esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Physical Interaction of ARG1 with ERas  
Besides the investigation of protein expression and intracellular localization, we furthermore analyzed the direct interaction of ARG1 and ERas as well as their complex association in vitro. Association of purified ARG1 with different purified ERas variants, including _hs_Nex, _rn_Nex and the full-length ERas orthologs _hs_FL and _rn_FL, was first analyzed in a GST pull-down assay (Figure 2F). Purified GST was used as a control. Figure 2F shows that ARG1 bound much tighter to human than rat ERas proteins. Next, we analyzed the interaction of ARG1 with six human and rat ERas constructs: _hs_FL, _rn_FL, _hs_Nex, _rn_Nex, _hs_\(\Delta\)C and _rn_\(\Delta\)C (C-terminal truncated variants), in immunoprecipitation experiments using ERas overexpression in HEK293 cells. Notably, ERas FL and \(\Delta\)C interaction with ARG1 appeared much stronger as compared to Nex, which indicates that ARG1 might be engaged via additional binding sites other than the N-terminal extension, which contribute to a higher affinity (Figure 2G). Therefore, a pull-down of purified ARG1 with overexpressed N-terminally truncated ERas (\(\Delta\)N; a 39-233) revealed that ERas \(\Delta\)N is considerably impaired in binding ARG1 but still exhibited a weak binding as compared to ERas WT and EYFP, which was used as positive and negative controls (Supplementary Figure S4A). This result confirms Nex as a critical binding site of ERas for ARG1 but also confirms that other regions of ERas also contact ARG1. The pull-down of ARG1 with purified GST-ERas G-domain from _rn_ and _hs_ clearly showed that ERas possess with the G-domain of ERas a second binding site for ARG1 (Supplementary Figure S4B).  
Subsequent kinetic of the ERas-ARG1 interaction was investigated using surface plasmon resonance. In this approach, we injected different concentrations of ARG1 (0.5, 5,10, and 20 \(\upmu\)M) to immobilized GST-_hs_Nex (10 \(\upmu\)M) and measured their kinetic of interaction (Figure 2H). A dissociation constant (K\({}_{\text{d}}\)) of 2.1 \(\upmu\)M was determined for GST-_hs_Nex-ARG1 interaction using 1:1 binding Langmuir algorithm model, supporting the previous cell-based analysis of direct protein binding.
## 2 PV System Description  
The implemented PV system models were developed in MATLAB/Simulink to analyze the performance of different MPPT control strategies under realistic conditions. Figure 0(b) presents the system's block diagram and its component interactions. Two configurations were considered: (i) a photovoltaic model with a single bypass diode, representing the actual SOLARPOWER XUNZEL 30 W 24 V panel (Figure 0(a)), and (ii) an extended model with three series-connected cell groups, each equipped with an individual bypass diode to simulate PSC (Figure 2). This latter approach allowed independent adjustment of irradiance and temperature for each cell group, enabling a comprehensive evaluation of MPPT algorithms under non-uniform operating conditions.  
Figure 0(a) shows the Simulink implementation, structured into colored blocks:  
* Blue (MPPT Controller): Computes PV voltage, current, and power, implementing the MPPT algorithm to generate the optimal duty cycle for the Pulse Width Modulation (PWM) generator.
* Green (DC-DC Buck Converter): Models the buck converter circuit, which adjusts the output voltage and current to transfer power efficiently to the load based on the MPPT control.
* Orange (Measurements): Monitors key variables such as voltages, currents, power, efficiency, temperature, and irradiance, displaying them for performance analysis.
* Yellow (Performance Metrics): Calculates and exports input/output power, efficiency, and tracking error to the MATLAB workspace for further evaluation.  
Figure 0(a): PV MPPT control architecture: (**a**) Simulink model of the PV system; (**b**) block diagram of the MPPT control structure.  
This modular structure enables a clear and organized simulation environment for testing and comparing MPPT algorithms under different operating conditions.  
The photovoltaic panel operation was characterized through detailed electrical calculations based on the manufacturer's specifications presented in Table 1.  
As shown in Figure 3, the red curve on both I-V and P-V plots indicates the MPP of the panel.  
