esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Advanced Strategies for eNO\({}_{3}\)R Toward Ammonia and Nitrogen
### Copper-Based Electrocatalyst for the eNO\({}_{3}\)R: A Case Study for Ammonia Production
#### 4.1.3 Copper-Based Bimetallic Catalysts  
Copper bimetallic catalysts, particularly CuNi alloys and Cu/Ni heterostructures, have shown remarkable potential in electrochemical nitrate reduction due to their synergistic effects and enhanced performance [167]. CuNi alloy cathodes, such as Cu\({}_{80}\)Ni\({}_{20}\) and Cu\({}_{50}\)Ni\({}_{50}\), have demonstrated superior activity in nitrate reduction compared to mono-component metals. The synergistic mechanism between Cu and Ni, where Cu facilitates nitrate reduction while Ni preferentially adsorbs hydrogen atoms, enhances the reduction process by hindering competitive hydrogen adsorption [65; 168]. Operando spectroscopy studies have elucidated the structural-performance relationship, revealing the upshifting of Cu d-band center towards the Fermi level upon alloying with Ni. This enhances the adsorption energies of intermediates, reducing overpotential and promoting nitrate reduction [65]. Porous CuNi alloy electrodes, prepared via hydrogen evolution-assisted electrodeposition, exhibit high stability and effective nitrate reduction, attributed to their large surface area and enhanced mass transport [169]. The mechanism proposed by [PERSON] et al. [84] involves the formation of infinite galvanic cells, facilitating electron transfer and adsorption of nitrate on Cu sites, leading to enhanced reaction kinetics and selectivity (Figure 12). While copper bimetallic catalysts offer promising advantages in nitrate reduction, challenges such as leaching, and particle aggregation need to be addressed for prolonged operation.  
Figure 11: (**A**) Cyclic voltammograms obtained from Cu(111) (**a**) and Cu(100) (**b**) surfaces in 0.1 M HClO\({}_{4}\) + 1 mM HNO\({}_{3}\). (**B**) In situ EC-STM images of the Cu(111) surface were obtained in 1 mM HNO\({}_{3}\) + 0.1 M HF during cathodic potential sweeping between \(-\)0.35 V—0.47 V (**a**), \(-\)0.57 V—0.69 V (**b**), and \(-\)0.79 V—0.80 V. (**c**) The image size is 150 nm \(\times\) 150 nm. The tip bias and tunneling current are 10 mV and 10 nA, respectively [166].
## 3 Results  
### Proliferation and Morphology of Chondrogenic MSCs Cultured in 3D Silica Fabrics  
The proliferation and morphology of chondrogenic-differentiated MSCs cultured in 3D silica fabrics were observed using CLSM (Figure 1). CLSM images of MSCs in silica fabrics cultured with normal medium (Diff (\(-\))) and chondrogenic differentiation medium (Diff (\(+\))) after 2 weeks and 4 weeks in culture are shown in Figure 1. After 2 weeks of culture, MSCs were detected at a depth of approximately 48 \(\upmu\)m under both culture conditions, and no difference was observed in terms of MSC morphology. After 4 weeks of culture, differentiated MSCs but not normal MSCs were detected at a depth of approximately 73 \(\upmu\)m. Moreover, normal MSCs were crowded on the 3D silica fabric surface. These results suggested that differentiated cells cultured in Diff (\(+\)) preferentially migrated vertically in 3D silica fabrics without proliferation. These proliferation results are related to cell number.  
Figure 1: Confocal-laser scanning microscopic (CLSM) images showing proliferation and morphology of mesenchymal stem cells (MSCs) cultured in normal medium (Diff (\(-\))) and chondrogenic differentiation medium (Diff (\(+\))) on three-dimensional (3D) silica nonwoven fabrics for 2 weeks (**a–f**) and 4 weeks (**g–l**). In each case, single optical slices near the surface (5.0 \(\upmu\)m: a, b, g, h), in the middle (15 \(\upmu\)m: c, d, 30 \(\upmu\)m: i, j), and near the bottom (30 \(\upmu\)m: e, f, 60 \(\upmu\)m: k, l) of the silica fabrics are shown. Cellular nuclei and skeletons were stained with Hoechst 33342 (blue) and Alexa Fluor 594 phalloidin (red), respectively. Scale bars: 20 m.  
Figure 2 shows the number of cells adhered to the 3D silica fabrics under normal and differentiation conditions after 4 weeks of culture. The number of adhered cells in the 3D silica fabrics under differentiation condition was controlled compared with that in normal condition.
