatenation of several specialized metabolites, sometimes of different metabolic classes. In the maize specialized metabolome, phenylpropanol glycosides are coupled to benzoic acids, flavonoids, phenylethanolaids and indolics [6]. Hexosylated flavonoids, but also free hexoses or dihexoses, often carry multiple aromatic acylations, often of different natures [3; 6; 131]. Less frequently, the hexose moiety in poly-acylated aromatic conjugates is replaced by an alternative polyol, such as glucaric acid [2]. The feruloyl moieties, both in glucaric acid and hexose conjugates, are sometimes coupled by radical-radical coupling to mono- or oligoliglons, allowing these coupling products to be stored as such in the leaf vacuole [2; 4]. In contrast to glucaric acid conjugates, where the two carboxylic acid groups of glucaric acid remain unesterified, other aromatic conjugates invoke esterification to the carboxyl groups of dicarboxylic acids such as succinic acid or 3-hydroxy-3-methylglutaric acid. For example, in blue Agapanthus flowers, a \(p\)-coumarylated deblinidin diglycoside is attached to a favoured triglycoside via a succinic acid diester link [132]. In this conjugate, the aromatic acyl group and the colourless flavonoid both stabilize the blue color of the anthocyanin delphidin diglycoside. Esterification involving the dicarboxylic acids 3-hydroxy-3-methylglutaric acid (HMGA) and succinic acid allowed the formation of a macromolecular polyester structure carrying glucosylated lignans, glucosylated flavonoids, phenylpropanoic acids and ferulic acid coupled oligolignols in the outer integument of flax seeds [127]. More common polyesters in higher plants are cutin and suberin, of which the latter contains aromatic domains derived from cinnamic acids [133]. Cutin and suberin function as protecting barriers against desiccation or biotic and abiotic stresses.
# Multivariate Statistical Process Control Using Enhanced Bottleneck Neural Network
## 2 Materials and Methods
### Process Monitoring and Diagnosis
#### 2.2.3 Upper Control Limit by KDE  
\(KDE\) is a powerful data-driven technique for the nonparametric estimation of density functions [27; 28; 29]. Given a sample matrix with \(n\) variables and \(l\) samples \(\left\{x_{1},x_{2},\ldots,x_{l}\right\}^{T}\in R^{l\times n}\), a general estimator of the density function \(f(x)\) can be defined as:  
\[\hat{f}(x,H)=\frac{1}{l}\frac{1}{|H|^{(n+1)/2}}\sum_{i=1}^{n}K[H^{-1/2}(x-x_{ i})H^{-1/2}] \tag{15}\]  
where \(H\in R^{n\times n}\) is the bandwidth matrix, \(|H|\) indicates the determinant of \(H\), and \(K(.)\) is the kernel function, where the kernel estimator is the sum of the bumps placed at the sample points. The \(K(.)\) defines the shape of the bumps, while the bandwidth defines the width. A popular choice for \(K(.)\) is the Gaussian kernel function:  
\[k(x)=(2\pi)^{-n/2}exp\left(-\frac{1}{2}x^{T}x\right) \tag{16}\]  
The critical issue for determining \(\hat{f}(x,H)\) is the selection of bandwidth matrix \(H\). In order to determine \(H\), we must first determine its matrix structure. Generally, there are three possible matrix structure cases:1. a full symmetrical positive definite matrix with \(n(n+1)/2\) parameters \(h_{rj}^{2}\)--for example, in which \(h_{rj}^{2}=h_{jr}^{2}\) ;
2. a diagonal matrix with only \(l\) parameters, \(H=diag(h_{1}^{2},h_{2}^{2},\ldots,h_{n}^{2})\);
3. a diagonal matrix with one parameter, \(H=h^{2}I\), where, I is unit matrix.  
According to [30], the first case is the most accurate, but is unrealistic in terms of computational load and time, while the third case is the simplest but can cause problems in certain situations due to the loss of accuracy by forcing the bandwidths in all dimensions to be the same. In this study, the second matrix structure bandwidth will be studied as a compromise of the first case and the third case, and an optimal selection of bandwidth matrix algorithm will be used [31].  
The above \(KDE\) approach is able to estimate the underlying probability density function (PDF) of the \(SPE_{j}^{(\ell)}\) matrix. Thus, the corresponding upper control limit \(UCL_{j}^{(KDE)}\) can be obtained from the \(PDF\) of the \(SPE_{j}^{(\ell)}\) matrix with a given confidence bound \(\alpha\) by solving the following equation:  
\[\int_{\alpha}^{UCL_{j}^{(KDE)}}P(SPE_{j}^{(\ell)})dSPE_{j}^{(\ell)}=\alpha,j= 1,2,\ldots,m \tag{17}\]  
where \(P(SPE_{j}^{(\ell)})\) is the probability density function of \(SPE_{j}^{(\ell)}\) for normal operating data, \(\alpha\) is the confidence level, and \(SPE_{j}^{(\ell)}\) are the local \(SPE_{j}\).  
Therefore, the KDE is a well-established approach to estimating the PDF, particularly for random variables like \(SPE\) which are univariate although the underlying processes are multivariate.
