esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential of Plant Stem Cells as Helpful Agents for Skin Disorders--A Narrative Review
## 4 Mechanistic Insights into the Skin Aging of Plant-Derived Stem Cells
### Regulation of Gene Expression  
As cell aging progresses, the synthesis of genes related to collagen fiber production decreases, while the presence of matrix metalloproteinases (MMPs), cytokines, chemokines, and growth factors becomes more pronounced [90]. Interactions between plant stem cell extracts, with the proteins involved in the transcription and expression of genes related to cell metabolism, proliferation, inflammation, as well as growth, lead to the elimination of the senescent cells and neutralization of SASP. Plant callus extracts have demonstrated substantial benefits in the treatment of animal cells. A recent study showed that the Swiss apple callus extract stimulated the growth of human stem cells, protected umbilical cord blood stem cells from UV radiation-induced cell death, and reversed the aging process in fibroblasts, resulting in an extended lifetime of human skin cells [72]. Further studies demonstrated that plant stem cell extracts inhibit the activity of elastase [48; 61; 63], tyrosine [52], hyaluronidase [61; 63], collagenase/MMP-1 [48; 56; 70], and gelatinase/MMP-2,9 [22; 50], thus maintaining skin integrity and containing skin alterations such as skin tones, deep wrinkles, and loss of resilience [61; 63]. Higher MMP-2 expression is one of the biomarkers for skin senescence, as it leads to the degradation of the extracellular matrix, causing wrinkles and loss of elasticity [91]. Inhibition of that gene resulted in improved skin elasticity, dermal density, and periodital wrinkles [22]. Of note, a positive correlation was found between higher MMP-2 expression and longevity, which might be indicative of a link between inflammaging and longevity [92]. Ceramide production through the expression of glucocerebosidase (GBA) and sphingomyelin phosphodiesterase 1 (Smpd1) increased significantly in keratinocyte cells treated with raspberry stem cell extract. Moreover, the extract also increased the expression of the genes involved in skin hydration, suggesting the effectiveness of the raspberry stem cell extract in maintaining skin hydration and preventing excessive water loss in aged skin [68]. These results indicated that the extract of plant stem cells might effectively exhibit a protective effect in UV-induced photo-aged or photodamaged models [10]. Recently, a combined therapy with plant stem cells hasbeen proposed. [PERSON] et al. reported that plasma-treated plant stem cells showed increased expression of collagen synthesis protein-related genes, resulting in skin regeneration [93].
# Adapting the GACT-X Aligner to Accelerate Minimap2 in an FPGA Cloud Instance
## 2 Materials and Methods
### Acquiring Mapping and Alignment Accuracy  
Minimap2's mapping accuracy was evaluated by checking whether the simulated reads were mapped to the correct RefSeq and, from the correct results, by checking how much the alignment overlaps with the original position of the read, calculated as a percentage of its length. Under the optimizations provided in the previous section, GACT-X's alignment accuracy was evaluated against Minimap2's ksw function for the first 100,000 reads of each of the three datasets. Minimap2's alignment results could be collected from the CIGAR (Concise Idiosyncratic Gapped Alignment Reports) strings in the SAM files, whereas the nucleotide input files had to be generated from Minimap2 for GACT-X, and the alignment results from these were reported as CIGARs on an output file. The accuracy was measured according to the alignment score, considering Minimap2's parameters in Table 2. Ideally, all algorithms would find the optimal alignments, i.e., the maximum score possible for any pair of sequence inputs. However, heuristics such as banding and tiling can eventually miss the optimal alignment path in exchange for great performance advantages. The alignment lengths, with respect to the query sequences, were also measured and calculated as percentages. This measurement is relevant because both the ksw and the GACT-X functions perform semi-global alignments on the edges of the reads, which can result in higher alignment scores but, at the same time, can also discard the information contained on these edges. Ideally, the complete read sequences should be aligned with the reference. All of the data are presented in Section 3.
