esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 FRP Confinement Mechanism  
In literature, the proposed strain models contain some common variables such as confinement stiffness ratio \(\rho_{\mathrm{k}}\), strain ratio \(\rho_{\mathrm{\varepsilon}}\), hoop rupture strain of fibers \(\epsilon_{\mathrm{h,rup}}\) and maximum confinement stress \(\mathrm{f_{l}}\). The relations for \(\rho_{\mathrm{k}}\) and \(\rho_{\mathrm{\varepsilon}}\) were presented by [32].  
\[\rho_{\mathrm{\varepsilon}}\ =\ \frac{\epsilon_{\mathrm{h,rup}}}{\epsilon_{ \mathrm{co}}} \tag{1}\]  
\[\rho_{\mathrm{k}}\ =\ \frac{2\mathrm{E_{f}t}}{(\frac{\epsilon_{\mathrm{ mo}}}{\epsilon_{\mathrm{co}}})\mathrm{D}} \tag{2}\]  
where 't' is the thickness of FRP sheets, 'E\({}_{f}\)' is the elastic modulus of confinement sheets in the transverse direction of specimen, '\(\epsilon_{\mathrm{co}}\)' is the unconfined strength of concrete and '\(\epsilon_{\mathrm{co}}\)' is the ultimate compressive strain that corresponds to the compressive strength of concrete. Figure 1 represents the confinement stresses due to FRP-jackets with a hoop diameter D. The relationship between the maximum confinement stress and different variables as shown in Figure 1 can be represented by Equation (3). The hoop rupture strain can be represented by Equation (4) [33].  
\[\mathrm{f_{l}}\ =\ \rho_{\mathrm{\varepsilon}}\,\rho_{\mathrm{k}}\,\mathrm{f^{ \prime}_{\mathrm{co}}}\ =\ \frac{2\mathrm{E_{f}\,\epsilon_{\mathrm{h,rup}}t}}{\mathrm{D}} \tag{3}\]  
\[\epsilon_{\mathrm{h,rup}}\ =\ \frac{\epsilon_{\mathrm{f}}}{\mathrm{f^{ \prime 0.125}_{\mathrm{co}}}} \tag{4}\]  
where, \(\epsilon_{\mathrm{f}}\) is the maximum tensile strain of fibers.  
First of all, the strain model for FRP confined concrete was proposed by [15]. According to this model, the ultimate strain increases with the circumferential stiffness of confined concrete. [PERSON] et al. [16] gave the strain model for concrete confined with steel stirrups which was adopted by ACI 440-2R-02 [34] for design guidelines of FRP confined concrete. In strain model proposed by [17], it was assumed that the FRP confinement reaches to its maximum tensile strength in hoop direction which is not possible. [PERSON] et al. [18] developed the strain model for FRP-tubed confined concrete and concluded that the effective hoop strain at rupture is less than that obtained from uniaxial tensile tests. The first strain model which includes the actual rupture strain of FRPs (\(\varepsilon_{\text{h,rup}}\)) was developed by [19]. [PERSON] and [PERSON] [20] proposed the strain model for FRP confined concrete based on a small experimental database of 76 sample points by taking into consideration the actual failure strain of wraps. Later on, this model was refined by [PERSON] et al. [21] based on 18 tested specimens which gave close agreement with the experimental results of axial compressive strains. Table 1 represents the strain models used in the present study for the evaluation of currently developed database and to select a general form the currently proposed strain model.
## 3 Materials and Methods
### Learning-Style Classification  
Three well-known classification techniques, random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), were applied and evaluated for EEG signal classification under various conditions, including reading, lecture, discussion, and video viewing using each EEG feature set described above, as well as a combination of all feature sets.  
Random forest: The random forest (RF) classifier functions as a meta-estimator, in which several decision tree classifiers are fitted to different subsamples of the dataset [39]. By averaging the results, the RF classifier enhances predictive accuracy and mitigates the issue of overfitting. The machine learning algorithm is highly durable and is capable of effectively managing classification and regression tasks. The number of trees in the forest, the maximum depth of each tree, the minimum number of samples needed to divide an internal node, and additional factors may be among the optimized parameters for RF.  
