esa-sceva/satcom-chunk-collection huggingface dataset:
# Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks
###### Abstract  
The Deep Operator Network (DeepONet) framework is a different class of neural network architecture that one trains to learn nonlinear operators, i.e., mappings between infinite-dimensional spaces. Traditionally, DeepONets are trained using a centralized strategy that requires transferring the training data to a centralized location. Such a strategy, however, limits our ability to secure data privacy or use high-performance distributed/parallel computing platforms. To alleviate such limitations, in this paper, we study the federated training of DeepONets for the first time. That is, we develop a framework, which we refer to as _Fed-DeepONet_, that allows multiple clients to train DeepONets collaboratively under the coordination of a centralized server. To achieve Fed-DeepONets, we propose an efficient stochastic gradient-based algorithm that enables the distributed optimization of the DeepONet parameters by averaging first-order estimates of the DeepONet loss gradient. Then, to accelerate the training convergence of Fed-DeepONets, we propose a moment-enhanced (i.e., adaptive) stochastic gradient-based strategy. Finally, we verify the performance of Fed-DeepONet by learning, for different configurations of the number of clients and fractions of available clients, (i) the solution operator of a gravity pendulum and (ii) the dynamic response of a parametric library of pendulums.  
deep learning; federated learning; deep operator networks; stochastic-gradient descent +
Footnote †: journal:  
15325[[https://doi.org/10.3390/a15090325](https://doi.org/10.3390/a15090325)]([https://doi.org/10.3390/a15090325](https://doi.org/10.3390/a15090325))
# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 5 Experimental Results and Discussion
### Comparative Experiment Analysis
Similarly, the reduction in MAE is 21.14% compared with GRU, 17.09% compared with LSTM, 22.40% compared with LSTM-GRU, and 27.61% compared with RS. These reductions indicate that while the increase in the Pearson correlation coefficient may seem marginal, the overall predictive accuracy and precision of the proposed model show significant improvement. Therefore, the improvement is considered both statistically meaningful and practically significant, particularly when considering the reduction in prediction error across multiple metrics. In summary, across all the metrics and predicted variables, the hybrid deep learning model demonstrated superior accuracy and trend alignment, validating its effectiveness for precise and reliable predictions in hydropower unit operations.
## 5 Analysis and Results  
### Respondent Profile and Cyberattacks Levels and Controls  
The descriptive statistics of the respondents for the current sample are listed as shown in Table 6. Questions (1-8) were about respondents' profile and demographic information, while questions (9-11) concerned specific information on types of cyberattack encountered, rating level of cyberattack leadership practices and rating levels of success of cyberattack prevention among individuals working in financial organizations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables (Question Items)** & **Response** & **Number** & **\%** \\ \hline \multirow{2}{*}{1.} & \multirow{2}{*}{Gender} & Male & 263 & 85\% \\ \cline{3-5}  & & Female & 47 & 15\% \\ \hline \multirow{6}{*}{2.} & \multirow{6}{*}{Age} & 18–24 & 13 & 4.2\% \\ \cline{3-5}  & & 25–34 & 120 & 38.7\% \\ \cline{3-5}  & & 35–44 & 128 & 41.3\% \\ \cline{3-5}  & & 45–54 & 45 & 14.5\% \\ \cline{3-5}  & & 55–64 & 4 & 1.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Respondent profile.  
265 (85.5%) who work in the banking sector, while only 45 respondents (14.5%) were UAE citizens. The number of workers in Islamic banks were the most dominant number of respondents with a frequency of 160 (51.6%), followed by commercial banks, 75 (24.2%), industrial banks, 45 (14.56%), and merchants banks, 30 (9.7%). The results revealed that the investigated financial organizations suffer high rate of several cyberattacks. Viruses and malware attacks represented the most common cyberattacks with a rate of (38.7%) and (25.5%), respectively, followed by DoS and phishing emails attacks each with similar rate of (10.6%), then lastly identity theft and cyberstalk attacks with rate of (7.1%) and (7.4%), respectively. Respondents declared a variety of responses about assessing the rate of applying cybersecurity leadership practices in their financial organizations. However, almost one third of the respondents, nearly (35%) in total, stated that the rate of practicing cybersecurity leadership concept is approximately less than 50%. As a result, such a low rate of practicing the cybersecurity leadership concept was reflected in the responses to the next question, which showed no high rate of cyberattack prevention among individuals in financial organizations.
