esa-sceva/satcom-chunk-collection huggingface dataset:
# Fractional Sliding Mode Harmonic Control of an Active Power Filter
## 2 Dynamic Model of an Active Power Filter
\begin{cases}\dfrac{di_{1}}{dt}=-\dfrac{R_{c}}{L_{c}}i_{1}+\dfrac{v_{1}}{L_{c} }-\dfrac{v_{dc}}{L_{c}}d_{n1}\\ \dfrac{di_{2}}{dt}=-\dfrac{R_{c}}{L_{c}}i_{2}+\dfrac{v_{2}}{L_{c}}-\dfrac{v_{ dc}}{L_{c}}d_{n2}\\ \dfrac{di_{3}}{dt}=-\dfrac{R_{c}}{L_{c}}i_{3}+\dfrac{v_{3}}{L_{c}}-\dfrac{v_{ dc}}{L_{c}}d_{n3}\end{cases}  \tag{7}\]  
We define two state variables  
\[x=i=\left(i_{1}\quad i_{2}\quad i_{3}\right)^{T},\;\dot{x}=\dot{i},\;x=\left(x _{11}\quad x_{12}\quad x_{13}\right)^{T},\;\dot{x}=\left(x_{21}\quad x_{22} \quad x_{23}\right)^{T}.\]  
Taking the time derivative with respect to time yields  
\[ \begin{array}{ll}\dot{x}_{k}&=-\dfrac{R_{c}}{L_{c}}l_{k}+\dfrac{1}{L_{c}} \dfrac{dv_{k}}{dt}-\dfrac{1}{L_{c}}\dfrac{dv_{dc}}{dt}d_{nk}\\ &=\dfrac{R_{c}^{2}}{L_{c}^{2}}k-\dfrac{R_{c}}{L_{c}^{2}}v_{k}+\dfrac{1}{L_{c} }\dfrac{dv_{k}}{dt}+\left(\dfrac{R_{c}}{L_{c}^{2}}v_{dc}-\dfrac{1}{L_{c}} \dfrac{dv_{dc}}{dt}\right)d_{nk}\end{array} , \tag{8}\]  
where \(k=1,2,3\). Considering the external disturbances, the model of an active power filter can be rewritten as  
\[\dot{x}=f(x)+bu+d, \tag{9}\]  
\[\text{where }f(x_{1k})=f(i_{k})=\dfrac{R_{c}^{2}}{L_{c}^{2}}i_{k}-\dfrac{R_{c}}{L_ {c}^{2}}v_{k}+\dfrac{1}{L_{c}}\dfrac{dv_{k}}{dt},\;b=\dfrac{R_{c}}{L_{c}^{2} }v_{dc}-\dfrac{1}{L_{c}}\dfrac{dv_{dc}}{dt}\]  
\[u=d_{n}=\left(d_{n_{1}}\quad d_{n_{2}}\quad d_{n_{3}}\right)^{T},\;d=diag(d_{ 1}\quad d_{2}\quad d_{3}).\]  
**Assumption 1**. : \(d\) _is an unknown disturbance satisfying the condition \(\rho-1d1>\sigma_{1}\). where \(\rho\) is a positive constant, and \(\sigma_{1}\) is a tiny positive constant._
## 2 Materials and Methods
### Experimental Description
As the allowable measurement range of the data logger was within \(\pm\) 10 V, the output voltage from the generator was reduced to within 10 V by using a voltage divider circuit. Figure 5 illustrates schematic diagrams of the voltage divider circuit. \(V_{in}\) is the voltage converted to DC by the rectifier, and \(V_{out}\) is the voltage measured by the data logger. To reduce the voltage of \(V_{in}\), one resistor of 15 k\(\Omega\) and five resistors of 1 k\(\Omega\) (total resistance of 20 k\(\Omega\)) were connected in series on the breadboard. \(C\) is a condenser. \(V_{in}\) was calculated using Equation (1) as follows:  
\[V_{out}=\frac{\text{\emph{resistor}}}{\text{\emph{Total resistors}}}\times V_{in }=\frac{1}{20}V_{in}, \tag{1}\]  
A gyro sensor (wit-motion, BWT61 CL) that could measure the rotation angle of the flap in real-time was introduced to examine the behavior pattern of the pendulum. It was attached to the upper part of the flap together with the round rack gear. The measured data could be obtained wirelessly via Bluetooth. Additionally, the gyro sensor was sealed with a freezer bag so that it would not come in direct contact with the water. To investigate the behavioral characteristics of the flap, the rotation angle \(\theta_{x}\) of the flap was measured. The experiments were composed of two parts: investigating the natural oscillation period of the flap, and determining the power generation efficiency, depending on the hydraulic conditions. In the former experiment, a time series of the rotation angles of the flap was measured when the flap was tilted in one direction in still water and then released. The experiments were conducted six times each on both sides (12 times in total).