K-nearest neighbors: The K-nearest neighbors (KNN) algorithm is a refined and user-friendly machine learning technique utilized to address problems involving classification and regression [48]. KNN utilizes the principle of similarity to forecast the label or value of a novel data point by considering its K-nearest neighbors from the training dataset. The optimized KNN parameters may consist of, among other things, the distance metric employed and the quantity of neighbors to be considered.  
Multilayer perceptron: The multilayer perceptron (MLP) classifier falls under the category of feedforward artificial neural networks [49]. The architecture comprises a minimum of three node layers and employs a non-linear activation function. MLP is an algorithm for supervised learning in which a function is learned through training on a dataset. The number of hidden neurons, the type of activation function, the solver for weight optimization, and additional factors may be among the optimized parameters for MLP.
## 5 Discussion
### Uncertainties Associated with Optirrig's Simulations
The results of the present study demonstrate the effectiveness of our approach and offer valuable insights into the precision and dependability of irrigation date and amount detections, providing reliable information for monitoring irrigation practices. With further validation using a larger dataset encompassing diverse crops and irrigation methods, we could potentially develop a near-real-time tool for irrigation detection.
## 1 Introduction
[19] used TDLAS in combination with acoustic levitation to investigate the mass transport between liquid pure water droplets and the surrounding gas by directly measuring the absolute concentration field around a single levitated droplet. With this in situ approach, an absorption length of only 4 cm could be achieved, resulting in a concentration resolution of 67 ppm [19]. Typical spectral regions for the measurement of water via TDLAS are the overtone band at 1.4 um (7140 cm\({}^{-1}\)), the combination band at 1.8 um (5555 cm\({}^{-1}\)) or the first fundamental oscillation band at 2.6 um (3850 cm\({}^{-1}\)) [20]. Although the combination band and the fundamental band are two and 20 times stronger than the overtone band, respectively, the latter is often used for water vapor detection as the optical components are very advanced due to the intensive usage of this band in optical telecommunication techniques [21]. In this paper, we describe a novel approach to study the drying process of levitated single droplets of pure liquids as well as droplets of protein solutions using tunable diode laser absorption spectroscopy. To that end, we applied a self-developed TDLAS system to an acoustic levitator and measured the exhaust gas coming from the levitation chamber in terms of its water content by analyzing the 110 \(\rightarrow\) 211 transition at 1369.97 nm (7299.43 cm\({}^{-1}\)) in the 1.4 um (7140 cm\({}^{-1}\)) overtone band. This extractive approach of analyzing the exhaust gas of an acoustic levitator enables the use of a multipass cell of the Herriott type [22] to extend the absorption length to a path length of 18 m. This allows the detection range to be increased and the lowest possible water content to be determined, especially towards the end of the drying process. With this newly developed setup, we can investigate the whole drying process of levitated microdroplets from pure water as well as from protein solutions, including after the critical point of drying, and can determine the water concentration in the exhaust gas with a resolution of 1.7 ppm.
# Effect of Magnetic Nanofluids on the Performance of a Fin-Tube Heat Exchanger
## 3 Methodology
### Effect of One Permanent Magnet on Heat Transfer  
Numerical analysis was performed for the case in which no magnetic field was applied. Figure 5a,b are the velocity and temperature contour results, respectively. Since it is not affected by the magnetic field, the flow velocity was very low. Additionally, looking at the temperature contour, cooling did not occur sufficiently around the tubes. To compare the cooling performance according to the strength of the magnetic field, numerical analysis was performed for four cases. The intensity of magnetization was increased at the same intervals from \(6.0\times 10^{6}\) A/m to \(2.4\times 10^{7}\) A/m.  
Because ferrofluid has kinetic energy in magnetic fields, flows began to change, as shown in Figure 6. When compared to the initial magnetization of Figure 6 with Figure 5a, a change in the velocity field was confirmed. As the magnetization value increased, ferrofluid reacted more actively due to nanoparticles. Because the low-temperature ferrofluid near Figure 1a region rose toward a permanent magnet, this fluid pushed the high-temperature ferrofluid near the magnet. Therefore, the circulation shape is clearly visible. Through circulation, the low-temperature ferrofluid at Figure 1a can reach the area (c). In the case of \(2.4\times 10^{7}\) A/m, the most distinct circulation occurred. Therefore, a small vortex was additionally generated in the tube (f).  