# Research on Sparse Representation Method of Acoustic Microimaging Signals
## 2 Materials and Methods
### Constraint Dictionary-Based Sparse Representation  
Mathematically, the above Gabor dictionary is concise and complete. However, when dealing with overlapping echoes, the above Gabor dictionary contains quite a lot of atoms that do not match the actual echoes, which affect the calculation efficiency. In addition, the above Gabor dictionary may contain atoms that match the echoes after overlap, so that the echoes before the overlap cannot be distinguished and the improvement of longitudinal resolution is limited. In order to effectively improve longitudinal resolution, the CD-SR method is proposed. The constraint dictionary and algorithm design are as follows.
###### Abstract  
Background: Ethanolamine plasmalogen (PIsEtn) is a subclass of ethanolamine glycerophospholipids (EtnGpls) and is abundantly found in some marine invertebrates, including ascidian _Halcyutthia roretzi_. PlsEtn is reported to exhibit physiological and nutritional hepatic functions; however, the effects of dietary PlsEtn on continuous acetaminophen (APAP)-induced hepatic injury, including oxidative stress and impaired lipid metabolism, remain unclear. Herein, we investigated the dietary effects of PlsEtn from ascidian on chronic hepatic injury in APAP-treated mice. Methods: Five-week-old male ICR mice were divided into four groups (\(n=12\)), which were treated with the respective experimental diet for two weeks and then the respective APAP-containing diet for five weeks. The results obtained after administering the PlsEtn-rich diet were compared with those obtained after the administration of a phosphatidylethanolamine (PtEtn)-rich diet, a major subclass of hepatic EtnGpls. Results: The PlsEtn-rich diet effectively suppressed the APAP-induced decrease in body and liver weights of mice; however, this suppressive effect was not observed in mice fed a PtdEnt-rich diet. APAP administration decreased the total fatty acid content in the liver, whereas a PlsEtn-rich diet alleviated this decrease and increased the hepatic content of docosalexaenoic acid (DHA), which exhibits various hepatic functions. Moreover, dietary EtnGpl rich in PlsEtn or PtdEnt suppressed APAP-induced lipid oxidation in the liver. The protein expression results revealed that dietary EtnGpls reduced the expression of certain apoptosis-related proteins in the livers of APAP-administered mice compared to that in the APAP group. This reduction was particularly more effective in mice fed the PlsEtn-rich diet than in those on the PtdEnt-rich diet. Conclusions: Dietary EtnGpls, particularly PlsEtn, alleviated the hepatic cellular stress caused by continuous APAP consumption. These beneficial effects may depend on the subclass and may be related to DHA metabolism in the liver. The results of this study contribute to the understanding of the role of PlsEtn in maintaining liver health.  
actimonber; ascidian; docosalexaenoic acid; liver disease; phosphatidylethanolamine; plasmalogen 2020
## 4 Conclusions  
This work reports a new Zn-MOF-8@AgQDs composite material for the detection of 2,4-DNT. The Zn-MOF-8@AgQDs modified Au electrode possessed outstanding adsorption and electrochemical sensing of 2,4-DNT via cyclic voltameter and amperometric methods. Under optimal conditions, the fabricated Zn-MOF-8@Ag modified Au electrode possessed higher sensitivity, and no interference was discerned from other explosive compounds including hydrazine, thiourea, glucose, ascorbic acid, ethanol, urea, ammonium and KI. The Zn-MOF-8@Ag modified Au electrode provides a low detection limit of 0.041 uM, high sensitivity \(-\)0.238 (uA\({}^{-1}\)uM\({}^{-1}\)cm\({}^{-2}\)) and shows good repeatability. The detection limit and sensitivity of Zn-MOF-8@AgQDs sensor was superior to other reported 2,4-DNT sensors.  
Conceptualization, N.D., S.R.; methodology, S.R. and B.S.; validation, S.R., B.S. and S.K.; formal analysis, investigation, S.R.; data curation, writing, R.M.; review and editing, R.S.V. All authors have provided critical feedback and assistance in the conducted research, analysis and finalization of the manuscript.  
This research was funded by [PERSON], Council of Scientific and Industrial Research (CSIR) sanction latter no/file no 09/752(0057)/2014/EMR-1, New Delhi. and the research work was funded by Life Science Research Board- Defence Research and Development Organization. RSV acknowledges the support by the Operational Programme Research, Development and Education - European Regional Development Fund, project no. CZ.02.1.01/0.0/0.0/16_019/0000754 of the Ministry of Education, Youth and Sports of the Czech Republic.  
