esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results and Discussion
### Performance Evaluation and Discussion
#### 4.4.2 FAT with FGSM Samples Results  
Numerous experiments on adversarial attacks and federated adversarial training were conducted using FGSM adversarial samples. As explained in Section 4.3, the attack strategy involved white-box insider attacks on the FL. The primary goal was to gain a comprehensive understanding of the impact of poisoning attacks on the classification performance of the global model. In these experiments, each client received 1224 images for training, validation, and testing. The client datasets contained a variety of compositions, including mixed data, adversarial data, or exclusively normal data, as detailed in Table 3. The accuracy of the results obtained from these investigations is graphically depicted in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Test Result** & **Test Result in Unseen Data** \\ \hline Normal data & 91.92\% & 90.36\% \\ Mixed normal data and FGSM samples & 91.65\% & 91.01\% \\ Mixed normal data and PGD samples & 94.28\% & 92.16\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test accuracy of natural training and natural adversarial training accuracy.  
The accuracies across all scenarios were found to be comparable, as shown in Table 5. The baseline accuracy, represented by the fl-normal scenario (generating a global model without any attackers), was 91.34%. Notably, this accuracy was identical to that observed in fl-fgsm-50, where 50% of all clients were categorized as attackers. The accuracies for fat-fgsm, fl-mixed-fgsm, fl-fgsm-25, and fl-fgsm-75 were 88.55%, 88.15%, 91.18%, and 88.53%, respectively. Based on these empirical findings, we conclude that adversarial attacks utilizing FGSM adversarial samples with epsilon (\(\epsilon\)) = 0.01 were unable to inflict catastrophic damage on the global model.
# Calculation of Three-dimensional Energy Product for Isotropic Nd\({}_{2}\)Fe\({}_{14}\)B Magnet
## 2 Materials and Methods  
We adopted an Nd\({}_{2}\)Fe\({}_{14}\)B multi-grain magnet with dimensions of \(2000\times 2000\times 1000\,\text{nm}^{3}\), as illustrated in Figure 1. The material is commonly used with high coercive force and the energy product, and it has been investigated with the use of various models [14, 15, 16, 17, 18, 19]. In order to investigate the magnetic properties during multidomain reversal, we assumed that a model magnet consists of grains with a diameter of 100 nm, and that the uniaxial magnetic anisotropy direction of the grains is randomly distributed. For the micromagnetic simulation, the unidirectional magnetic anisotropy constant of \(4.9\times 10^{6}\) J/m\({}^{3}\) was used. The saturation polarization was assumed to be \(1.26\) MA/m\({}^{3}\), and we set the exchange stiffness constant to \(3.0\times 10^{-12}\) J/m [20].  
To investigate the model system, a full hysteresis loop was simulated with Mumax3 [21, 22], which implements simulations in magnetic solids described by a finite differential micromagnetic solver. In the finite differential calculation of the micromagnetic properties, a cubic cell of \(10\times 10\times 10\) nm\({}^{3}\) was assumed, which was larger than the exchange length of the magnetic material. While the larger cell size can make a difference with the use of correct magnetic quantities of the materials, it is sufficient to investigate the tendency of the magnetizations and the demagnetizing fields during the switching process [23, 24]. The hysteresis loop was computed through the minimization of the Gibbs free energy for an external magnetic field ranging from \(-10\) to \(+10\) T, which was applied along the cartesian \(z\)-axis. The total magnetic Gibbs free energy is described by the following equation:  
\[E=E_{\text{ex}}+E_{\text{K}}+E_{\text{d}}+E_{\text{Zeeman}}, \tag{2}\]  
where \(E_{\text{ex}},E_{\text{K}},E_{\text{d}}\), and \(E_{\text{Zeeman}}\) are the exchange energy, the anisotropy energy, the magnetostatic energy, and the Zeeman energy, respectively.