## 4 Results
### Spatial Autocorrelation Analysis of Vegetation Dynamics in the ARB
#### 4.3.1 Regional Impacts of Climate Changes on Vegetation  
As shown in Table 5, the global results of the PLSR model of the drivers prone to vegetation changes revealed the important role of Pcp in vegetation growth. Pcp is one of the factors among the independent variables that had a strong influence on LAI, FVC, and GPP (VIP \(>\) 1.5); in addition, the effects of Pcp on LAI, FVC, and GPP were all generally positive. Compared to Tmin and Tavg, Tmax and Pcp had a relatively strong effect on vegetation growth status, and the global results suggested that Tmax had a positive effect on LAI and GPP and a negative effect on FVC. Nevertheless, owning to significant spatial variability of climate changes over the study area [36; 38], the relationships between vegetation and climate factors varied geographically, and thus, exhibited significant spatial heterogeneity. The spatial distributions of the regression coefficients for Pcp with the LAI, FVC and GPP GTWR models during the four studied periods are presented in the upper part of Figure 6. Although the effects of Pcp on LAI, FVC, and GPP differed in time and space, a general tendency for the coefficients is displayed on the figures: generally lower in the northeast and relatively higher in the southwest. The areas with high coefficient values were primarily concentrated at the borders of Mongolia, Russia, and China and near the \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**LAI**} & \multicolumn{4}{c}{**FVC**} & \multicolumn{4}{c}{**GPP**} \\ \cline{2-13}  & **OLS** & **TWR** & **GWR** & **GWTR** & **OLS** & **TWR** & **GWR** & **GWTR** & **OLS** & **TWR** & **GWR** & **GWTR** \\ \hline AICc & \(-215.4\) & \(-279.2\) & \(-745.9\) & \(-851.6\) & \(-1955.4\) & \(-2096.1\) & \(-2328.2\) & \(-2436.6\) & \(6442.5\) & \(6382.5\) & \(5665.5\) & \(5564.1\) \\ R\({}^{2}\) & 0.838 & 0.866 & 0.955 & 0.970 & 0.871 & 0.908 & 0.952 & 0.970 & 0.781 & 0.818 & 0.962 & 0.975 \\ Adjusted R\({}^{2}\) & 0.836 & 0.864 & 0.955 & 0.970 & 0.870 & 0.907 & 0.952 & 0.970 & 0.778 & 0.816 & 0.961 & 0.974 \\ \hline \hline \end{tabular} NOTE: AICc: a version of Akaike information criterion (AIC) with a correction for small sample size.  
\end{table} 
Table 6: The summarized results of the ordinary least squares regression (OLS) model, temporally weighted regression (TWR) model, and geographically weighted regression (GWR) model for comparisons.
## 5 Application to Real-Case Scenarios  
After the V&V, the BeSYC software has been applied to several real-case scenarios. In the first scenario, the BeSYC software was applied against several pressure drop characterization tests performed on a first wall mock-up [20]. This mock-up is a P92 steel plate crossed by 10 channels having a 15 mm \(\times\) 15 mm square section. The characterization tests were performed to investigate the pressure losses across them thanks to the injection of air at 20 \({}^{\circ}\)C and \(\sim\)6 bar. A calibration considering four parameters and one response was performed to reproduce these tests. The four parameters are the channel surface roughness and the three concentrated pressure drop coefficients at the geometrical discontinuities within the channel. Instead, the response is the differential pressure between the inlet and the outlet of the channel.  
Figure 5 shows the results achieved from the application of the best-estimate methodology against the tests performed for two channels of the first wall mock-up (MELCOR 1.8.6 code, see [9] for further details). The analysis was mostly performed to demonstrate that the best-estimate input deck (blue curve) was capable to better reproduce the experimental trends compared to the default input deck (red curve) considered as starting point of the analysis. As it can be depicted from the two graphs, the goals of the analysis were achieved since the blue curve was much closer to the experimental results (black curve) than the red one. The differences between the results obtained from the best-estimate methodology and those of the best-estimate code run are again correlated to the necessity to use single values for the different parameter in the system/thermal-hydraulics codes.  
Then, the BeSYC software was used for the post-test analysis with RELAP5-3D and MELCOR 1.8.6 of a LOFA experimental campaign performed in the KIT HELOKA-HP facility [8; 9]. For this analysis, 10 responses and about 100 parameters were selected to reproduce the different experimental scenarios (11 tests). The BeSYC software was in charge to write the input decks, control their execution (about 200 code runs for each of the 11 tests), apply the best-estimate methodology, write the best-estimate input deck, and execute it. Moreover, in these scenarios (not discussed here for sake of brevity), the software demonstrated its capabilities when dealing with the different aspects of the analysis. Further information on the LOFA experimental campaign and the post-test analysis performed with BeSYC and the RELAP5-3D code can be found in [8], while the post-test analysis with BeSYC and the MELCOR 1.8.6 code are described in [9].
