esa-sceva/satcom-chunk-collection huggingface dataset:
# CAA-RF: An Anomaly Detection Algorithm for Computing Power Blockchain Networks
## 4 Experimental Results and Analysis
### Experimental Setting and Assessment Criteria  
The experiments were all conducted on a personal computer manufactured by Compal Electronics in Nanjing, China and equipped with a 12 th-generation Intel i7-12650H processor, 64 GB of RAM, and an NVIDIA RTX 4060 GPU, running Python 3.9. Various attacks, including DDoS attacks and Sybil attacks, were simulated within the network environment, and data under both normal and abnormal network conditions were collected. Accuracy evaluations were carried out using several models, including autoencoder, DBN, GANs, LSTM, RNN, VAE, CAA, and CAA-RF. Based on this dataset, deep learning neural networks were trained, and the accuracy for each training epoch was recorded. For every training round, the accuracy was assessed using the test set. Accuracy reflects the model's overall predictive ability on all samples and serves as a fundamental metric for assessing the global performance of the model. Through accuracy, we can intuitively understand the proportion of correct classifications on the entire dataset, which facilitates comparative analysis with other methods. However, in anomaly detection tasks with extremely imbalanced data, accuracy can sometimes be dominated by the most normal samples. Therefore, we use it as a reference for overall performance. Moreover, the F1 score, as the harmonic mean of precision and recall, comprehensively measures the model's ability to identify anomalous samples. In anomaly detection scenarios, relying solely on accuracy may mask the omission of anomalous samples, while the F1 score effectively balances the two types of errors: missing anomalies and misclassifying normal samples as anomalies. Thus, the F1 score can more accurately reflect the model's actual performance in detecting minority anomalies, which is especially suitable for the class imbalance problem faced in this study.
# Performance Tuning for Power Electronic Interfaces Under VSG Control
## 6 Case Study and Simulation
### Frequency Response with Renewable Participation and Parameter Tuning  
In order to satisfy the performance standards, the equivalent inertia constant \(H_{\text{eq}}\) is set as 5 by the parameter tuning. The generation units in a power system can be divided into two parts: (1) synchronous generators coupled to the power system, and (2) renewable generation units with virtual inertia. From Equation (5), \(H_{\text{eq}}\) obeys  
\[H_{\text{eq}}=\frac{\sum\limits_{i=1}^{SG}\left(H_{i}S_{\text{rated, }i}\right)+\sum\limits_{j=1}^{RG}\left(H_{\text{vir}},\,jS_{\text{RT, }j}\right)}{S_{\text{sys}}} \tag{35}\]  
where \(H_{i}\) is the inertia constant of synchronous generators \(i\), and \(S_{\text{rate, }i}\) is the rated power of a single unit. \(H_{\text{vir},j}\) is the virtual inertia constant emulated by renewable generation units, and \(S_{\text{RT, }j}\) is the output power of the \(j\)-th unit in real-time. \(S_{\text{sys}}\) is the rated power of the mix power system. Then, from Equation (35), the equivalent inertia property emulated by the RG units can be derived.  
When subjected to a step increment of demand by 0.03 pu, the frequency response is shown in Figure 18. By the predetermined settings, the proposed parameter tuning method satisfies the standards of performance indices. From Figure 18a, the maximum frequency deviation is 0.1799 Hz, and the PFR settling frequency is 49.9237 Hz. From (b), the maximum ROCOF is 0.1602 Hz/s. From (c), the output power of the converter interface increases to suppress the ROCOF. The final value of the power in the PFR loop is a little higher than the actual load increment because of the power loss in the switches and the filter.  
Figure 18: \(Cont.\)The comparison of the simulation results between the frequency response with or without the participation of renewable generation is listed in Table 6.  
From Table 6, it can be concluded that:  
(1) Due to the growing penetration of renewable generation and the consequent loss of inertia, the overall inertia property decreases, and the system performance could not satisfy the standard of the nadir index.  
(2) Previous research on VSG focuses on the validity of control strategies but lacks the parameter analysis of the equivalent inertia based on conditions in actual power systems. Therefore, the emulation approaches based on typical inertia constants could not satisfy the standard of the nadir index as well.  
