esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Example NPP Structure and Earthquake Loads  
### NPP Example Structure Model  
In this study, the OPR1000 auxiliary building, as depicted in Figure 2, was selected as the example structure for predicting the dynamic responses of an NPP structure under seismic loads. The analysis model illustrated in the figure was created using Abaqus/CAE 2022, a widely recognized structural analysis software. The majority of structural components in the auxiliary building, including walls, slabs, and beams, were modeled using a four-node shell element with reduced integration (S4R). The main columns were represented using three-dimensional two-node beam elements (B31). The finite element model comprised a total of 19,689 elements and 17,765 nodes. It is important to note that only the auxiliary building of the OPR1000 was modeled, given that the foundations of the containment and auxiliary buildings are distinct.  
The reinforced concrete material utilized in the analysis possesses an elastic modulus of 2.78 \(\times\) 104 N/mm\({}^{2}\), a Poisson's ratio of 0.17, and a density ranging from 7.32 \(\times\) 10\({}^{-7}\) to 10.59 \(\times\) 10\({}^{-7}\) N/mm\({}^{2}\). Damping was modeled using Rayleigh Damping, which incorporated a 5% damping ratio. The eigenvalue analysis determined that the natural frequencies of the primary modes in the X and Y directions were 6.16 Hz and 4.25 Hz, respectively.  
Figure 3 depicts the input/output relationship of the database that was utilized to develop the machine learning model. As shown, the time history analysis was performed iteratively, incorporating variations in ground motion and material properties owing to aging deterioration as input variables. The machine learning model output was designated as the floor acceleration response, crucial for assessing the seismic safety of the MCC cabinet installed in the auxiliary building. The response was measured at the highest floor (Z = 103 ft), where the acceleration is most significantly amplified. Acceleration time  
Figure 1: Proportion of ML methods utilized in structural engineering.  
histories in the X and Y directions were calculated at the four corners indicated in Figure 2b, using finite element analysis. The calculated acceleration time history was further processed into the floor response spectrum corresponding to the resonance frequency band of the MCC cabinet. Further discussion on this topic will follow in the subsequent section.
## 2 Bearing Failure Modes
### Current Analysis  
Current analysis, as one of the supplementary methods of vibration analysis [45; 46], is often used for fault diagnosis of motor bearings [47]. Similar to the previous two methods, the failure mode and fault location of the bearing are different, and the frequency spectrum of the current generated is also different, which can be used for diagnosis. The principle of current analysis is shown in Figure 5.  
Current analysis is often combined with new technologies such as deep learning, implementing automated detection. For example, [PERSON] et al. used current signals as a key feature for bearing fault diagnosis, combining adaptive kernel functions and semi-supervised learning algorithms to adapt to complex working conditions and different operating states [48]. Research on advanced denoising algorithms, such as Barcelos AS and others, has introduced advanced denoising methods and deep learning algorithms to extract relevant features from noisy current signals [49]. It can also be combined with vibration signals and acoustic signals to achieve multimodal measurement. For example, [PERSON] et al. proposed processing signals from multiple phases and using multi-scale feature fusion modules to enhance features, thereby solving the problem of weak fault features in current signals [50].  
The advantages of current signal fault detection lie in its non-contact nature, high sensitivity, and real-time monitoring capability. It is less affected by environmental noise and can detect bearing defects at an early stage, preventing excessive mechanical damage. It is suitable for the online monitoring of bearings in industrial equipment such as motors, generators, and pumps, and is widely used in high-demand applications such as wind turbines and aerospace equipment. However, there are still limitations in identifying complex fault patterns, and in some cases, changes in current signals may be affected by external factors such as power supply fluctuations.
# Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks
## 5 Results and Analysis
### Communication Area and Number of Alive Nodes  
Figure 5c illustrates the communication area involved as the number of nodes increased in the network. Results show that the communication area increased as per the increase in number of nodes, whereas the value decreased with 35 to 45 nodes, to 4846 m\({}^{2}\) and 4756 m\({}^{2}\), respectively. For 30 nodes, the communication area coverage was 5008 m\({}^{2}\). It varied as per the topology of the nodes after deployment, i.e., the nodes deployed very close to each other may not increase the communication area. Figure 5d shows the number of alive nodes while the simulation time passed gradually. Results show that the nodes consumed energy for different data collection operations to expire battery life as per the increase in simulation time. After 800 s, there were 42 alive nodes out of a total of 70 nodes.
