esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Computational Fluid Dynamics Analyses
#### 3.3.1 Mesh Sensitivity  
A mesh sensitivity study is performed to examine the relationship between computational time, which is related to total nodal count, and convergence of results. Two sets of boundary conditions definition are imposed to check discrepancies between mesh densities with results listed in Table 8. The largest observed changes refer to the efficiency values which can be imputed to improved wall boundary layer resolutions as number of elements increase.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Variable** & **Unit** & **Value** \\ \hline \(\dot{m}\) & kg/s & 354.6 \\ \(\dot{W}\) & MW & 101.6 \\ Total-to-total efficiency (\(\eta_{tt}\)) & \% & 88.25 \\ Total-to-static efficiency (\(\eta_{ts}\)) & \% & 83.40 \\ \(\
u_{s}\) & - & 0.344 \\ Stage loading (\(\psi\)) & - & 1.42 \\ \(\phi\) & - & 0.433 \\ Degree of reaction (R) & - & 0.282 \\ \(\Pi_{ts}\) & - & 5.52 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Anticipated performance results.  
Figure 4: Meridional dimensions of selected turbine.  
The finest mesh of 971,613 elements is used for flow visualisation. The y\({}^{+}\) on the rotor surfaces is \(\leq\)2; it is harder to obtain low values of the non-dimensional distance on the stator and rotor shroud because of the high Reynolds number (Re \(\approx\)1.7 \(\times\) 10\({}^{7}\), given that the fluid has a high density). The range of y\({}^{+}\) values on all surfaces is between 0.14-200 (200 being the highest acceptable limit of y\({}^{+}\) as referred to SoftInWay manual recommendations). [PERSON] et al. (2013) also noted high values of y\({}^{+}\) in specific regions of the CFD mesh (0.3-92.2). Lower y\({}^{+}\) can be obtained by further increasing mesh element count but owing to the licensing restrictions of AxCFD and the limited computational resources available to run the simulations, the mentioned values are accepted. H-O grid topologies are used in both the stator and rotor domains for smooth alignment with the flow direction, and refinement made in regions near blade walls and leading/trailing edges to account for high normal gradients ([PERSON], 2016).  
Inlet/outlet mass imbalance, monitoring of total-to-total efficiency stabilisation, and root mean square residuals of mass, density, pressure and turbulence equations (k and \(\omega\)) are used as criteria to assess solution convergence. For lower mesh densities, the residual values are set at \(\leq\)10\({}^{-6}\) but for finer meshes, residuals require more iterations to reach steady higher values. This is observed in similar works by [PERSON] (2016) where increased residual values are accepted for off-design conditions and for finer mesh simulations.
# Towards Optimizing Multi-Level Selective Maintenance via Machine Learning Predictive Models
## 4 Proposed Methodology
### Selective Maintenance with Multi-Strategies  
Due to machine degradation, the failure rates increase, and the maintenance operation plays a crucial role in both eliminating the effects of this degradation and improving the overall system age. As perfect maintenance incurs significant expenses, in this context, we consider the possibility of implementing multi-level maintenance activities to effectively reduce both maintenance costs and time. In this context, we posit that maintenance action proportionally enhances the component's age compared to its state before the maintenance activity. The impact of maintenance actions on the component's age is expressed by the following equation:  
\[\gamma_{i}^{*}(m)=\begin{cases}\gamma_{i}^{*}(m)+\zeta(k_{i})&\text{if }k_{i}\in\{1,2, \ldots,K_{i}-1\}\\ A_{i}^{*}&\text{if }k_{i}=K_{i}\end{cases} \tag{21}\]  
where \(\zeta(k_{i})\geq 0\) is the age improvement value. \(\zeta(k_{i})=0\) for \(k_{i}=1\) or before maintenance operation and \(\zeta(k_{i})>0\) is the age after maintenance operation with \(k_{i}>1\).  
Figure 4: Actual and predicted degradation coefficient with ANN model.  
Figure 3: Actual and predicted degradation coefficient with SVR-Linear Kernel.
