esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Optimization of the Procedure and Assessment of the Reliability of the Data  
For the optimization of the procedure, and to evaluate its ability to discriminate between different viral concentrations, four multiplicities of infection (MOI) were tested (from 0.1 to 0.0001), and the correlation between the viral doses and the different parameters was assessed by regression analysis. As shown in Table 1, the percentage of infected cells decreased with the MOI and both parameters correlated significantly (R\({}^{2}\) = 0.9882; Figure 1A). Using the obtained MESF data and the percentages of positive cells, the corresponding MFI values were calculated, which as expected also showed a significant correlation with the infected cells (R\({}^{2}\) = 0.9682; Figure 1B).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & \multicolumn{3}{c}{**Fluorescence Parameters**} & \multicolumn{2}{c}{**Viral RNA**} \\ \hline
**Sample** & **\% \({}^{+}\)Cells 1** & **AF 2** ** & **MESI 3** ** & **Per All \({}^{+}\)Cells 6** ** \\ \hline \end{tabular}
\end{table}
Table 1: Preliminary assays to evaluate the performance of the method for discrimination between different virus concentrations.  
Figure 1: Correlation between multiplicity of infection (MOI), infected cells, Mean of Fluorescence Intensity (MFI) and RNA molecules. Each data corresponds to a single data point. (**A**) Regression between MOI and percentage of infected cells; (**B**) Regression between MOI and MFI; (**C**) Regression between MFI and viral RNA copies per cell; (**D**) Regression between MFI and viral RNA copies per positive cells. In graphs with 2 equations, the first one corresponds to a regression line equation (and R\({}^{2}\) value) using the values shown on both axes; the second one corresponds to a regression line equation (and R\({}^{2}\) value) using logarithm of the values shown on both axes. Dashed lines: regression curves.  
In addition, the viral RNA was extracted from a number of positive cells and quantified by RT-qPCR to determine the number of RNA copies per cell. The total number of copies (per 100 cells) was calculated multiplying that value by the percentage of infected cells (Table 1). As shown in Figure 1C,D, a clear correlation was observed between either parameters (RNA copies per cell and per all positive cells) and the MFI values, following a second-degree regression with R\({}^{2}\) values 0.9734 and 0.9998, respectively.
# Investigation of Element Migration from Aluminum Cooking Pots Using ICP-MS
## 3 Results and Discussion
### The Results of SEM-EDS Analysis  
SEM-EDS analysis was conducted to confirm the detected element concentrations in the cooked food samples, mainly consisting of rice that was dried and ground into powder. It should be mentioned that SEM-EDS analysis was performed on all samples from various food experiments, as shown in Table 1; however, only one example related to the ACP-5 condition will be presented to avoid repetition. Figure 5 presents SEM-EDS results for food samples prepared under the conditions of ACP-5 as outlined in Table 1. In Figure 5a,b, secondary electron images at low (200\(\times\)) and high magnification (500\(\times\)) display the size and morphology of the prepared food samples. Figure 5c highlights the selected area for chemical analysis, outlined in red in the same images. Figure 5d displays the EDS spectra of the examined area, and the top-right insert in Figure 5d provides a table of chemical analysis listing the quantitative values of elements in atomic percent. The results of EDS spectra and quantitative chemical analysis showed that only carbon and oxygen were detected in the examined samples. During EDS analysis, five elements of interest, namely Al, Fe, As, Cd, and Pb, were selected to check their concentrations in the cooked food samples. However, the EDS technique did not detect any of these elements in the food samples prepared using aluminum cooking pots. No peaks for these elements were observed in the obtained EDS spectra, and quantitative chemical analysis did not yield any values for the selected elements. The reason for this is that the concentrations of the detected elements, as per the ICP-MS results presented in Table 1, as well as Figures 1 and 2, fall below the detection limit of the EDS technique. EDS cannot detect trace elements with concentrations below 0.1 wt.%, and its reported detection limit is 0.1 wt.% (1000 ppm or 1000 \(\upmu\)g/g) [42; 43; 44].  
