esa-sceva/satcom-chunk-collection huggingface dataset:
# Emergence of White Organic Light-Emitting Diodes Based on Thermally Activated Delayed Fluorescence
## 3 Approaches for WOLEDs Based on TADF  
### Basic Aspects of WOLEDs Based on TADF  
TADF emitters can be used to develop high-performance WOLEDs, because TADF emitters can (i) harness triplet excitons; (ii) exhibit excellent efficiency; (iii) show usually broad emission spectra with rather large full width at half-maximum of about 100 nm, which is wider than that of conventional fluorescent materials due to their charge-transfer nature [100, 101, 102]. For WOLEDs based on TADF, apart from the selection of excellent emitters, the careful manipulation of device engineering also plays a significant role in the performance [103, 104, 105, 106]. Therefore, to attain the high performance, the design strategies, device structures, working mechanisms and electroluminescent processes of the WOLEDs based on TADF should be well manipulated [107, 108, 109]. For example, unlike conventional fluorescence emitters, the T\({}_{1}\) of TADF emitters is necessary to be considered when designing a WOLED architecture, since hosts or nearby layers with low T\({}_{1}\) would quench the triplet excitons, which leads to the low efficiency [110, 111, 112]. Besides, the location of TADF emitters is needed to be investigated, since the energy transfer would occur between the contacted different emitters (e.g., energy can transfer from high-energy TADF emitters to low-energy emitters) [113, 114, 115]. With the step-by-step understanding of the insight of TADF, various approaches have been reported to develop WOLEDs based on TADF, including the exploitation of all TADF emitters, the combination of TADF and conventional fluorescence emitters, the mixture of blue TADF and phosphorescence emitters and introduction of TADF exciplex host and phosphorescence dopants.
# Biomechanical Optimization of Lumbar Fusion Cages with a Porous Design: A Finite Element Analysis
## 1 Introduction
Similarly, the Voronoi pattern--drawing inspiration from natural structures like giraffe skin and turtle shells replicates the anisotropic porosity and connectivity of trabecular bone [15]. [PERSON] et al. [16] reported that strategic regulation of structural irregularities in Voronoi designs can boost yield strength by up to 30% by minimizing stress concentrations and redirecting load pathways. Through parametric modeling, the Voronoi approach enables fine control over pore size, porosity, and shape, yielding complex structures that closely mirror the morphology and mechanics of natural trabeculae [17]. Despite these innovations, biomechanical evaluations of personalized porous fusion cages with diverse lattice designs remain scarce. The influence of varying structural configurations on subsidence risk post-implantation is yet to be fully elucidated. Finite element (FE) analysis emerges as a robust methodology to bridge this knowledge gap, offering a non-invasive means to simulate and compare implant performance. FE modeling allows for precise alterations to cage geometry, accurately capturing interactions between structures, and facilitating direct comparisons across experimental conditions while minimizing the confounding effects of material variability [18; 19]. By forecasting biomechanical behavior, functional outcomes, and potential complications, FE analysis provides insights that are challenging to obtain through traditional in vitro experiments, overcoming limitations such as time constraints and parameter inflexibility [20]. Based on this, this study created a variety of lumbar intervertebral fusion device designs based on the intervertebral fusion devices required for L3-L4 lumbar intervertebral lesions through personalized design combined with Gyroid and Voronoi structural parametric modeling. The biomechanical properties of different fusion devices implanted in the upper and lower end plates of the L3-L4 intervertebral disk were compared based on finite element (FE) analysis, and the risk parameters related to intervertebral fusion device subsidence and adjacent segment degeneration were evaluated based on von Mises stress and displacement changes. These preliminary studies will provide guidance for the design of fusion cages and offer new ideas for solving the problem of fusion cage sinking.
# Review of Code Similarity and Plagiarism Detection Research Studies
## 2 Code Clones: Definition and Types
### Type IV Code Clone  
Type IV code clones are discriminated based on semantic similarity, and not on the similarity of the code itself. Therefore, clones of this type may not have similar code structures and are less likely to be pieces of code copied from the original, which are generally agreed upon to be code clones. Type IV code clones can occur when similar logic is implemented in different forms for the same purpose. Consider this code that calculates a factorial:  
```
intfactorial(intn,intacc){ if(n==1) returnacc; else returnfactorial(n-1,n*acc); }
```  
The code snippet above forms a Type IV code clone with the following code snippet:  
```
intfact(intn){ intf=1; for(inti=2;i<=n;i++) f=f*i; returnf; }
```  
The functions presented in the two code snippets have little lexical or syntactic similarities. Therefore, based on the criteria for type I-III code clones presented thus far, we can conclude that the two code snippets are not at all similar. However, regarding the meaning of the actual code, both code fragments perform the same function of calculating n! for a given n. Thus, the occurrence of the type IV code clone is determined based on the semantic similarity of this form.  