# 3D-Printed Lightweight Foamed Concrete with Dispersed Reinforcement
## 3 Results and Discussions
### Stability and Buildability
Control samples without fibers (Figure 9) served as a baseline for comparison. These specimens exhibited the smoothest surfaces and the most uniform layer structures among all tested samples, underscoring the impact of fiber reinforcement on the printability and surface quality of foamed concrete mixtures. The variations in printability and structural stability of the fiber-reinforced foamed concrete samples can be attributed to the specific type of supplementary cementitious materials employed, namely fly ash and coal slag. Fly ash, characterized by its finer particle size and higher pozzolanic reactivity, enhanced the workability and cohesion of the concrete mix, a phenomenon particularly pronounced in samples with a lower fiber content (0.5%). This improved rheology facilitated a more uniform extrusion process and mitigated the occurrence of  
Figure 9: Printed samples without fibers. nozzle clogging compared to mixtures incorporating coal slag. Conversely, coal slag, due to its coarser granulometry and diminished pozzolanic activity, contributed to increased viscosity and reduced flowability of the material. This effect was further exacerbated in fiber-reinforced mixtures, where fiber entanglement impeded particle mobility, thereby increasing the propensity for material blockage within the screw feeder. Additionally, the presence of unburnt carbon residues in the slag may have induced inconsistencies in foam stability, adversely affecting layer uniformity during the printing process. These findings align with previous research highlighting the pivotal role of supplementary cementitious materials in governing the rheological properties of 3D-printable foam concrete. The results indicate that fly ash-based mixtures provide a more conducive matrix for fiber dispersion and extrusion, whereas coal slag-based compositions present greater challenges, particularly at elevated fiber concentrations. This distinction is instrumental in elucidating the observed variations in structural integrity, surface quality, and deformation patterns across different sample compositions. Furthermore, a comprehensive understanding of the interaction between these additives and the selected fibers during the printing process enabled the formulation of an optimized mixture incorporating both materials in precisely controlled proportions to achieve the desired performance characteristics.
###### Abstract  
Aroma is a crucial attribute affecting the quality of pepper and its processed products, which has significant commercial value. However, little is known about the composition of volatile aroma compounds (VACs) in pepper fruits and their potential molecular regulatory mechanisms. In this study, HS-SPME-GC-MS combined with transcriptome sequencing is used to analyze the composition and formation mechanism of VACs in different kinds and development stages of pepper fruits. The results showed that 149 VACs, such as esters, alcohols, aldehydes, and terpenoids, were identified from 4 varieties and 3 development stages, and there were significant quantitative differences among different samples. Volatile esters were the most important aroma components in pepper fruits. PCA analysis showed that pepper fruits of different developmental stages had significantly different marker aroma compounds, which may be an important provider of pepper's characteristic aroma. Transcriptome analysis showed that many differential genes (DEGs) were enriched in the metabolic pathways related to the synthesis of VACs, such as fatty acids, amino acids, MVA, and MEP in pepper fruits. In addition, we identified a large number of differential transcription factors (TFs) that may regulate the synthesis of VACs. Combined analysis of differential aroma metabolites and DEGs identified two co-expression network modules highly correlated with the relative content of VACs in pepper fruit. This study confirmed the basic information on the changes of VACs in the fruits of several Chinese spicy peppers at different stages of development, screened out the characteristic aroma components of different varieties, and revealed the molecular mechanism of aroma formation, providing a valuable reference for the quality breeding of pepper.  
pepper; volatile aroma compounds; non-targeted metabolome; transcriptome; co-expression network +
Footnote †: journal: Molecular Sciences
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 2 Methodology
### Data Preprocessing  
In the data preprocessing phase, since there are no missing values or outliers in the datasets, the focus is on preparing the data for analysis by arranging it into a suitable format. The datasets--comprising hourly energy demand data from the LUCIA dataset, the limited data from FUHEM, and the combined heating and cooling demands from the EII building--are organized to ensure consistency across different sources. This involves aligning the timeframes, ensuring that timestamps are correctly synchronized, and verifying that all relevant variables are correctly labeled and structured for further analysis. Once the data is properly organized, the next step involves exploring the datasets to understand inherent patterns and relationships. This includes visualizing the demand profiles over time, identifying peak usage periods, and examining seasonal or operational trends. By carefully preparing and analyzing the data in this way, meaningful insights can be gained regarding the interactions between heating and cooling demands, which can inform strategies for improving energy efficiency and optimizing building management systems.  