## 3 Results  
### Concentrations of Bisphenol A in Recyclate Containing PET Bottles in Europe  
Overall, a total of 375 rPET bottles collected in Europe between 2019 and 2023 were analyzed regarding their BPA content. The bottles had varying contents of rPET ranging from 11% to 100%. The results of the quantitative determination of BPA in the rPET bottles are given in Table 1. In addition, five reference bottles made from virgin PET were analyzed. These reference bottles did not contain BPA above the detection limit of 0.007 mg/kg.  
The average rPET content in the investigated PET bottles increased from 71% in 2019 to 100% in 2022 and 2023. The average content of rPET in the bottles over the last five years was 90%. The results show that the mean value of BPA concentration increases from 0.715 mg/kg in 2019 to 4.16 mg/kg in 2022, which correlates with the increasing  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Concentration [mg/kg]**} \\  & **2019** & **2020** & **2021** & **2022** & **2023** & **2019–2023** \\ \hline maximum & 5.28 & 4.68 & 20.0 & 56.6 & 18.4 & 56.6 \\ minimum & 0.033 & 0.020 & 0.073 & 0.260 & 0.050 & 0.020 \\ mean value & 0.715 & 0.737 & 2.42 & 4.16 & 1.50 & 1.74 \\ median & 0.450 & 0.592 & 0.893 & 0.867 & 0.760 & 0.703 \\ \hline mean rPET & 71\% & 76\% & 98\% & 100\% & 100\% & 90\% \\ \multicolumn{10}{l}{analyzed samples} & 23 & 116 & 51 & 56 & 129 & 375 \\ \hline \hline \end{tabular}
\end{table}
Table 1: BPA concentrations in rPET bottles (\(n=375\)) supplied from European bottling companies between 2019 and 2023.  
rPET content in the analyzed PET bottles. In 2023, the mean BPA concentration drops significantly to 1.50 mg/kg. The median also increases between 2019 and 2021 but then drops to around 0.760 mg/kg in 2023. The highest BPA concentration was measured in 2022 at 56.6 mg/kg in one PET bottle. In 2021 and 2023, the highest concentrations were 20.0 mg/kg and 18.4 mg/kg, respectively. The median in 2022 was below that of 2021, which means that the high mean value in 2022 was triggered by the high outlier (and three further outliers with 34.4 mg/kg, 14.8 mg/kg, and 14.7 mg/kg). Without these four outliers, the mean value in 2022 would have been 1.99 mg/kg instead of 4.16.