Figure 4: Case classification applied in this study.  
Figure 5: Contours of the reference model: (**a**) velocity contour, (**b**) temperature contour.  
In Case 4 of Case B, mixing was most actively performed, and this case shows the best cooling performance among the four cases, as shown in Table 4. When the magnetization value was increased based on \(1.8\times 10^{7}\) A/m, the temperature contour gradually started to change as shown in Figure 7. However, three tubes showed results to suggest that cooling was not happening equally. Temperatures decreased mainly at the Figure 1f tube section. Circulation only affected the tube corresponding to the restricted area, where one permanent magnet was arranged. Additionally, there were stagnation flows at the edge of the chamber. These are factors that deteriorate heat transfer performance.
## 4 Simulations and Experiments  
In order to verify the performance of the proposed method in this paper, this section conducts simulations and experiments. Simulations are given in Section 4.1 which includes two parts. In the first part, the attitude estimation method is simulated to verify the accuracy of the method. In the second part, the LOS motion control of optoelectronic platform is simulated to verify the practicability of the method. In Section 4.2, the attitude  
Figure 4: Design diagram of phase-lag-free filter.  
estimation method is tested on the embedded system to verify the accuracy and real-time performance of the method.
## 2 Background and Related Works
### Software-Based Fault Tolerance  
Hardware-based hardening techniques, although effective, are not always suitable mainly due to their high costs and because they require physical modifications to the components of the system. In the case of COTS components, such as commercial microprocessors and microcontrollers, hardening techniques based on software modifications are very attractive. They are also known as software-implemented hardware fault tolerance (SIHFT) techniques [6].  
These techniques can be used to protect the control flow [12; 13] or the data flow [7; 14; 15] of a microprocessor. The SIHFT techniques are based on software redundancy at different levels. Each level of granularity implies a level of protection, but it also has associated overheads.  
For instance, a representative example of these techniques is SWIFT-R [16], which uses triple modular redundancy (TMR) at the instruction level of the software for the detection and correction of faults. It is based on the creation of three copies of the data to be protected, and then, through majority voters, it is able to detect and correct faults.
# Analytical Modeling and Empirical Analysis of Binary Options Strategies
## 2 Materials and Methods
### Modeling Binary Options
#### 2.5.3 Mathematical Notation  
The mathematical notation for formally modeling binary options and the introduced investment strategies is as follows:  
* \(\mathcal{T}:\) set of periods, \(t\in\ \mathcal{T}=\{1,\dots,T\}\), where \(T\) is the _trading horizon_.
* \(X_{0}\): initial capital available in period \(0\) before the series of successive investments begins.  
* \(p\): prediction accuracy, \(0\leq p\leq 1\); probability that a prediction (put or call) correctly predicts the future.
* \(r\): payout percentage, \(0\leq r\leq 1\); percentage of initial investment received if the prediction is correct.
* \(T\): trading horizon, number of periods for which successive investments are made.  
**State Variables**
* \(X_{t}\): balance available at (beginning of) period \(t\).  
**Decisions**
* \(z\): investment proportion (proportion of the current balance invested in each period) in Strategies A, C, and D, \(0\leq z\leq 1\);
* \(z^{\prime}\): investment proportion (proportion of the initial balance invested in each period) in Strategy B, \(0\leq z^{\prime}\leq 1\);
* \(w\): multiplier for withdrawal; multiplier of initial capital \(X_{0}\) in deciding whether to withdraw money in Strategies C and D, \(w\geq 0\);
* \(v\): withdrawal proportion (proportion of current balance withdrawn) in Strategy C, \(0\leq v\leq 1\);
* \(y\): withdrawal proportion (proportion of surplus withdrawn) in Strategy D, \(0\leq y\leq 1\).