## 5 Lactic Acid Bacteria
### Antimicrobial Compounds Produced by LAB
#### 5.3.3 Exopolysaccharideaccharides  
Exopolysaccharides (EPSs), another group of functional components produced by LAB, are biodegradable polymers formed from monosaccharide units linked by a glycosidic bond [63]. These polymers can be divided into homopolysaccharides (HoPSs) and heteropolysaccharides (HePSs). HoPSs are synthesized extracellularly by _Lactobacillus_, _Streptococcus_, _Leuconostocus_, _Oenococcus_, _Pediococcus_, and _Weissella_ genera. Meanwhile, LAB-producing HePSs are synthesized intracellularly and belong to _Lactobacillus_, _Lactococcus_, _Streptococcus_, and _Bifidobacterium_ species. Some examples of monosaccharides that compose HoPSs are glucan, fructans, and galactans, consisting of D-glucose, D-fructose, and D-galactose, respectively. In the case of HePSs, these contain rhamnose, glucose, galactose, fructose, and mannose [89; 90].  
EPSs can be used as a stabilizer, thickener, emulsifier, and gelling agent to improve fermented foods' appearance, taste, rheological properties and texture. Beyond their positive technological effects, EPSs may also induce beneficial antimicrobial properties, increasing their potential applications in the food industry [91]. A study conducted by [PERSON] et al. [92] showed that EPSs produced by _Lactococcus lactis_ F-mou exhibited inhibitory activity against various microorganisms such as _Staphylococcus aureus_, _Pseudomonas aeruginosa_, _Escherichia coli_, _Listeria monocytogenes_, _Bacillus cereus_, _Proteus mirabilis_, _Acinetobacter baumantii_, _Enterobacter cloace_, and _Candida albicans_. Likewise, the EPSs isolated from _Bifidobacterium longum_ inhibited the growth of several bacteria, such as _Vibrio parahaemolyticus_, _Salmonella typhimurium_, _Staphylococcus aureus_, and _Bacillus cereus_[93].  
These numerous characteristics related to EPS from LAB highlight the relevance of these microorganisms in the clean label trend by producing antimicrobial metabolites that are responsible for improving the technological and sensory properties of food products. Therefore, it is interesting that more research investigates the potential use of EPS from LAB to replace preservatives in yogurts.
###### Abstract  
In the packages and printed circuit boards (PCBs) of high-speed and mixed-signal electronics, parallel plate waveguides (PPWs) are frequently used as the power/ground plane pair, resulting in a significant problem of electrical switching noise such as simultaneous switching noise and ground bounce noise. This noise inevitably deteriorates system performance. In this paper, we propose an electromagnetic bandgap (EBG) structure using an inductance-enhanced patch (IEP) to suppress PPW modes in high-speed and compact packages and PCBs. The noise suppression characteristics of the proposed IEP-EBG structure were thoroughly analyzed using a dispersion diagram based on a full-wave simulation as well as an equivalent circuit model of a unit cell structure with a Floquet boundary condition. The proposed IEP-EBG structure has the advantages of substantial reductions in the low cut-off frequency of the first stopband as well as unit cell size when compared to a conventional mushroom-type EBG structure without the inductance-enhanced technique. The suppression of the PPW modes of the proposed IEP-EBG structure was verified using measurements of scattering parameters. In the measurements, the low and high cut-off frequencies of the first stopband of the IEP-EBG structure were found to be 1.55 GHz and 2.48 GHz, respectively, while those of the conventional mushroom type EBG structure were 3.52 GHz and 5.3 GHz. For the low cut-off frequency, a 56% reduction was achieved, resulting in substantial miniaturization suitable for compact packages and PCBs.  
electromagnetic bandgap (EBG); inductance-enhanced; simultaneous switching noise; printed circuit boards (PCBs) +
Footnote †: journal: Electronics Letters
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 4 Fuzzy Q-Charging Algorithm
### Fuzzy Logic-Based Safe Energy Level Determination  
#### 4.4.1 Motivation  
We observe that \(E_{sf}\) contributes significantly to the algorithm's performance. When \(E_{sf}\)'s value is small, the MC's maximum energy amount, charging to the sensors, is also small. Accordingly, after being charged, the sensor's battery may quickly go below the threshold. On the contrary, if \(E_{sf}\) is too large, the MC needs to spend a long time at every charging point. Consequently, the sensors far from the charging points may run out of energy while waiting for their turn. To this end, we leverage Fuzzy logic to adjust \(E_{sf}\)'s value adaptively. In the following, we first analyze factors that affect \(E_{sf}\). We then describe the proposed algorithm. Remember that according to Equation (5), \(E_{sf}\) is determined by \(E_{sf}=E_{th}+\theta E_{max}\). Therefore, below, we will show how to adjust the value of \(\theta\) adaptively.  