The PV parameters, summarized in Table 2, were calculated using the following formulas:  
\[P_{in}=V_{mpp}\times I_{mpp} \tag{1}\]  
\[P_{out}=\eta\times P_{in} \tag{2}\]  
\[I_{out}=\frac{P_{out}}{V_{out}} \tag{3}\]  
\[R_{o}=\frac{V_{out}^{2}}{P_{out}} \tag{4}\]  
Figure 2: Simulink models: (**a**) PV system with three groups of series-connected photovoltaic cells; (**b**) demonstration of bypass diode operation during PSC [27].
## 3 Approach
### Phase Shifts Between Frames
#### 3.3.2 Blooming  
Blooming is a phenomenon in which excess charge from a pixel spills over into adjacent pixels, causing blurring and related image artifacts. Blooming may cause the neighboring pixels to look brighter than an accurate representation of the light absorbed by the photo diode in that pixel [21]. In case of UDPSOOK modulation, blooming causes bright stripes of the resulting stripe pattern to be wider than dark stripes, because the charge from a bright stripe spills over to the adjacent pixels of the dark stripe. Figure 10 shows this effect in an example image, where one can see that the blooming effect is strong in the center of the image where the bright center of the LED was captured and is weaker on the left and right side due to the slightly shifted angle of the emitted light. The green lines indicate the edges of the rectangular signal used for the modulation. Due to the blooming effect the bright stripes are wider than the dark stripes when the pulse width of the modulation signal is 50%. The parts of the image affected by blooming cannot represent the encoded data correctly, because in these parts of the image, the modulated LED always appears to be ON, even if the signal is inverted by a phase shift. This paper proposes to adjust the pulse width ratio of the modulation signal to counteract the blooming effect. If the pulse width ratio is intentionally below 50%, the blooming effect is neutralized and the BER should be improved.  
A similar effect occurs if the exposure time of the recording camera is set slightly longer, e.g., \(1/1000\) s instead of \(1/8000\) s. It makes sense to make the exposure time longer, as it is difficult to detect a car and its modulated taillights in a dark image. However, the exposure time needs to be as short as possible to enable the desired use of the rolling-shutter effect. If the exposure time is slightly longer, the image sensor of the camera is exposed longer to the light of the modulated LED and the transitions from ON to OFF and vice versa are slightly washed out. This means the bright stripes of the stripe pattern are wider than with an extremely short exposure time. This can again be neutralized by having a lower pulse width ratio to optimize the BER.
# Research on Characteristics of Flow Noise and Flow-Induced Noise
## 6 Conclusions  
In this paper, we conducted research on flow noise and flow-induced noise, and validated the numerical simulation methods used. Based on this, an acoustic comparative analysis of flow noise and flow-induced noise in a cylindrical shell was performed. The following conclusions were drawn:  
1. The validation of the computational method was performed using a teardrop submarine model, and the pressure curves at various detection points exhibited consistency with experimental trends.
2. According to the result of detection points at different distances in the X, Y, and Z axes, the spatial characteristics of flow noise and flow-induced noise were compared and analyzed. The results indicate that, at the same frequency, flow-induced noise was greater than flow noise. In the near-field region, both flow noise and flow-induced noise exhibited a significant decay rate, which stabilized as the distance increased.
3. The experimental results demonstrate that the simulated sound pressure values at the detection points exhibited a consistent trend with the experimental values, with an error of 8% at the peak. Therefore, the simulation results can effectively reflect the characteristics of the actual underwater acoustic environment.  
Conceptualization, B.L.; methodology, B.L.; software, J.W.; validation, L.Z.; formal analysis, J.W.; investigation, X.X.; resources, J.W. and L.Z.; data curation, X.X.; writing--original draft preparation, X.X. and J.W.; writing--review and editing, X.X. and Z.W.; visualization, L.Z.; supervision, B.L.; project administration, B.L.; funding acquisition, B.L. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (contract No. 62201527) and the Science and Technology on Sonar Laboratory Foundation of China (contract No. 6142109 KF201803).  
Not applicable.  
Data Availability StatementData are unavailable due to privacy.  
We sincerely thank [PERSON] for the assistance provided during the experimental process.  