Type IV code clones are very difficult to detect because they cannot be determined using simple textual, lexical, or grammatical similarities. The example presented here is intended to help in understanding type IV code clones by clearly indicating the purpose of the code. However, in general, research on detecting this type of code clone aims to find a piece of code that implements a similar type of logic, regardless of its purpose.
## Abbreviations  
AMF Adaptive morphological filter  
SI Sparsity index  
ES Envelope spectra  
SK Spectral kurtosis  
WT Wavelet transform  
EMD Empirical mode decomposition  
VMD Variational mode decomposition  
MF Morphological filtering  
GDE Dilation and erosion gradient operator  
GCO Cloosing and opening gradient operator  
GCOOC Closing-opening and opening-closing gradient operator  
AHDE Dilation and erosion average-hat operator  
AHCOO Closing and opening average-hat operator  
AHCOOC Closing-opening and opening-closing average-hat operator  
SE Structure element  
FCF Fault characteristic frequency  
CSI Cubic spline interpolation
# RMCSat: An F10.7 Solar Flux Index CubeSat Mission
## 4 Satellite Architecture and Subsystems
### Power Budget
The proposed COTS EPS offers integrated batteries that have a capacity of 22.4 W-h and a battery heater for eclipse conditions. This size is more than sufficient to meet the required battery capacity of 12.4 Wh for RMCSat in full eclipse (7.6 W).
# MRU-NET: A U-Shaped Network for Retinal Vessel Segmentation
## 2 Proposed Method
### Image Preprocessing
#### 2.1.2 Image Enhancement  
Original retinal blood vessel image data are three-channel image data. Due to the optical effect when taking pictures, contrast between the blood vessels and the background in the fundus image is low, so it cannot effectively distinguish the fundus blood vessels from the background image. Image enhancement preprocessing can effectively enhance the contrast between retinal blood vessels and the background. The image enhancement preprocessing process is divided into the following four types:  
* Through the contrast experiment, the green channel of the retinal blood vessel image has a higher contrast, so the fundus blood vessel image of the green channel is selected for experiments.
* The green channel image is processed for data standardization, and the conversion formula is as follows: \[x=\frac{x_{i}-x_{min}}{x_{max}-x_{min}}\] (1) where \(X_{i}\) is the current pixel value, \(x_{min}\) is the minimum pixel value, and \(x_{max}\) is the maximum pixel value.
* Limited contrast histogram (CLAHE) is used to equalize the normalized image, thereby increasing the contrast between the blood vessel and the background image and making the image easier to segment.
* Gamma adaptive correction is performed according to different pixel characteristics of blood vessels and background images, thereby suppressing uneven light and centerline reflection in the fundus image, and the gamma value was set to 1.2.  
The top-down images in Figure 3 are DRIVE and STARE data images, respectively. From left to right: the first column is the original image, and columns 2-5 are the green channel image, the normalized image, the Clahe equalized image, and the gamma locally adaptive correction image, respectively.
