esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Verification and Robustness
As shown in Figure 14, an overall reduction of the point-local strain error is documented for both the longitudinal and shear strains. In particular, a significant reduction of the minimum longitudinal strain error is shown in Figure 14a. Figure 14b shows that the converged point-local relative error for both the maximum and minimum shear value is still relatively high at the end of the FEMU process (respectively 17.8% and 13.3%). However, this point-local error should be compared to the total weighted strain error, which takes into account the full-field strain data for all DIC images taken throughout the loading. The weights for calculation of the RMS strain error are defined in Equation (2). The evolution of the total weighted RMS during FEMU iterations is shown in Figure 15 for the strain data presented in Figures 12 and 13. As shown in Figure 15, the total RMS strain error is reduced from 32% to about 6%. Among the reasons for the difference in error reduction between the point-local strain error and the overall total weighted RMS strain error, small misalignments from the mapping of the DIC data on the FEM grid and local material heterogeneities are worth mentioning. This highlights the importance of A) careful DIC strain mapping and B) using full-field data for FEMU characterization, rather than point-local strain data. After the FEMU procedure has converged, the material parameters (\(E_{11}\), \(E_{33}\), \(\
u_{13}\), \(G_{13}\)) are used in a 2D plane stress static FE analysis run at failure load to obtain the maximum interlaminar tensile strength at the 12 and 6 o'clock locations, which corresponds to the ILT strength. For fatigue analysis, the maximum amplitude of the fatigue load is applied and the maximum ILT stress is used to generate the fatigue ILT S-N curves.
## 4 Weather Forecast Service  
When an MOD subscriber watches the TV, he/she can access weather/traffic information by selecting an icon on the screen. Figure 5 shows how the weather forecast service is presented in the UE.  
Figure 4: Location Information Message Flow.  
The message flow for accessing the weather forecast service is illustrated in Figure 6 and is described as follows:  
**Step 1:**: The Central Weather Bureau (Transportation Bureau) periodically sends the weather/traffic information to the Media Server through FTP. The Media Server then forwards the information to the IPTV-CS. The BMM parses the received information and stores the results into the database [Figure 2 (e)].
**Step 2:**: To access the weather information, the browser on the STB first access the location information described in Section 3, and then sends an HTTP request to the MOD-SDP (if MOD-SDP is equipped with SIP, then the following message exchange can also be implemented by SIP). The URI of the HTTP request is  
http://cs_ip:port_no/weather/WeatherServlet?black={$string}&{other_parameters}  
where cs_ip and port_no are the IP address and port number of the IPTV-CS, respectively. The parameter \"black\" is an encrypted string containing the IP address and the identification number of the originating STB, the customer ID, and the time stamp. Function WeatherServlet is located at directory weather/, which instructs the IPTV-CS to execute the user's request. \"other_parameters\" specify area, time and data type (text, image, video, or mixed data) of the weather information to be retrieved.  
Figure 5: Weather Forecast Service.  
**Step 3:**: Upon receipt of the HTTP request, the IPTV-CS decrypts the \"black\" parameter by _Data Encryption Standard_ (DES) or DES3 to authorize the request. Specifically, it queries the database [Figure 2 (e)] to obtain the subscriber's data and compare them with the decrypted customer ID and IP address of the STB.
**Step 4:**: The IPTV-CS retrieves the required weather/traffic content including texts and images from its database, and uses them to create the web pages for the ANT Fresco or Firefox browser according to the STB type. The HTTP response with the content (web page) is returned to the STB through the MOD-SDP, and the weather/traffic information is displayed on the TV screen.
