esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 VSG-Enabled Grid Service for Power Systems
### Microgrid  
A microgrid (MG) can be divided into three categories: the AC-MG, the DC-MG, and the hybrid AC-DC MG [65]. An AC-MG is akin to a small-scale duplication of a distribution system, as it has autonomous operation ability. As the demand for efficiency and performance increases, many AC loads are connected through converters under variable frequency control. Meanwhile, huge quantities of renewable energy sources, energy storage, EV, and electronic instruments that are inherently DC-generating or DC-consuming units are utilized [2]. For the DC-MG, its main advantages are its seamless integration and efficient energy conversion (as DC/AC energy conversion is omitted) [72; 73; 74]. The central device of the MG system is its bidirectional power converter (BPC), which interconnects interior DGs, energy storage, and local loads with an exterior upper grid. By implementing a VSG control strategy on a BPC, the voltage fluctuation on the AC or DC bus can be restrained. Also, by utilizing internal energy reserves, the MG can provide grid services, too [75]. In some applications, a hybrid AC-DC microgrid is implemented, which combines the advantages of both the AC and DC systems [76]. The two AC sub-MG and DC sub-MG are connected by a bidirectional interlinking converter (BIC). In order to maintain the stability of both sub-MGs in terms of AC frequency and AC/DC bus voltage against generation and consumption fluctuations in both grid-connected and autonomous modes, accurate power exchange, inertia emulation, and dynamic power oscillation damping are the crucial tasks for a BIC [77]. Thus, the VSG control strategy is feasible for interlinking converters between AC and DC microgrids.
# An Overview of Bioactive Flavonoids from _Citrus_ Fruits
## 6 _Citrus_ Flavonoids and Chronic Diseases
### Anti-Carcinogenic Activity  
_Citrus_ flavonoids (flavanones and polymethoxylated flavones) were found to have interesting properties for the pharmaceutical field. These compounds, due to their properties, help prevent certain diseases, such as cancers [68]. In recent years, many studies have shown that there is a connection between flavonoid intake and their potential therapeutic application against cancer. [PERSON] et al. [69] showed that flavonoids have an anti-mutagenic effect by protecting DNA from oxidative damage and neutralize free radicals, which cause mutations. Other studies showed that flavonoids might be implicated on anti-proliferative mechanisms [42]. Studies on mice showed that hesperetin intake promoted the inhibition of proliferating cell nuclear antigen and growth inhibition of aromatase-expressing MCF-7 tumor in ovariectomized athymic mice [70; 71]. Hesperidin, as the glycoside of hesperetin, led to cell apoptosis via conducting the expression of p53 and peroxisome proliferatorator-activated receptor gamma [72]. In a recent study, naringenin showed anti-mutagenic modification by activating DNA repair, following oxidative damage in human prostate cancer cells [73]. Current research indicates that didymin, a typical dietary glycoside flavonoid also known as neoponcinirn, demonstrated an anti-proliferative effect on breast cancer [74]. Moreover, tangeretin and nobiletin can exhibit anti-angiogenesis activity by the inhibition of angiogenic differentiation and exertion of cell cycle arrest in breast and human colon cancer cell lines [75; 76]. In summary, several studies showed that flavonoids might exert an anti-carcinogenesis effect by blocking the metastasis cascade, the inhibition of cancer cell mobility in circulatory systems, proapoptosis, blocking cell cycle progression and antiangiogenesis [19].
# Anticipatory Troubleshooting
## 6 Evaluation
### Long-Term Experiments
#### 6.3.4 Discussion  
The results above show the benefits of using our AT algorithm. However, there are cases where using AT algorithms is not beneficial. For example, when the cost of replacing is much more expensive than the cost of fixing, then it is clear from the results that always-fix policy keeps the long-term costs the lowest. Similarly, when the gap between the costs is very small, always-replace is the best policy. In between, our AT algorithms are beneficial. Thus, to save computational resources of the MDP-based methods, it may be worthwhile recognizing in advance the cases where always-replace or always-fix methods are dominant.  