The authors declare no conflict of interest.  
Figure 16: Frequency spectrum diagram: (**a**) no. 1 detection point; (**b**) no. 4 detection point.
## 1 Introduction
### Background and Motivations
This situation is even more critical in wind farms located in complex terrain, which can lead to long downtimes of the WT. In addition, early fault detection is crucial for WT maintenance, as it saves significant time and costs. Implementing hybrid models and effectively using SCADA data can improve the reliability and efficiency of predictive maintenance, leading to a reduction in wind farm O&M costs. Finally, Table 1 summarizes key studies on failure prediction, from which some conclusions can be drawn. Most studies do not indicate how far in advance the models predict the failure before it occurs. Furthermore, there is a small body of scientific research focused on predicting WT converter failures, while more studies are needed to address the prediction of failures in WTs located in complex terrains, such as the wind farm studied in this work. Therefore, our motivation is to help fill the knowledge gap mentioned above. The remainder of the paper is structured as follows: Section 2 describes the methodological process, the model architecture, the failure prediction system, and the evaluation methods. Section 3 details the results of the evaluation of the fault prediction model. Finally, Section 4 offers conclusions and suggestions for future research. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Ref. \& Results** & **Learning Algorithms** & **Tools/Methods** & **Main Component** & **Results \& Limitations** \\ \hline
[31], 2019 & Classification & BPNN & Converter & Accuracy = 99.98\% in identifying converter faults. There is limited discussion on the scalability of the proposed strategy to handle a wider variety of fault types. Does not use SCADA data. & Accuracy = 99.
# Cumulative Impact of Wind Farm Noise
## 2 Initial Position  
Cibuk Wind Farm and Vetrozelena Wind Farm (case study) are planned in the northern part of the Republic of Serbia (East Europe), some 35 km from the capital Belgrade and about the same distance from the border with Romania (Figure 1). It is a low-lying, plain area, which is of special importance for determining the characteristics of the spatial propagation of noise. Cibuk Wind Farm (156 MW) has already been developed and is operational, while Vetrozelena Wind Farm (300 MW) is in the document preparation stage.  
In the course of Vetrozelena Wind Farm Urban Plan preparation, it is necessary to include the cumulative acoustic effect of both the existing and the planned wind farms, as well as the change in the outline of the impacted area relative to identified receptors.  
The assessed cumulative impact of noise produced by turbines is part of the expert analysis and multicriteria evaluation (semiquantitative method) in SEA [34; 35; 36], aimed at determining the best possible microlocations of each turbine that is to be part of the future Vetrozelena Wind Farm and also applying preventive measures against noise from the very first stage of planning and developing Vetrozelena Wind Farm [37]. The basis for definingcriteria for determining the acceptable wind farm noise effect was local legislation in the area, leaning on the Environmental Noise Directive (2002/49/EC) [38] and the Guidelines for Community Noise (WHO, 1999) [39].  
The European Strategic Environmental Assessment Directive 2001/42/EC [40] stipulates the implementation of the SEA process in planning and drafting programs in different areas, one of them being the energy management sector, ergo the wind power industry. Despite the widely accepted negative impact of wind farms on the environment, it must be pointed out that their benefits are far greater. Negative impacts cannot be completely disregarded, though, as stated in EU Guidance on Wind Energy and EU environmental regulations [41]. Plenty of research highlights the negative impacts of wind farms on the environment and ecosystem services [42].  
Figure 1: The case study location of Cibuk and Vetrozelena wind farms in Serbia (marked red).  
When assessing the territorial impacts in SEA, the planning stage includes taking stock of the wind farms effects on biodiversity, people living in the area (those being shadow flicker effects, noise pollution, or the possibility of accidents), and landscape. These results are of prime importance for deciding on the proper number of turbines and their microlocations. Each of the above aspects can be partially examined (The term \"partial\" is used here for assessing the effect of a specific project, i.e., a wind farm, on a single element of the environment (more specifically noise), therefore the noise impact only), but it can also be a part of the holistic wind farm effects environmental assessment, preferably by means of software models that make the process more objective [43; 44; 45; 46; 47].