###### Abstract
For the extraction of VSCs in wines and wine distillates, liquid-liquid extraction [3], static headspace [3], counter-current supercritical fluid extraction [9], purge and tramp concentrators [10], solid-phase extraction (SPE) [5], solid-phase microextraction (SPME) [3; 11], headspace solid-phase microextraction (HS-SPME) [1; 5; 12; 13; 14], and Stir Bar Sportive Extraction (SBSE) [15] have been used. SPME and SBSE techniques have certain advantages over other extraction techniques, since they do not require solvents; they simultaneously combine the extraction and concentration of analytes in a single step; they are simple and fast techniques that require a low sample volume; and they are easily combined with analytical techniques such as GC. SBSE has a larger sorbent phase and higher sensitivity and is more robust than SPME [16], which makes it an ideal green analytical method for determining the concentration of volatile minor compounds such as VSCs at a stage prior to their analysis. The most common detection system used in the analysis of VSCs in wine and distillate samples is MS [2; 11; 14; 17; 18], although other types of more specific detectors such as chemi-luminescent detectors [19], atomic emission detectors [1], and pulsed flame photometric detection [12] are also found. There are few recent papers that focus their studies on the analysis of VSCs in brandy samples [1; 5; 20; 21], and there have been no studies on Brandy de Jerez samples. It is for this reason that the aim of this paper was the optimization of an SBSE-GC-MS method for the quantitative determination of volatile sulphur compounds in wine distillates and brandy samples. After optimization and validation, the analytical methodology was applied to a high number of distillates employed for the elaboration of Brandy de Jerez.
# Study of the Impact of PV-Thermal and Nanofluids on the Desalination Process by Flashing
###### Abstract  
In this study, a mathematical and numerical modeling of the photovoltaic (PV)-thermal solar system to power the multistage flashing chamber process is presented. The proposed model was established after the mass and energy conservation equations written for finite control volume were integrated with properties of the water and nanofluids. The nanofluids studied and presented herein are Ai\({}_{2}\)O\({}_{3}\), CuO, Fe\({}_{3}\)O\({}_{4}\), and SiO\({}_{2}\). The multiple flashing chamber process was studied under various conditions, including different solar radiation levels, brine flows and concentrations, and nanofluid concentrations as well as flashing chamber temperatures and pressures. Solar radiation levels were taken as 500 w/m\({}^{2}\), 750 w/m\({}^{2}\), 1000 w/m\({}^{2}\), and finally, 1200 w/m\({}^{2}\). The nanofluid volumetric concentrations considered varied from 1% to 20%. There is clear evidence that the higher the solar radiation, the higher the flashed flow produced. The results also clearly show that irreversibility is reduced by using nanofluid Ai\({}_{2}\)O\({}_{3}\) at higher concentrations of 10% to 20% compared to water as base fluid. The highest irreversibility was experienced when water was used as base fluid and the lowest irreversibility was associated with nanofluid SiO\({}_{2}\). The irreversibility increase depends upon the type of nanofluid and its thermodynamic properties. Furthermore, the higher the concentration (e.g., from 10% to 20% of Ai\({}_{2}\)O\({}_{3}\)), the higher the availability at the last flashing chamber. However, the availability is progressively reduced at the last flashing chamber. Finally, the predicted results compare well with experimental data published in the literature.  
Keywords: desalination; flashing chamber; PV-thermal; nanofluids; numerical model; validation +
Footnote †: journal: Applied Systems
## 3 Results and Discussion
### Change in Amount of Volatile Compounds Associated with Desirable Arona Descriptors
#### 3.4.4 Alcohols  
Isomer A of 2,3-butanediol was consistently found in higher amounts across all agroclimatic cacao production clusters, with its amount peaking between 96 and 120 h of fermentation, as shown in Figure S7. Conversely, the behavior of 2,3-butanediol Isomer B throughout fermentation exhibited more variability. Our research substantiates the fact that chocolates derived from cocoa seeds fermented for 96-120 h possess volatile compounds strongly correlated with high-quality aroma descriptors, leading to enhanced scores for desirable sensory attributes and overall quality. The observed decline in fine-flavor sensory attributes outside this fermentation window may be attributed to variations in the quantities and types of volatile precursors formed during fermentation, which undergo reactions during the roasting stage, notably through Maillard reactions [27].  
Furthermore, the agroclimatic region C9 demonstrated high amounts of acetaldehyde, 2,3-butanediol isomers, and pyrazines, contrasted with lower amounts of the esters 1-methoxy-2-propyl acetate and 2-phenethyl acetate. This region, distinguished by its hotter and drier conditions compared to the markedly rainier region C4, exhibits a distinct geographical and climatic profile. Agroclimatic region C4 displayed different volatile compound marker profiles, such as higher amounts of 1-methoxy-2-propyl acetate, 2-phenethyl acetate, and acetoin compared to region C9. Additionally, sensory profiles varied between these regions; at 96 h, the nuttiness was significantly more pronounced in cluster 9, while fruitiness was slightly higher in cluster 4. However, by 120 h of fermentation, a greater reduction in fruitiness was noted for the cluster. These findings highlight the significant influence of climate and geographical location on the volatile and sensory profiles of chocolate.
