esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, the performance of this method is affected by the presence of noise. In [12], the authors proposed an algorithm supported by the Stockwell transform for the protection of the power system network integrated with solar PV generation. This algorithm overcomes the demerits of the DWT-based scheme; however, it has the disadvantage of a low protection speed due to the requirement of the large size of the input data. Hence, a new algorithm for protection of the power system with SE penetration which has the merits of a high protection speed, independently of the direction of the power flow, fault current, nature of loads and generation, needs to be designed. These are considered as key factors in this work and the main contributions are detailed below. * This paper introduced an algorithm which is effective for providing high-speed protection to the power system incorporated with SE. * The proposed protection scheme is based on the use of fault index computed from the combined features which are extracted from current signals using the Wigner distribution function and alienation coefficient. The type of fault is recognized using the number of faulty phases identified with the help of the proposed fault index. However, a ground fault index computed using the Wigner distribution function-based decomposition of zero sequence current is proposed for the discrimination of the double phase fault and the double phase to ground fault from each other. * The performance of the algorithm will not be affected by the presence of noise. Furthermore, this algorithm works well in various operating scenarios in the presence of solar energy such as fault location, variations in the fault impedance, fault incidence angle, all types of transformer windings and hybrid combination of the OH line and the UG cable. * Algorithm effectively discriminates the switching transients from the faulty transients. The contents of this article are arranged into ten sections. After the introduction in Section 1, Section 2 describes the IEEE-13 node test network, incorporated with SE. The algorithm of the proposed protection scheme is detailed in the Section 3. Section 4 includes simulation results for the fault identification. Fault classification results are included in Section 5. The results for various case studies are included in Section 6. The discrimination of switching transients from the faulty transients is discussed in Section 7. The validation of the protection scheme on the real-time network of Rajasthan State in India is included in Section 8. A performance comparison of the proposed protection scheme with other existed schemes in the presence of solar energy is detailed in Section 9, followed by Section 10, which presents the conclusion.
# Power System Reliability Assessment Considering Coal-Fired Unit Peaking Characteristics
## 3 Multi-Stage Probabilistic Model
### Coal-Fired Unit Multi-State Probabilistic Model  
In conventional PPS, coal-fired units typically use a simple two-state model, considering the forced outage state and the rated operating state of the unit [26]. However, in practice, coal-fired units perform load-following tasks, requiring dynamic adjustment of output based on load demand. The load-following operation of coal-fired units is typically divided into flexible load-following and start-stop load-following. The former allows the unit's output to be adjusted within a certain power range to respond to load demand, while the latter involves frequent start-stop operations to meet load requirements. Additionally, coal-fired units often operate in off-design conditions during load-following, and their load change rate and depth of load-following can affect the unit's reliability. For example, high-temperature components, such as the economizer and rotor, are more susceptible to thermal stress during load fluctuations, which may lead to failures such as tube ruptures, significantly impacting the unit's reliability [27]. Therefore, this study focuses on the operating characteristics of coal-fired units at different load positions and examines the  
Figure 1: Discretization of probability curve.  
impact of coal-fired units on the overall power system's reliability during load-following by approximating the failure of high-temperature equipment during load-following as equivalent to the failure of the entire coal-fired unit.
# A Narrative Review of Metabolomic Insights into Olive Oil's Nutritional Value
## 2 Olive Oil Metabolomics
### Mono- and Diacylglycerols  
Mono-and diacylglycerols, which are relatively understudied components of olive oil compared with their counterparts, have not been overlooked in certain research endeavors, where the findings highlight some of their potential health benefits. For instance, monoacylglycerols enriched in OA have demonstrated antioxidant, antiatherogenic, and antidiabetic effects, as they contribute to the inhibition of insulin levels in the blood following the ingestion of food [33]. In contrast, diacylglycerols possess the ability to activate enzymes involved in \(\beta\) oxidation of fat. Furthermore, they reduce the risk of CVDs by decreasing the levels of CRP, TNF-\(\alpha\), inducible nitric oxide (i-NOS), cyclooxygenase (COX)-2, and vascular cell adhesion molecule-1 (VCAM-1), which have antidiabetic effects. Diacylglycerols can control the production of inflammatory mediators and cytokines, highlighting their involvement in inflammation [34].