Figure 5: SEM-EDS results collected from food samples prepared using an aluminum cooking pot according to ACP-5 experiment conditions: (**a**) secondary electron image of the morphology and size of the prepared food sample at 200\(\times\); (**b**) secondary electron image of the prepared food sample at 500\(\times\); (**c**) the selected area for chemical analysis (outlined in a red color); (**d**) the EDS spectrum and chemical analysis results of the selected area in (**c**).
# The Impact of Skinsuit Zigzag Tape Turbulators on Speed Skating Performance
## 1 Introduction  
During the past decades, research and innovations in the field of sport apparel and materials have helped to increase skating speed tremendously. The introduction and fine-tuning of the clap skate mechanism, the thickness and metal compound of blades and the application of water-additives to reduce ice-friction all had their impact on improved speed-skating performance [1]. Skating suits evolved from the knitted outfits used during the early 1970s to the present suits with distributed rough patches [2, 3].  
In the almost 25 years following the introduction of the one-piece skinsuit by the Austrian [PERSON], in 1974/75 until February 1998, the suits worn by elite speed skaters developed gradually, mostly aiming at a better fit and at improved cooling characteristics. While until 1998 the trend was to use increasingly smooth fabrics, at the 1998 Nagano Olympics, foam zigzag tape strips on the lower legs and the cap of the skating suit were introduced, aiming at significant aerodynamic drag reduction, Figure 1. The application of the zigzag tape to racing suits by the Dutch team followed earlier measurements by [PERSON], a Delft University of Technology (TU Delft) aerodynamics professor who experimentally investigated the impact of trip wires and seams on the aerodynamic drag of cylinders and a mannequin leg covered with a skinsuit fabric [4]. He found that the drag coefficient of such a mannequin leg could be reduced by 22% when the entire leg was equipped with a 0.8 mm thick trip wire on both sides of the leg front end. These results unfortunately lay dormant for over 10 years, until in 1997 and January 1998, wind-tunnel measurements at TU Delft were performed on live skaters and cylinders, this time using zigzag tape as a turbulator. Zigzag tape proved to be an efficient means to trip the laminar boundary layer on a sailplane wing, thus avoiding drag rise due to laminar separation at low Reynolds numbers. This flow phenomenon is typically also present around the body of speed skaters with smooth skinsuits. Total drag area reductions on static male skaters with zigzag-tape strips on lower legs and cap were found up to 9%. Although some results were presented in low-circulation media [5, 6, 7, 8], unfortunately little of this work on the impact of zigzag tape, specifically on speed-skating performance, has been widely published.  
The Dutch team was quite successful in Nagano; they gathered more gold medals than ever before. Although the wind-tunnel tests were very promising, the contribution of the zigzag strips to reduce the aerodynamic drag in the actual races remained unclear. The aim of this paper is to present results of wind-tunnel measurements showing the effects of the strips and, in addition, to quantify the impact on speed skating performance the zigzag strips may have had in a real skating environment.
###### Abstract  
In this study, platinum (Pt) nanocatalysts were synthesized via a sol-gel method over the non-stoichiometric, Magneli phase titanium oxides (\(\mathrm{TmO_{2n-1}}\)) at varying Pt loadings (10-40 wt.%). Their structural and morphological properties were characterized, and after preliminary electrochemical screening, the catalysts were integrated into commercially available gas diffusion electrodes (GDEs) with a three-layer structure to enhance mass transport and catalyst utilization. Membrane electrode assemblies (MEAs) were fabricated using a Nafion[8] 117 polymer membrane and tested in a laboratory PEM cell under controlled conditions. The electrochemical activity toward the hydrogen reduction reaction (HRR) was evaluated at room temperature and at elevated temperatures to determine the catalytic efficiency and stability. The optimal Pt loading was determined to be 30 wt.%, achieving a current density of approximately 0.12 A cm\({}^{-2}\) at 0.25 V, demonstrating a balance between catalyst efficiency and material utilization. The chronoamperometry tests showed minimal degradation over prolonged operation, suggesting that the catalysts were durable. These findings highlight the potential of Pt-based catalysts supported on Magneli phase titanium oxides (\(\mathrm{TmO_{2n-1}}\)) for efficient HRRs in electrochemical hydrogen pumps/compressors, offering a promising approach for improving hydrogen compression efficiency and advancing sustainable energy technologies.  