## 3 Results
### Identification of Foodborne Pathogens Using Protein Fingerprinting  
The analysis of the isolated bacteria was carried out with the MALDI Biotyper (MBT) equipment, and the produced spectra were compared to those available in the MBT database. The MBT database contains the spectral profiles of recognized bacterial species, and the bacteria were identified on the basis of the results of the analysis. Based on the MBT results, specific details regarding the strains' species and subspecies were provided. According to the MBT Compass software, 100% of the 102 _E. coli_ isolates, 93 _S. aureus_ isolates, 32 _Salmonella_, 22 _L. monocytogenes_, 20 _A. baumannii_, and 12 _H. alvei_ isolates were identified. MBT was able to identify all isolated bacteria in the current investigation, with a log score ranging from 2.00 to 2.56. According to this analysis, the MBT is capable of correctly detecting bacteria. It was observed that several spectrum proteins were distributed throughout the zone's initial bands, ranging from 3000 to 11,000 Daltons (Da), with the strongest peak signals located between 4100 and 9900 Da for the majority of _E. coli_ strains (Figure 1). In the analysis of _S. aureus_ proteins, the peak signals ranged from 3000 to 18,000 Da, and robust signals were observed between 4300 and 6900 Da. As seen in (Figure 2), all the identifiable strains of _S. aureus_ were synchronized with the eight reference strains kept in the MBT's Compass software library.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Foodborne Pathogen**} & **Positive Shawarma Samples** & **Positive Chicken Burger Samples** & **Total (_n = 300_)** \\ \cline{2-6}  & **No.** & **\%** & **No.** & **\%** & **No.** & **\%** \\ \hline _E. coli_ & 58 & 38.67 & 44 & 29.33 & 102 & 34.00 \\ _S. aureus_ & 48 & 32.00 & 45 & 30.00 & 93 & 31.00 \\ _Salmonella_ & 19 & 12.67 & 13 & 8.67 & 32 & 10.67 \\ _L. monocytogenes_ & 13 & 8.67 & 9 & 6.00 & 22 & 7.33 \\ _A. baumannii_ & 12 & 8.00 & 8 & 5.33 & 20 & 6.67 \\ _Hafnia alvei_ & 7 & 4.67 & 5 & 3.33 & 12 & 4.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Frequency of various foodborne pathogens in samples taken from shawarma sandwic (_n_ = 150) and chicken burgers (_n_ = 150).  
Figure 1: A spectral protein profile analysis of _E. coli_ isolated from fast food samples compared to the reference strain RV412_A1_2010_06a LBK maintained in the MBT is shown. The lower parts of the spectra are colored blue, indicating that the stored spectra in the Compass library were used to match the pattern; the upper parts of the spectra are colored green, indicating that the peaks were excellently matched. The upper yellow and red colors of the spectra are characterized by mismatched and intermediate peaks, respectively.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 5 Security Analysis
### Formal Analysis
#### 5.1.7 Real-or-Random Model  
The real-or-random (RoR) model [34] is a formal security analysis method that proves the semantic security of the session key in the authentication protocol. In the proposed protocol, the participants are the vehicle user, fog node, and cloud server: \(\textit{Ta}_{V_{i}^{k_{1}}}^{k_{1}},\textit{Ta}_{FN_{i}}^{k_{2}}\), and \(\textit{Ta}_{CS}^{k_{3}}\), respectively. In the RoR model, adversary \(A\) can intercept, eavesdrop, replay, and modify all insecure channel messages to determine the session key, \(SK\). \(A\) can perform the queries \(Execute(\textit{Ta}_{V_{i}^{t}}^{k_{1}},\textit{Ta}_{FN_{i}}^{k_{2}},\textit{Ta }_{CS}^{k_{3}})\), \(CorruptSC(\textit{Ta}_{V_{i}^{t}}^{k_{1}})\), \(Send(\textit{Ta}_{x}^{k_{n}},\textit{Msg})\), and \(Test(\textit{Ta}_{x}^{k_{n}})\). Table 3 presents the queries performed by \(A\). **Theorem 1**. : _We define \(q_{\textit{thr}}\), \(|Hash|\), \(q_{\textit{send}}\), and \(1\) as the number of hash queries performed by \(A\), the range space of the hash function, the number of send queries performed by \(A\), and the length of the identity \(V_{i}\), respectively. Furthermore, the breaking possibility of the ECCDH problem is \(Advp_{M}^{ECC}(A)\), and the Zipf parameters are \(C^{\prime}\) and \(s^{\prime}\). When \(Advp(A)\) is the probability that \(A\) breaks the session key in polynomial time, we prove the following equation:_  