# Bacterial Indole-3-Acetic Acid Influences Soil Nitrogen Acquisition in Barley and Chickpea
## 2 Results
### Klebsiella Demonstrates Effective Mobilization and Use of Nitrogen in Barley and Chickpea
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Plants** & **Traits** & & & **Treatment** & & \\ \hline \multirow{8}{*}{Barley} & Chlorophyll & \multirow{2}{*}{1.079 \(\pm\) 0.0547 *} & Control & _K.p._ SSN1 & _K.q._ SGM81 & _K.o._ M5a1 & _AnifH_ \\  & (mg mL\({}^{-1}\)) & & 1.530 \(\pm\) 0.1281 ** & 1.372 \(\pm\) 0.110 * & 1.362 \(\pm\) 0.0614 * & 1.165 \(\pm\) 0.04829 * \\ \cline{2-6}  & Protein & & & & & \\  & (mg g\({}^{-1}\) FW & 1.200 \(\pm\) 0.0774 * & 2.130 \(\pm\) 0.1134 ** & 2.042 \(\pm\) 0.1428 ** & 1.395 \(\pm\) 0.1919 ** & 1.133 \(\pm\) 0.1436 ns \\  & \multicolumn{4}{c}{} \\  & reductase & & & & & \\  & (mg g\({}^{-1}\) FW & 1.22 \(\pm\) 0.3156 ** & 14.20 \(\pm\) 0.5447 *** & 14.60 \(\pm\) 0.2781 *** & 10.93 \(\pm\) 0.6344 * & 10.12 \(\pm\) 0.4257 * \\  & \multicolumn{4}{c}{} \\  & Nitrate & & & & & \\  & (mg g\({}^{-1}\)) & & & & & \\  & (mg g\({}^{-1}\)) & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & \\ \hline \multirow{8}{*}{Chickpea} & Chlorophyll & \multirow{2}{*}{1.295 \(\pm\) 0.1350 *} & \multirow{2}{*}{1.760 \(\pm\) 0.1395 *} & \multirow{2}{*}{1.967 \(\pm\) 0.0928 **} & \multirow{2}{*}{1.577 \(\pm\) 0.0954 ns} & \multirow{2}{*}{1.433 \(\pm\) 0.0954 ns} \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{8}{*}{Chickpea} & Nitrate & & & & & \\  & reductase & & & & & \\  & (mg g\({}^{-1}\) FW & 15.53 \(\pm\) 1.322 *** & 16.57 \(\pm\) 0.9735 *** & 17.35 \(\pm\) 0.6020 ns & 13.67 \(\pm\) 0.2257 ns \\  & \multicolumn{4}{c}{} \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{8}{*}{Chickpea} & Nitrate & & & & & \\  & reductase & & & & & \\  & (mg g\({}^{-1}\) FW & 15.53 \(\pm\) 1.322 *** & 16.57 \(\pm\) 0.9735 *** & 17.35 \(\pm\) 0.6020 ns & 13.67 \(\pm\) 0.2257 ns \\  & \multicolumn{4}{c}{} \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & \\  & & & & & & \\ \hline \multirow{8}{*}{Chickpea} & Nitrate & & & & & \\  & (mg g\({}^{-1}\)) & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{8}{*}{Chickpea} & Nitrate & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & \\  & & & & & & \\  & & & & & \\  & & & & & \\ \hline \end{tabular} Effect of five treatments on biochemical traits of barley and chickpea. Statistical analysis was done using GraphPad Prism 5 software calculated at \(p\leq 0.05\). Data represents mean values of six replicates with standard error. Significance of data was analyzed using ANOVA and mentioned through *.*. Key: * (\(p\leq 0.05\)), ** (\(p\leq 0.01\)), ** (\(p\leq 0.001\)), ns non-significant.  
\end{table} 
Table 2: Measurements of biochemical traits in barley and chickpea plants. Both barley and chickpea had the similar influence of RSA on NUE, showing positive correlation between the two. This result suggests that stronger root establishment in the soil gives the plant an improved access to soil nitrogen.
## 2 Methodology
### Experimental and Diagnostic Approach
Key components, including thruster location, probe alignment and data acquisition systems, are labeled to provide a comprehensive overview of the experimental arrangement. Figure 2: Bottom view of probe positioning. Four probes are aligned vertically with (1) nearest to and (4) most distant from the thruster. The distance between probes (1) and (4) is 40 mm. The positions of probe holders, probe heads and the fixed thermionic cathode (TC) are indicated by arrows. The translational movement in the horizontal direction is conducted from left to right. The vertical movement is performed in the direction out of the image plane. The secondary star ground (SSG) was connected to the facility ground via a _Kethley 2710_ (all _Kethley_ devices from: Tektronix Inc., Beaverton, Oregon, United States) multimeter operating as the ammeter. This setup delivered the value of neutralizing electric current flowing to the thruster platform and was in good agreement with the electric current measured in the first grid, which is equal to the ion beam current, as it is assumed that all electrons from the discharge chamber that are within the vicinity of the ion extraction grid will be absorbed by the positively charged screen grid. All connections of the probes were referenced to the SSG potential. The probe measurements were carried out by _Keithley 2410_ and _Keithley 2450_ source measurement devices, which are capable of sourcing 1000 V and 200 V, respectively. The translational positioning of the measurement apparatus was controlled by software written in the LabView 2019 SP1 environment.