When the MC stays at a charging point, the nearby sensors receive a more significant energy amount than the faraway sensors. Therefore, each near sensor's charging amount is likely more significant than its energy consumption for sensing and communication. Consequently, the nearby ones tend to increase their battery level gradually. On the contrary, the faraway sensors tend to decrease due to the energy consumption for sensing and communication tasks. It is expected that the MC should spend a longer time at charging points where sensors far from it do not encounter critical situations (i.e., having low residual energy or high energy consumption rate). Based on this observation, our algorithm is designed so that \(E_{sf}\) tends to receive a small value in the following cases.  
* The residual energy of all sensors is small.
* Many sensors need to be charged.  
We propose a Fuzzy logic-based \(E_{sf}\) determination algorithm, which utilizes the following two variables. The first one is the minimum residual energy of all sensors \(E_{min}\). The second is the number of charging requests, denoted as \(L_{r}\).  
The details of the algorithm is presented in Algorithm 1.
## 1 Introduction
### Objective of the Review Paper  
The objective of this review paper is to provide a comprehensive analysis of the application of GAs for multi-objective message routing in EnFVs. By examining existing research papers and studies, our aim is to achieve the following objectives:  
* Explore the state-of-the-art approaches and methodologies for multi-objective message routing in EnFVs using GAs.
* Identify the key challenges and considerations in optimizing message routing for these transportation systems.
* Discuss the benefits and limitations of GA-based routing approaches and their impact on the performance and functionality of EnFVs.
* Highlight the implications and potential applications of GAs in improving the efficiency, reliability, and the sustainability of message routing in EnFVs.
* Identify future research directions and opportunities to further enhance the effectiveness of GAs in optimizing message routing in these transportation systems.  
By providing a comprehensive overview and analysis, this study contributes to the understanding of multi-objective message routing in EnFVs and provides insights for researchers and practitioners interested in leveraging GAs for efficient and reliable communication within these innovative transportation systems. It provides critical challenges to the message routing of EnFVs and suggests many promising research directions.  
The subsequent sections of this paper delve into the multi-objective nature of message routing in EnFVs (Section 2), GAs for multi-objective routing optimization (Section 3), a review of existing studies on routing optimization for EVs and FVs (Sections 4 and 5), respectively, and a detailed discussion on the benefits and limitations of GA-based routing approaches (Section 6). This paper concludes by summarizing key findings, providing implications for EnFV message routing, and suggesting promising future research directions in this rapidly evolving field (Sections 7 and 8).
## 3 Results and Discussion
### Delay
#### 3.2.1 Verification
In the example, the prediction model with n \(\geq\) 2 is used to compensate for the nonlinear relation, and the second-order Taylor expansion is not accurate enough to model PHIG1 nonlinearities. The model achieves a MAPE of 4.07% when n = 3. The non-Gaussian distribution of delay can be predicted with n = 5, as shown in Figure 9. A strong nonlinearity of delay with respect to TOX1 is also observed, while its coefficient of variation is only 0.958%. The proposed method with n = 2 correctly predicts the non-Gaussian distribution for low \(V_{dd}\) (0.3 V), which is shown in Figure 10.  
Figure 9: The comparison of delay versus PHIG1 and the probability density between 1000 MC SPICE simulations and the prediction model with (**a**) n = 1 (**b**) n = 3 (**c**) n = 5. The CV of the delay with respect to L is 19%, which is second only to PHIG1 among the considered process parameters. However, differing from the strong nonlinearity exhibited by the relation between delay and PHIG1, a good result can be achieved by prediction, either with a single-point transient simulation or with a single-point DC simulation when n = 1. As shown in Figure 11, the prediction accuracy improves significantly when n = 2, and handles the nonlinearity well. The improvement is almost negligible with n > 2, and this is a common characteristic of the delay variability with respect to [L, W, TOX1, TOX2, TSI]. However, a stepwise decrease in the MPAE of delay with respect to PHIG1 is found in Figure 12. MAPEs of the predicted inverter delay and slew in the subthreshold region (0.3 V) are compared in Figure 6:0  
Figure 6:1 MAPE comparison in terms of process parameters in the subthreshold region (0.3 V) for (**a**) Slew H-L prediction with a single-point DC simulation (**b**) Slew H-L prediction with a single-point transient simulation. Figure 6:2 The comparison of delay versus TOX1 and the probability density between 1000 MC SPICE simulations and the prediction model with (**a**) n = 1 (**b**) n = 2.