(3) By parameter tuning, all the standards are satisfied. It is also noted that the settling frequency stabilized by the PFR is also improved, which is advantageous to cope with another possible disturbance before the frequency is restored by the SFR.
## 2 Materials and Methods
### Methods
#### 2.3.4 Trend Analysis  
The slope of the univariate linear equation over the same time period for each year from the starting year to the ending year was calculated using the least squares method for the variation in NEP over the year in that time period. Generally, a positive slope indicates an increase in NEP over the years, a negative slope indicates a decrease in NEP, and a slope equal to zero indicates a constant NEP.
## 3 Proposed System Architecture
### Abnormal and Normal Event Analysis
#### 3.3.2 Modified Difference (MD)  
The difference calculus is formulated to determine stationary points on the moving average of aspect ratio (\(r\)) and point distance (\(d\)), as shown in Equation (9). The observation can clearly provide information on the high possibility of an abnormal point, confirmed according to the crossing point of moving average and the maximum or minimum stationary point.  
\[MD(t,F,N_{0},N_{1})=MA(t,F(t+N_{0}+N_{1}),N_{1})-MA(t,F(t-N_{0}-N_{1}),N_{1}) \tag{9}\]  
where \(MD\)(\(t\),\(F\),\(N_{0}\),\(N_{1}\)) represents the modified difference for the selected features (\(r\) and \(d\)) at time \(t\) between the predefined moving averages. The selected optimal value for \(N_{0}\) is 0 and \(N_{1}\) is 51.  
Since the possibility of an abnormal point is estimated when the aspect ratio decreases relative to the pixel's location, the maximum difference value (\(l_{max}\)) on the moving average of aspect ratios can give the highest abnormal action point. When a person falls, point distance \(d\) and its moving average immediately increases relative to the pixel's location. In that case, a minimum difference value (\(l_{min}\)) must be considered in order to detect an abnormal event. Concepts for consideration are sketched out in Figure 8a,b.
## 2 Materials and Methods
### Environmental Forcing Data  
Daily climate data from the Lesvos airport weather station (Hellenic National Meteorological Service) were used to force the model for the 1980-2020 model evaluation period. The forcing data included T\({}_{\text{mean}}\), T\({}_{\text{min}}\), T\({}_{\text{max}}\), RH\({}_{\text{mean}}\), RH\({}_{\text{min}}\), RH\({}_{\text{max}}\), Precipitation, Radiation and CO\({}_{2}\) concentration.  
The daily simulated climate data from the Coordinated Downscaling Experiment--European Domain (EURO-CORDEX) were extracted for the region of interest at 0.11\({}^{\circ}\) resolution and used to force over the 21 st century. We used the downscaled outputs of the MPI-ESM-LR Global Circulation Model [67; 68] that provide continuous daily climate outputs for the periods 1970-2005 and 2006-2100, following the RCP4.5 and RCP8.5 emissions scenarios. The daily 1970-2005 climate data were compared with the data from the Lesvos airport weather station to derive a site-specific elevation (Z--m asl)-driven temperature correction equation:  
\[T_{mean}=T_{GCM}-(0.638*z/100) \tag{11}\]  
with T\({}_{\text{mean}}\) being the daily corrected average temperature and TGCM the temperature from the downscaled daily temperature from the MPI-ESM-LR general circulation model. The 0.638 coefficient represents the local lapse rate.
# Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer
## 4 Overall Flow and Discussion of the Experiment  
In the present study, the daily load data were used in combination with the economic and environmental data of Jiangsu Province to construct the dataset. The load data were obtained from a thermal power plant in Jiangsu Province, China, for the period from 1 July 2019 to 28 September 2022, with a time granularity of days, as shown in Figure 5. A total of 1096 data points were used to train, test, and validate the model from 1 July 2019 to 30 June 2022. The training, validation, and test set ratio was 7:2:1. Model performance was evaluated using data from 1 July to 28 September 2022 for prediction. The external variable data for the corresponding period were obtained from the official website of the National Bureau of Statistics of China [32] and the public meteorological information website [33].  