# Changes in Selected Quality Indices in Microbially Fermented Commercial Almond and Oat Drinks
## 1 Introduction
For example, microbials such as _Lactobacillus_ spp., _Bifidobacterium_ spp., _Streptococcus_ spp., _Citrobacter_ spp., _Enterobacter_ spp., _Helicobacter_ spp., _Klebsiella_ spp., _Yersinia_ spp., _Escherichia coli_, and _Yarrowia lipolytica_ are able to synthesize and accumulate odd-chain fatty acids (OCFAs) [19], branched-chain fatty acids (BCFAs), and cyclic fatty acids (CFAs) [20; 21]. For example, the ratio of CFAs to unsaturated fatty acids is the relevant parameter associated with membrane rigidification in _Lactococcus lactis_ TOMSC161 cells and cell resistance to freeze-drying and storage [22]. Apart from the usefulness of these compounds for microflora, they also seem to be important for decreasing/preventing many health problems. For example, higher concentrations of blood-circulating pentadecanoic and heptadecanoic acids (representatives of OCFAs) are associated with lower risks of cardiometabolic diseases, lower mortality, and anti-inflammatory properties. They also counteract anemia, dyslipidemia, and fibrosis in vivo, and pentadecanoic acid is proposed as a potential essential fatty acid [23]. BCFAs have anti-diabetic, anti-inflammatory, and anti-cancerogenic activity [24; 25; 26]. Similarly, animal studies indicate that sterculic acid (one of the main representatives of CFAs) can prevent metabolic syndrome development and have an anxiolytic-like effect [27; 28]. In general, plant materials are the main source of even-chain carbon fatty acids. In this context, even a small accumulation of OCFAs, BCFAs, or CFAs should be profitable for the pro-healthy quality of fermented products. The estimated daily intake of these fatty acids from the typical diet in Western countries is approximately 1.5 g, including approximately 1.0 g odd, 0.5 g branched, and 12 mg of cyclic fatty acids [17]. The present study aimed to determine whether commercial almond and oat drinks, differing in their protein, fat, and carbohydrate contents, are valuable nutrient mediums for the growth and multiplication of three strains of lactic acid bacteria and five strains of food-grade yeasts and their influence on selected quality indices.
## 2 Brain-Computer Interfaces
### BCI Control Paradigms  
The second component is feature extraction, which in the case of BCI, unfolds the brain signal characteristics from nonessential material and shows them in a more meaningful form, manageable for either humans or computers. Hence, it is important to show which commands would be used for control and the features that better represent the signal to be analyzed.  
**BCI control paradigms** depend on choosing the feature and the type of signal used as a pattern for the BCI. There are mainly two types of EEG paradigms used on BCI: Evoked potentials (EP) [39] and changes in the spontaneous oscillatory EEG activity.  
Generally, there are two types of brain paradigms: Evoked Potentials and changes in the spontaneous oscillatory EEG activity.
# Study of RF Stray Currents in ITER Neutral Beam Test Facilities
## 3 Circuital Model
### Beam Source  
The source elements represented in the model are the RF load and its interface to the system of grids and other beam-source structures. The load of ISRF, consisting of two RF plasma drivers, is represented by a R-L series. The equivalent resistances and inductances of the RF drivers are not constant and depend upon plasma conditions. The parallel capacitor \(Cp\) and two series capacitors \(Cs\) represent the matching network. The external conductor of the RF coaxial line is referred to the plasma source body, thus to the BI conductor. The bank of capacitors installed between the PG and the source body, used for mitigation of breakdown overvoltage, is also represented in the model. The system of grids and the electrostatic screen surrounding the source are modeled in terms of their parasitic capacitances.