# Aerodynamic Efficiency Improvement on a NACA-8412 Airfoil via Active Flow Control Implementation
###### Abstract
The NACA-0018 profile at AoA = \(10^{\circ}\) and at \(Re=1000\), was evaluated using direct numerical simulation (3D-DNS) by [PERSON] and [PERSON] [34]. Three non-dimensional frequencies (\(F^{+}=0.5\), \(1\) and \(4\)) were considered. The optimum frequency was \(F^{+}=1\) although improvement was observed in all of them. In [PERSON] et al. [35], the SD/7003 airfoil at \(Re=6\times 10^{4}\) and under three AoA = \(4^{\circ}\), \(11^{\circ}\) and \(14^{\circ}\) was numerically studied. At AoA = \(14^{\circ}\) they obtained an aerodynamic efficiency increase of 124%. The same Reynolds number and wing profile but at AoA = \(14^{\circ}\), and AoA = \(13^{\circ}\) & \(16^{\circ}\) was studied by [4; 5], respectively. Due to the fact that in these studies optimization processes were employed, the maximum efficiency increase was obtained to be 280% and 591%, respectively. The airfoil chosen for the present study is the NACA-8412, a Reynolds number of \(Re=68.5\times 10^{3}\) and AoA = 15 deg were considered. According to the authors knowledge no previous studies for this airfoil and Re configuration have been found in the literature. The usage of the NACA-8412 airfoil was motivated by its large curvature, which in combination with the high AoA used will produce a separation of the flow close to the leading edge. This early separation will remark the effects of introducing SJA, and enable a clearer comparison of the results with the baseline case. Furthermore, the methodology employed in the present manuscript, which is based on a previous knowledge of the flow field and consist in initially fixing some AFC parameters, is proven to be very useful in obtaining good results while saving computational resources. The rest of the manuscript is structured as follows. The formulation of the problem, the numerical methods employed and the mesh independence study are presented in Section 2. The definition of the AFC parameters and their implementation are introduced in Section 3. The Results section is introduced in Section 4 and the summary of the work is presented in Section 5.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
## 5 ECM-Derived Peptide Sequences and Their Use in Biomimetic Hydrogel Functionalisation
### Laminin: YIGSR Motif  
The pentapeptide sequence YIGSR is found in the \(\beta\)1 chain of laminin and has been identified as a cell adhesion site, which can bind to the 67 kDa laminin adhesion receptor [119]. The YIGSR peptide was first found between residues 929-933 on the \(\beta\)1 chain [120]. YIGSR-NH\({}_{2}\) has been used to modify a poly (N,N-diethylacrylamide) (PDEAAm) hydrogel scaffold [121]. After initially seeding human embryonic stem cells (hESCs) onto the hydrogels, the PDEAAm gels did not possess cytotoxic traits and could stimulate increased cell attachment and proliferation compared with an unmodified hydrogel control, where cell attachment was significantly decreased. This confirmed the significance of the pentapeptide motif YIGSR in creating a biomimetic surface for the culture of hESCs [121].  
In a study by [PERSON] et al. [122], degradable and non-degradable PEG hydrogels modified with YIGSR could promote a biomimetic environment suitable for adipocyte differentiation [122]. Although studies have shown that YIGSR can have a profound effect on cell attachment and proliferation, this work demonstrated that there were no significant differences in cell number between hydrogels modified with YIGSR, and hydrogels containing no YIGSR [122]. Significant differences were noted for adipocyte differentiation depending on the presence or absence of YIGSR; triglyceride accumulation in degradable hydrogels decreased from 400 ug per 100,000 cells in the presence of YIGSR to 260 ug per 100,000 cells in the absence of YIGSR [122]. Functionalising hydrogels with integrin-binding motifs such as YIGSR therefore support the accumulation of lipids within mature, differentiated adipocytes [122]. Future applications could involve further optimization of cell culture conditions to produce mature fat pads. The hydrogel-cell mixture could also be injected directly into a soft tissue defect, where in vivo crosslinking will produce functionally active tissue substitutes [122].