catalystis; hydrogen; membrane electrode assembly; compression; electrochemical hydrogen pump/compressor Article
# Laser Vibration Characteristics of Marble Specimens and Failure Criterion
## 3 Results
### Characteristics of Vibration Signals
#### 3.3.2 Brazilian Tests  
The whole waveform of marble specimens in the Brazilian test is shown in Figure 5a. Although obvious plastic deformation is not reflected from the stress-strain curve, the waveform is divided into elastic stage (I), plastic stage (II), and failure stage (III) according to the laser waveform features. Typical frequency spectra in each stage are shown in Figure 5b-f, and the detailed parameters are listed in Table 2.  
In the elastic stage (I), fluctuation phase (a), stable phase (b), and fluctuation phase (c) are identified. In the fluctuation phase (a), unimodal low frequency waveforms are predominant, the dominant frequency is 27.5 Hz, and the corresponding amplitude is 0.01325 mm/s. In the stable phase (b), bimodal low frequency waveforms are detected, the dominant frequency and second dominant frequency are 10 Hz and 27.5 Hz, and the corresponding amplitudes are 0.00801 mm/s and 0.00656 mm/s. Bimodal low frequency waveforms are representative in the fluctuation phase (c). The maximum velocity amplitudes in each phase are almost the same, generally smaller than 0.2 mm/s. In the plastic stage (II), severe fluctuation of the laser waveform is monitored in the fluctuation phase (d), wide frequency signals are representative, and then a stable phase (e) is followed. In this stage, the maximum velocity amplitude reaches up to 0.64 mm/s, more than twice the maximum velocity 0.35 mm/s in the elastic stage (I). Combined with the AE activities around 200 s in Figure 3b, it can be found that cracks are unstably developing, and thus this stage can be recognized as the plastic stage. This is an advantage of LDV signals versus stress-strain curves and AE monitoring, as it can prompt the plastic state. Furthermore, severe vibration of the specimen leads to signals overlap and wide frequency bands are formed, forecasting the oncoming specimen failure. It can be seen that there is a significant difference between laser and AE signals (Figures 5 and 3b) that the laser signals can reflect more abundant information about global vibration, rather than the waveform released by local microcracks of AE signals. In the failure stage (III), unimodal low frequency signals are predominant. Figure 5f shows the typical frequency spectra; the dominant frequency is 7.5 Hz and the corresponding amplitude is 0.04553 mm/s. The maximum velocity amplitude sours as high as 1.16 mm/s, occupying approximately 3.3 and 1.8 times the maximum velocity amplitude of the elastic stage (I) and plastic stage (II).
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 2 Methodology
### On-Site Personnel: The Reflectance-Based Approach
#### 2.3.4 Surface Reflectance Measurements  
The surface BRF is typically measured using a portable hyperspectral spectroradiometer having a wavelength range of 350 nm to 2500 nm, and it is carried across the site in a suitable predetermined pattern. The foreoptic is mounted to a boom arm to ensure shadow-free measurements. Periodic measurements of a Spectralon reference panel are made at predetermined points throughout the site collection, and the output digital numbers (DNs) over the two targets (Spectralon and ground) are ratioed to determine the hyperspectral surface BRF for each measurement. Routine measurements of the UArizona and SDSU Spectralon panel BRFs as a function of wavelength and angle are made at the UArizona spinometric laboratory to monitor changes caused by regular field use, including ultraviolet (UV) light and dust exposure [89; 90; 103]. In the final BRF processing step, the individual transect measurements are averaged together to create one hyperspectral surface BRF to be used in the radiative transfer code for the given sensor overpass.  
