esa-sceva/satcom-chunk-collection huggingface dataset:
# Automotive Cybersecurity Application Based on CARDIAN
## 3 Intrusion Detection Systems in Automotives  
The design and implementation of an intrusion detection system must take into account the following critical issues [10]:  
* Limited resources: the ECUs inside a vehicle are typically equipped with small memories, little computational power and limited bandwidth.
* Real-time operation: CAN messages are generated and transmitted in real time; delaying a message and generating queues can therefore become critical during communication. The messages must be processed by the other ECUs as soon as they are received, in order to guarantee the correct functioning of every part of the vehicle.
* CAN traffic management: the CAN traffic management protocol is different from typical internet communication protocols; for example, CAN messages always have a broadcast type operation.
* Unstable connections: since these are moving systems, they could move in areas with limited or even no connection to the network. The use of IDS systems connected to the network must take into account the fact that such areas may exist, guaranteeing smooth operation offline, even for long periods of time.
* Weight, dimensions and cost: the installation of an IDS can affect the topology of the network to which it is connected; it must therefore be chosen appropriately so as not to require an excessively expensive modification of the network.
# An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms
## 4 Revision of the State-of-the-Art
### [PERSON] et al.'s HybGA-Based Proposal  
In [PERSON] et al. [56], the authors faced a hard optimization problem based on a multi-objective approach for tackling deformable image registration (DIR) problems. In this multi-objective DIR algorithm, which is a version of gradient descent method, the set of all pareto-optimal improving directions for a vector function \(f\) is initially calculated. The directions that are of interest, i.e., that lead to maximum improvement of all objectives, correspond to the negative non-dominated directional derivatives. One of these promising directions is randomly chosen and a multi-objective line search is performed along that direction. This scheme is named Combined Objectives Repeated Line-search (CORL). The authors provided several hybridizations based on the CORL and the Genetic Local Search (GLS) algorithms. Specifically, the Adaptive GLS variant integrates the CORL method in an adaptive hybridization scheme taken from the literature, which exploits gradient information in three different ways. For achieving maximum efficiency, AGLS decides during the run which exploitation method to use at the end of each evolutionary cycle based on the number of improved solutions found. Moreover, instead of applying conjugate gradients to a randomly chosen objective for a solution, AGLS applies the procedure to only members of the population following again a rank-based scheme, i.e., RL-CORL. Therefore, conjugate gradient is accordingly applied to the solution that ranks best in one objective to further improve it. The proposed multi-objective DIR approach was tested in medical IR scenarios considering 2D MRIs. The reported results showed the improvement of their proposal over the existing adaptive hybridization schemes. Actually, the unbiased improvement of all objectives in a multi-objective gradient technique as part of a hybridization scheme was indeed successful at addressing the tested hard DIR cases, thus providing the best balance between exploitation and exploration capabilities.
## 3 Results
### Source and Year Distribution  
The final dataset includes 185 articles, with the following distribution by source: MDPI, Elsevier, and Google Scholar. This balance ensures diversity across editorial policies and research approaches. Figure 1 illustrates the article distribution by type and source. As shown in Figure 2, there has been a steady increase in the number of publications, with the peak in 2023.  
The bibliometric profile of the reviewed literature was analyzed in terms of source and publication year. As illustrated in Figure 1, the 185 articles selected for this study were distributed across three major sources: Elsevier contributed the largest share with 70 articles (37.8%), followed by Google Scholar with 63 articles (34.1%), and MDPI with 52 articles (28.1%). This distribution reflects both the breadth and diversity of scientific output on fault detection and diagnosis in power plant systems.  
\begin{table}
\begin{tabular}{c c} \hline
**Power Plant Type** & **References** \\ \hline Thermal & [122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134; 135; 136; 137; 138; 139; 140; 141; 142; 143; 144; 145; 146; 147; 148; 149; 150; 151; 152; 153; 154; 155; 156; 157; 158; 159; 160; 161; 162; 163; 164; 165; 1666; 167; 168; 169; 170] \\ \hline Nuclear & [171; 172] \\ \hline Wind energy plant & [173] \\ \hline Other & [143; 174; 175; 176; 177; 178; 179; 180; 181; 182; 183] \\ \hline \end{tabular}
\end{table}
Table 3: Bibliographic references of power plant type on the Google Scholar platform, in alphabetical order.  