## 4 Task Allocation Based on CEAGA in IUWSNs
### Adaptive Operator  
In the CEAGA, the adaptive strategy is applied to the crossover and mutation operations to make the crossover probability and mutation probability more flexible and rea  
Figure 4: Variation process diagram.  
sonable, so as to maintain the optimization performance of the algorithm. If the crossover probability is large, the search solution space will become larger, and new individuals will be generated faster. At the same time, this may damage the genetic structure of excellent individuals. On the contrary, if the crossover probability is small, excellent genes are easily inherited to the next generation, but the search process will slow down or even stop the iteration. If the mutation probability is high, the search solution space will become large, but it will increase the probability of destroying the individuals obtained by the elite operation, and the algorithm will also become a random search algorithm. On the contrary, if the mutation probability is low, the excellent genes of the previous generation are not easy to be destroyed and are easy to be inherited to the next generation, but it is not easy to produce new individuals and it is impossible to have population diversity. In the CEAGA, it is assumed that the crossover probability is denoted as \(Pc\), while the mutation probability is represented by \(Pm\). In order to get the finest value of individual crossover probability and mutation probability, the two Equations (24) and (25) are designed to calculate the excellent crossover and mutation probability in real time.  
\[P_{c}(i)=P_{c_{\min}}+\frac{P_{c_{\max}}-P_{c_{\min}}}{1+\exp\bigl{[}(h\cdot F (i)-F_{avg})/(F_{\max}-F_{avg})\bigr{]}} \tag{24}\]  
\[P_{m}(i)=P_{m_{\min}}+\frac{P_{m_{\max}}-P_{m_{\min}}}{1+\exp\bigl{[}(h\cdot F (i)-F_{avg})/(F_{\max}-F_{avg})\bigr{]}} \tag{25}\]  
In Equations (24) and (25), the upper and lower limits of the cross probability are expressed by \(Pc_{max}\) and \(Pc_{min}\), respectively. The upper and lower limits of the change probability are expressed by \(Pm_{max}\) and \(Pm_{min}\), respectively. \(F_{max}\) represents the highest fitness value within the population, \(F_{avg}\) denotes the average fitness value across the population, and \(F(i)\) represents the fitness value of the individual \(i\).
## 3 Platform for Real-Life Testing of Communication Protocols
### Monitor Messaging
#### 3.3.6 The Message Broker  
The message broker has been developed in Python to take advantage of the improved database access support. The protocol message parsing is performed at the library level in C++ to prevent the need for compiling the PROTO files in Python. This approach ensures that the message structure remains internal to the library, which only exposes the parser. In Figure 11, you can see that the ZMQ subscriber is implemented in Python and passes the raw messages it receives to the parser through Python's buffer protocol (as pybind11 py:buffer argument) [56]. The parser returns the parse result structure that contains the message payload type (PMT or PROTO) and the payload. If the PMT payload type is used, the message is parsed in Python using the pmt:to_python implementation. A PMT message is simply a shared pointer to the PMT structure, so the amount of data copied between C++ and Python is minimal. If the PROTO payload type is used, the data are parsed by the registered parser, and a dictionary (i.e., std::unordered_map) with the result is set in the parsed result structure. To avoid copying the dictionary, pybind11 opaque types [57] are used. The parse result object is returned to Python as a unique_ptr to transfer the ownership and release it via the garbage collector once the Python object is collected. As mentioned earlier, the parser implementation is part of the monitoring library, and the broker implementation is available in [44].