# Space-Ground Remote Sensor Network for Monitoring Suspended Sediments in the Yellow River Basin
## 2 Data and Methods
### Methods  
For the Wangtuan station, after quality control and temporal alignment, 47 paired measurements of water spectral data and SSC were collected for model development. The construction of the SSC model involves the following steps: (1) Resampling the raw spectra to 10 nm to ensure applicability to hyperspectral data acquired from drones or satellites; (2) Using single-band reflectance, band differences, and band ratios as input variables (X), and SSC as the output variable (Y); (3) Fitting the data after transforming X and Y into a linear relationship; (4) Selecting the optimal model based on the coefficient of determination (R\({}^{2}\)). Among them, the band difference model, SSC = 2 \(\times\) 10\({}^{-5}\) (R690\(-\)R680)\({}^{-2.512}\) (R690 represents the reflectance at 690 nm) yielded the best performance, with an R\({}^{2}\) value of 0.67. Therefore, this model was used for temporal SSC monitoring at the Wangtuan station [37].  
For monitoring SSC in the Xiaolangdi reservoir and the adjacent coastal areas of the Yellow River estuary, this study primarily relies on multispectral satellite imagery. We adopted the SSC monitoring model developed by [PERSON] et al. (2014) [38] using HJ satellite data for the Yellow River estuary. The model is defined as SSC = 2.3224 exp(59.445 \(\times\) R830), with an R\({}^{2}\) value of 0.93, demonstrating its suitability for SSC monitoring in highly turbid waters.
# Effect of Human Hair Fibers on the Performance of Concrete Incorporating High Dosage of Silica Fume
## 1 Introduction
Ref. [24] investigated compressive and tensile strengths. They discovered that the mechanical characteristics with SF addition are not constant and that the optimal replacement ratio is dependent on the water-cement (w/c) ratio of the concrete matrix. The tests revealed that split tensile and flexural strengths improved the most. Ref. [25] concluded that the chloride diffusion coefficient in concrete was found to be lower when the water-to-binder ratio was reduced and SF was added at a dosage of 10%. The impact of human hair as a fiber in concrete has been studied extensively. To evaluate the performance of concrete HHF as a fiber and SF as a cementitious material has been incorporated in different percentages. Fresh concrete properties such as slump were evaluated, as were hardened properties such as flexural, compressive, and split tensile strengths after 7, 14, and 28 days of curing. Secondly, to offer something concrete that could aid in overcoming environmental issues, waste products such as human hair fiber at 2% were incorporated. Furthermore, as a cementitious material, up to 30% of cement is replaced with SF. To the best of the authors 'knowledge, most researchers have investigated the mechanical properties of human hair fiber-reinforced concrete [15; 26]. On the other hand, this study addresses both mechanical and statistical aspects of the investigation, making it unique. This research contributes to the United Nations' Sustainable Development Goals (SDGs) 9 (Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation) and 12 (Ensure sustainable consumption and production patterns), targeting goals 9.1, 9.4, 9.5, 12.2, 12.5, and 12.a, respectively [27]. These targets specifically focus on sustainable and resilient infrastructures, adoption of clean and environmentally friendly technologies, enhanced scientific research, efficient use of natural resources, reducing waste generation through recycling and reuse, and by supporting developing countries to strengthen the research capabilities to move towards sustainable consumption and production. **Research Significance**  
This study shows that solid waste human hair can be used as a fiber to improve the concrete performance by improving the mechanical properties such as compressive, split tensile, and flexural strength. Moreover, the silica fume also helps in improving the microstructure of concrete matrix by densification and CSH formation. This research also indicates that SF improves the mechanical properties of concrete by incorporating human hair fibers. The RSM for optimization provides a forecast indication of concrete properties. The optimization results are also conditionally approved. In addition, the expectation method is used to predict the HHFC and SF findings, demonstrating its usefulness. Unlike previous research, our study adds to the literature by investigating a wide variety of results using the RSM technique and filling the gap in the literature given in Table 1.
# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 4 Volumetrics
### Cross-Docking and Its Role in Industry 4.0  
Cross-docking is a pivotal operation within modern supply chain management, especially in the context of Industry 4.0. This logistics practice involves the streamlined movement of goods through a distribution centre, reducing storage time and enhancing efficiency [17].  
In an Industry 4.0 framework, cross-docking plays a critical role in optimising the flow of products. Technologies such as the Internet of Things (IoT), Big Data, and Robotics have revolutionised cross-docking operations. The optimisation of mixed pallets further enhances the efficiency of cross-docking, by reducing the number of pallets required and minimising the use of space, aligning with the lean and efficient principles of Industry 4.0 [17].  