# A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm
## 3 The IACO-Based Spectrum Allocation Method
### The Process and Description of IACO
Adopting DE to accelerate the global convergence speed by optimizing the searching mode of the ants and guaranteeing the diversity of the population. Initialize DE with the subpopulation of ACO, \(E_{convergence}=E_{convergence}+1\). **Step 8:** Local search. Employing VNS to improve the local searchability of the improved algorithm by searching around the initial solution.
## 3 Results and Discussion
### HPLC-PDA Method Validation
#### 3.2.2 Linear Range, Linearity, Limits of Detection (LODs), and Limits of Quantification (LOQs)  
Linearity was analyzed in the concentration range 3.00-360.00 \(\rm\mu g/mL\) for L7 GC and C7 dGlu and 5.00-600.00 \(\rm\mu g/mL\) for CDR and DSM. The correlation coefficients (\(r^{2}\)) of all curves were >0.999, which showed that the established analysis range showed a good linearity. The LOD and LOQ results were 2.152 and 6.521 \(\rm\mu g/mL\) (for L7 GC), 1.927 and 5.840 \(\rm\mu g/mL\) (for CDR), 0.379 and 1.147 \(\rm\mu g/mL\) (for C7 dGlu), and 0.901 and 2.732 \(\rm\mu g/mL\) (for DSM), respectively. These results suggest that the minimum detection concentrations of L7 GC, CDR, C7 dGlu, and DSM in \(L\). _chinensis_ extract lay in the range 0.379 to 2.152 \(\rm\mu g/mL\)  
Figure 3: PDA spectra of the standard mixture (**A**) and \(L\). _chinensis_ extract (**B**).  
Figure 2: Chromatogram of \(L\). _chinensis_ extract at 340 nm (**A**); chromatogram of the standard mixture of L7 GC, CDR, C7 dGlu, and DSM at 340 nm (**B**).  
and that quantitative analysis was possible from 1.147 ug/mL. The results are summarized in Table 1.
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
## 8 Evaluation  
We evaluate the behavior of the presented control policies, and their interplay when combined through simulation. After presenting the experimental setup (Section 8.1), we propose three main sets of experiments. First, we investigate the ability of the ACS and ICS to adapt the application deployment and the computing infrastructure, respectively, when different control policies are adopted (Section 8.2). Second, we evaluate the flexibility of the proposed RL-based policy for infrastructure-level elasticity control; we show how it can control the computing infrastructure when we change the relative importance of avoiding resource wastage with respect to the ability of satisfying the application demand for resources (Section 8.3). Third, we discuss about the learning time experienced by the RL policy to learn a good infrastructure adaptation policy (Section 8.4).