The higher representation of Elsevier publications may be attributed to the journal family's long-standing focus on engineering applications and data-driven modeling approaches, particularly in fields such as control systems, energy systems, and process optimization. In contrast, MDPI's share has grown significantly in recent years, coinciding with its increased publication volume in open access journals related to artificial intelligence and industrial informatics. The presence of Google Scholar in this review ensures the inclusion of gray literature and early-stage or preprint contributions that may not be indexed in traditional databases but are still relevant to the research landscape.  
Figure 2 shows the distribution of publications over time, ranging from 2015 to 2024. The temporal analysis reveals a significant upward trend in publications related to power plant fault diagnosis, especially starting in 2019. This growth aligns with broader research trends in digitalization, predictive maintenance, and the integration of AI in industrial diagnostics. The year 2023 stands out as the most prolific, both in terms of total article count and methodological diversity.  
These observations underscore the increasing importance and academic attention directed toward fault detection frameworks in power systems. They also point to the growing maturity of the field, as diagnostic methods are increasingly evaluated not only in theoretical models but also in simulation environments and real-world applications.
## 2 Background
### Mathematical Optimisation  
Hyperparameter optimisation falls under the umbrella of mathematical optimisation, which can be broadly defined as the process of maximising or minimising an objective function given a set of candidates (solution alternatives). The search space of the objective function is determined by the decision variables, \(x\), which can take any value between a given range for all real numbers, \(\mathbb{R}\) in one-dimensional space. Unconstrained optimisation is then defined as finding the maximum/minimum value of an objective function without imposing any restrictions on its solution (i.e., within all possible values). Generally, unconstrained optimisation can be defined as:  
\[\min_{x\in\mathbb{R}}f(x), \tag{1}\]  
where \(f(x)\) is the objective function to minimise.  
However, in real-world applications, the constraints of the decision variables are generally known, and hence, Equation (1) then becomes:  
\[\min_{x\in\mathbb{X}}f(x), \tag{2}\]  
\[a_{i}(x)\leq 0,\ i=1,2,\ldots,m\]  
\[b_{j}(x)=0,\ j=1,2,\ldots,n\]  
where \(a_{i}\) are the inequality constraint functions and \(b_{j}\) is the equality constraint functions over the constrained domain \(X\) of \(x\). The constraints define the boundaries for the search space of hyperparameters and thus lead to a feasible region, \(F\), where the solution exists, which can be denoted by:  
\[F=\{x\in X|a_{i}(x)<=0,\ b_{j}(x)=0\} \tag{3}\]  
Thus, the goal of mathematical optimisation is to find a global optimum of \(f(x)\) given the set of \(X\) in the feasible region \(F\). However, a global optimum of an objective function can only be found if the objective function, \(f(x)\), is a convex function and \(X\) is a convex set over the domain \(F\) as follows:  
\[\min_{x\in\mathbb{X}}f(x), \tag{4}\]  
Figure 3: Common topology of transfer learning in a convolutional neural network. The features learned from a dataset can be transferred to a new dataset by sharing the convolutional layers and by fine-tuning the fully connected layers.  
For time \(t\),  
\[\text{as }t\rightarrow\infty, \tag{5}\] \[f(x_{t})-f(x^{*})\to 0 \tag{6}\]  
where \(f(x_{t})\) is the objective function at time \(t\) and \(f(x^{*})\) is the optimal objective value.  
However, NNs have an objective function that is non-convex and therefore has multiple local optimums. As illustrated in Figure 4, the optimiser still can get stuck in one of these local optima. As such, numerical optimisation approaches based on gradient descent are used to minimise the objective function within a given search space; however, this can become computationally intensive when dealing with large search spaces. To address this issue, derivation-free algorithms such as heuristics have been developed to help discover optimal solutions more efficiently.