\[Advp(A)\leq\frac{q_{\textit{thr}}^{2}}{|Hash|}+2 Advp_{M}^{ECC}(A)+2 max\{C^{ \prime}q_{\textit{send}}^{s^{\prime}},\frac{q_{\textit{send}}}{2^{l}}\} \tag{1}\]  
Proof. : \(A\) plays five games, \(GM_{n}(n=0,1,2,3,4)\), based on the RoR model. \(AVTG^{WIGM_{n}}(A)\) represents the advantage of \(A\) to break the session key after playing the game \(GM_{n}\). * \(GM_{0}\): In the first game, \(A\) selects a random bit, \(r\). \(A\) does not know any information required to calculate the session key, \(SK\), and has no queries to perform. Thus, we derive the following equation: \[Advp(A)=|2 AVTG_{WIGM_{0}}(A)-1|.\] (2)
* \(GM_{1}\): \(A\) performs the \(Execute\) query to conduct an eavesdropping attack. From that query, \(A\) obtains all public channel messages \((D_{1}=R_{1},RID_{i},Q_{i},ID_{j}),(D_{2}=R_{1},RID_{i},R_{2},r_{2}R_{1},L_{j},ID_{j}),(D_{3}=K_{ij},R_{3},r_{3}R_{1},Auth_{j})\), and \((D_{4}=K_{ij},r_{2}R_{3},Auth_{i})\). Afterward, \(A\) performs a _Test_ query to calculate the session key, \(SK\). However, \(A\) cannot calculate the session key, \(SK\), because it is masked by long-term keys \(N_{i}\) and \(N_{j}\) and the short-term key \(R_{i-j-cs}\). Thus, we obtain the following equation:
## 5 Discussion
### Distribution of Mixed Sedimentation and the MSR Reservoirs  
#### 5.3.1 Mixed Sedimentation Controlled by Provenance Supply and Tidal Current  
Previous studies have identified sea-level fluctuations, paleogeographic conditions, and provenance supply as the primary factors influencing the distribution of mixing sedimentation [41; 42; 43; 44; 45; 46]. In the transition zone between the shallow-water delta and tidal flat on a gentle slope, the distribution of mixed sedimentation in Member B of the Asmari Formation in the C Oilfield is mainly controlled by provenance supply and tidal currents. Over extended periods, persistent provenance supply and the repeated action of tidal currents transform the original pure sedimentary system into a mixed sedimentary system. Provenance supply profoundly affects the spatial variation, properties, and amounts of terrigenous material, thereby controlling the distribution of mixed sedimentation. River channel sands are repeatedly reworked by tidal processes, gradually transitioning into tidal flat deposits. Tidal currents are a critical force determining the final depositional site of terrigenous clastic particles, as well as the composition and spatial distribution of MSRs. Tidal energy influences transport distance, sediment distribution, mixed layer thickness, particle size, and compositional ratios. More MSRs occurred around the tidal flat than in other depositional environments.  
The provenance supply of terrigenous siliciclastics directly impacts the compositional characteristics and scale of mixed sedimentation. The distance to the provenance area and regional topographic features define the intensity and attributes of provenance supply. In the southeastern area of the M Oilfields Group, there was a large-scale Ahwaz Delta identified in previous studies [47]. The C Oilfield, located in the northeast of the M Oilfields Group, was therefore influenced by provenance supply from the southeast. There are obvious differences in mixed sedimentation between the provenance-affected zone and the northwestern and southeastern areas of this oilfield. As shown in Figure 10, the thickness ratios of MSRs vary among the provenance-affected zone and the northwestern and southeastern areas of the oilfield. In the B4 sublayer, which is strongly influenced by provenance supply, a relatively large scale of distributary channels developed, resulting in no MSRs being distributed in the provenance-affected zone. Relatively clean depositional water during the B3-2 to A3 sequences resulted in more carbonate precipitation in these sublayers. From the B3-2 to B1 layers, with reduced influence from provenance, there was more intensive mixed sedimentation in the provenance-affected zone, where some sands could still be supplied. During the A3 sequence, as provenance supply further weakened, very few MSRs deposited.  