Using deep learning models on very small datasets like FUHEM (100 points) and EII (260 points) is a significant challenge, typically addressed through strategic methodological choices. Justification centers on the specific deep learning approach, often involving a simple architecture and crucially, transfers learning from a larger, relevant pre-trained model to leverage existing learned features. To prevent severe overfitting, aggressive regularization techniques like dropout, L1/L2 penalties, and early stopping are essential. Furthermore, the definition of random sampling is critical; it must clarify how the chosen points, despite their scarcity, were ensured to be representative of the full data's diversity, particularly in addressing inherent seasonality by distributing samples across different months and time periods, potentially augmented by feature engineering for time-based attributes. Finally, rigorous evaluation using methods like K-Fold Cross-Validation and comparison against traditional machine learning baselines is vital to demonstrate the model's performance and assess its limited, but justifiable, generalization capabilities [34; 35; 36].
## 3 Spectrum Management Issues
### Small Cell
#### 3.3.3 Coverage Planning  
The authors in [145] have discussed two critical tasks of mmWave small cell network, i.e., coverage and spectral efficiency. To evaluate the performance of spectrum resources, two techniques pursued are frequency reuse-1 and frequency reuse-3. Similarly, an interference mitigation scheme is introduced to validate the performance of both scenarios. The researchers have also added points on how small cell radius inserts affect Multiuser MIMO (MU-MIMO) mode performance. Therefore, maximum throughput achieves almost 2.5\(\times\) when three additional channels operate within a small radius of up to 50 m. However, ultra-dense small cells are profoundly interfered with by intercell interferences; thus, an additional investigation is required on small cells. The author in [146] has focused on the issue of coverage holes (i.e., a location where a user does not receive an optimal signal level) in a macro to small cell two-layer network. To validate this shortage, an energy-efficient coverage approach is designed for implementation in a respective network. In conclusion, quality coverage, power usage, and average transmission rate are observed by utilizing the proposed algorithm for comparison with conventional methods. Regardless, a concrete algorithm that can extend to more than two-layer networks is required. A density-aware, energy-efficient, and spectrum-efficient sleep scheduling technique is presented in [147]. The solution is based on BS density adaptation and cell-zooming algorithms. The idea is to prevent a coverage hole and increase network throughput along with reducing Signal-to-Interference-plus-Noise Ratio (SINR) for cell-edge users. The results help improve throughput, energy, and spectral efficiency.
## 4 Results
### Simulation Results
#### 4.2.3 Effects of the Move Speed  
The moving speed of nodes represents the degree of network topology change. The change in topology will cause the interruption of the established route. The nodes' speed changes between 0.5 and 3 m/s, and the random movement model is adopted.  
Figure 8a shows the effects of the nodes' speed on the discovery success ratio. The metric of the RFAODV is 25% better than that of the AODV and DSAODV in the high-speed scenario. The movement speed will cause the existing route to break. The discovery success ratio of the route indicates the adaptability of the strategy to the highly dynamic topology. RFAODV adopts a more conflict-avoiding route discovery strategy, which has a higher probability of route establishment.  
Figure 8b presents the effects of the nodes' speed on the packet delivery ratio. Obviously, with the change in movement speed, the packet delivery ratio of RFAODV is always  
Figure 8: Effects of the move speed. (**a**) Discovery success ratio vs move speed; (**b**) packet delivery ratio vs move speed; (**c**) protocol overhead vs move speed; (**d**) consumption energy vs move speed.  
better than that of AODV and DSAODV, and the packet delivery ratio is improved by at least 4%.  