## 4 Discussion  
Previous studies show that the effect of MICP mainly depends on the concentration of Ca\({}^{2+}\), the concentration of CO\({}_{3}\)\({}^{2-}\), the pH, and the nucleation site [63]. Bacterial activity and urease activity can be affected by high concentrations or low concentrations of Ca\({}^{2+}\)[37]. [PERSON] et al. found that 0.5 M of cementation solution is suitable for mineralization [64]. It can be seen from Section 3.4 that the alkaline bacteria liquid and cementation solution neutralize the acidity of phosphogypsum, making the pH of the final mineralized environment range from 7 to 8, which does not inhibit the activity of the bacteria and the formation of CaCO\({}_{3}\). Therefore, the improvement of the hydraulic erosion resistance of phosphogypsum depends on the CaCO\({}_{3}\) content and the structure of the phosphogypsum connected to CaCO\({}_{3}\) crystals.  
In our study, the water stability and hydraulic erosion resistance of microbially modified phosphogypsum materials are significantly improved compared with that of untreated materials. In the case of spray solidification, the bacterial solution is transported by gravity, and the bacteria will be adsorbed on the pore throat or the surface of the particles under the action of capillary suction and electrostatic force. As the concentration of bacteria increases, more soluble organic matter is carried in the solution, which makes the liquid viscous and increases the infiltration resistance of the liquid, thus reducing the permeability coefficient and improving the water stability. The bacterial density increase leads to the bacterial cells being prone to aggregation and flocculation, and the infiltration of the bacterial solution is limited by the size relationship of the bacteria and pores throats. During microbial treatments, the pores between the phosphogypsum particles are filled with CaCO\({}_{3}\) and the treatment solution, and the bacteria easily accumulate on the surface of the phosphogypsum slope. Compared with the slopes treated by OD\({}_{600}\) = 0.3 and OD\({}_{600}\) = 0.5, the migration and distribution of bacteria in the slope treated by OD\({}_{600}\) = 0.1 are relatively dispersed. CaCO\({}_{3}\) filled in the macropores and formed a uniform and dense solidified layer, decreasing the porosity of phosphogypsum and enhancing the erosion resistance of the slope. However, as for the phosphogypsum slope treated by higher bacterial concentrations, although the CaCO\({}_{3}\) content increases, the particles on the surface were agglomerated during cementation, which caused uneven solidification, and some macropores did not fill effectively, resulting in the infiltration of rainwater and more severe erosion during the rainfall scouring test.
# The Wettability and Numerical Model of Different Silicon Microstructural Surfaces
## 3 Numerical Methods and Setup  
The calculation results of the theoretical contact angles in the last section indicated the difficulty in scientifically determining the parameters (such as \(f_{s}\) and \(r\)) when applying the Cassie-Baxter theoretical model to predict the contact angle of the complex microstructural surface, which resulted in large prediction errors. In this section, the VOF numerical simulation method was used to study the simulation of the static contact angle according to the microstructural characteristics of the sample.  
The macroscopic static contact angle was the comprehensive expression of the local infiltration characteristics of the droplets on the microstructural surface. The fluid was assumed to have continuum characteristics and satisfied the basic Young's equation in any local area of the microstructure. In addition, the contact angle on the smooth surface and the local area of the microstructure were assumed to be equal. Therefore, the VOF model in the computational fluid dynamics (CFD) analysis method was used to study the macroscopic static contact angle of specific microstructural surfaces.  
According to the analysis of the PIII preparation technology in Section 2.1, the locally exposed surface after corrosion can be assumed to have the same wettability as the initial surface. In addition, the 3-D morphology model was established according to the AFM pictures. This section systemically describes the CFD analyzing model and the numerical simulation results of the static contact angle on the microstructure surface prepared by PIII technology.