Figure 10: Thickness ratio of MSRs in six sublayers of the C Oilfield in different region. Note that the codes of different sub-members are shown in the upper left corner of each subfigure, including A3, B1, B2, B3-1, B3-2, and B4 layers.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Biodegradable Graphene ECG Electrodes
#### 2.3.2 Graphene-PHA ECG Electrode  
Polyhydroxyalkanoaote (PHA) is a kind of aliphatic polyester bioplastic that is accumulated by Ralstonia eutropha [57]. With PHA nanofibers and graphene, [PERSON] et al. created a graphene-PHA ECG electrode that is biocompatible and can be completely biodegraded by microbes in terrestrial environments [58]. The PHA nanofibers were produced by electrospinningning and served as the scaffold for graphene. The conductive graphene was derived by hydrazine-hydrate-reduced graphene oxide, and was applied to the PHA scaffold to form the graphene-PHA composite by vacuum filtration. After being compressed by a roller to boost its conductivity, the graphene-PHA ECG electrode showed a high-performance ECG monitoring capability. Besides, although the electrode is biodegradable, it can be kept for 1-2 years at the right humidity and oxygen levels.
## 6 Algorithms and Frameworks in Federated Learning
### Adaptive Learning Rates in Federated Contexts
#### 6.4.1 Federated Adaptive Algorithms and the Importance of Adaptive Learning in a Federated Setting  
Traditional centralized learning benefits from consistent, IID data, but federated learning encounters challenges with non-IID data from diverse devices. This variability requires adaptive learning rates to handle each client's unique data and model progress. Adaptive mechanisms are essential for quicker and more stable convergence in federated settings [89; 90]. Two notable adaptive algorithms in FL are as follows.  
* Federated Adagrad: Enhances the Adagrad algorithm by adjusting the learning rates based on unique client data gradients, ensuring personalized optimization [91].
* Federated Adam: Builds on the Adam optimizer, which combines Adagrad and RMSprop's advantages, maintaining running averages of gradients and their squares for more balanced learning rate adaptation.  
These adaptive methods promise improved convergence and stability but face challenges, such as computational overhead and synchronization issues, particularly with non-IID data. They require careful management to avoid divergence and still necessitate hyperparameter tuning. Research highlights the effectiveness of Adagrad, especially in communication-efficient strategies and diverse datasets. Future FL advancements will need to balance the adaptive learning rates' benefits with the management of computational and communication complexities [91].
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 2 Materials and Methods
### Total Loss of Algorithm
#### 2.7.2 Training Loss  
The total loss can help us check the feasibility of the dataset between trainings, because it can specify the range to test to obtain an appropriate dataset. Practically, the mean loss is calculated for each batch and then averaged across all the batches within an epoch. This provides a comprehensive assessment of the model's performance on the training data, helping to follow its progress over time. Considering the fast-processing time, calculating the total number of training rounds in epochs is performed as follows:  
\[T_{B}=T_{D}/B_{S} \tag{26}\]  
where \(T_{B}\) is the total number of batches, \(T_{D}\) is total data size, and \(B_{S}\) is the batch size. For determination of the number of samples that will be distributed across the network, the batch size is essential. When training a deep learning model, the average loss is typically computed over the entire training dataset, in small batches of samples. This is known as batch training. This is because training the model on the entire dataset in one go could be computationally expensive, and it could also cause the model to overfit on the training data.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 2 Related Work
### GAN-Based Intrusion Detection Without Major Loss Modifications  
Many studies have employed standard GAN or Wasserstein GAN (WGAN) frameworks to improve intrusion detection by oversampling rare attack classes. For instance, [PERSON] and [PERSON] [3] utilized a GAN-based approach to generate synthetic samples for underrepresented attack types in the CIC-IDS-2017 dataset, successfully mitigating class imbalance. Similarly, [PERSON] et al. [4] proposed a hybrid IDS model integrating GANs with Deep Autoencoders (DAE) and Random Forest (RF), enhancing the detection of rare attack patterns in 5G IoT environments.  