In addition, there are cases where it is never worthwhile to replace a healthy component. An obvious setting where this occurs is when the overhead cost is close to the cost of replacing. Another setting when replacing a healthy component is useless is when the component's age has no effect on the probability that it will survive more time. That is, the probability that a component that has survived up to time \(t\) will fail before time \(t+t^{\prime}\) is equal to the probability that a new component will fail before time \(t^{\prime}\). This occurs when the conditional probability of a component to survive \(t^{\prime}\) more time given that it survived up to time \(t\) is equal to the unconditional probability that it will survive \(t^{\prime}\) more time. That means the probability of failure is the same in every time interval, no matter the age of the component. Such distributions are known as \"memoryless\" probability distributions. An example of such a distribution is the _Weibull_ distribution with the parameter \(b\) equal to 1 (known as exponential distribution). We experimented with this setting, and as expected, observed that there is no difference between a regular \(N\text{-}MDP_{FixRep}\) solver and an \(N\text{-}MDP_{RepHeal}\) solver.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 4 Simulation Model
### Characteristics of Simulation Scenarios  
Our developed simulation model has been designed with the following premises:  
* We have chosen IEEE 802.15.7 star topology, due to its importance and wide range of applications.
* For the MAC layer, we opted to use the superframe, since it allows the use of both contention (CAP) and no contention (CFP) access methods. In addition, the use of the superframe enables devices to enter the energy save state during the idle period.  
Figure 6: Random access algorithm implemented in our simulation model.  
Our developed simulation model has been designed to evaluate the performance of uplink transmissions during the CAP of the superframe. For this purpose, we have defined four basic scenarios that differ in the number of devices, \(N\), that communicate with the coordinator. We have defined scenarios with \(N=4,8,12\) and \(16\) devices, respectively. In all scenarios, network nodes are contained in an enclosure of \(4\) m height by \(5\) m wide and \(5\) m long. The coordinator is located in the center of the ceiling and points towards the ground. Devices point to the coordinator and are located in fixed positions distributed according to a grid pattern on a plane situated at the height of \(1\) m above the ground. Figure 7a illustrates the simulated network layout of the scenario with four devices. Figure 7b shows the top view of scenarios with \(4,8,12\) and \(16\) devices. All scenarios share the same configuration parameters for PHY and MAC (taking into account that the orientation vector of each device changes with its position).  
Next, we present the most important features of our simulation model, which are needed to adequately interpret the results obtained from the simulations performed.  
Figure 7: (**a**) Star network scenario with 4 devices in \(4\) m \(\times\)\(5\) m \(\times\)\(5\) m enclosure and (**b**) top-view layout for scenarios with \(4\), \(8\), \(12\) and \(16\) devices.
## 4 Practical Mobility Management in Modern Scenarios  
With the huge proliferation of mobile devices, and therefore, different kind of mobility (pedestrian, vehicular, autonomous, flying, etc.), modern networks must accommodate the effects of movement, strictly reflected to the physical layer of the involved devices (Doppler shifts, fading, path loss, shadowing, reflection, refraction, etc.). Standard solutions, such as VLANs and VPNs, which originally have been designed for static networks, are not inherently ready for mobility management, so several solutions have been proposed. In this section, we will overview the main existing solutions which give the opportunity to manage mobility, while maintaining the advantages of the previous solutions.