# Congestion-Free Ant Traffic: Jam Absorption Mechanism in Multiple Platoons
## Appendix A Variables in the Ant Trail Model
### Pheromone Saturation Level \((\sigma_{sat})\)  
The maximum possible velocity of the agents in the ATM depends on \(\sigma_{sat}\). Thus, from a system perspective, the capacity flow and capacity flow density depend on \(\sigma_{sat}\). As shown in Figure 11, at \(\sigma_{sat}=1\), ATM had a similar capacity flow and capacity flow density as TASEP with a low hopping probability (Figure 2). The above similarity happened because at \(\sigma_{sat}=1\), even a small amount of evaporation reduced the pheromone concentration to undetectable levels \((\sigma<1)\), leading to the condition where most of the agents traveled without pheromones (constant low velocity). In this scenario, ATM behaved similarly to TASEP with a low hopping probability. After \(\sigma_{sat}=1\), initially, with the rise in \(\sigma_{sat}\) for the range \(1\leq\sigma_{sat}\leq 5\), a sharp rise in capacity flow, as well as capacity flow density was observed; thereafter, for a wide range of \(\sigma_{sat}\)\((5\leq\sigma_{sat}\leq 80)\), both the capacity flow and capacity flow density remained constant. Based on the above observations, we can state that, for a considerable range of \(\sigma_{sat}\) (\(5\leq\sigma_{sat}\leq 80\)), ATM simulations were independent of \(\sigma_{sat}\). We intend to analyze the above behavior of ATM concerning \(\sigma_{sat}\) in future work; however, for the purpose of this paper, the above behavior analysis was considered as out of scope.  
Then, afterwards, at a very high \(\sigma_{sat}\) (\(\sigma_{sat}>80\)), the capacity flow and capacity flow density started decreasing, and eventually (\(\sigma_{sat}>4000\)), the ATM again behaved similarly to TASEP with a low hopping probability. This happened because at very high \(\sigma_{sat}\) values, due to the effect of pheromone evaporation, the pheromone never accumulated and remained at a comparatively meager value, which led to a low velocity. In this scenario, the pheromone became ineffective; hence, most of the agents behaved similarly to agents in TASEP with a low hopping probability.
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 4 Experimentation Methodology
### Topology  
As presented in Section 2.1, the experiments were performed over fat-tree topologies varying the \(k\) factor. The factor \(k=4\) was chosen to validate the emulation environments. Afterward, the \(k\) factor was gradually increased to test performance and scalability. Figure 1 shows the basic topology fully labeled with the terminology introduced before. Note that the figure omits the leaves-ToR links; for a \(k\) factor of 4, two ports per leaf are available for connecting ToR switches.
# Unravelling the Molecular Regulation Mechanisms of Slow Ripening Trait in _Prunus persica_
## 2 Results
### Hormonal Differences between Normal and Slow Ripening Phenotypes  
Fruit development is a complex process that is coordinated carefully by the interplay of many plant hormones. Furthermore, a normal ripening process involves the participation of these hormones in specific developmental stages. Therefore, hormone biosynthetic pathways related to fruit development were analyzed, and differences in expression profiles were observed in the genes related to ethylene, abscisic acid (ABA), gibberellins (GA), and jasmonic acid (JA) production (Figure 3).  
Figure 2: Differential expression analysis between NR and SR phenotypes. Graphical representation of differentially expressed transcripts for the four developmental stages analyzed (T1, T2, T3, T7) between the NR and SR phenotypes. **(A)** Principal component analysis using differentially expressed transcripts. The different stages are separated in colored circles of red, green, blue and purple for T1, T2, T3 and T7, respectively. The NR phenotype is represented with fullly colored figures and the SR phenotype is represented with line colored figures. **(B)** Heatmap of differentially expressed genes between NR and SR phenotypes. The red and blue lines represent genes with up and down regulated expressions, respectively. The four developmental stages are separated, and each column represents one independent biological replicate expression result. **(C)** Venn diagram representing the differentially expressed transcripts. In yellow, green, blue and purple, the differentially expressed transcripts only occurring in T1, T2, T3 and T7, respectively are represented, and the number of identified genes differentially expressed in all developmental stages are presented in grey. **(D)** Gene ontology term enrichment analysis of normal and slow ripening phenotypes in fruit development. The blue-red scale color represents the adjusted p-value and the point size represents the DE gene ratio.