# Yeast Starter Culture Identification to Produce of Red Wines with Enhanced Antioxidant Content
## 2 Materials and Methods
### Determination of Polyphenic Profile of Wines  
A reversed-phase HPLC analytical method was used for the analysis of polyphenolic compounds. The apparatus was an Agilent-1100 liquid chromatograph (Agilent Technologies, Santa Clara, CA, USA) equipped with a DAD detector (Agilent 1260 Infinity) and the separation was performed on a C18 column (5 \(\upmu\)m UltraSphere 80 A, 4.6 i.d. \(\times\) 250 mm length) following the conditions described by [PERSON] et al. [32]. Chromatograms were acquired at 520, 280, 320, 370 and 306 nm. The following reference compounds (purchased from Sigma-Aldrich, Saint Louis, MO, USA) were used, each with its retention time indicated in parentheses: quercetin (37.17 min), gallic acid (5.57 min), catechin (12.09 min), oenin (27.60 min), trans-resveratrol (35.74 min), cafratz acid (10.42 min), coutaric acid (14.43 min).  
Identification of compounds was based on the comparison of peak retention time with the retention time and UV-vis spectra of pure standards while quantification was performed by adopting the external standard method.
## 3 Results
### Extent of Rice Paddies in the Mun River Basin  
After obtaining the phenological characteristics, the rice paddies were extracted using the set thresholds of LVS. The spatial extent of the paddies can be observed in the final time-series Sentinel-1 rice map in the Mun River basin (Figure 8a,c). The extracted rice cultivation area obtained using our proposed rice identification method was 32,066.81 km\({}^{2}\) in 2017, mostly concentrated in the central and northern plateau areas. Rice paddies are also distributed in low-lying plain areas along the Mun River and its tributaries, while the non-rice cover such as forest and dry land are mainly distributed in the upland mountain areas in the south of Mun River basin. Among the ten changwats (provinces) covered by the Mun River basin, the rice cultivation areas are mainly distributed in the changwats of Nakhon Ratchasima, Ubon Ratchathani, Surin, Buri Ram, and Si Sa Ket, with areas of 7945.94 km\({}^{2}\), 5141.29 km\({}^{2}\), 4620.90 km\({}^{2}\), 4499.79 km\({}^{2}\), and 3887.34 km\({}^{2}\), respectively.  
Figure 6: Statistical histogram of backscattering values of permanent water. (**a**) Maximum and minimum of VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization. (**b**) Average values of the maximum and minimum of VH and VV polarization. Vertical error is \(\pm\)1 standard deviation.
## 2 Materials and Methods  
### The Study Area and Geophysical Data  
A study area was adopted with the dimensions of 26 km \(\times\) 26 km, covering almost the entire city of Krakow, with the emitters of the MSWI Plant located in the middle of this area (Figure 1). Krakow is one of the most urbanized areas in Poland (760,000 inhabitants), and the adopted test area is characterized by a particular topography (location of the city in low terrain impeding the undisturbed air flow, relatively high hills surrounding the city from two sides) and a high diversity of land cover classes (Figure 2).  
The CALMET/CALPUFF modeling system requires preparation of the so-called geophysical data, which include the following information: elevation and land use, aerodynamic roughness length, albedo, Bowen ratio, anthropogenic heat flux parameter, soil heat flux parameter and leaf area index. For the purpose of this analysis, four rectangular computational grids of geophysical parameters of different resolutions (100, 250, 500, and 1000 m) were prepared. The grids, reflecting the land height, were prepared using the TERREL pre-processor, while the grid concerning land coverage, using the ArcMAP application (Esri, Redlands, CA, USA), in accordance with the methodology described by [PERSON] and [PERSON] [45]. The source of the input data constituted the Digital Evaluation Model SRTM3 obtained from the resources of the United Stated Geological Survey [1] and the raster data of Corine Land Cover available at the European Environment Agency (EEA) service [2]. The MAKEGEO  
Figure 1: Location of the study area.  
pre-processor was used to integrate the two abovementioned sets of data and to assign geophysical factors in respect of the land use category.  