## 3 Result Analysis
### Proposal and Showcase Studies Analysis  
A total of six studies were categorized as Proposal and Showcase studies [70; 71; 72; 73; 74; 75]. These studies, in addition to theoretical contributions, have also presented a prototype of a system or application. However, these studies have not applied their system within educational settings yet.  
[PERSON] et al. [70] went over the design and development of their proposed author tool and intelligent tutor which focused on integrating intelligent tutoring systems within virtual reality and 360-degree educational environments. Their study presented a related project and discussed in detail the specifications of their proposed tool and its aspects which worked within the Unity game engine. Specifically, they showcased the different stages of the project, presented the author tool, and analyzed the learning experiences and scenarios that it offers. [PERSON] et al. [71] presented an authoring tool to develop immersive intelligent tutoring systems for virtual reality experiences. Their tool provides a no-code approach to creating realistic industrial environments to aid students and educators in developing relevant skills and acquiring practical knowledge. Following the proposed authoring tool, a prototype application, which involved training scenarios on handling laser-based cutting machines, was developed and presented. [PERSON] et al. [72] focused on integrating intelligent tutoring systems within augmented reality environments to support students' programming learning. Their application provided automatically generated graphic visualizations to aid both students and teachers in understanding and explaining programming concepts. The extensibility and flexibility of such applications were highly regarded.  
[PERSON] et al. [73] explored the use of intelligent tutoring systems within virtual reality environments. They presented their prototype and evaluated it based on the views of experts. Additionally, they provided an overview of their system design and implementation and commented upon the system ability and potential to integrate and support open educational resources. [PERSON] et al. [74] presented their virtual reality application which integrated an intelligent tutoring system to support students' learning of electrical operations. Specifically, they focused on a model that mapped students' errors to features using a bag-of-errors scheme to categorize students based on their performance. Additionally, they commented that such an approach can help identify aspects which students need further assistance with. [PERSON] et al. [75] presented an adaptive learning system that combines augmented reality with an intelligent tutoring system. They went over the specifications of their prototype and commented upon its use cases. To better support psychomotor learning, the specific application can be used across different interfaces, such as desktops, hand-held devices, and head-mounted displays.
## 3 Experimental Results
### Spectral Radiance Pattern Analysis of Pothole Surface from UAV Original Bands  
Upon the successful masking of asphalt, radiance values for each spectral channel were presented in Table 2. Descriptive statistics of the spectral radiance data sampled from pothole surface were generated in this study, guided by the null hypothesis that \"there were no significant differences in spectral radiance properties of pothole surface across original spectral channels of UAV imagery\". Table 2 presents the descriptive statistical analysis of radiance properties of the potholes.  
Minimum spectral radiance values at N = 25 were 0.855 (blue), 0.875 (green), 0.937 (NIR) and 0.675 (red edge) spectral channels for UAV imagery. These spectral channels were also found to have maximum radiance values of 0.969, 0.992, 0.984, 0.337 and 0.953, in that order. For these respective spectral channels, the corresponding mean radiance values were found to be 0.896, 0.936, 0.962, 0.273 and 0.872. Their spectral radiance standard deviation values were also found to be 0.032, 0.029, 0.012, 0.028 and 0.078, respectively, with the computed \(p\)-values of 0.008, 0.000, 0.002, 0.021 and 0.017, respectively. Then, the spectral radiance pattern of the potholes was computed at different spectral channels. Figure 4 presents the behavior of the potholes at different original spectral bands.  
Based on the descriptive statistics presented in Table 2, the potholes were subsequently demarcated based on the analysis of their upper and lower radiance threshold values. Figure 5 provides demarcated potholes based on the original spectral channels of UAV imagery.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Original Spectral Band** & **N** & **Min** & **Max** & \(\mu\) & \(\sigma\) & \(p\)**-Value** \\ \hline Blue & 25 & 0.855 & 0.969 & 0.896 & 0.032 & 0.008 \\ Green & 25 & 0.875 & 0.992 & 0.936 & 0.029 & 0.001 \\ Red & 25 & 0.937 & 0.984 & 0.962 & 0.012 & 0.002 \\ NIR & 25 & 0.208 & 0.337 & 0.273 & 0.028 & 0.021 \\ Red edge & 25 & 0.675 & 0.953 & 0.872 & 0.078 & 0.017 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of radiance samples obtained from original spectral channels.  