## 1 Introduction
Another research work was carried out to predict the resonance frequency of patch antennas using ANN in [31]. In this research work, the percentage of error was calculated from the difference between actual value and predicted value divided by the actual value. It is noteworthy that the percentage of error in terms of MSE, MAE, and RMSE was not evaluated. In addition, most of the existing ML-based antenna design research work has not measured the variance score. In this research work, six regression ML models, such as linear regression (LR), random forest regression (RFR), decision tree regression (DTR), Lasso regression, ridge regression (RR), extreme gradient boosting (XGB) regression, and one DL model (CNN), are used to predict the operating frequency of the proposed antenna. Various performance metrics, such as mean square error (MSE), mean absoluteerror (MAE), root MSE (RMSE), and variance score are observed to validate the prediction of the \(f_{r}\) using the mentioned models [32]. Recently, a new technique has been explored by using multiple regression algorithms along with convolutional neural networks (CNN) to predict the \(f_{r}\) by using CST electromagnetic (EM) simulation tools. Furthermore, the simulated \(f_{r}\) using CST and ADS and measured \(f_{r}\) is compared with the predicted \(f_{r}\) using the proposed ML algorithms. The main contributions of this research work are summarized as follows:  
* Simulate and analyze the performance of a microstrip patch antenna using CST EM simulation tools. * Validate the CST simulation results using ADS simulation software, and the simulated S\({}_{11}\) is compared with the measured S\({}_{11}\). * The resonance frequency (\(f_{r}\)) is predicted using six ML regression algorithms and CNN. A comparative study of the different models based on the different predicted results is incorporated. The remaining sections of this study are organized as follows. Section 2 explains several generic design formulas and the geometrical structure of the proposed antenna and the required performance analysis of the various machine learning algorithms. The results of the necessary simulations and measurements are presented in Section 3 and, consequently, the concluding remarks are highlighted in Section 4.
## 3 Results and Discussion  
The efficiency of UV/persulfate (UV/PS) and photo-Fenton (UV/PF) methods was evaluated by following monitoring UV absorbance at 276 nm (a typical UV-visible spectrum of TA aqueous solution presents two bands at 210 nm and 276 nm) and TOC. The absorbance at 276 nm is used to follow the degradation of TA and its aromatic intermediates (% aromatics removal = \(\frac{\text{A}_{\lambda=276\text{ nm}}^{0}-\text{A}_{\lambda=276\text{ nm}}^{1}}{\text{A}_{\lambda=276\text{ nm}}^{0}}\times 100\), where \(\text{A}_{\lambda=276\text{ nm}}^{0}\) and \(\text{A}_{\lambda=276\text{ nm}}^{\text{t}}\) are absorbencies measured at 276 nm at t = 0 s and instant t). TOC is used to evaluate the mineralization of the organic content (% TOC removal = \(\frac{\text{TOC}^{0}-\text{TOC}^{t}}{\text{TOC}^{0}}\times 100\), where TOC\({}^{0}\) and TOC\({}^{t}\) are TOC measured at t = 0 s and instant t). Persulfate, hydrogen peroxide, ferrous iron and TA concentrations, temperature, and initial pH are the main factors to achieve the highest TA degradation and mineralization yields.
## 3 Data
### Flood Event Data  
Flood event data utilized in this study were obtained from the Emergency Events Database (EM-DAT), maintained by the Centre for Research on the Epidemiology of Disasters (CRED). EM-DAT is a comprehensive global database documenting the occurrence and impacts of over 26,000 mass disasters from 1900 to the present. The database integrates information from diverse sources, including United Nations agencies, non-governmental organizations, insurance entities, research institutions, and press outlets. It provides critical data on both natural disasters--such as floods, droughts, storms, famines, and epidemics--and technological disasters.  
In EM-DAT, floods are defined as the overflow of water onto normally dry land, including riverine flooding (overflow from stream channels), coastal flooding (higher-than-normal levels along coasts, lakes, or reservoirs), and flash floods (water pooling near the point of rainfall). Detailed records in the database encompass information on human casualties, economic losses, and the geographic distribution of flood-affected areas, offering a valuable resource for disaster analysis and management.