# Assimilation of Earth Observation Data Over Cropland and Grassland Sites into a Simple GPP Model
## 1 Introduction
In principle this means that the model benefit from EO data but does not depend on it. Assimilating EO data (i.e., NDVI) into a simple process-based present two main advantages against classical data-driven GPP algorithms: (_i_) the process of growth is modelled also in absence of EO data (thus increased robustness against missing or noisy EO data); (_ii_) weather forecasts can be used to run the model to make short-term GPP prediction (given the current model state) that are progressively adjusted when more observations become available. This latter point is of particular interest in view of operational monitoring and forecasting applications. In this study, GPP estimates by the Sim model are validated using GPP derived at eddy covariance flux sites in Europe and compared to the standard MODIS GPP product (MOD17) used as benchmark ([PERSON], 1998). The research questions addressed in this study are: (1) can a simple growth model use modelled meteorological variables and assimilate EO data to adequately simulate crop and grassland GPP? and, (2) what is the estimation accuracy and how does it compare with that of the standard MODIS GPP product?
## 1 Introduction
### Contributions and Structure of This Paper  
Recently, the use of customer-side inverter compensation methods has been shown to be more economical in comparison with the installation of additional SVC devices due to the high penetration of DGs and inverters with DGs. However, voltage control of this proposed method [30] depends on an adequate reactive power output from the customer (player in game theory). If customers do not provide the reactive power requested by the distribution company (DisCo) to the grid, the distribution system will be unstable due to large voltage fluctuations. In order to obtain proper reactive power output from the customer, the DisCo should frame the system in such a way as to encourage the customers to follow DisCo requests.  
Therefore, this paper proposes a system called the reactive power incentive system. The system is intended to encourage customers to output the appropriate reactive power as requested by the DisCo by allowing them to obtain rewards corresponding to the reactive power requirement of the DisCo. If customers output the proper reactive power, some burden of the DisCo, such as the need for reactive power compensators or LRTs and SVRs, will be mitigated. The compensation that the DisCo pays to its customers is covered by the burden that has been alleviated by coordination with the customers. By using this proposed method, the DisCo can save on costs due to mitigating the burden of some control equipment; customers also get the advantage of incentives by following suitable reactive power output regimes as requested by the DisCo. This reactive power DR program serves to improve the power quality of the power system without requiring a change in normal load consumption, as compared with a conventional DR handling only active power. The effectiveness of the proposed method is confirmed using MATLAB(r) simulations. Not only does this paper provide a detailed technical assessment, but it also shows the economic benefits of both of the DisCo and customers. In addition, optimal operation is calculated by using the particle swarm optimization (PSO) algorithm, and the solution is provided as optimal one-day scheduling. In order to avoid over-control, a modified scheduling method is proposed in this paper.  
The paper is organized as follows: Section 2 explains DR regarding active and reactive power. In Section 3, in order solve the problem of high penetration of DGs using RES causing voltage deviation and reverse power flow, formulations of the distribution model, objective function, optimization method and control device configuration are provided. The method of reactive power incentive is provided in Section 4. The optimization algorithm and modified scheduling method are explained in Section 5. In Section 6, the simulation results are presented as case studies. Section 7 concludes this paper.
## 3 Results and Discussion
### Analysis of Orthogonal Experiment Results
#### 3.4.2 Analysis of Variance  
Analysis of variance is the estimation of the magnitude of errors and the significance of factors affecting experimental results. It is necessary to take the blank column as the error column, compare the sum of squared deviations of parameters with the sum of squared errors, and perform an F-test to determine the significance level of each parameter. Specifically, the significance level of factors is divided into four levels: (1) when F \(\geq\) F\({}_{0.01}\), the factor is considered to have a highly significant impact on the experimental results, marked as \"**\"; (2) when F\({}_{0.01}\) \(>\) F \(\geq\) F\({}_{0.05}\), the factor is considered to have a significant impact on the experimental results, marked as \"**\"; (3) when F\({}_{0.05}\) \(>\) F \(\geq\) F\({}_{0.1}\), the factor is considered to have a less significant impact on the experimental results, but still has a certain impact, marked as \"**\"; (4) when F \(<\) F\({}_{0.1}\), the factor is considered to have no significant impact on the experimental results, marked as \"**\". The analysis results are shown in Table 8. It is evident that slurry thickness is a highly significant influencing factor, while the geotextile thickness is a moderately significant factor and the clay content is a non-significant factor.  
In summary, for the thermal insulation temperature of slurry-coated samples, the slurry thickness is the primary influencing factor. As the slurry thickness increases, its thermal resistance also enhances, leading to improved insulation performance. Geotextile thickness is a moderately significant factor, indicating its limited impact on insulation's effectiveness. The clay content shows no significant influence, suggesting that its effect on thermal insulation may have reached saturation within a broad range, resulting in a relatively minor impact on insulation performance.  
Figure 10: Direct view of influencing factors of test indexes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source of** & **DEVSQ** & **Freedom** & **Variance** & **F Value** & **Significance** \\
**Variance** & & & & & \\ \hline factor A & 1.6458 & 2 & 0.8229 & 6.3446 & * \\ factor B * & 0.1206 & 2 & 0.0603 & 0.4649 & \(\times\) \\ factor C & 6.1215 & 2 & 3.0608 & 23.5991 & *** \\ error e * & 0.3980 & 2 & 0.1990 & 1.5343 & \\ error e & 0.5186 & 4 & 0.1297 & & \\ \hline comprehensive & 8.2859 & 8 & & & \\ \hline \hline \end{tabular} Note: (1) *** represents that the impact of this factor on the experimental results is not very significant; (2) *\(\times\)* represents that this factor has no significant impact on the experimental results; (3) *\(\times\)* This represents a highly significant impact of this factor on the experimental results.  
\end{table}
Table 8: Analysis of variance results.