Other works have explored integrating GANs with classification models to enhance feature representation learning. [PERSON] et al. [5] introduced an auxiliary classifier into the GAN framework, ensuring that the generated samples not only resemble real network traffic but also preserve distinctive attack characteristics. [PERSON] et al. [6] leveraged WGAN with Temporal Convolutional Networks (TCN) and self-attention mechanisms to improve the efficiency of sequence modelling in IDS.  
Several studies have focused on balancing the quality and diversity of generated samples. [PERSON] et al. [7] incorporated WGAN-GP with ANOVA feature selection to reduce high-dimensional feature complexity while effectively generating minority-class samples. [PERSON] et al. [8] introduced CL-GAN, a semi-supervised architecture that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, improving detection accuracy for DoS attacks in IoT environments.  
To enhance scalability, [PERSON] and [PERSON] [9] proposed a distributed multi-discriminator GAN, reducing computational overhead in large-scale IDS deployments. [PERSON] et al. [10] introduced a novel termination criterion based on box-plot distribution matching, ensuring that the generated data closely aligns with real-world traffic. Furthermore, [PERSON] et al. [11] and [PERSON] et al. [12] demonstrated that augmenting IDS datasets using GAN-generated samples significantly improves classification accuracy when integrated with deep learning models such as CNN, BiLSTM, or LightGBM.
## 2 Materials and Methods
### Aplid Settling Bioassay  
The experiments were carried out using peach-potato aphid _Myzus persicae_ (Sulz.) (1-7- day old apterous females) and _Brassica pekinensis_ (Lour.) Rupr. (3-week old plants). Aphids and plants were reared in the laboratory under controlled conditions of 20 \({}^{\circ}\)C, 65% r.h., and long day photoperiod (L16:8D) in the growing chamber Sanyo MLR-352H. Due to the cryptic mode of aphid feeding (they feed on plant sap and need to penetrate plant tissues with sucking-piercing mouthparts in order to reach phloem elements that are the food source), it is not possible to monitor plant acceptance directly. Neither it is possible to directly monitor the effect of exogenously applied compounds. Therefore, according to the known facts on aphid feeding behaviour [25], aphid settling bioassay is the commonly applied method to determine the level of plant acceptance by these insects. The studied compounds were applied as 0.1% solutions in 70% ethanol by immersing plant leaves for 30 s in a given solution. Leaves immersed in pure 70% ethanol were used as controls. Treated leaves (both, control and lactone-treated ones) were offered to aphids after 1 h that followed the immersion. The time was necessary to let the solvent evaporate. During the experiment, aphids had a choice between treated and control leaves. The experiment was replicated 8 times for each substance and 20 aphids were used in each replicate. The number of aphids that settled, which was determined by the position of their antennae [26], was counted on each leaf 1, 2, and 24 h after the onset of the experiment. The numbers of aphids on control and treated leaves were compared using Student's \(t\)-test at \(p=0.05\) to reveal significant differences in preference for control or treated leaves. Based on the number of aphids settled on the control leaf (\(C\)) and the number of aphids settled on the treated leaf (\(T\)), the relative index of deterrence (_ID_) was calculated, according to the formula:  
\[ID=\ \frac{C-T}{C+T} \tag{2}\]  
The values of _ID_ ranged between 1 (ideal deterrent) and \(-1\) (ideal attractant).