In addition to the typical hyperspectral spectroradiometer used to collect surface reflectance data, a recently developed multispectral instrument was deployed by GSFC personnel during the November 2021 field campaign. It is known as the Calibration Test Site SI-Traceable Transfer Radiometer (CaTSSITTR), and it was developed by UArizona after the RadCaTS became operational and with the increasing involvement of UArizona in RadCalNet. The main goal in the development of the CaTSSITTR is to allow one-person operation in the field while maintaining an SNR and absolute accuracy on par with laboratory transfer radiometers. There are currently two identical CaTSSITTR instruments as follows: CaTSSITTR-A (Arizona) and CaTSSITTR-G (Goddard) [104; 105; 106; 107], and their design specifications mirror those of a RadCaTS GVR, minus the SWIR channel at 1550 nm. The CaTSSITTR measurements at Ivanph Playa were modeled on the reflectance-based approach, but instead of walking the site taking continuous measurements between panel measurements, the instrument was moved to eight randomly predetermined locations within the overall test site. Operation of a portable spectroradiometer and the CaTSSITTR at Ivanph Playa and in S. Dakota is shown in Figure 11.
## 4 Conclusions  
Based on our experiment, it can be concluded that the use of different quantities of whey proteins (5, 7, 9, 11 or 13% WPC80) significantly influenced the textural and rheological properties and appearance of high-protein fat-free dairy desserts in varying degrees. Generally, the increase of WPC80 content increased the hardness (5-11% WPC80 content), adhesiveness (5-13% WPC80 content), cohesiveness (5-13% WPC80 content), springiness (5-11% WPC80 content) and viscosity (5-13% WPC80 content) of the high-protein fat-free dairy desserts tested. A correlation of storage (G') and loss (G\") moduli, and back-extrusion with hardness values, indicating that the obtained product becomes harder and more compact with the increased addition of WPC80 (5-11%), was noted. This may be the result of interactions between the individual ingredients of the product, for example, carrageenan with proteins through electrostatic or ionic bonding, or by phase separation caused by \(\beta\)-lactoglobulins and k-carrageenan interactions. The tested product showed a significant decrease (\(p<0.05\)) in the value of water activity along with an increase in the content of whey-protein concentrate. Every tested sample showed antioxidant properties; however, the sample with 13% WPC80 exhibited the highest value. The dessert with 9% WPC80 was described as the most favorable among panelists in terms of its sensory properties. At the same time, the use of high quantities of whey proteins makes these proteins very noticeable in the product in terms of taste and smell. In addition, the greatest technological problems during the preparation of the samples occurred in the production of the dessert with 13% WPC80, which can be ascribed to the phase separation between whey proteins and k-carrageenan.  
The conducted research and the obtained results allow us to conclude that it is possible to use whey proteins as a fat substitute in the preparation of high-protein fat-free dairy desserts. Moreover, these types of products, having health-promoting properties, can benefit consumers.  
Conceptualization, B.G.S. and K.K.; methodology, B.G.S. and W.R.; software, B.G.S. and W.R.; validation, B.G.S. and W.R.; formal analysis, K.K.; investigation, K.K.; resources, J.O.S. and B.G.S.; data curation, K.K. and J.O.S.; writing--original draft preparation, K.K. and J.O.S.; writing--review and editing, B.G.S.; visualization, K.K. and J.O.S.; supervision, B.G.S. and W.R.; project administration, B.G.S.; funding acquisition, B.G.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 6 Conclusions  
In this paper, we proposed a microwave photonic converter based on microwave pre-upconversion. The transmitted and received signals before entering the MZM are upconverted to HF by microwave pre-treatment. The HF and LO signals are coupled to drive the MZM by means of the electrical combiner, and CS-DSB modulation is introduced to the MZM by simply controlling the bias voltages of the modulator. Only a single MZM is employed in the converter system so that the complexity and bandwidth limiting in the link can be reduced. Experimental results demonstrate that the interference components including harmonics and intermodulation as well as original signals are all out of the system frequency band from 0.8-18 GHz, and in-band spurious suppression of at least 40 dBc is achieved. In addition, the system has SFDR of 86.23 dB\(\cdot\)HZ\({}^{2/3}\) for upconversion and 80.95 dB\(\cdot\)HZ\({}^{2/3}\) for downconversion. The proposed system shows improved conversion capability for both the upconversion and downconversion in the applications of radars and microwave signal processing.  