# Efficiency Improvement Strategy for Multiple Operating Points in Doubly Fed Magnetic Geared Motor
## 6 Conclusions  
The operating principle and the division of the operating region are presented based on the individual frequency control in the inner and outer windings. In addition, the iron loss and efficiency characteristics are compared for three cases of different winding frequency conditions where the frequency control sequence of the inner and outer windings are defined and the inner and outer windings are operated as the armature and field parts, respectively. On the basis of the iron loss analysis, the results show that increasing the inner winding frequency can produce considerable iron loss due to magnetic saturation and the effect of inner winding flux on the outer stator. Furthermore, in the efficiency analysis, the maximum efficiency maps over the constant torque region are obtained using all winding frequency combinations, and it shows that, maximally, there is a difference of 3.5% in the efficiency with the different control strategies and a maximum efficiency of 93.1% is obtained. In addition to this inner-armature case, the frequency control sequence and the roles of the inner and outer windings as the armature and field parts are changed, and the analysis of the iron loss and efficiency are repeated. In the outer-armature case, the results show that, maximally, there is a difference of 7.7% efficiency between different strategies and a maximum efficiency of 92.5% is obtained. Namely, by selecting this frequency combination and the individual frequency control with respect to the inner and outer windings, the possibility of improving the overall operating efficiency over the multiple operating points is presented. Moreover, the performances of a DFMGM could be significantly improved if this study regarding individual frequency design and efficiency improvement by selection of the frequency combinations was expanded to comprehensive research that considered a greater number of frequency combinations, the whole operating region including the field-weakening region, and an optimization process for the frequency combination.  
[PERSON] developed the basic concept of the efficiency improvement strategy and implemented the validation of the concept, formal analysis, investigation, and original draft preparation. [PERSON] took charge of review, editing, supervision, and project administration. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, grant number NRF-2018R1D1A1B07050279.  
The authors declare no conflict of interest.
# Yield and Quality of Essential Oils in Hemp Varieties in Different Environments
## 3 Results and discussion
### Characterization of EOs Composition
[14] and [PERSON] and [PERSON] [11], with the exception of the \(\beta\)-myrcene content that was lower in our study. Our results do not confirm what was reported by [PERSON] et al. [14] and [PERSON] et al. [16], who stated that the terpinolene concentration in French varieties would be around 16%; in fact, in the present study, although the varieties were of French origin, the concentration of terpinolene reached 5.1% as a maximum value in Ferimon. Among the 65 compounds identified in the GC-MS analysis, \(\alpha\)-terpinene and \(\gamma\)-terpinene were found exclusively in Futura with 0.25% and 0.24%, respectively of total components, while o-cymene, p-cimen-8-ol and \(\beta\)-curcumene, representing 0.32%, 0.34% and 0.23% of the total, respectively, were found exclusively in Fedora. The analyzed EOs, along with the main terpen, showed very interesting minor components that, even if in small quantities, may perhaps contribute to the final aroma and characterize one EO over another. Often, these minor compounds, terpenoids, are a modified class of terpen with different functional groups, containing oxygen and oxidized methyl moved or removed at various positions. The oxidation, in fact, leads to the formation of highly oxygenated molecules, which are often further rearranged in similar compounds with different chemical reactivity, through a process that is not yet completely clear [50; 51]. The antibacterial properties of many EOs are probably due to these minor components [52], which, after the variation of their structures, become biologically active and are used worldwide for the treatment of many diseases. This could be the case of 1,8 cineol (eucalypt), used in the treatment of bronchial complaints, sinusitis and colds [53] or of linalool, a tertiary alcohol present in very low quantities in EOs, but with antimicrobial activity and a fresh, clean, mild, light floral fragrance with a slight citrus impression [54]. This may explain the particular aroma of some hemp EOs obtained, for example, from Futura at Aviano and Campoformido (Table 4). Instead, cyanophyllene oxide is an oxidation derivative of sesquiterpene carophyllene. It has a strong wooden odor and anti-inflammatory, antioxidant, antiviral, anti-carcinogenic and analgesic properties [55] and in all EOs obtained, it was present in quantities similar to those of some important monoterenes such as myrcene and \(\beta\)-ocimene (Figure 5). Other oxygenated sesquiterpenes that are found in smaller quantities are humulene epoxide II (derived from humulene), globulol, aromadendrene oxide-(2), allohimachalol and \(\alpha\)-bisabolol; all these compounds have anti-inflammatory, analgesic, antibiotic and anticancer activities.