## 3 Results
### Diurnal Variation and Validation of a HONO Collection System for Ambient Air Measurement  
To determine the feasibility of using the HONO collection system in ambient air measurements, the typical diurnal behavior of HONO (i.e., nighttime increase and daytime decrease) was validated. Figure 3a-c illustrate the diurnal fluctuations in gas-phase concentrations of HONO, HNO3, and O3. The highest average HONO concentration was recorded at 08:00, measuring 0.372 ppb, closely followed by 21:00, with a concentration of 0.369 ppb. The lowest levels occurred from 11:00 to 16:00, at 0.27 ppb. In contrast, the peak concentration of O3 was recorded at 15:00, reaching 36.27 ppb. The corresponding diurnal peak of solar insolation (Figure 3d) was observed between 13:00 and 14:00. These trends align with prior research, which indicates that HONO increases after sunset and decreases due to photolytic degradation [56; 57]. HNO3 demonstrated its highest concentration of 0.38 ppb at 15:00, with the lowest at 20:00, measuring 0.27 ppb. The daytime increase in HNO3 is believed to result from the reaction of OH radicals with NO2, whereas the second-highest concentration (0.36 ppb at 00:00) is attributed to heterogeneous reactions involving water vapor and N2O5 during the night [57; 58; 59].
## 4 Experimental Settings
### Vocoder  
The final vocoder plays a critical role in ensuring a high-quality speech resynthesis in the context of speech anonymization, and this is irrespective of the de-indentification processes implemented beforehand. The present work adopts HiFi-GAN [80]: a state-of-the-art vocoder that consists of one generator and two discriminators (multi-scale and multi-period), which are trained in an adversarial fashion along with two additional losses for improving training stability and model performance. The generator is a fully convolutional neural network. Specifically, it uses a mel-spectrogram as the input, and it upsamples it through transposed convolutions until the length of the output sequence matches the temporal resolution of the waveform. In this work, the synthesis model was trained using LibriTTS and VCTK together with the speech disentanglement mechanism.
# Design of a Planar Cable-Driven Parallel Robot for Non-Contact Tasks
###### Abstract
The parallelogram arrangement strategy can also be applied in non-contact tasks where the \(EE\) must move on a prescribed vertical plane without interacting with the surrounding environment. This is the case of welding, laser engraving, thermal treatment, and decoration or inspection of building facades, to name a few. This paper focuses on the design of a _CDPR_ for this kind of applications. Specifically, an overconstrained planar machine is presented. This prototype employs a parallelogram cable arrangement to constrain the \(EE\) to move on a vertical plane. Four cables, closed in a parallelogram loop, are used to control the mobile platform's three degrees of freedom (_DoFs_). The cable guidance system comprises a pair of swivel pulleys whose swivel axes are orthogonal to the work surface: the intersections of these swivel axes with the work surface effectively define the cable proximal and distal points. Each cable loop is driven by one winch, whose drum has two helicoidal starts. Each winch includes a rotary encoder for cable length control. In addition, the pulley swivel axes are equipped with rotary encoders to achieve redundant kinematic measures, and cable tensions are measured with shear beam load cells integrated into the proximal anchor systems. Like other planar _CDPR_ that share the idea of parallelogram mechanisms [23; 26], this system allows the platform to move on a defined plane, excluding low-amplitude oscillatory motions induced by external disturbances [27; 28]. On the other hand, unlike the others, the proposed system shows three advantages: the use of a single cable loop, instead of two separate cables, ensures that the parallel cable segments are in tension when controlled by a single actuator, thus reducing the number of actuators to be used; the cable anchor system includes a measurement unit to continuously monitor cable tensions and cable angles with respect to the frame; the mounting arrangement of the swivel pulleys allows to estimate the cable lengths as distances between two precisely defined points, which are the intersections between the swivel axes and the work plane, without introducing geometrical approximations. Consequently, a simple geometric model can be adopted to control the \(CDPR\) without inherent geometrical errors. The paper is organized as follows. The main aspects to be taken into consideration for the mechanical design of a \(CDPR\) are described in Section 2. Then, the design of the planar overconstrained \(CDPR\) prototype is presented in Section 3, focusing on the actuation and guidance system, and the influence of the kinematic model. In Section 4, as an application example, an experiment of the robot performing laser engraving is reported and discussed. Lastly, some conclusions are drawn.
# High-Precision Light Spot Position Detection in Low SNR Condition Based on Quadrant Detector
## 2 Description of QD Characteristics  
As Figure 1 shows, QD consists of four identical p-n junction photo-diodes, which can turn optical signals into electric signals through the photoelectric effect when a beacon light is received.  
The outputs of QD are four electric signals, which are independent of one another, whose amplitude is closely related to the energy of the four parts of the optical signals that illuminate each photo-diode and their transition gain [8]. The photoelectric effect can be expressed as follows.  