## 2 Literature Review
The results indicate that combining BERT with various deep learning algorithms yields strong performance, with BERT and CNN achieving the highest accuracy of 93%. Sentiment analysis in the Arabic language has made significant progress in recent years. In ref. [12], various feature extraction techniques, including TF-IDF and AraVec, are compared for Arabic sentiment analysis across both standard and dialectal Arabic datasets. TF-IDF combined with logistic regression achieves an accuracy of 82.10%, outperforming AraVec with SVM, which achieves 71.47%. The lower accuracy of AraVec is attributed to its dataset, which contains a blend of comments written in both styles, complicating the semantic capture of relationships between words. Another comparison of feature extraction techniques between word embedding methods, Word2 Vec, global vector for word representation (GloVe), and FastText, was conducted by [13] on a multi-dialect Arabic dataset to classify positive and negative reviews. The combination of FastText with NuSVC classifiers demonstrated superior performance compared to other combinations. Ref. [14] proposes a hybrid feature extraction and selection approach that combines term document matrix (TDM), term frequency matrix (TFM), and supervised lexicon weighting, along with the K-nearest neighbors (KNN) classifier. This method demonstrates high accuracy compared to other models in experiments conducted on an Arabic dataset, which includes reviews from a mobile store application. Focusing on Arabic sentiment analysis, ref. [15] compares several classifier models to their approach, which is based on TF-IDF and ensemble classifiers of machine learning algorithms, to enhance sentiment analysis of hotel customer reviews. This model achieves an accuracy of 89.2%, demonstrating high performance on the hotel datasets used in the study. Research in French language processing, particularly in sentiment analysis and text classification, has seen limited progress. In ref. [16], researchers use a French dataset of customer reviews from an e-commerce platform to analyze sentiment. This study employs TF-IDF alongside machine learning algorithms such as RNN and LSTM, achieving precision metrics between 73% and 77% over two iterations. In ref. [17], a French dataset containing restaurant and museum reviews was used to analyze sentiments at the aspect level. The study applied various BERT variants for word representation alongside multiple classifiers to compare their effectiveness. The combination of FlauBERT with a pretrained fully connected model (PTM-FC) achieved the highest accuracy, reaching 84.68%.
# Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute
## 4 Experimental Results
### Experiment C: Cross-Dataset, Cross-Camera Testing  
To investigate the robustness of the proposed method, some cross-dataset testing should be performed on multiple datasets. In this experiment, the combination dataset of Gehler-Shi and Cube+ is used by these testing through the 3-fold cross-validation technique. Table 5 provides the statistical metrics of the experimental results. Since the combined dataset can cover a wider range of image features and illuminant regions, the classification and models obtained by the proposed method commonly receive better generality on illuminant estimation. Table 5 validates this viewpoint. It can be seen that the obtained statistical metrics for all images in the Gehler-Shi and Cube+ datasets reach the good performances, e.g., the Mean and Maximum of recovery AE respectively are 2.13 and 14.83 for the selected optimal features of RGBuv+ChengSF+IIF. Figure 2 shows the recovery and reproduction AEs' distribution for some images from the Gehler-Shi and Cube+ datasets using some conventional methods and the proposed method. These results indicate the proposed method can work well and reach excellent performances. It should be pointed out that the selected optimal features of RGBuv+ChengSF+IIF are prerequisites for these results. In real life, photographers usually cannot capture images under perfect or standard illumination. A complex lighting environment results in changing brightness and contrast. A robust color constancy algorithm should be able to deal with these challenges. Our algorithm essentially is a weight-based method in which the features more related to illuminant color are highlighted and used to deduce dynamic weights. Figure 3 shows some visual results to face some challenging image scenes. As shown in this figure, our algorithm with the feature combination of RGBuv+ChengSF+IIF reaches the better performance compared with some conventional methods. Consequently, the proposed method has the great potential to color constancy of a wide range of images. For more examples, please refer to the related materials at our GitHub repositories.
## 2 Research Methodology
### Cracks
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Advantages** & **Disadvantages** & **Domain*** \\ \hline Finite element method (FEM) & Ability to take nonlinear effects into account [40]. & Sensitivity to the quality of initial parameters and boundary conditions [41]. & N/A \\ \hline Hilbert–Huang transform (HHT) [40] & Efficient analysis of nonstationary and nonlinear signals. & High computational costs when calculating complex signals. & \\ \hline \multirow{3}{*}{Fourier expansion} & Better resolution compared to classical methods of spectral analysis. & Noise can affect the accuracy of the results. & \\ \cline{2-4}  & It approximates periodic signals with high accuracy [40; 42]. & It is unsuitable for analyzing nonstationary signals without additional modifications [42]. & \\ \cline{2-4}  & Facilitates the calculation of complex functions by representing them as series [40]. & It requires many harmonics to represent complex waveforms accurately [40]. & \\ \hline Strain energy release rate (SERR) [40] & It allows us to determine the stress level and predict the cracks’ development. & Sensitive to geometric parameters and measurement accuracy. & N/A \\ \hline \multirow{3}{*}{Newmark-\(\beta\) method} & It provides a stable numerical solution for effective analysis of vibrations [41]. & It requires a small step for accuracy, increasing computational costs [40]. & \\ \cline{2-4}  & & Sensitive to the choice of integration parameters, which affects the accuracy of the results [41]. & N/A \\ \hline \hline \end{tabular}  
* Signal processing domain (if applicable): FD—frequency domain; TFD—time-frequency domain.  