**Author Contributions:** Conceptualization, Y.Z., W.W. and W.Z.; hardware, C.W. and Q.J.; validation, Y.Z., W.Z. and Z.Z.; writing--original draft preparation, C.W.; writing--review and editing, C.W., W.W. and Y.Z.; project administration, J.L.; C.W. and Y.Z. contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the National Natural Science Foundation of China (61727815).  
**Acknowledgments:** The authors are grateful to the State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences and School of Integrated Circuits, University of Chinese Academy of Sciences.  
**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Materials and Methods
### Experimental Setup and Implementation
#### 3.3.3 Definition of Factors and Their Levels  
Vehicular ad hoc networks rely on wireless communication with no infrastructure support; this yields numerous vulnerabilities [1], such as black hole and wormhole attacks [27].  
In the current study, black hole (BKH) and wormhole (WMH) are selected as input variables or factors. Further, this study aims at evaluating AODV under different density and traffic levels. Hence, the number of vehicles (ND) and the number of connections (NC),which is the number of source-destination pairs, are also considered. Table 2 lists these factors and their levels.
## 4 Discussion
### Limitations and Future Work  
Some potential reasons may lead to poor consistency between the remote sensing evaluation results and measured results of CLQ. First, inconsistent indicators lead to inconsistent results. In this study, the measured results were calculated based on thirteen field-measured indicators, while the remote sensing evaluation results were obtained from eight spatial attribute indicators, which may be the one reason for the inconsistency between the two results. Second, the remote sensing evaluation indicators of CLQ were more easily affected by factors such as crop varieties and climate (such as sun radiation and precipitations). In addition, one limitation of this study was that the spatial resolution of data was inconsistent. For example, the resolution of the NPP data was 500 \(\times\) 500 m, that of the topsoil texture was 1 \(\times\) 1 km, and that of the remaining data was 30 \(\times\) 30 m. This can result in poor reliability of the evaluation results [10].  
In the future, we will try our best to obtain indicators consistent with the measured data, so as to improve the accuracy of remote sensing evaluation of CLQ. The impact of crop varieties, land use and climate factors on the CLQ should be explored. Moreover, constructing a more comprehensive and representative evaluation indicator system is still the key of CLQ evaluation.
## 5 Discussion
### Mid Attention Layer Analysis  
The first two rows in Figure 7 show the mid-layer convolutional outputs, where the network's feature maps become more abstract and complex. Here, the model is no longer capturing only basic spectral patterns; instead, it begins to identify mid-level structures,  
Figure 6: Early feature and attention maps for a trumpet and bass sample showing the initial layer outputs of the Log-Mel and scaled CST (Chroma, Spectral contrast, and Tonnetz) features. The maps highlight the fundamental frequency components and outline basic musical structures through a combination of Log-Mel’s high-resolution representation and the CST features scaled to 1/4 of the Log-Mel size. The grayscale shades represent values in the feature maps and attention maps, where darker shades indicate lower values, and lighter shades indicate higher values, providing a visual representation of the extracted features.  
such as harmonic relationships and timbral qualities specific to each instrument. The feature maps display more intricate textures and organized clusters, reflecting the model's refined understanding of trumpet and bass sounds. These mid-level Conv layers capture more nuanced aspects of sound, helping the network to distinguish between instruments with increased specificity.  
Figure 7: Mid-level feature and attention maps for a trumpet and bass sample, capturing intermediate representations with heightened detail through the combination of Log-Mel spectrogram and CST features at 1/4 scale. The mid layers reveal more refined frequency and harmonic structures, enhancing the model’s ability to distinguish between complex timbral characteristics. The grayscale shades represent values in the feature maps and attention maps, where darker shades indicate lower values, and lighter shades indicate higher values, providing a visual representation of the extracted features.  
The height and width attention maps in the mid layers, shown in the third and fourth rows, demonstrate a sharper and more targeted focus. The height attention maps now isolate specific harmonic frequencies more accurately, suggesting that the model has identified frequency bands that are especially characteristic of each instrument's tonal quality. In the width attention maps, the network's focus on temporal segments is also more refined, capturing rhythm and temporal dynamics with greater precision. This enhanced attention to both frequency and time dimensions enables the model to extract timbral and rhythmic cues that contribute to the mid-level understanding of each instrument.