Cross-docking operations, in the context of Industry 4.0, are becoming smarter and more responsive. IoT devices and sensors provide real-time data on inventory, enabling predictive maintenance and efficient routing, while robotics and automation streamline the movement of goods within the facility, reducing human error and increasing speed. By aligning cross-docking with Industry 4.0 principles, businesses can achieve greater agility, adaptability, and competitiveness in the modern era of logistics and manufacturing [17].
# Plastic Properties of Prestressed High-Strength Steel Reinforced Concrete Continuous Beams
## 4 Test Results and Analysis
### Analysis of the Moment Redistribution
#### 4.6.3 Moment Redistribution Based on the Relative Plastic Rotation Angle  
Taking the relative plastic rotation angle \(\frac{\theta_{p}}{h_{0}}\) as the abscissa and the amplitude modulation coefficient \(\Delta M\) of \(\beta\) as the ordinate, the measured relative plastic rotation angle and bending moment amplitude modulation coefficient were placed in the coordinate system. Distribution of the \(\beta\) and \(\left(\frac{\theta_{p}}{h_{0}}\right)\cdot 10^{5}\) test points was almost linear, and the straight line obtained by first-order linear fitting is shown in Figure 11.  
The bending moment amplitude modulation coefficient can be calculated using the following formula:  
\[\left\{\begin{array}{ll}\beta=0.3558\left[\left(\frac{\theta_{p}}{h_{0}} \right)\cdot 10^{5}\right]+0.075&\left(\frac{\theta_{p}}{h_{0}}\right)\cdot 10^{5} \leq 0.829\\ \beta=0.37&\left(\frac{\theta_{p}}{h_{0}}\right)\cdot 10^{5}>0.829\end{array}\right. \tag{9}\]  
Figure 11: Coordinate point distribution and the fitting curve.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimen Number** & \(M_{load,t}\) & \(M_{load,c}\) & \(M_{a}\) & \(\Delta M_{t}\) & \(\Delta M_{c}\) & \(\beta\) \\  & **kN\(\cdot\)m** & **kN\(\cdot\)m** & **kN\(\cdot\)m** & **kN\(\cdot\)m** & **kN\(\cdot\)m** & **kN\(\cdot\)m** & **\(\beta\)** \\ \hline A\({}_{1}\)Q\({}_{5}\)S\({}_{4}\)P\({}_{2}\)R\({}_{5}\) & 375.19 & 290 & 146.1 & 143.9 & 229.09 & 0.37 \\ A\({}_{2}\)Q\({}_{4}\)S\({}_{4}\)P\({}_{1}\)R\({}_{5}\) & 424.13 & 348 & 161.1 & 186.9 & 263.03 & 0.29 \\ A\({}_{3}\)Q\({}_{4}\)S\({}_{4}\)P\({}_{2}\)R\({}_{5}\) & 435 & 353.8 & 160.2 & 193.6 & 274.8 & 0.3 \\ A\({}_{4}\)Q\({}_{5}\)S\({}_{4}\)P\({}_{3}\)R\({}_{5}\) & 451.31 & 353.8 & 163.9 & 189.9 & 287.41 & 0.34 \\ A\({}_{5}\)Q\({}_{6}\)S\({}_{5}\)P\({}_{2}\)R\({}_{5}\) & 429.56 & 350.9 & 161.1 & 189.8 & 268.46 & 0.29 \\ A\({}_{6}\)Q\({}_{6}\)S\({}_{4}\)P\({}_{2}\)R\({}_{5}\) & 522 & 461.1 & 248.3 & 212.8 & 273.7 & 0.22 \\ A\({}_{7}\)Q\({}_{5}\)S\({}_{7}\)P\({}_{2}\)R\({}_{5}\) & 527.44 & 493 & 279.5 & 213.5 & 247.94 & 0.14 \\ A\({}_{8}\)Q\({}_{5}\)S\({}_{4}\)P\({}_{2}\)R\({}_{6}\) & 525.26 & 458.2 & 248.3 & 207.8 & 276.96 & 0.24 \\ A\({}_{9}\)Q\({}_{6}\)S\({}_{4}\)P\({}_{2}\)R\({}_{8}\) & 527.44 & 458.2 & 248.3 & 209.9 & 279.14 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Moment redistribution coefficient of the test beams.