## 2 Research Significance  
Improving the thermal conductivity of EG-Al2O3 nanofluids is crucial for heat transfer in cooling systems and heat exchangers. This enhancement allows for more efficient energy use, reduces operational costs, and facilitates the design of more compact systems. Additionally, higher thermal conductivity can expand the range of applications in industries  
Figure 1: Illustration of a workflow for predictions of the thermal conductivity of EG–Al2O3 nanofluids.  
requiring effective thermal management, such as electronics and high-performance engines. The accurate prediction of thermal conductivity in EG-Al\({}_{2}\)O\({}_{3}\) nanofluids is pivotal for optimizing energy utilization in various industries, yet it presents significant challenges. Traditional models like those of Maxwell and Hamilton-Crosse have typically simplified complex phenomena such as Brownian motion and particle interactions, leading to significant discrepancies between the predicted and experimental results. This limitation necessitates a shift towards more adaptable and nuanced approaches.  
ML models, by virtue of their design, excel in managing complex, nonlinear data interactions without necessitating explicit programmatic formulations of the underlying physical laws. This capability is particularly advantageous in handling the multi-variable nature of nanofluid systems, where factors such as particle size, temperature, and concentration interact in nonlinear ways to affect thermal conductivity. By implementing advanced ML models, this research leverages computational power to derive insights from existing data in a way that is both cost-efficient and robust, offering substantial improvements over conventional methods. This study contributes to the field by: (i) employing six different supervised machine learning models (ANN, DT, GBDT, KNN, MLP, and XGBoost) to predict the thermal conductivity of EG-Al\({}_{2}\)O\({}_{3}\) nanofluids, and (ii) utilizing SHAP sensitivity analysis to provide crucial insights that enhance the theoretical understanding and practical application of thermal management in various industries. The six supervised models were developed using four published studies that focused on temperature, particle size, and volume fraction as the primary analytical parameters. These studies were chosen because they provide high-quality and well-organized data.
# A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data
## 2 Materials and Methods
### Methods  
#### 2.5.1 Satellite Data Pre-Processing  
To ensure a consistent spatial resolution of 10 m across all bands, spatial resampling of the satellite data was performed using the S2 Resampling Processor tool in the open-source ESA SNAP software. Subsequently, cloud masking was applied to each image using the 'Land/Sea Mask' tool by selecting the 'Use Vector as Mask' option and incorporating the 'Opaque_clouds' and 'Cirrus_clouds' masks provided by ESA. Finally, a subset of each dataset was created for the study area, ensuring that the area of interest, including the fields, remained free of cloud cover.
## 4 Discussion
To date, a number of regulatory factors involved in modulating anthocyanin accumulation in strawberry fruits has been characterized, including _FaRAV1_, _FaRAP_, _FaSnRK2.6_, _FaMADS9_, _FaRIF_, _FaMYB9_/_MYB11_, and _FaGAMYB_[13; 14; 15; 16; 17; 18; 19; 20; 21]. Besides these transcription factors, some important regulatory proteins downregulate the level of anthocyanin, such as protein phosphatase AB11, protein kinase FaSnRK2.6, FaMRLK47, and FvMAPK3 [22; 23; 24; 25]. Taken together, the key question that needs to be addressed is whether the interplay between _FaAKR23_ and _FaMYB10_ results inanthocyanin accumulation or whether FaAKR23 regulates anthocyanin by directly binding to the promoter of _FaMYB10_. Nevertheless, when _FaAKR23_ is overexpressed in strawberry fruits, _FaAKR23_-OE fruits do not show higher content of either AsA or anthocyanin compared to EV-OE fruits (Figure S1). We speculated that, for one thing, there might be a precisely controlled mechanism of AsA and anthocyanin accumulation that protects the plants from overloading the antioxidants, as, even though _GGP_ or _GalIIR_ genes are overexpressed in plants, the AsA level did not increase as remarkably as that of gene expression; in some cases, AsA was only moderately promoted [49; 50]. Moreover, the nuclear-located FaAKR23 possibly needs other cooperative factors, such as transcription factor _FaMYB10_, to improve the biosynthesis of the two antioxidants. Furthermore, RNA-seq data revealed that many DEGs were involved in plant-pathogen interaction, starch and sucrose metabolism, and the MAPK signaling pathway. We discovered here that the expression of 23 _FaWRKY_ transcription factors was significantly upregulated and that 3 _FaWRKY_ members were downregulated in _FaAKR23_-RNAi transiently transformed fruits (Figure S3). _WRKY_ genes compose one of the largest families of transcription factors in land plants and are characterized by the highly conserved WRKY domain; WRKY proteins have been reported to play key roles in regulating growth, development, and biotic and abiotic stresses [51; 52; 53; 54]. In strawberry, _FaWRKY1_ has been characterized and involved in mediating defense responses to the fungus _Colletotrichum acutum_[55]; moreover, FvWRKY48 binds to the pectate lyase _FvPLA_ promoter to control fruit softening [56]. These results hint that _FaAKR23_ may play multiple roles in strawberry in addition to AsA and anthocyanin accumulation in fruits. To sum up, the specific regulatory mechanism of _FaAKR23_ that modulates downstream structural genes and regulatory factors should be addressed, and the interacting proteins that assist _FaAKR23_ in regulating AsA and anthocyanin content need to be further identified. In addition, whether plant hormone or abiotic stress acts as upstream signaling to regulate the expression of _FaAKR23_ and then is involved in AsA and anthocyanin accumulation needs further research evidence to be clarified. Overall, our study provides new clues about the regulatory mechanisms of AsA and anthocyanin accumulation and offers a promising candidate gene for strawberry breeding with high antioxidants.