Figure 10: Schematic representation of the synthesis of Zn–MOF-8, its composite form, and its fabrication on a gold electrode; CV response depicted for reduction of 2-4-DNT.  
**Acknowledgments:** [PERSON] is thankful to CSIR, New Delhi, for providing financial assistance in the form of a Junior Research Fellowship. The authors thank LSRB-DRDO for providing research funding and MHRD-RUSA, DST-PURSE grant for providing funding for purchase of equipment and infrastructure. Authors are thankful to SAIF, AIIMS, Delhi for the TEM facility.  
**Conflicts of Interest:** Authors declare no conflict of interest.
## 3 Proposed Methodology
### Numerical Study  
Numerical modeling consists of three stages, namely pre-processing, processing, and post-processing. In the pre- processing stage, the three-dimensional geometry is modeled by using Solid Works 10.0 software. Flow channel designs are illustrated in Figure 2, and the design parameter is listed in Table 1. Finite volume discretization of the PEMFC is modeled by using the ANSYS ICEMCFD 15.0 code. In the processing stage, governing equations, which are used to capture the flow physics and electrochemical reactions of PEMFC are solved by using the ANSYS Fluent 15.0. CFD code. The modeling of the proton exchange membrane fuel cell is created by using solidworks 10.0, which consists of nine parts, namely membrane electrolyte, catalyst layers, gas diffusion layers, flow channels, and the current collectors in both anode and cathode side. The proton exchange membrane fuel cell is discretized through a finite volume approach by using ANSYS ICEMCFD 15.0. The PEMFC parts are meshed individually by adopting the blocking technology to generate hexahedral elements. The angle and determinant value mesh lies in the range of 85\({}^{o}\) and 0.9, respectively.  
Figure 1: Architecture of proposed method.
## 4 Conclusions  
A device which accomplishes the main requirements for being used in monitoring physical parameters for cultural heritage purposes has been successfully developed. One Master, one Slave and two Wireless prototypes modules have been designed to verify the consistence of the designs.  
The modules can be connected conveniently to configure a monitoring system that suits the cultural heritage site. In addition, third party hardware is used in order that the system can be very versatile because different modules available on the market can be attached for sending an alarm by SMS, recording data in the cloud, switch on/off an HVAC system, _etc._  
As the system reads a four channel ADC (DS2450), each integrated circuit can connect up to four sensors that transform the measured physical parameter into a change in the electric potential.  
Libraries have been programmed for a failure protection system using a Watch-Dog and identifying corrupted bytes inside data transmission. Thereby, it is ensured that stored data is correct and the system does not come into infinite loops or crash. Furthermore, an internal clock that controls the frequency of data capture has been programmed.  
Moreover, due to the time it takes to save every data, the prototypes have been able to save the measures from 80 sensors extracted from the Slave module inside a USB memory in less than 30 s of time and record measures of temperature and humidity from a surface of 2000 m\({}^{2}\).  
The system has a data storage capacity which depends on the USB memory used. For a 2 GB USB memory and 20 sensors recording data every minute the system will be able to save data continuously during 10 years. Even low level memories around 100 Mb ensure the autonomy of the data-logger for more than one year.  
The whole system has been made using free hardware-software or trial versions so its development has been possible thanks to the open scientific knowledge on the Internet. A practical testing of the system has revealed its suitability and matching results with commercial data-loggers used in cultural heritage.  
Figure 12: Mean daily trajectories of sensor inside the display cabinet (red), sensor at the outside of the display cabinet (blue) and control data-logger at the outside of the display cabinet (green). **(a)** Temperature; **(b)** RH.
# A Trajectory Optimisation-Based Incremental Learning Strategy for Learning from Demonstration
## 3 Research Methodology
### Extended Demonstration via G-PRM
#### 3.2.1 Generation of Sampling Points  
According to Figure 5, the sampling points in PRM are generated randomly, which makes it difficult to control the shape of the resulting trajectory. By using Gaussian distribution-based sampling points (Gaussian sampling points, _GSP_), the adjustment of the generated trajectory \(Tra_{gen}\) can be effectively facilitated.  