## 3 Results and Discussion
### Evaluation of Potentially Robotic Traits
#### 3.2.2 Growth and Survival Under Gastrointestinal Tract Conditions  
All the examined strains were subjected to in vitro digestion and presented with various survival abilities under simulated gastrointestinal (GI) conditions (Figure 2). Only 11 strains showed the same or higher survivability than the positive control (commercial probiotic _Saccharomyces cerevisiae_ var. _boulardii_), with survivability at 48.70% in experimental conditions. These strains belonged to _Candida metapsilosis_ (058_Cand_metapsilosis, 66.22%), _Metschnikowia pulcherrima_ (105_[PERSON]_pulcherrima, 52.18%; 107_[PERSON]_pulcherrima, 54.31%; 110_Metsch_pulcherrima, 85.25%; 111_Metsch_pulcherrima, 71.55%; 112_Metsch_pulcherrima, 62.37%; and 113_Metsch_pulcherrima, 104.99%), and _Pichia kudriavzevii_ (052_Pich_kudriavzevii, 100.61%; 053_Pich_kudriavzevii, 103.47%; 099_Pich_kudriavzevii, 82.44%; and 101_Pich_kudriavzevii, 78.94%). Similar to the findings from our last study, where yeast strains were derived from wines, some strains of _Hanseniuspora uvarum_, _Metschnikowia pulcherrima_, and _Saccharomyces cerevisiae_ presented with above 100% survivability rates, which may suggest that GI tract conditions favour their proliferation [6]. In the cases of spoilage yeast, three out of the twenty-seven strains presented with such properties, including two _Pichia kudriavzevii_ (052_Pich_kudriavzevii, 053_Pich_kudriavzevii) and one _Metschnikowia pulcherrima_ (113_Metsch_pulcherrima). A similarly high ability to survive the GI tract was found for _M. pulcherrima_, and its probiotic potential is presented by [PERSON] et al., 2024 [30] and [PERSON] et al. [31]. Also, in the case of _P. kudriavzevii_, various authors present its promising ability to survive GI tract conditions and suggest its probiotic potential [10; 11; 15; 19; 32; 33; 34; 35]. [PERSON] et al. [13] isolated six strains of _P. kudriavzevii_ from fermented products like traditional cheese, sourtough, olive brine, and Shalgam, which showed survival rates ranging from 51% to 84.4% for the most resistant strains and yielded similar results to those presented here. In the other article, [PERSON] et al. [33] described the food-derived strain _Pichia kluyveri_ LAR001 which showed probiotic potential and properties that may allow its use in further beverage production. Strains with survivability lower than the _Saccharomyces cerevisiae_ var. _boulardii_ CNCM I-745 were excluded from further studies.  
Figure 2: Survival of examined strains under gastrointestinal tract conditions (One-Way ANOVA: strain: F (27,56) = 85.544, \(p\) = 0.0000, se \(\pm\) 0.031514).
# Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks
## 2 Stacked RBM-AE Model  
### Architecture of the Stacked RBM-AE  
The Stacked RBM-AE model contains two parts. The first part of the Stacked RBM-AE model we call encoder \(\mathbf{E}\), which includes four standard RBMs (in Figures 1 and 2).  
Each standard RBM is an undirected graph model, which includes two layers. The vector \(\mathbf{v}\left(v_{1},v_{2},\ldots,v_{n}\right)\) represents the visual layer, which is the input vector with \(n\) scalar components from the training dataset; The vector \(\mathbf{h}\)\(\left(h_{1},h_{2},\ldots,h_{m}\right)\) represents the hidden layer vector with m scalar components. The standard RBM takes the state space \(\left(\mathbf{v},\mathbf{h}\right)\in\left\{0,1\right\}^{m+n}\). The goal of RBM is to find \(P_{data}(\mathbf{v})\) the unknown true high dimensional distribution of the visual layer variables [30]. To achieve this goal, the high-dimensional distribution of the training dataset is modeled to get \(P(\mathbf{v}|\theta)\), where the sample distribution depends on the model parameters \(\theta\)[31].  
For the second part of the Stacked RBM-AE model, firstly we use the characteristics of auto-encoder to flip the encoder \(\mathbf{E}\) to get a symmetrical scale decoding output named decoder \(\mathbf{D}\); the structure is shown in Figure 3. The initial input of the encoder \(\mathbf{E}\) is used as the input of the multilayer auto-encoder. Our goal is to satisfy the top output approximately equal to the bottom input.  