## 4 End-to-End Language Development Screening Model
### Tokenization for Korean  
By processing words at the character level, subword tokenization can solve the Out-of-Vocabulary (OOV) problem. For example, an abbreviation such as \"OMG (Oh my god),\" which is not in the vocabulary, is tokenized into subwords such as \"O\", \"M\", and \"G\". A word vector for \"OMG (Oh my god)\" may be represented by a combination of each alphabet (\"O\", \"M\", and \"G\") in the vocabulary when handled as a subword in this manner. This method also has the benefit of allowing all cases of the alphabet to be included in the vocabulary at a low cost. However, because Korean words are agglutinative languages, they are represented by combining syllables made up of consonants and vowels, and a Korean-specific subword tokenization approach was investigated [29; 30; 31].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{5}{c}{**Type of Korean Part-of-Speech**} \\ \hline  & Noun & Pronoun & Numeral & Verb & Adjective & Adverb \\  & Determiner & Postposition (Josa) & Interjection & Ending \\ \hline \hline \end{tabular}
\end{table}
Table 4: Korean part-of-speech system.  
Figure 2: Example of part-of-speech system of Korean and English.  
In addition, the Korean language has a different method of generating noun and verb phrases. The postposition is merged after the stem in noun phrases, while the ending is merged after the stem of the verb in verb phrases. By applying Byte-Pair-Encoding (BPE) [32] in bi-directions, the BidirectionalWordPiece Tokenizer [16] proposed by KR-BERT can solve this problem. Therefore, the BidirectionalWordPiece Tokenizer was employed in this study.
# Structural Health Monitoring for Prefabricated Building Envelope under Stress Tests
###### Abstract  
This paper details the comprehensive activities conducted in a laboratory setting to assess the structural health monitoring (SFM) of prefabricated building envelopes. Integrating sensors into building components like curtain wall facades poses challenges but offers opportunities for monitoring structural health, requiring compliance with regulatory standards. The research investigates the possibility of defining a kit of conventional and multi-parameter sensors integrated within the building envelope to monitor its behavior during the performance test conducted. The kit of sensors also includes Fiber Optic Sensors for effectively monitoring building envelope behavior and structural integrity. In this context, the European project InComEss (H2020-GA862597) aims to define a stand-alone solution for SFM using Piezoelectric Energy Harvesting Systems (PE-EHS) for facade monitoring through FBG/FOS system. After analyzing the main facade structural stress, a series of FBGs, accelerometers, and force washers were integrated within a 1:1 scale facade prototype and tested in a laboratory following the test sequence parameters required by the curtain wall standard EN 13830. The data collected were analyzed with the aim of monitoring the facade behavior before and after the tests. The results show that the facade's performance passed the assessing test criteria without reporting any damages. In addition, the outcomes demonstrated the effectiveness of the defined kit of multi-parameter sensors for the building envelope's SFM.  