The initialisation of _GSP_ relies on Gaussian noise and coordinate differences. Here are the details: a Gaussian distribution is defined as \(N(\mu,\sigma^{2})\), where \(\mu\) is the mean, and \(\sigma^{2}\) represents the variance. Gaussian noise points directly generated from the \(\mathcal{N}(\mu,\sigma^{2})\) are defined as \(\textbf{GN}=\left\{GN_{1},GN_{2},\ldots,GN_{sample}\right\}\). The '\(\sim\)' symbol indicates that each dimension of the element in the _GN_ follows \(\mathcal{N}(\mu,\sigma^{2})\), which is expressed as Equation (1). _sample_ denotes the number of _GN_, and \(GN_{i}=(GN_{i_{x}},GN_{i_{y}},GN_{i_{z}}),i\in[1,sample]\).  
\[GN_{i_{x}}\sim\mathcal{N}(\mu,\sigma^{2}),GN_{i_{y}}\sim\mathcal{N}(\mu,\sigma ^{2}),GN_{i_{z}}\sim\mathcal{N}(\mu,\sigma^{2}) \tag{1}\]  
_GN_ needs further processing to yield _GSP_, which is distributed between the segmentation point and the new target point. The number of elements in _GSP_ matches that in _GN_, hence \(\textbf{GSP}=\left\{GSP_{1},\ GSP_{2},\ldots,GSP_{sample}\right\}\), and \(GSP_{i}=(GSP_{i_{x}},GSP_{i_{y}},GSP_{i_{z}})\), with the same \(i\in[1,sample]\). A time series \(\textbf{t}=\left\{t_{1},t_{2},\ldots,t_{sample}\right\}\) serves as a control variable to calculate the \(GSP_{i}\) from \(GN_{i}\) based on the coordinate differences between the segmentation point and the new target point. The segmentation point and new target point for \(Tra_{gen}\) are \(seg=(x_{s},y_{s},z_{s})\) and \(n\_tar=(x_{e},y_{e},z_{e})\), respectively. The calculation for \(t_{i}\) in \(t\) and \(GSP_{i}\) is described in Equation (2):  
\[\left\{\begin{aligned} & GSP_{i_{x}}=x_{s}+t_{i}\cdot(x_{e}-x_{s})+ GN_{i_{x}}\\ & GSP_{i_{y}}=y_{s}+t_{i}\cdot(y_{e}-y_{s})+GN_{i_{y}}\;,\;t_{i}= \frac{i}{sample},\;i=0,1,\ldots,sample\\ & GSP_{i_{z}}=z_{s}+t_{i}\cdot(z_{e}-z_{s})+GN_{i_{z}}\end{aligned}\right. \tag{2}\]  
Figure 5: Schematic diagram of PRM. (**a**) Sampling. (**b**) Roadmap construction. (**c**) Trajectory generation.  
Figure 6: Overall flowchart of the G-PRM.
###### Abstract  
A multiphase flow measurement technique plays a critical role in the studies of heat and mass transfer characteristics and mechanism of the gas-liquid two-phase, the practical measurement of the gas-liquid flow and the improvement of multiphase theoretical models. The four-sensor electrical probe as an emerging measurement method has been proved to be able to get the local flow parameters of multi-dimensional two-phase flow. However, few studies have been reported using the four-sensor probe to obtain the interface information (e.g., the interface direction and velocity). This paper presents a new signal processing method by which the interface direction and velocity can be obtained, besides void fraction, interfacial area concentration (IAC) and bubble chord length. The key solution is to employ the vector-based calculating method, which possesses the merits of simplicity and efficiency, to gain the interface velocity vector through legitimately assuming a direction of the interface velocity. A miniaturized four-sensor electrical probe was made and a gas-liquid two-phase flow experiment was performed to test the proposed signal process scheme. The two-phase flow was controlled to be in cap-bubble flow regime. To validate the availability and reliability of the proposed method, the local flow parameters obtained by the probe measurement were compared with the results from visual measurement technique in the same flow conditions. The comparison indicates that the above local flow parameters from four-sensor probe measurement are in good agreement with the visual measurement results, with maximum deviations of chord length of 8.7%, thereby proving the correctness of the proposed method.  