Figures 2 and 3 present four grids of land use and digital elevation model prepared for the CALMET model for the particular examined resolutions. The graphic visualization of these grids indicates that an increase in the calculation resolution did not result in a greater diversity of land cover classes, however, it significantly influenced the spatial diversity of the occurrence of classes such as bare lands, rivers and forests. The grids with higher resolutions also enable a more accurate representation of the land orography.
## 2 HEV Modeling and Baseline Energy Management
### HEV on-Board Energy Management Strategy  
The considered HEV embeds an _ICE_, a _MGP_0, and a _MGP_4. These three power components allow the HEV to choose between the three following operating modes while driving [25]:  
1. Electric mode, in which the traction is provided by the _MGP_4 alone;
2. Hybrid mode, which is characterized by the three power components being controlled in order to have the _ICE_ working close to its optimal operating line (OOL). The OOL is particularly defined by the values of _ICE_ torque that maximize the engine efficiency for each given value of rotational speed;
3. E-save mode, where the _ICE_ can be simultaneously employed for propelling the HEV and charging the battery by means of _MGP_0 and _MGP_4 working as generators. In particular, the instantaneous _ICE_ power is controlled to be the maximum value between the one corresponding to its OOL and the one required to propel the HEV alone. All the operating modes allow performing regenerative braking, while the baseline HEV controller implemented in this study is assumed to operate as follows:  
* As the HEV starts a given driving mission, pure electric operation is selected if the battery is sufficiently charged. Then, an automatic shift to Hybrid mode is performed if the _SOC_ goes below 0.30 in order to preserve battery charge sustenance. Moreover, if the driver's power demand exceeds the value that can be provided by the _MGP_4 alone in Electric mode, then the powertrain temporarily operates in Hybrid mode.
* In case the value of battery _SOC_ falls below 0.25, the rule-based HEV controller switches to the E-save mode to charge the battery until _SOC_ reaches 0.30. Hybrid mode is subsequently set as the operating mode.
* Regenerative braking is disabled when the battery _SOC_ exceeds the upper limit, which is set to 0.8 here. This is performed as Li-Ion cells significantly degrades when being charged at high _SOC_ values [27].
## 3 Results  
The results of this study are presented in Figures 3 and 4, which depict the findings for peak AceIT, mean AceIT, SampEn and time for each lateral displacement. Peak AceIT was significantly higher in the non-dominant leg than in the dominant leg (\(p<0.001\)). In contrast, SampEn was significantly greater in the dominant leg (\(p<0.001\)).  
Figure 2: Placement of the accelerometer on the lower back.  
Regarding mean AceIT, it was superior in VNS (\(p<0.01\)) and VS (\(p<0.05\)) compared to the NVS condition. For SampEn, the values were higher when vibration was added than with no vibration. Specifically, there were significant differences between NVNS and the VNS and VS conditions (\(p<0.01\)), as well as between NVS and the VNS and VS conditions (\(p<0.01\)).  
No significant interactions were observed, and there were no differences in the time of the sliding action.  
Table 1 shows the data corresponding to the differences between leg dominance (dominant and non-dominant) and conditions (NVNS, NVS, VNS and VS) in the sum of squares, \(F\) and \(\eta_{\rm p}\)\({}^{2}\) for the different effects. Large effect sizes were reported on peak AceIT and SampEn for the dominance effect; in the condition effect, large effect sizes were reported on mean AceIT and SampEn.  
Figure 3: Peak and mean AceIT in different conditions. (**A**): Peak AceIT; (**B**): Mean AceIT; AceIT; acceleration; NVNS: no vibration and no stick; NVS: no vibration with stick; VNS: vibration without stick; VS: vibration with stick; \({}^{*}\)\(p<0.05\), \({}^{**}\)\(p<0.01\) for differences between conditions.  
Figure 4: SampleEn and sliding action time in different conditions. (**A**): \(\text{SampEn}\); (**B**): Time; \(\text{SampEn}\): sample entropy; NVNS: no vibration and no stick; NVS: no vibration with stick; VNS: vibration without stick; VS: vibration with stick; \({}^{**}\)\(p\) < 0.01 for differences between conditions.  
Table 2 shows the data obtained in the Bonferroni post hoc analysis (multiple comparisons) between the analysed conditions. Significant differences were reported in mean AceIT, specifically in NVS vs. VNS and in NVS vs. VS (\(p<0.05\)). Similarly, in SampEn, differences were found in NVNS vs. VNS, NVNS vs. VS, NVS vs. VNS and NVS vs. VS (\(p<0.001\)).