building envelope; facade; structural health monitoring; fiber optic sensor; fiber brag grating; IoT +
Footnote †: journal: Applied Sciences
# Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization
## 2 Methodologies and System Description
### Particle Swarm Algorithm Combined with Pareto Efficiency Process  
Table 1 shows the multi-objective particle swarm optimization algorithm parameters for MOPSO. Figure 1 shows a flow chart of the PSO algorithm combined with Pareto  
efficiency to form a MOPSO algorithm. It starts at random by generating a group of particles and initializing them. Next, the fitness value of each example according to the multi-objective function is evaluated, and the individual and global optimal solutions are updated based on the Pareto dominance rule. The particle updates its position and velocity based on these optimal solutions while updating the Pareto front to reflect the best non-dominated solution found. Finally, this process is repeated until the termination condition is met. The objective function can be changed according to the type of filter to be designed. And the fitness function of MOPSO can be expressed in the following mathematical form:  
\[f(x)=\left(f_{1}(x),f_{2}(x),f_{3}(x),\ldots,f_{n}(x)\right) \tag{16}\]  
Among them, \(f_{1}(x)\) can represent the passband oscillation, \(f_{2}(x)\) can represent the stopband suppression, \(f_{3}(x)\) can represent the range of the numerator or denominator coefficient of the filter, and \(f_{n}(x)\) can be increased or decreased according to the set parameters. \(x\) is the parameter vector of the particle. In Pareto efficiency, target constraints and correction sorting mechanisms are added. The concept of objective constraints is to transform specific objectives into constraints and optimize other objectives only under these constraints. For example, a threshold for a specific goal can be set, and only when this threshold is met will the optimization of other \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Coefficient** \\ \hline Acceleration factor (c1, c2) & 1.5 \\ Weights & 0.7 \\ Number of particles & 30 \\ Number of iterations & 100 \\ Maximum/minimum speed & 5/\(-\)5 \\ Search maximum/minimum boundary & 50/0 \\ random number & [0, 1] \\  & \(\delta_{p}(dB)=20 log10(1+\delta_{p})-20 log10(1-\delta_{p})\) \\ Objective function & \(H(z)=\frac{Y(z)}{X(z)}=\frac{\sum_{i=0}^{N}h_{i}z^{-i}}{1+\sum_{i=0}^{N}h_{i}z^ {-i}}\) \\  & \(H\left(e^{i\pi T}\right)=\frac{\sum_{i=0}^{N}h_{i}z^{-i}}{1+\sum_{i=0}^{N}h_{i} z^{-i}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MOPSO algorithm parameter settings. Figure 1: PSO algorithm combined with Pareto efficiency process. goals be considered, which can be expressed by a mathematical formula (17). The concept of modifying the ranking is to introduce additional ranking criteria. For example, Pareto ranking can be adjusted based on the distribution, uniformity, or diversity of solutions to ensure the diversity of solutions and the uniformity of distribution. \[minimize\ f_{1}(x)\quad subject\ to\ f_{2}(x)\ \leq\ \delta \tag{17}\]  
Among them, \(\delta\) is the preset constraint threshold. The following is the pseudocode architecture process of proposal method.
## 2 Persisters of Foodborne Pathogens
### Persister Types and Mechanisms of Formation
#### 2.3.4 Small Colony Variants  
SCVs are defined as slow-growing pin-prick-sized colonies that are less sensitive to antibiotic treatments compared to wildtype pathogens and can revert to a normal phenotype after stress removal [97]. Since the first report about associations between SCVs and persistent infection in 1995 [98], researchers have found that SCVs exist in a wide range of bacteria and are linked to several chronic diseases [99]. In addition, SCVs can promote pathogenicity by promoting biofilm formation [81], presumably facilitated by overexpressing adhesins [100] and by impairing host immunity via necrotosis induction [101].  
It was found that environmental stress, antibiotic therapy, and adverse physiological conditions inside eukaryotic cells, are inducers of SCVs. For example, long-term cold stress raises the proportion of _S. aureus_ SCVs with thicker and diffuse cell walls [102]. Exposure to quinolones [103] and aminoglycosoides [104] can also lead to _S. aureus_ SCVs. [PERSON] et al. [105] observed that _S. aureus_ SCVs isolated from clinical infections and culture under in vitro stress conditions, displayed a prolonged lag time. Further research showed that such a prolonged lag time that can be induced after is antibiotic exposure in vitro. This suggests that antibiotics may increase SCV formation, and, thus, the risk of persister formation in the clinic.  
Though most SCVs-related studies use _S. aureus_ as model strains, SCV formation is also used as a survival strategy by _Salmonella_. SCVs form in _Salmonella_ planktonic and biofilm cultures when exposed to a high concentration of ciprofloxacin and ceftazidime [106]. Moreover, the formation of SCVs is recognized as part of persistent _Salmonella_[99] infection. Such SCVs, localized within host cells, can last for several weeks and contribute to a relapse of infection.