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 2 UAVs Overview
### UAV Networks and Communications
#### 2.3.1 General Systems  
Every UAV is different in that not every model has the same systems nor the capabilities of another. Many autonomous UAVs are equipped with a global positioning system (GPS) or global navigation satellite system (GNSS), but UAVs that are a little less advanced and require a remote pilot are not equipped with such systems [24]. GNSS is a generic term used to describe the various types of satellite navigation systems worldwide. GNSS applications for UAVs include positioning, navigation, and timing services. GPS is the most prevalent GNSS, and it is owned and operated by the United States. Figure 3 describes the various satellite systems of different countries that are typically included in GNSS.  
UAVs equipped with GPS or GNSS typically can fly farther than those not equipped with either. Additionally, autonomous UAVs, or UAVs that do not require human intervention, is the UAV group kitted with the more independent systems. They are, in many cases, equipped with advanced navigation, collision avoidance, and environment detection systems, among others [26]. In the context of UAVs, the terms 'autonomous' and 'automated' are often used interchangeably, but these terms refer to different concepts. Automated systems are often limited in their tasks, and these systems function within a well-defined set of parameters. In contrast, an autonomous system is adaptive in nature and evolves following the environment around it. Autonomous UAV systems are built on the foundation of artificial intelligence and underlying machine learning capabilities, due to which they can adapt to dynamic environments [27].
# Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review
## 3 Search Results and Meta-Analysis
### Clustering Based Methods  
Clustering is an unsupervised learning method that involves grouping identical instances into the same sets [70; 102]. Although Clustering techniques are popular in financial fraud detection, they were, however, implemented considerably less than classification techniques in the reviewed articles [5; 102; 103]. [PERSON] and [PERSON] [56] used text-mining hierarchical clustering to design a financial transaction fraud-detection model. [PERSON] and [PERSON] [56] proposed an approach for financial fraud detection using the text dimension reduction method and document clustering. The authors used the SVDs technique to achieve text dimension reduction. The dual GHSOM technique is created to detect the non-fraud-central spatial hypothesis [18]. The model is capable of detecting the topological patterns of fraudulent financial transactions. [PERSON] and [PERSON] [102] integrated K-means clustering and SOM to design a clustering-based fraud-detection method. To address the uncertain clustering borders of nodes, which is one of the shortcomings of the SOM, this model additionally applied K-means clustering. Logistic regression (LR) techniques are mainly applied in binary and multi-class classification problems [35, 78]. It operates by performing regression on a set of variables. It is typically a useful technique for describing patterns and clarifying connections between numerous dependent binary variables. In line with a review article by [PERSON] et al. [104], the logistic regression method is one of the most used machine learning (ML) techniques for detecting financial misstatement models. Based on that review work, the majority of the studies used LR techniques for financial fraud detection. An appropriate technique for identifying characteristics related to fraudulent transaction detection using LR was proposed by [PERSON] and [PERSON]. [81] after reviewing published data. The authors compared the predictive ability of the suggested method against other detecting methods. Table 6 presents ML techniques used for financial fraud detection. 2.3 RQ3: What Are the Performance Evaluation Metrics Used for Financial Fraud Detection Using Machine Learning Methods  
To evaluate the performance of a model, the evaluation metric is very important in financial fraud detection [38, 40, 84]. However, there are no specific evaluation measures that are strictly used for evaluating ML techniques for fraud detection [38, 72]. In recent times, several performance evaluation metrics have been employed by different researchers that include accuracy, precision, recall, F1 measure, false-negative rate (FNR), the area under the curve (AUC), specificity, etc. In this section, we present evaluation metrics that have been employed in the reviewed papers based on the selected articles. Table 7 shows the formulas of the different performance measures.