Figure 4: Spectral radiance patterns of pothole surface generated from UAV original bands.
## 3 Results
### Comparison of MODIS-ET and Flux Measurements  
Generally, MODIS-ET shows a good seasonal dynamic for each site (Figure 3). For mixed forests (Figure 3a), evergreen needleleaf forests (Figure 3c) and alpine grasslands (Figure 3f), MODIS-ET shows higher \(R^{2}\) (\(>\)0.5) with flux measured ET. MODIS-ET products at mixed forests site (Figure 3a) have very good fitting accuracy with flux measured ET (\(R^{2}\) = 0.88). MODIS-ET products in evergreen broadleaf forests (Figure 3b,d), croplands (Figure 3e) and temperate grasslands (Figure 3g) show relative higher \(R^{2}\) (slightly lower than 0.5) with flux measured ET. MODIS-ET in alpine meadow site  
Figure 2: Comparison between the flux derived GPP (site name_derived) and MODIS-GPP (site name_MODIS) for all sites, pink solid line and blue solid line represent the flux derived GPP and MODIS-GPP, respectively. (**a**) in mixed forests site; (**b**) in evergreen broadleaf forests site; (**c**) in evergreen needleleaf forests site; (**d**) in evergreen broadleaf forests site; (**e**) in croplands; (**f,g,h**) in grasslands site. (DOY: Day of year).  
(Figure 3h) shows the lowest fitting degree with flux measured ET (\(R^{2}\) = 0.26). We also analyzed bias and RMSE in different land cover types. Bias in croplands (Figure 3e) is far greater than that in other land cover types with bias of 18 mm/8-day. Evergreen broadleaf forests (Figure 3b,d) also have very high bias (from 9 mm/8-day to 14 mm/8-day). RMSE (from 10 mm/8-day to 20 mm/8-day) of evergreen broadleaf forests (Figure 3b,d), croplands (Figure 3e) and alpine grasslands (Figure 3h) are greater than the RMSE of other land cover types. For alpine grasslands (Figure 3f,h), measured ET is underestimated by MODIS-ET in the growing season and overestimated in non-growing season. For croplands (Figure 3e), MODIS-ET greatly overestimates the whole period of measured ET, thus causing greater RMSE (20 mm/8-day) and bias (18 mm/8-day).
## 7 Implementation and Evaluation
### Adaptation Services
#### 7.3.1 Experimental Setup  
We performed our experiments using three similar servers as video scaling web services (see Table 1). Since every server consists of four CPUs every server can simulate up to four independent PUMA service providers. Therefore, our longest evaluated service chain contains 12 scaling services. The workflows are built in a way that after every adaptation step the chunk must be routed through the  
Figure 14: Validating service policies against the protocol role.  
network between servers. Thereby, we got a realistic service environment, although our servers can act as more than one service provider. We varied the chunk sizes and the number of services included in the workflow during our experiments. All servers are connected via 1 GBit/s connections, except the client which is connected by 100 MBit/s. We used an IBM T41p laptop as client playback device.
## 4 Experiments
### Singing Voice-Separation Experiment
#### 4.1.2 Setup  
We computed the magnitude \(X\) of the mixture spectrogram downsampled at 16 kHz and converted to monophonic to be used as input to the model. The spectrogram is obtained by short-time Fourier transform (STFT) with 1024 window size and 75% overlap. In the network, the mask \(\hat{M}\) of the target is estimated, and the estimated target spectrogram is obtained by element-wise multiplication of \(X\) and \(\hat{M}\). The estimated target signal is restored by taking inverse STFT of the estimated target spectrogram and then performing overlap-add. The estimated separated signal is up-sampled to 44.1 kHz for evaluation.
## 2 System Description  
### Experiment Platform  
A typical configuration of antagonistic configuration of PAMs is shown in Figure 1a, and the proposed experiment platform is demonstrated in Figure 1b. The experimental system consisted of two PAMs which had 1.0 inches of diameter and 22 inches of length. The PAMs were fabricated at our local institute. The pressure inside each PAMs was regulated by two proportional electric control valves series ITV 2030-212S-X26 from SMC company. One potentiometer CP-20H from Midori Precision (Japan) was used to measure the actuator's angle. All the control systems were implemented by using computer-based controller NI cDAQ-9178 from National Instrument (USA). The real-time controller collected the data from the potentiometer via analogue input module and sends the control signal to the electric control valve via analogue output modules. The developed control algorithm was implemented and compiled by LabView software before downloading it to the hardware controller.