# Influence of Material on the Density of a Point Cloud Created Using a Structured-Light 3D Scanner
## 1 Introduction
The points from the original point cloud are compared with the points from another point cloud after the surface of the scanned object has been treated [17]. It is known that the reflectivity of the surface and its shape have a great influence on the resulting density of the point cloud [11]. The article aims to suggest the treatments of the surface of materials and conditions in data acquisition so that the density of the point cloud reaches the required values. Based on previous experience with structured-light 3D scanning measurements and work with point clouds, we assume that materials with a glossy surface will have significantly worse results than materials with a matte surface. It can be expected that after the application of the opacifying agent, the results of materials with a glossy surface and transparent materials will be improved. Furthermore, we assume that materials with a fine structure will perform worse than materials with a uniform appearance [12]. The structure of this article is as follows. Section 1 presents the topic and issues of working with point clouds in the creation of digital models. Section 2 examines the current identified problems with point cloud density and clarifies the research questions for the described research. Section 3 delves into a comprehensive description of the proposed research methods, selection of research materials, and sample acquisition. This section also describes the research process and the equipment used. Section 4 describes the results of all measurements divided into four subsections according to the main properties of the investigated materials. Section 5 discusses the research results. Finally, Section 6 concludes the article by summarizing the essential findings and insights gained from the study, answering the research questions, and suggests potential directions for future research.
## 2 Background and Methods  
First, this section discusses the proposed framework and introduces two ML models, i.e., XGBoost and RF. Then, the Bayesian search and importance-based feature selection are presented. Two metrics used to quantify the performance of the proposed framework are provided at the end of this section.
# Environmental-Impact-Based Multi-Agent Reinforcement Learning
## 2 Background: MARL and Markov Games  
A Markov game [19] is a standard framework for modeling MARL problems such as SSDs. It is defined on a state space \(\mathcal{S}\), a collection of \(N\) agents' action sets \(\mathcal{A}=\{\mathcal{A}^{1},...,\mathcal{A}^{N}\}\), and a state _transition distribution_\(\mathcal{T}\) preserving the Markov property, that is, the next state of the environment is independent of the past states and depends only on the current environment state and the agents' applied actions [20; 21]. The global state of the environment at time step \(t\) is given by \(s_{t}\in\mathcal{S}\). Each agent \(k\) observes the environment to some extent as a local state \(s_{t}^{k}\) and selects an action \(a_{t}^{k}\in\mathcal{A}^{k}\) based on its _policy_\(\pi^{k}\), that is, the probability distribution of selecting each of the available actions. A _joint action_\(\mathbf{a}_{t}\) is the stacking of all agents' actions \([a_{t}^{1},...,a_{t}^{k},...,a_{t}^{N}]\). Applying the joint action \(\mathbf{a}_{t}\) at the global state \(s_{t}\) causes a transition in the environment state according to the transition distribution \(\mathcal{T}(s_{t+1}|s_{t},\mathbf{a}_{t})\). Each agent \(k\) then receives a reward \(r_{t+1}^{k}\), that is, originally, the agents' extrinsic reward \(e_{t+1}^{k}\), or can be reshaped by utilizing some intrinsic reward \(\hat{t}_{t+1}^{k}\). Using a Reinforcement Learning (RL) algorithm, the agent evaluates its policy by calculating its associated _value function_\(V^{\pi^{k}}(s_{t}^{k})=\mathbb{E}[R_{t}^{k}|s_{t}^{k},\pi^{k}]\) over every state \(s_{t}^{k}\), where \(R_{t}^{k}=\sum_{i=0}^{\infty}\gamma^{i}r_{t+i+1}^{k}\) is the cumulative discounted future rewards starting from the current local state \(s_{t}^{k}\) following policy \(\pi^{k}\), where \(\gamma\in[0,1]\) is the _discount factor_. Then, agent \(k\) learns to improve its policy such that in each state \(s_{t}^{k}\), the agent chooses the action that transfers the environment to the next state \(s_{t+1}^{k}\) where \(V^{\pi^{k}}\) is maximized.