## 4 Proposed Scheme
### One-Time Key Generation and Message Signature  
A message \(M_{i}\) could include status, emergency information or other related requests. When \(OLD_{i}\) tends to send message \(M_{i}\), it will firstly select a number \(r_{i}\) randomly and compute \(R_{i}=r\cdot P\). Let \(w_{i}=H2(M_{i}\|PID_{i}\|T_{t}\|R_{i}\|PK_{i})\), where \(T_{t}\) is the current timestamp. Then it signs \(M_{i}\) with \(\sigma=sk_{i}+w_{i}\cdot r_{i}\ mod\ q\). Then, \(OLD_{i}\) will send encapsulated \(msg:\{M_{i},T_{t},PK_{i},R_{i},PID_{i}\}\) to nearby communication-related entities.
###### Abstract  
A closed-loop modeling method was established here to evaluate the performance of new battery technology from lab research to scaled-up developed electric vehicle (EV) applications. As an emerging energy-storage device, the lithium-sulfur battery (LSB) is a very promising candidate for the next generation of rechargeable batteries. However, it has been difficult to commercialize the LSB up to now. In this work, we designed and built a battery, EV, and driver system loop model to study the key performance parameters of LSB operation in EVs, in which the tested data from the lab were introduced into the model followed by simulating driving cycles and fast charging. A comparison with the lithium-ion batteries used in real vehicles verified the high reliability of the model. Meanwhile, the simulation results showed that the LSB needs more improvements for EV application; in particular, developments are still highly needed to overcome the high power and energy loss and sharp voltage vibration for practical applications. The novelty of this work relies on the created closed-loop modeling method to simulate lab research results for evaluating new battery technology in scaled-up EV applications in order to not only vividly predict EV operation performance and commercialization feasibility, but also thoughtfully guide researchers and developers for further optimization and problem solutions. Therefore, this method holds great promise as a powerful tool for both lab research and the industrial development of new batteries for EV applications.  
lithium-sulfur battery; modeling; EV application; simulation analysis +
Footnote †: journal: Applied Sciences
## 7 Results and Discussion
### Classifiers Benchmark  
The pre-processed dataset was then split in a 75-25 proportion for training and testing, respectively. In addition, the trained classifier was later evaluated with data from a driving test that was not included in the original dataset. The results for this new dataset were consistent with the previous results, achieving an average F1-Score of 93%.  
In this work, 14 classification algorithms were tested in a benchmark evaluation using the pre-processed data. As it is explained further in this document, the analysis then proceeded with only the three algorithms with the highest scores in the benchmark: Random Forest, KNN, and SVM-RBF. The other classification algorithms initially tested were: Ridge Classifier, Perceptron, Passive-Aggressive, Stochastic gradient descent, Nearest Centroid (Rocchio classifier), and Naive Bayes. For each one of those, extensive documentation can be found [42]. In this work, the default configuration parameters of these algorithms in python scikit-learn were used [43].  
The benchmark allowed us to compare 14 different classifiers' performance, by using the same dataset in all of them. The results showed that Random Forest, KNN, and SVM classifiers provided consistently the best classification scores (Figure 11). In attention to the former, subsequent analyses were carried on with these three classifiers only.  
Figure 10: Speed averages in windows of 2 (s) and total average in one minute.
# Numerical Simulation Study on Welding Process of Upper Frame of Hydropower Unit
## 4 Simulation Analysis of Welding Deformation of Upper Frame Based on ISM
### Simulation of Welding Deformation for the Upper Frame of Hydropower Unit
#### 4.2.3 Establishment of Finite Element Model of the Upper Frame  
The overall size of the upper frame is large, and using hexahedral meshing will result in a huge number of elements, leading to low computational efficiency. Due to the relatively small thickness of the plates compared to the overall size, 2D shell elements were used for meshing. Tetrahedral shell elements were mainly used for meshing, with a few areas meshed with triangular elements. The mesh near the welds and their adjacent areas was dense. The finite element model of the upper frame shell is shown in Figure 23, which has a total of 61,607 nodes and 57,155 2D elements.  
The upper frame mesh model was imported into the welding deformation prediction software, where the thermal expansion coefficients of the materials at the corresponding welds were modified, and boundary conditions were set. Different welding sequences were simulated by changing the application sequence of inherent strains and elastic calculations were conducted.