Figure 8c depicts the effects of the nodes' speed on the protocol overhead. Because both RFAODV and DSAODV adopt forwarding control, the protocol overhead is significantly lower than that of AODV. Since RFAODV has a higher probability of route discovery, this index is also better than DSAODV. Compared with AODV and DSAODV, RFAODV increased by at least 300% and 20%, respectively.  
Figure 8d shows the relationship between the consumption energy and move speed. With the change in movement speed, the consumption energy of RFAODV is always better than that of AODV and DSAODV and was decreased by at least 5%. The results show that RFAODV can adapt to topology changes in terms of energy saving.
# Residential Demand Response Scheduling with Consideration of Consumer Preferences
###### Abstract  
This paper proposes a new demand response scheduling framework for an array of households, which are grouped into different categories based on socio-economic factors, such as the number of occupants, family decomposition and employment status. Each of the households is equipped with a variety of appliances. The model takes the preferences of participating households into account and aims to minimize the overall production cost and, in parallel, to lower the individual electricity bills. In the existing literature, customers submit binary values for each time period to indicate their operational preferences. However, turning the appliances \"on\" or \"off\" does not capture the associated discomfort levels, as each appliance provides a different service and leads to a different level of satisfaction. The proposed model employs integer values to indicate household preferences and models the scheduling problem as a multi-objective mixed integer programming. The main thrust of the framework is that the multi-level preference modeling of appliances increases their \"flexibility\"; hence, the job scheduling can be done at a lower cost. The model is evaluated by using the real data provided by the Department of Energy & Climate Change, UK. In the computational experiments, we examine the relation between the satisfaction of consumers based on the appliance usage preferences and the electricity costs by exploring the Pareto front of the related objective functions. The results show that the proposed model leads to significant savings in electricity cost, while maintaining a good level of customer satisfaction.  
demand response; mixed integer linear programming; scheduling; smart grids +
Footnote †: journal: Applied  
1
## 5 Results
### Development of XAI in Different Application Domains
#### 5.3.2 Models for Primary Tasks  
The majority of the applications built on the concepts of AI perform two basic types of tasks, i.e., supervised (classification and regression) and unsupervised (clustering) tasks which have undoubtedly remained unchanged in the XAI domain. The authors of the selected articles used different established AI/ML models depending on the tasks. The methods were clustered based on the basic type of the models, specifically, neural network (NN), ensemble model (EM), Bayesian model (BM), fuzzy model (FM), tree-based model (TM), linear model (LM), nearest neighbour model (NNM), support vector machine (SVM), neuro-fuzzy model (NFM), and case-based reasoning (CBR). Works related to these models were clustered on the basis of their types and are presented in Table 5. Moreover, the table contains the names of different variants of the AI/ML models references to the articles featuring the models, and the number of studies performed. It was observed that neural network-based models were exploited in most of the studies (63) from the selected articles. The second-highest number of studies (21) utilised the ensemble techniques for performing the primary supervised or unsupervised tasks. Based on the increased interest of researchers in neural networks and ensemble techniques, it can be inevitably assumed that these models were chosen to incorporate explainability because of their wide acceptability over various domains in terms of their performances. In addition to the renowned algorithms, there are some other algorithms, such as probabilistic soft logic (PSL) [100], LSP [103], sequential rule mining (SRM) [168], preference learning [113], Cartesian genetic programming (CGP) [122], Predomics [129], and TriRank [162]. The acronyms of the model types are further referenced in Table 6 to indicate their relation to the core AI/ML models.  