For the external variable data, the following factors were considered: GDP of Jiangsu Province, work days in China, thermal power share in Jiangsu Province (hereafter referred to as proportion), ambient temperature, rainfall records, wind speed, and the maintenance status of the power plant from which the load data were derived.
# A Controller Switching Mechanism for Resilient Wireless Sensor-Actuator Networks
## 3 System Overview
### Wireless Communication System
Thus, the carrier frequency offset is resolved using receiver implementation or an error-correction mechanism. Therefore, the LWB provides a higher-layer protocol and shared-bus application abstraction. In every slot, one initiator node transmits a message to all the others using a Glossy flood [8]. Figure 2 presents an illustration of each round and slot structure in the LWB. Each round consists of a slot for the current round schedule, n data slots, and round finish slots. All nodes repeat each round at every time period, and time for the round is \(T_{round}\). Each round begins with the schedule of the host, \(T_{sched}\). In this slot, the host node computes the scheduling for the network and broadcasts it to the source nodes. Upon receipt of the scheduling information, one source node becomes the initiator in the allocated slot and launches the Glossy flooding communication. One-hop nodes within the initiator's transmission range simultaneously receive the transmission and relay all packets simultaneously after the precise processing period. Repeated flooding enables nodes greater than two hops to receive packets sent by the initiator. In a multihop network, the LWB provides reliability exceeding 99.9% under real-world scenarios [8]. Additionally, nodes do not maintain any network state, making the LWB highly robust to network dynamics that result from misbehaving or failing nodes and wireless interference. To implement the constructive interference of Glossy flooding, wireless signals must be overlapped and synchronized. Therefore, application software executions on communication nodes should be minimized for strict time synchronization. Wireless signal receptions can also fail due to inherent wireless channel uncertainty and interference by other wireless communications. For network failures, Glossy flooding supports packet retransmission, which enhances the reliability of wireless communications.
# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 5 Challenges and Future Research Directions
### Adversarial Threats to Generative Models  
Despite the promising capabilities of Generative AI models in enhancing intrusion detection systems within IoV, these models themselves are vulnerable to various adversarial threats that can compromise their reliability and security. One prominent risk involves GAN poisoning and training data manipulation, where attackers inject maliciously crafted or mislabeled samples into the training dataset. This contamination can cause the generator to produce misleading attack signatures or confuse the discriminator, ultimately degrading the IDS' detection performance. In federated learning contexts, where data is distributed and often not centrally verified, poisoning attacks become even harder to detect and mitigate.  
Another significant threat category is input tampering and evasion attacks, in which adversaries introduce subtle perturbations to input data designed to mislead the IDS. These adversarial examples are often imperceptible to humans but can result in a complete misclassification by generative models such as VAEs- or Transformer-based detectors. For instance, a manipulated CAN message with minimal alterations might evade detection if the model overly relies on learned patterns rather than robust features. This raises concerns about the deployment of such models in real-time, safety-critical vehicular environments.  
Additionally, model stealing and reverse engineering attacks pose risks to the intellectual property and functional integrity of generative IDS. In scenarios where models are deployed at the edge (e.g., within OBUs or RSUs), adversaries can query the model extensively to replicate its behavior, recover its architecture, or even expose sensitive training data. Once replicated, stolen models can be studied offline to generate adversarial traffic or optimize evasion strategies. These threats necessitate the integration of robust defensive mechanisms, such as adversarial training, watermarking, differential privacy, and model access limitations, in generative IDS design.