## 3 Proposed Key Exchange Protocol  
### Security Model Assumption  
The proposed protocol was designed for a wireless sensor network model consisting of an array of IoT sensor nodes attempting to communicate with a trusted server. Each sensor node is assigned an identity \(ID\). The server is also assigned with an \(ID_{S}\). Some IoT applications, such as medical wearable sensor devices, are required to provide an anonymity property (hiding the true \(ID\) of a node device) to the protocol. However, the anonymity property was out of the scope of this study. The server is a legitimate entity responsible for gathering sensor data and distributing materials for the generation of public-private key pairs. In the trusted manufacture domain, the server generates its asymmetric key pair \((k_{S},K_{S})\). The server's private key \(k_{S}\) must be securely stored on the server side. The server's public key \(K_{S}\) and its identity \(ID_{S}\) are distributed to all sensor nodes in the network before deployment. The proposed protocol implements a physical unclonable function entity in each sensor node. Therefore, the server is required to store the challenge-response pairs \((\mathcal{C}^{t},\mathcal{R}^{t})\) of all sensor nodes in its database.
## 4 Scenario Analysis  
A base model was created first and calibrated to best resemble the current conditions experienced during the data collection phase. The measured volume and routing decisions were input at incremented 5-minute intervals. The routing decisions were entered as a proportion of all approaching vehicles selecting a certain route. These routing proportions were assumed to be static through each scenario in this research. After building the road network within the simulation model, the model was run for 4500 simulation-seconds, including the first 900 s for warming-up to the 3600 s of observation from the field. Eleven replications were run for each scenario with the simulation resolution of 10 time-steps per simulation-second and with random seeds from 25 to 35 by one-unit increments.
# Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication
## 5 Simulations Verification
### Model Verification  
The proposed analytical model was validated in the above mentioned environment and compared with [PERSON]'s model and [PERSON]'s model. In these experiments, we set \(\lambda=100\), \(p_{b}=1\times 10^{-5}\) and \(K=20\). The simulation and analytical results for delay and packet dropping rate are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(R\) & 300 m & \(T_{m}\) & 5000 \\ \(D_{l}\) & 4096 bits & \(\tau\) & 20 \(\upmu\)s \\ \(H_{mac}\) & 224 bits & CW & 15 \\ \(H_{phy}\) & 192 bits & \(DIFS\) & 64 \(\upmu\)s \\ \(H_{ack}\) & 112 + 192 bits & Data rate & 6 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 4: Simulation scenario: (**a**) real map; and (**b**) road topology.  
In Figure 5, we can observe that the results of our analytical model perfectly match that of experiment, while those of [PERSON]'s model and [PERSON]'s model have a great gap with the simulation results. This suggests that our analytical model is more exact compared with two existing models. This is because we not only consider the queuing process, but also take channel errors into consideration in the modeling process.
# A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data
## 3 Experimental Results
### Model Error Correction  
As we can see from Figure 7b, asymmetric sidelobes, which are caused by a cubic phase error [5] (pp. 210-212) can be found in the compression results. At the same time, the PSLR and ISLR in azimuth are not perfect enough because of model error. After model error correction, the results can be found in Table 6 and Figure 8. Obviously, the performance in azimuth has been greatly improved: as azimuth resolution becomes higher, PSLR and ISLR become lower. Unfortunately, there still is a small amount of cubic phase redundancy in both range and azimuth. Meanwhile, the contour is still mussy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Resolution (m)** & **PSLR (dB)** & **ISLR (dB)** \\ \hline Range & 0.754 & \(-26.343\) & \(-21.132\) \\ Azimuth & 0.605 & \(-12.776\) & \(-14.421\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance of compression after model error correction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Resolution (m)** & **PSLR (dB)** & **ISLR (dB)** \\ \hline Range & 0.754 & \(-26.343\) & \(-21.132\) \\ Azimuth & 0.605 & \(-12.776\) & \(-14.421\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance of compression just processed by two-step algorithm.  
Figure 7: The compression result of the corner reflector processed by the two-step processing algorithm. (**a**) The compression result of the corner reflector in slant range; (**b**) The compression result of the corner reflector in azimuth; (**c**) The two-dimension contour in azimuth and slant range.