Throughout this study, it was evident that most of the research works were domain-agnostic. For specific domains, healthcare, industry, and transportation were revealed to be more exploited than other domains. In these domains, as stated above, diverse forms of neural networks had been invoked to perform different tasks (see Figure 9) followed by other types of models, as listed in Table 5. The numbers associated with different model types stated in Figure 9 and Table 5 varied because the illustration presents the number of articles and the table lists the number of variations of
## 3 Mathematical Formulation of the Problem
### The Effectiveness of the Control Chart and the Average Restoration Maintenance Cycle  
A control chart's effectiveness can be measured by studying the risk of not detecting a change when using a sample of size \(n\). The efficiency of the chart is inversely proportional to the probability. Assume that at some point there is an adjustment that only affects the average. After the adjustment, the new value of the central tendency of the production process is:  
\[\mu=\mu_{0}+\Delta \tag{22}\]  
where \(\Delta\) is the amplitude of the adjustment. Considering that the adjustment is positive, we have: \(\Delta=\mu-\mu_{0}\).  
Assume that \(\beta_{1}\) is the probability that the point \(\overline{X}_{s}\) falls within the limits of control, i.e., \(\beta\) is the non-detection probability of the adjustment \(\Delta\) with a single sample taken after the adjustment. So, \(\beta\) is the non-detection probability of the critical state. Therefore, the quality indicator average does not exceed the limits of control \(LCL\leq\overline{X}_{s}\leq UCL\) as long as the production process is in the critical state and perfect preventive maintenance action with duration is necessary.  
\[\beta_{1j}=\left\{P\big{(}LCL\leq\overline{X}_{s}\leq UCL\big{)}\left|\mu=\mu _{0}+\Delta\right.\right\} \tag{23}\]  
After adjustment, the law of the quality characteristic followed is:  
\[\overline{X}_{s}\sim N(\mu,\sigma_{0})\]  
The sample mean then has for law:  
\[\overline{X}_{s}\sim N\bigg{(}\mu,\frac{\sigma_{0}}{\sqrt{n}}\bigg{)}\]  
By expressing the adjustment as a function of the standard deviation of \(X\),  
\[\Delta=\delta_{pj}\times\sigma_{0}\]With \(\delta_{pj}\): the magnitude of the move to the \"out of control\" for assignable cause \(j\) with \(j=\{1,:,npc\}\).  
The Equation (22) is written as:  
\[\beta_{1}=\left\{P\left(\frac{LCL-\mu}{\frac{\sigma_{0}}{\sqrt{n}}}\leq\overline {X}_{s}\leq\frac{UCL-\mu}{\frac{\sigma_{0}}{\sqrt{n}}}\right)\right\} \tag{24}\]  
\[\beta_{1j}=F\left(\frac{UCL-\mu}{\frac{\sigma_{0}}{\sqrt{n}}}\right)-F\left( \frac{LCL-\mu}{\frac{\sigma_{0}}{\sqrt{n}}}\right) \tag{25}\]  
Hence:  
\[\beta_{1j}=F\big{(}k_{p}-\delta_{pj}\times\sqrt{n}\big{)}-F\big{(}-k_{p}- \delta_{pj}\times\sqrt{n}\big{)} \tag{26}\]  
\[j=\big{\{}1,\ \ldots,\ n_{pc}\big{\}}\]  
with \(F\): reduced centered normal distribution function.  
Consequently, the average operational period \(AOP_{1}\) describes the average number of consecutive samples leading to detect the first out of order (critical state). It is defined, for each \(j\), as follows:  
\[AOP_{1,j}=\frac{1}{1-\beta_{1j}} \tag{27}\]  
\[AOP_{1,j}=\frac{1}{1-F\big{(}k_{p}-\delta_{pj}\times\sqrt{n}\big{)}-F\big{(}- k_{p}-\delta_{pj}\times\sqrt{n}\big{)}}\]  
The average restoration of maintenance cycle duration after perfect preventive maintenance due to the shift to the critical state \(RMCD_{j}\), with \(j=\big{\{}1,\ \ldots,\ n_{pc}\big{\}}\), is given by:  
\[RMCD_{1,j}=AOP_{1,j}\times h+t_{pj} \tag{28}\]