Figure 1: Standard RBM.  
Figure 2: Stacked RBM.
# A Novel Overcurrent Suppression Strategy during Reclosing Process of MMC-HVDC
## 5 Discussion  
There has been wide research on the fault current limiter (FCL), such as the current limiting inductor (CLI), superconducting fault current limiter (SFCL), Solid State FCL and resistive fault current limiter. All the limiters will work when the fault occurs to limit the current to a lower amplitude and then break the fault circuit. For CLI, it is capable of limiting the overcurrent rise, but too many inductors in the DC grid will influence the transient response, stability and the speed for DCCB interrupting the fault current. Moreover, a large value inductor will bring large size and manufacturing burdens. For SFCL, it can reduce the fault current peak and the size of CLI, but it costs too much. A solid state FCL can interrupt the DC fault current very quickly in even hundreds of microseconds but the extra large amount of power electronic components bring much conduction loss. Resistive fault current limiter usually aims to accelerate the process of fault current decaying and is often activated by electronic switches but neglects the high voltage exerted on the electronic components at the switching. The above current limiting strategies all mean to limit the current to some level making the DCCB or other breakers to interrupt the fault current more quickly and easily. None of them pay attention to the post-fault process.  
Research on the current limiting during the reclosing process for MMC-HVDC is really rare. Reclosing is necessary for an MMC-HVDC system to determine whether the fault is temporary or permanent. For a permanent pole-to-pole fault, the arm will undergo overcurrent again during each  
Figure 12: Arm current and blocking signal with RCLR.  
reclosing process. So, the overcurrent suppression during the reclosing process is of great significance. In the present literature, resistive limiters have been proposed but there are two problems remaining unsolved. Firstly, none of them accurately calculated the overcurrent, which is the base of current limiting. Secondly, a detailed configuration method of the limiter, especially of the calculation of the resistor, was not given or well-thought-out. In this paper, accurate current is calculated based on the proposed mesh current method, and the detailed calculation method of RCLR is conducted. The selection of resistor is based on different limiting levels of the fault current which is flexible for different thresholds of SMs overcurrent protection. The proposed method in this paper can limit the overcurrent to a required level with a lower resistance value.
# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
###### Abstract  
Gas distribution stations (GDSs), pivotal nodes in long-distance natural gas transportation networks, are susceptible to catastrophic fire and explosion accidents stemming from system failures, thereby emphasizing the urgency for robust safety measures. While previous studies have mainly focused on gas transmission pipelines, GDSs have received less attention, and existing risk assessment methodologies for GDSs may have limitations in providing accurate and reliable accident probability predictions and fault diagnoses, especially under data uncertainty. This paper introduces a novel dynamic accident probability assessment framework tailored for GDS under data uncertainty. By integrating Bayesian network (BN) modeling with fuzzy expert judgments, frequentist estimation, and Bayesian updating, the framework offers a comprehensive approach. It encompasses accident modeling, root event (RE) probability estimation, undesired event (UE) predictive analysis, probability adaptation, and accident diagnosis analysis. A case study demonstrates the framework's reliability and effectiveness, revealing that the occurrence probability of major hazards like vapor cloud explosions and long-duration jet fires diminishes significantly with effective safety barriers. Crucially, the framework acknowledges the dynamic nature of risk by incorporating observed failure incidents or near-misses into the assessment, promptly adjusting risk indicators like UE probabilities and RE criticality. This underscores the importance for decision-makers to maintain a heightened awareness of these dynamics, enabling swift adjustments to maintenance strategies and resource allocation prioritization. By mitigating assessment uncertainty and enhancing precision in maintenance strategies, the framework represents a significant advancement in GDS safety management, ultimately striving to elevate safety and reliability standards, mitigate natural gas distribution risks, and safeguard public safety and the environment.  
gas distribution station; dynamic accident probability assessment; Bayesian network modeling; data uncertainty; safety and reliability enhancement +
Footnote †: journal: Applied sciences  
Article