# Study of the Phenolic Compound Profile of _Arbutus unedo_ L.
## 2 Materials and Methods
### Conditions Used for the Optimization of the Extraction Procedure
#### 2.3.1 Total Phenolic Compounds (TPC)  
The concentration of PC in the sample extracts was determined according to the Folin-Ciocalteu method [29] with some modifications: 75 \(\mu\)L aliquot of the sample extract was mixed with 645 \(\mu\)L of Milli-Q water and 30 \(\mu\)L of FCR. Then, 75 \(\mu\)L of 20% (\(w/v\)) sodium carbonate and 675 \(\mu\)L of Milli-Q water were added, and the total solution was mixed briefly in the vortex. The mixture was incubated for 60 min at room temperature in darkness. Each sample extract was incubated in duplicate (\(n\) = 4). At the end of the incubation period, the absorbance was measured in triplicate using a UV-Vis spectrophotometer (Cary 60, Agilent, Santa Clara, CA, USA) at a wavelength of 725 nm. A standard calibration curve was prepared with gallic acid at a concentration range of 10-500 mg/L (\(w/v\)) (y = 0.0033x + 0.0498;R\({}^{2}\) = 0.99). The results were expressed as mg gallic acid equivalents (GAE)\(/\)100 g of fresh weight (FW).
## 1 Introduction
### Organization  
The structure of this article is organized to provide readers with a systematic understanding of the presented research. Section 2 establishes a strong foundation by elucidating the mathematical background of BEC. Section 3 delves into the core of our contributions, where we detail the proposed hardware architecture. This section introduces readers to the key components of our design. Moreover, it also offers insights into the design considerations and intricate implementation aspects. Section 4 is dedicated to our proposed optimizations, particularly focusing on scheduling techniques. It presents a thorough exploration of how we fine-tuned our architecture for optimal performance. Section 5 presents the main findings and results derived from a rigorous analysis. It allows for readers to gauge the efficiency and efficacy of our design in relation to existing solutions. Finally, Section 6 summarizes key takeaways and suggests intriguing directions for future investigations.
## 3 Results
### Device Structure and Environment Variation  
#### 3.3.1 Fill Factor Variation  
For a TEG placed on the human body, the area of the material compared to the entire area of the device is optimized to balance the temperature difference across the device and the power factor of the material, which is called the optimum fill factor (FF). The analyses above were conducted with the condition of FF = 10%.  
In general, materials based on Bismuth Telluride have high thermal conductivity and therefore have a very low optimum FF [19; 20]. As shown in Figure 5b, the optimum FF of a given material A-D is always below 10%. On the other hand, in order to utilize thermoelectric elements in power generation applications, a sufficiently high output voltage must be ensured [40]. Since 1 couple of TE legs cannot generate more than 1 mV for 1 K of temperature difference, a thermoelectric device should have many TE legs, which increases the fill factor of the device. In addition, considering the stability of the device structure, the actual device should be fabricated with larger FF than the optimum FF. Therefore, in practical applications, the FF should be more than 10%.  
As shown in Figure 5a, the tendency of the maximum power density increasing with lower thermal conductivity is always maintained. Figure 5b shows the difference in the optimum carrier concentration between ZT and power density. The thick dashed line indicates the carrier concentration at the maximum ZT, and the carrier concentration at the maximum power density is always located below it. In addition, the difference becomes smaller for a lower thermal conductivity material and smaller for a lower FF, but the tendency is still maintained even if the FF changes within the fabrication conditions of the practical device.  
Figure 4: **(a)** The material data obtained by multiplying the electrical conductivity and the thermal conductivity of the P-type BiSbTe\({}_{3}\) data (D) by 0.4 (A), 0.6 (B), and 0.8 (C). (**b**) Device simulation using the thermoelectric material with different thermal conductivity but with the same ZT. From material D to material A, the thermal conductivity decreases and the maximum power density increases. The carrier concentration of the maximum power density shifts to the right as the thermal conductivity decreases.