## 3 Nanosized-Lipid-Based Drug Delivery Systems in Wound Healing
### Lipid Nanoparticles in Wound Healing
#### 3.2.2 Nanostructured Lipid Carriers in Wound Healing
A molecule widely used for treating hemorrhage, blood stasis, bruises, swelling and pain is 20(\(S\))-protonanaxadiol. Studies also reported its effectiveness in diabetic wound healing, given its anti-inflammatory and pro-neovascularization effects. Contrary to these great properties, its applicability in commercial use is limited due to its low solubility. [PERSON] et al. developed a new formulation consisting of silicone elastomer, a polymer reported as being effective in wound healing [174], and 20(\(S\))-protonanaxadiol loaded in NLCs. 20(\(S\))-protonanaxadiol-NCLs resulted in a good EE% and sustained drug release properties. Aside from that, the in vivo studies showed the anti-inflammatory potential of this new formulation. In vivo experiments on a murine model revealed the enhancement of all wound-healing phases, the regulation of collagen settlement, inflammation decreases and angiogenesis promotion [56]. A recent study evaluated the efficacy of phenytoin loaded in NCLs as a topical treatment for patients with neuropathic diabetic foot ulcerations. Previous experimental research has emphasized that topical phenytoin can stimulate collagen synthesis, improve fibroblast proliferation and enhance wound healing. Still, local adverse reactions associated with commercially available products have restricted its application. In this study, the application of phenytoin loaded in NCLs has shown a more extended release of the active substance, leading to a significant reduction in wound size and healing time [58]. NLCs represent a chemically and physically stable drug delivery system with increased drug loading ability and enhanced bioavailability. The aforementioned properties proved the ability to overcome the shortcomings of SLNs, the first generation of lipid nanoparticles.
# Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks
## 2 Related Work
### MIoT Routing Protocols
However, the above routing protocols used for MIoT networks adopt a clustering approach with predefined routing paths, which ignores the intermittent connection problem of wireless communication links after disasters in the routing process. When sparse topology occurs in the network, these existing methods cannot guarantee reliable data transmission for post-disaster MIoT networks.
# Nonparametric Probability Density Function Estimation Using the Pade Approximation
## 5 Quantitative and Comparative Assessment
### Example 2: The Beta Distribution
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & \(\downarrow\)**WD** & \(\downarrow\)**BD** & \(\uparrow\)**CC** & \(\downarrow\)**KLD** & \(\downarrow\)**L1** & \(\downarrow\)**L2** \\ \hline KDE & 18.4653 & 7.1327 & 0.9844 & 0.005 & 0.1070 & 0.0042 \\ \hline Adaptive KDE & 15.7541 & 7.1331 & 0.9800 & 0.017 & 0.1203 & 0.0048 \\ \hline GMM & 11.6768 & 7.1313 & 0.9868 & 0.009 & 0.0806 & 0.0035 \\ \hline QHBS & 11.8088 & 7.1360 & 0.9356 & 0.003 & 0.1780 & 0.0081 \\ \hline One-Point Padé & 13.7342 & 7.1319 & 0.9876 & 0.021 & 0.0798 & 0.0034 \\ \hline Proposed Method & **11.0687** & **7.1311** & **0.9897** & **0.003** & **0.0789** & **0.0032** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics for different methods for the beta distribution using 300 samples. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & \(\downarrow\)**WD** & \(\downarrow\)**BD** & \(\uparrow\)**CC** & \(\downarrow\)**KLD** & \(\downarrow\)**L1** & \(\downarrow\)**L2** \\ \hline KDE & 29.5680 & 7.1375 & 0.9811 & 0.012 & 0.1418 & 0.0055 \\ \hline Adaptive KDE & 33.4228 & 7.1376 & 0.9848 & 0.015 & 0.1460 & 0.0055 \\ \hline GMM & 28.7827 & 7.1368 & 0.9565 & 0.020 & 0.1716 & 0.0063 \\ \hline QHBS & 37.6501 & 7.1395 & 0.9107 & 0.025 & 0.2023 & 0.0092 \\ \hline One-Point Padé & 28.6066 & 7.1376 & 0.9644 & 0.018 & 0.1503 & 0.0058 \\ \hline Proposed Method & **27.1665** & **7.1356** & **0.9856** & **0.010** & **0.1105** & **0.0045** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics for different methods for the Beta distribution using 100 samples. Figure 7: Visual assessment of different density estimation methods. The true Beta distribution (black), KDE (blue), Adaptive KDE (red), GMM approximation (green), QHBS (pink), One-point approximation (orange), and our method (cyan). The results for different sample sizes are shown from left to right: 300 samples (**a**), 100 samples (**b**), and 50 samples (**c**).