# Plant Design for the Conversion of Plastic Waste into Valuable Chemicals (Alkyl Aromatics)
## 1 Introduction
### Purpose of the Study  
The purpose of this study is to design a plant that converts plastic waste, specifically high-density polyethylene (HDPE) and low-density polyethylene (LDPE), into valuable chemicals known as alkyl aromatics. The rapid increase in plastic production and consumption has resulted in a significant accumulation of plastic waste, leading to harmful effects on both human health and the natural environment. In this study, we acknowledge that plastic pollution arises not only from discarded plastics but also from the chemicals released during plastic production and decomposition. Various waste management strategies, including landfilling, recycling, conversion to liquid fuel, and upcycling, are explored. However, landfilling contributes to long-term environmental degradation, recycling rates remain low in certain regions, and the commercialization of plastic-to-liquid fuel conversion faces challenges. Upcycling, specifically depolymerization and repolymerization, presents an opportunity to transform plastic waste into valuable chemicals. HDPE and LDPE, which constitute a significant portion of plastic waste, are the focus of this study. By implementing controlled partial depolymerization, we propose a plant design that optimizes energy consumption, process efficiency, and product quality. The goal is to contribute to sustainable plastic waste management and reduce environmental pollution caused by plastic waste.
## 3 Results and Discussion
#### 3.1.2 One-Step Data Analysis and Evaluation of Model Performance  
Despite the acceptable two-step approach indices, [PERSON] and [PERSON] [13] and [PERSON] [11] stated that the one-step approach better fits the data than the two-step method, which produces directly interpretable regression diagnostics and standard errors. Therefore, one-step data analysis was applied for direct construction of the tertiary HM\({}_{1-\delta}\); however, it requires advanced knowledge and skills in statistics and computer programming [12]. With the assistance of the user interface in commercial process-engineering software Athena Visual Workbench, it was possible to adjust the initial values and visually inspect the agreement between the test call model data and experimental observations. In our study, for all cases of the one-step method, we used not only the parameters previously estimated by the two-step approach, but also the ability of Athena Visual Workbench to fix some of the parameters as constants as well as to optimize those that were not determined in the previous nonlinear regression analysis. The inability to estimate the parameters more reliably may also be caused by an insufficient number of experimental data points associated with the specific value of the observed factor. Therefore, for successful nonlinear regression one-step kinetic data analysis, high-quality data are necessary, and the initial estimated values should be as close to the final values of parameters as possible. However, according to [PERSON] [12], suitable software with a graphical-user-interface can make it easier to perform the regression analysis. Finally, the results may be used to improve the experimental design and data collection. The estimated parameters of the tertiary HM\({}_{1-\delta}\) constructed through one-step data analysis for single cultures of the strains considered in this study are summarized in Table 3, and the statistical goodness-of-fit indices are presented in Table 4.
# A Low-Cost Evaluation Tool for Synchronization Methods in Three-Phase Power Systems
## 2 Evaluation Tool and Methodology of Use
### The Methodology of Use
#### 2.2.1 Connection of the Emulator to the Digital Controller  
To avoid electrical damage to the board, the connection must consider the operating voltage ranges of the DACs in the emulator and the ADCs in the digital controller. The DACs and ADCs shown in Figure 4 operate in the \([0,3.3]\) V range, which enables the direct  
\begin{table}
\begin{tabular}{c c} \hline
**Variable** & **Description** \\ \hline Digital Controller & Arduino Due \\ \hline \(out_{0}(t)\) & Analog output \\ \(out_{1}(t)\) & Analog output \\ \(sw_{tc,A}(t)\) & Digital input, assign synchronization technique \\ \(sw_{tc,b}(t)\) & Digital input, assign synchronization technique \\ \(sw_{out10,a}(t)\) & To select the outputs to \(out_{0}(t)\) and \(out_{1}(t)\) \\ \(sw_{out10,b}(t)\) & To select the outputs to \(out_{0}(t)\) and \(out_{1}(t)\) \\ \(DT(t)\) & Duty cycle for execution time \\ \(A_{1,b}(t)\) & Amplitude of the fundamental voltage estimated. \\ \(\theta_{1,\sigma}\left(t\right)\) & Phase of the fundamental voltage estimated. \\ \(f_{1,\sigma}\left(t\right)\) & Frequency of the fundamental voltage estimated \\ \hline \end{tabular}
\end{table}
Table 3: Selections of harmonic content, case studies, and DAC signals in the emulator.  