If the incident light obeys normal distribution, its power density can be written as [9; 10]:  
\[\overline{p}(x,y)=\frac{2p_{0}}{\pi\omega^{2}}\exp\left\{-\frac{2\Big{[}\big{(} x-x_{0}\big{)}^{2}+\big{(}y-y_{0}\big{)}^{2}\Big{]}}{\omega^{2}}\right\} \tag{1}\]  
Here: \(\omega\) is the spot radius, \((x_{0},y_{0})\) is the coordinate of the spot center, \(p_{0}\) is the power of the incident light, and the center of the QD is the origin of the coordinates.  
So in one quadrant, the energy of the light in one second can be written as:  
\[E=\iint\limits_{S}p(x,y)dxdy \tag{2}\]  
After the photoelectric effect, optical signals are transformed into photocurrents, which can be expressed as [11]:  
\[I=KE=\frac{e\eta}{I\
u}\iint\limits_{S}p(x,y)dxdy \tag{3}\]  
Here: \(K\) is the transition gain of the diode, \(e\) is the elementary charge, \(\eta\) is the transfer efficiency, \(h\) is the planck constant, and \(\
u\) is the frequency of the incident light.
## 4 Results and Discussion
### Generalization Capability of the SSA-LSTM Model  
To delve deeper into the model's generalization potential, data from monitoring points 2, 3, and 4 were, respectively, input into the SSA-LSTM model for prediction and compared with the prediction results from monitoring point 1. The corresponding prediction results are shown in Figures 9-12. By combining the three evaluation indexes MAE, MSE, and  
\begin{table}
\begin{tabular}{c r r r r r} \hline \hline  & \multicolumn{5}{c}{**Sub subsidence Value (mm)**} \\ \hline
**Number** & **True Value** & \begin{tabular}{c} **Predicted** \\ **Value 1** \\ \end{tabular} & \begin{tabular}{c} **Predicted** \\ **Value 2** \\ \end{tabular} & \
\begin{tabular}{c} **Residual** \\ **Value 1** \\ \end{tabular} &
\begin{tabular}{c} **Residual** \\ **Value 2** \\ \end{tabular} \\ \hline
1 & 0.25 & 0.73 & 0.22 & \(-\)0.48 & 0.03 \\
2 & \(-\)0.21 & 0.16 & \(-\)0.21 & \(-\)0.37 & 0.00 \\
3 & \(-\)0.34 & \(-\)0.02 & \(-\)0.34 & \(-\)0.32 & 0.00 \\
4 & 0.38 & 0.90 & 0.36 & \(-\)0.52 & 0.02 \\
5 & 0.  
RMSE, the predictive accuracy of the model was assessed. Detailed comparisons can be seen in Table 7. From Figures 9-12, it was clearly observed that the prediction error ranges for monitoring points 1 to 4 were, respectively, (\(-\)0.0753 to 0.0593), (\(-\)0.0685 to 0.1237), (\(-\)0.0524 to 0.0579), and (\(-\)0.0876 to 0.0774). These findings indicate that the fluctuations in prediction errors were relatively minor, attesting to the model's stable performance. Based on the data from Table 7, the SSA-LSTM model excelled in its predictions for monitoring point 4, while it also performed commendably on the other three monitoring points. This further validated the high accuracy, stability, and superior generalization capability of the SSA-LSTM model in predicting ground settlement, aligning with the conclusions drawn in Section 2.4. Hence, with its robust and accurate predictive qualities, this model can provide strong decision-making support for early warning and mitigation of land subsidence.
# A Lightweight AI-Based Approach for Drone Jamming Detection
###### Abstract  
The future integration of drones in 6G networks will significantly enhance their capabilities, enabling a wide range of new applications based on autonomous operation. However, drone networks are particularly vulnerable to jamming attacks, a type of availability attack that can disrupt network operation and hinder drone functionality. In this paper, we propose a low complexity unsupervised machine learning approach for the detection of constant and periodic jamming attacks, using the Isolation Forest algorithm. We detail the tuning of the base model as well as the integration with a Majority Rule module which significantly reduced the number of false positives caused by environmental noise, achieving high accuracy and precision. Our approach outperforms the standard Isolation Forest model in the detection of both constant and periodic jamming attacks, while still correctly identifying nominal traffic. Finally, we discuss the potential integration of the proposed solution in 6G-enabled drone networks, as a lightweight edge-based solution for enhancing security against jamming attacks.  