\end{table} 
Table 3: Comparative analysis of methods for rotor crack detection. Figure 11: Bifurcation diagram for the undamped rotor system with breathing crack for different types of bifurcation. Adopted from [42].
# Robust Federated Learning for Mitigating Advanced Persistent Threats in Cyber-Physical Systems
## 2 Background
### Advanced Persistent Threats  
The integration of physical components, a network layer, and an application layer in modern CPSs makes them susceptible to cyber-attacks [22; 23; 24]. An APT is one of the most concerning types of these threats [25]. These sophisticated attacks have very specific objectives and are executed gradually, with a tendency to maintain long-term access to the system. Moreover, the attackers operating an APT often orchestrate the attack themselves rather than using automated tools and codes. They are skilled and well resourced, combining several tools and techniques to carry out each step of the APT. Furthermore, the continuous and persistent nature of APTs makes them hard to detect by security teams, as they cannot definitively categorize an attack as an APT due to a lack of information. Thus, the security team must promptly detect any APTs in the system so that enough resources and manpower can be allocated to eliminate such threats.  
The detection of APT attacks is often performed as part of the malware triage process, where different adversaries are ranked based on their degree of severity to set priorities for the security team. This data-driven process, however, is heavily reliant on the availability of data and historical patterns associated with critical threats such as APTs [5; 15]. Several research endeavors have presented solutions for detecting APTs [14; 26; 27; 28]. For instance, a risk management approach can be used to analyze the system's expected state [29]. Multi-label learning has been shown to be effective at categorizing each malicious sample into different groups to provide extended insight into the data [30]. A dynamic quarantine and recovery scheme was designed in [6] to minimize the impact of APTs when they are present in a system. Nonetheless, despite the scarcity of data for effectively combating APTs, there are not many studies that address this problem. Privacy and security concerns associated with revealing network data prevent organizations from sharing such data. This paper tackles this issue by employing FL.
## 4 Spatiotemporal Rebalancing Pricing Algorithm
### Markov Chain  
A four-tuple \((S,Ac,R,\gamma)\) represents a Markov process [5], where \(S\) represents the state space, \(Ac\) represents the action space, \(R\) represents the immediate reward space, and \(\gamma\) represents the discount factor. As shown in Figure 3, after the current state \(S_{t}\) receives action \(Ac_{t}\), the environment provides feedback \(R(S_{t},Ac_{t})\), and the current state is transferred to \(S_{t+1}\). As time goes on, a Markov chain can be obtained. Our bicycle rebalancing aims to consider the long-term benefits after performing actions in the face of the current state. In time series prediction, the greater the amount of future prediction results, the worse the accuracy. In addition, the current available gain [5] is more important than the future gain. Therefore, \(\gamma\) represents a gain-loss factor relative to future time.  
**Lemma 1**.: _The future gain of any Markov chain can be a formula, and the recursive relation can alsp be a formula, as follows:_  
\[G_{t}=R_{t}+\gamma R_{t+1}+\gamma^{2}R_{t+2}+\cdots=R_{t}+\gamma G_{t+1}. \tag{11}\]  
If we can determine the future gain of the Markov chain, we can choose the optimal incentive strategy (Action) at each time stage to obtain a Markov chain \(S_{0},A_{0},R_{0},S_{1},\ldots,S_{n}\) with the largest gain. In this Markov chain, we can obtain the incentive strategy of the whole stage, thus completing our rebalancing task. Determining the future benefits of each Markov chain is a huge project so that we can acquire the gain expectation of future possible states.  
**Lemma 2**.: _The state-action value function [5] of the Markov chain can be a formula, and the recursive relation can also be a formula, as follows:_  
\[V(S_{t},Ac_{t})=\mathbb{E}[R_{t}+\gamma R_{t+1}+\gamma^{2}R_{t+2}+\ldots|S_{t },Ac_{t}]=\mathbb{E}[\sum_{k=0}^{\infty}\gamma^{k}R_{t+k}|S_{t},Ac_{t}]. \tag{12}\]Note that \(V(S_{t},Ac_{t})\) is a conditional expectation for state \(S_{t}\) and action \(Ac_{t}\).  