gas-liquid two-phase flow; local flow parameters measurement; four-sensor probe; vector-based signal processing; visual measurement experiment +
Footnote †: journal: Applied Physics Letters
## 3 Round Robin Test (RRT) and Reliability Analysis
### PAUT Reliability Analysis Results
An 80% POD was obtained at 4.53 mm for carbon steel, 6.14 mm for austenitic stainless steel, and 11.58 mm for dissimilar metals. Ferrritic steel welds exhibit good flaw detection performance because carbon steel is an isotropic material and is less affected by attenuation or refraction in the propagation of ultrasonic beams. However, austenitic stainless steel (TP304H) has high attenuation and material anisotropy, which makes ultrasound testing difficult. In particular, for dissimilar metal welds (TP304H + T91), the attenuation and refraction of ultrasonic waves occurs at the interface between the base metal and the welds, which causes difficulties in inspection and data evaluation. The difficulty of ultrasound testing may be due to the coarse grain and crystal orientation of the metallization, which may cause significant changes in attenuation, reflection and refraction at grain boundaries, and changes in velocity within the grain. This reduces the sensitivity of PAUT, resulting in a lower POD. The dissimilar metal welds contain anisotropic materials, and signal interpretation is difficult owing to reflection and refraction \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Team** & **All** & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline Detection Size & 6.93 & 6.89 & 5.50 & 10.77 & 7.02 & 5.59 & 5.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: POD analysis results of each team for the detectable length with 80% probability [mm]. Figure 2: POD analysis results for the tube weld specimens. at the interface between carbon steel and austenitic stainless steel. For these reasons, the reliability of the PAUT technique for dissimilar metal welds was low. Figure 4: POD analysis results obtained by dividing the welds according to the materials: (**a**) Ferritic steel welds (SA210 GrA1); (**b**) Austenitic stainless steel welds (TP304H); (**c**) Dissimilar metal welds (SA210 GrA1 + TP 304H). Figure 3: POD analysis results obtained by classifying planar and volumetric flaws: (**a**) planar flaws; (**b**) volumetric flaws. Figure 5 shows the results of calculations using the least-squares method. In the results of the linear regression analysis, the carbon steel and austenitic stainless steel specimens exhibited high sizing accuracies, and the dissimilar metal specimens exhibited relatively large errors from the ideal regression line. Figure 6 shows the results of calculating the RMSE derived from the regression analysis for different specimens. The overall RMSE for the inspection results was approximately 5.8 mm, which was higher than the acceptance criteria at 19 mm for the PAUT technique presented in ASME Code Section XI, Appendix. For the results of the analysis based on the welding materials, the RMSE was 3.52 mm for carbon steel, indicating an excellent accuracy, and 6.79 mm for dissimilar metal welds, indicating a relatively low accuracy.
## 2 Materials and Methods  
### Sample Preparation  
For the purposes of this study, five chestnut (_Castanea sativa_ Mill.) trees were harvested from a coppice forest in Sithonia Peninsula (Chalkidiki, Greece). The trees were as straight as possible, without any apparent defects. The trees were aged 25-27 years and their diameter ranged from 19.1 cm to 24 cm. Tree trunk disks of 3 cm thickness were obtained for approximately every 1 m of height from the tree trunk base (starting from the height of 1 m) to the top, taking a total of 5 disks per trunk (25 disks in total).  
The disks were transferred to laboratory infrastructure and conditioned in a closed chamber under stable conditions (60% relative humidity, 20 \(\pm\) 3 \({}^{\circ}\)C) until a constant weight was achieved. The moisture content of the disk wood was recorded according to the ISO13061-1 [31] standard to be 7.6-8.5%. All the measurements were performed on the different surface planes of these disks (Figure 1).  
First of all, the disks were further cut using a band saw (Figure 2A) in order to form samples of smaller dimensions bearing clear transverse, radial, and tangential surfaces concerning the heartwood and sapwood areas of wood (Figure 2B). Afterwards, the samples were code marked, sanded using 80-girt sandpaper on a sanding machine under the same processing conditions (TC-US400, Einhell, Germany) (Figure 2C), and a polishing technique was applied to all the samples to ensure the comparability and reliability of the results. In general, if high-girt sanding is applied during surface preparation, roughness would be only affected by wood structure and ingenuine properties. However, it is difficult to relate wood anatomy to roughness, since sanding is a procedure that may be conducted in different conditions and with different means. For instance, when high-speed cutting is performed, softwood species are strongly affected [4; 32]. Therefore, in the current study, only one and the same person/operator implemented all the sample sanding processes, applying the same sanding process on the same device, spending the same time on each wood sample (approximately 2 min/surface). All the specimens were visually and empirically examined and determined to have been appropriately sanded.  
Figure 1: Configuration of sampling method of the 5 disks per tree (on the **left**) and areas of interest for measurement (on the **right**). The transverse surface/plane is perpendicular to the stem longitudinal axis; the radial surface is oriented along the direction of a ray of the circumference described by the stem; the tangential surface is perpendicular to the direction of a ray of the circumference described by the stem. The arrows depict the direction of measurement (parallel with or vertical to the wood grain); “H” corresponds to heartwood and “S” to sapwood area.  
Figure 2: Chestnut wood disk/specimen preparation ((**A**) cutting, (**B**) marking, (**C**) sanding processes).