Keywords:jamming attacks; 6G drone networks; isolation forest; edge AI; IoT security +
Footnote †: journal: _Future Internet_
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 5 Planning Methods in Penetration Path Planning
### Intelligent Planning
#### 5.1.3 Hierarchical Task Planning  
The Hierarchical Task Network (HTN) planning method [71] is a hierarchical intelligent planning technique. It utilizes domain knowledge to progressively refine complex task goals into smaller, executable atomic tasks through task decomposition. These atomic tasks are then efficiently combined to form a complete planning solution.  
One of the pioneering HTN planners, the Universal Method Composition Planner [72], was the first to demonstrate both reliability and completeness, capable of addressing a diverse range of planning issues and generating effective solutions. Building on the success of the Universal Method Composition Planner, simple hierarchical ordered planner (SHOP) [73] and its successor SHOP2 [74] were developed. SHOP2 is a more powerful and flexible planning system that not only inherits the features of SHOP but also introduces new capabilities, including parallel task execution and more complex task decomposition. Further developments have led to the creation of versions in Java (JSHOP2) and Python (PYHOP), extending the application of HTN planning techniques across different programming environments and application requirements.  
Expanding on the capabilities of HTN planning, [PERSON] [75] pioneered its application to penetration path planning. By integrating expert insights and utilizing PYHOP for planning, [PERSON] created an enhanced model that effectively addresses the complexities of large networks. Additionally, they introduced a topology-based multilayer partitioning algorithm and a maintenance scheme for managing local changes, significantly simplifying the planning process in complex network environments.
## 3 Photonic Technologies
### Fiber Optic Sensors (FOS)  
#### 3.5.1 Working Principles  
A fiber Bragg grating sensor (FBG sensor) is a periodic modulation of the refractive index of an optical fiber core. If an optical fiber that contains an FBG is illuminated by a broadband light source, only the wavelengths that satisfy the Bragg condition are reflected, all the others being transmitted. This condition is given by Equation (4):  
\[\lambda_{\text{B}}=2n_{\text{\emph{eff}}}\Lambda \tag{4}\]  
where \(\lambda_{\text{B}}\) is the reflected Bragg wavelength, \(n_{\text{\emph{eff}}}\) the effective refractive index of the fiber core and \(\Lambda\) the periodicity of the refractive index modulation [18]. When the grating is subjected to thermal variations and/or mechanical perturbations the reflected Bragg wavelength changes, according with Equation (5):  
\[\Delta\lambda_{\text{B}}=2\Bigg{(}\Lambda\frac{\partial n_{\text{\emph{eff}}} }{\partial\xi}+n_{\text{\emph{eff}}}\frac{\partial\Lambda}{\partial\xi}\Bigg{)} \Delta\varepsilon_{\text{z}}+2\Bigg{(}\Lambda\frac{\partial n_{\text{\emph{eff }}}}{\partial T}+n_{\text{\emph{eff}}}\frac{\partial\Lambda}{\partial T} \Bigg{)}\Delta T \tag{5}\]  
The first term represents the strain dependence on the Bragg wavelength variation and the second term, the thermal effect on the same parameter. Equation (2) shows that an FBG can be employed as a sensor element, by monitoring the back-reflected light from the Bragg grating, for longitudinal mechanical strain, \(\varepsilon_{\text{z}}\), and temperature, \(T\). The sensibility of an FBG to \(\varepsilon_{\text{z}}\) is given by Equation (6):  
\[\frac{\Delta\lambda_{\text{B}}}{\lambda_{\text{B}}}=\Bigg{\{}1-\frac{n_{\text{ \emph{eff}}}^{2}}{2}\Big{[}p_{12}-\
u\big{(}p_{11}+p_{12}\big{)}\Big{]}\Bigg{\}} \varepsilon_{\text{z}} \tag{6}\]where v is the Poisson's ratio and \(p_{\bar{y}}\) are the components of the strain-optic tensor. For a typical germanosilicate optical fiber, the expected strain sensitivity around \(\lambda_{\mathrm{B}}=1550\) nm is 1.2 pm/\(\mu\)e [18].