The state space and action space in MDP are continuous high-dimensional spaces. After we use the numerical discretization method to change the infinite action space into a finite set, even if the optimal action is selected by the instantaneous reward rather than the state-action value function, the workload of selecting the optimal action needs a lot of computation and storage space. Furthermore, the probability function cannot determine the action, for it is unknown. We must estimate the probability of selection action through random strategies with many samples. This work is huge, and the results are random. We use the deterministic strategy to describe the relationship between the current state and action, that is, \(Ac=\pi(S)\). This part is defined as the _actor module_ (as shown in Figure 1). To obtain the optimal strategy, we use the state-action value function to guide the promotion of the optimal strategy. This part is defined as the _critic module_.
## 2 Data and Methods
### Data
For the other cities, annual means of measured data were used for the ground monitoring sites that had collected data for over 50% of the year. Only sites that had co-located model-estimated values within a distance less than 500 m were included. We used a total of 33 (31) ground PM\({}_{2.5}\) (NO\({}_{2}\)) sites, for evaluation, with 11 (7), 8 (12), 8 (10), and 6 (2) sites in London, Vancouver, LA, and NYC, respectively (Table 1). Data were obtained from the following sources: London [34; 35], Vancouver [36], LA [37], and NYC [28; 37].
# Simultaneous Quantitation of Isoprenoid Pyrophosphates in Plasma and Cancer Cells Using LC-MS/MS
## Materials and Methods
### Liquid Chromatographic and MS/MS Conditions  
Sample analysis was conducted using a Shimadzu Nexera ultra high-performance liquid chromatography (UPLC) system (Columbia, MD, USA) equipped with two pumps (LC-30 AD), a column oven (CTO-30 AS) and an auto-sampler (SIL-30 AC). Peak resolution and separation for all samples were achieved by using an ACCQ-TAG Ultra C18 column (1.7 \(\upmu\)m, 100 mm \(\times\) 2.1 mm I.D., Waters, Milliford, MA, USA) equipped with a C18 guard column. Mass spectrometric detection was performed on an LC-MS/MS 8060 system (Shimadzu Scientific, Inc., Columbia, MD, USA), equipped with a dual ionization source (DUIS) operated in the negative electrospray ionization and multiple reaction monitoring mode in order to achieve unit resolution. The multiple reaction monitoring (MRM) transitions and compound dependent parameters, such as voltage potential Q1, Q3, and collision energy (CE), are shown in Table 1. Optimized parameters were obtained by the product ion scan mode of individual analytes (1 \(\upmu\)g/mL) at 0.25 mL/min in methanol/water (50/50, \(v/v\)). Parameters for multiple reaction monitoring (MRM) detection in the negative mode were as follows: Nebulizer gas: 2.0 L/min; heating gas: 10 L/min; drying gas: 10 L/min; interface temperature: 350 \({}^{\circ}\)C; desolvation line temperature: 250 \({}^{\circ}\)C; heat block temperature: 400 \({}^{\circ}\)C. Mass transitions were monitored at 30 ms dwell times and unit mass resolutions and individual parameters are listed in Table 1. Lab Solutions LCMS Ver.5.80 (Shimadzu Scientific, Inc. Columbia, MD, USA) was used for data collection and quantitation.  
The mobile phase consisted of a 10 mM ammonium carbonate buffer with 0.1% ammonium hydroxide (mobile phase A, pH 9.7) and 0.1% ammonium hydroxide in acetonitrile/methanol (75/25) (mobile phase B) at a total flow rate of 0.25 mL/min. The chromatographic separation was achieved using a 12 min gradient elution. The initial mobile phase composition was 10% B, increasing in a linear fashion to 65% B over 7 min, then held constant for 2 min, increased to 95% B over 0.5 min, then held constant for 2 min, and finally brought back to the initial condition of 10% B over 0.20 min followed by 1-min re-equilibration. The injection volume of all samples was 10 \(\upmu\)L.