## 3 Results and Discussion
### Pearson's Correlation Coefficient  
Pearson's correlation coefficient between the total phenolic and flavonoid contents, and various bioactivities including antioxidant and anti-inflammatory activities and cell viability determined for WS and NS is exhibited in Figure 5. As mentioned previously, phenolic compounds and flavonoids are natural antioxidants of plant materials [21; 22]. Therefore, positive correlations were found significantly between total phenolic and flavonoids contents with all antioxidant activities determined by three different assays, which are DPPH, ABTS, and reducing power assays. These three different assays also exhibited significant correlation between each other. The linear correlation between phenolics and flavonoid contents with radical scavenging activity and reducing power in plant material was reported previously [46].  
The correlation coefficient of NO production ratio, a biomarker of inflammation response, with other factors were not significant and this demonstrated the absence of linear correlation with them. Although \(p\)-coumaric acid is reported to exhibit anti-inflammatory activity in wheat flour [40], it is assumed that other compounds which does not belong to phenolics, may affect anti-inflammatory activity in WS or NS. Cell viability also did not exhibit linear correlation with any other factors and this implies that the major compounds related to the effect promoting cell viability are not phenolic compounds.  
Figure 5: [PERSON]’s correlation coefficient between the total phenolic and flavonoid contents, DPPH and ABTS radical scavenging activities, the reducing power, NO production ratio in MEF cells, and the viability of Caco-2 cells for Korean waxy and normal wheat sprouts (* \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\)).
# Active Learning for Biomedical Article Classification with Bag of Words and _FastText_ Embeddings
## 3 Results
### Active Learning Predictive Performance: Aggregated  
Tables 3 and 4 present the area under the precision-recall curve values for all classification algorithm and text representation combinations, obtained at different stages of active learning with random and cluster initialization, respectively, averaged over all datasets. Each of them contains separate subtables for uncertainty sampling, diversity sampling, random sampling, minority sampling, and threshold sampling. Active learning stages are described in table row labels by the percentages of the pool having been used, i.e., the percentages of pool articles for which class labels were obtained and which were used as training articles for model creation. The 100% row corresponds to standard passive learning, with all class labels available, and is therefore the same for all sampling strategies. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{(**a**) uncertainty sampling} & \multicolumn{6}{c}{(**b**) diversity sampling} \\ \hline  & SVM-BOW & SVM-FT & RF-BOW & RF-FT & & SVM-BOW & SVM-FT & RF-BOW & RF-FT \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 3: Average PR AUC values obtained for random initialization. The following observations can be made based on these results. * The random forest algorithm with the bag of words representation achieves the best overall performance regardless of the initialization and query selection methods and the percentage of data used. * The random forest and SVM algorithms with the _FastText_ representation perform nearly identically to each other and just marginally worse than RF-BOW. * For the SVM algorithm, the bag of words representation is clearly inferior to the _FastText_ representation, both with respect to the passive learning performance and, to an even greater extent, with respect to active learning effectiveness, which may suggest a negative effect of its higher dimensionality, which is particularly visible with small training sets. * The initialization method has little impact on the predictive performance except for the very earliest stages of learning when cluster initialization sometimes gives a minor improvement. * The uncertainty, minority, and threshold sampling strategies are similarly effective and make it possible to approach the passive learning level after using about 50% of the data for the the most successful algorithm-representation configurations, RF-BOW, RF-FT, and SVM-FT, and even with 25-33% of data, they are not far behind. * Random sampling is less effective than the three best query selection strategies except for the SVM algorithm with the bag of words representation, which performs worse than the other algorithm-representation combinations regardless of the query selection strategy.
# Ultra-Compact Digital Metasurface Polarization Beam Splitter via Physics-Constrained Inverse Design
###### Abstract
Therefore, the effective refractive index distribution of the waveguide is regulated by a two-dimensional code metasurface layer consisting of thousands of pixels. The simulation result shows a low insertion loss (IL) that is less than 0.73 dB and 0.87 dB, and a high extinction ratio (ER) that is greater than 12.59 dB and 10.76 dB for the TE and TM modes, respectively. Note that the device has a high performance under broadband inputs (1510 nm to 1590 nm, covering the C-band). In addition, we also demonstrate the device performance under certain manufacturing errors, and the simulation results indicate that the device has good performance and large manufacturing tolerances.