Figure 5: Proposed methodology of use.  
\begin{table}
\begin{tabular}{c c} \hline
**Variable** & **Description** \\ \hline \(Emulator\) & LAUNCHXL-F28379D C2000 Delfino LaunchPad \\ \hline \(v_{xx}(t)\), \(x=a,b,c\) & Voltage a, b, or c \\ \(v_{sat,ref}(t)\) & Fundamental voltage a \\ \(sync(t)\) & Synchronization signal \\ \(out_{A}(t)\) and \(out_{B}(t)\) & Analog output \\ \(sw_{H}(t)\) & To select the case of the harmonic \\ \(sw_{mode}(t)\) & To select the case study \\ \(sw_{outAB}(t)\) & To select the outputs for DACOUTA and DACOUTB \\ \(A_{1,ref}(t)\) & Reference of the fundamental amplitude \\ \(\theta_{1,ref}\left(t\right)\) & Reference of the fundamental phase \\ \(f_{1,ref}\left(t\right)\) & Reference of the fundamental frequency \\ \hline \end{tabular}
\end{table}
Table 2: Description of the emulator signals.  
connection between the emulator and digital controller in the 3.3 V CMOS standard. In particular, the analog outputs of the emulator, identified as DACx, are obtained through PWM outputs with a low-pass filter and, also, operate in the range of [0, 3.3] V.  
If the ADCs and DACs do not operate in the same range, it is possible (i) to program the internal signals of the emulator and digital controller to operate in an allowed voltage range or (ii) to use a conditioning circuit. An example of this type of circuit is shown in Figure 6 [32].
## 3 Results
### Evaluation of Adsorption Capacity of CyP(Ph)4-DVB
Moreover, the 1/n values for BY2 and BB3 adsorption on the CyP(Ph)4-DVB were calculated to be 0.63 and 0.76, indicating that it could be considered a favorable adsorption system. The \(k_{F}\) values were higher for BY2 (14.2 mg\({}^{1-1/n}\)L\({}^{1/n}\)/g) and BB3 (53.7 mg\({}^{1-1/n}\)L\({}^{1/n}\)/g) adsorption on the CyP(Ph)4-DVB than for the dyes' (4.56 mg\({}^{1-1/n}\)L\({}^{1/n}\)/g for BY2 and 20.1 mg\({}^{1-1/n}\)L\({}^{1/n}\)/g for BB3) adsorption on poly(DVB), which underlines the validity of the synthesis. The adsorption mechanism of BY2 and BB3 on CyP(Ph)4-DVB mainly involves \(\pi\)-\(\pi\) interactions between the aromatic rings of the dyes and the benzene rings in the adsorbent [53; 54; 55]. As illustrated in Figure 10, the formation of weak hydrogen bonds, as well as electrostatic interactions, between the positively charged BY2 and BB3 dyes and the negatively charged surface of the CyP(Ph)4-DVB under the experimental conditions may also be considered as the binding mechanism [56]. It was previously confirmed that BY2 adsorption on a C-SiO\({}_{2}\) composite [50], lignin-based hybrid adsorbents [53] and EGDMA-cellulose polymer sorbents [54] (where EGDMA--ethylene glycol dimethacrylate) took place in accordance with the Freundlich model and involved the above-proposed interactions. For example, the k\({}_{\rm F}\) values determined for C-SiO\({}_{2}\) and lignin-based hybrids were  
Figure 9: Fitting of the equilibrium experimental data to the [PERSON], Freundlich and Temkin isotherm models for the adsorption of (**a**) BY2 on poly(DVB), (**b**) BY2 on CyP(Ph)4-DVB, (**c**) BB3 on poly(DVB) and (**d**) BB3 on CyP(Ph)4-DVB. equal to 27.37-55.41 mg\({}^{1-1/n}\)L\({}^{1/n}\)/g [50] and 11.1-45.7 mg\({}^{1-1/n}\)L\({}^{1/n}\)/g [53] depending on the temperature changes, respectively. BB3 adsorption on sulfuric-acid-activated montmorillonite [55], C-SiO\({}_{2}\)[50] and functionalized lignin-based hybrids [53] also followed the Freundlich model.