esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Spectrophotometric Analyses
#### 2.5.1 Determination of Total Polyphenolic Content  
Total polyphenonic content (TPC) was determined according to the modified Folin-Ciocalteu spectrophotometric method [33]. The method is based on the reaction of polyphenols with the Folin-Ciocalteu phenol reagent what is accompanied by formation of blue product in the presence of basic medium as a result of reduction of molybdenum and tungsten oxidation state of 6+ by the hydroxyl group from phenols in the alkaline conditions leading to blue-coloured product [34]. Quantification of polyphenols is based on measurement of the intensity of blue colour. As a representative of polyphenolic compounds, a gallic acid (GA) is often used to express the total quantity of polyphenols in wine. A stock solution was prepared from 0.1 g of GA and its dilution with demineralized water up to 100 mL volume. Then the stock solution was appropriately diluted with demineralized water to prepare calibration solutions in the range of 5-200 mg L\({}^{-1}\) of GA. The blank was prepared with use of Folin-Ciocalteu reagent and distilled water instead of standard or sample. The calibration curve obtained had correlation coefficient R = 0.999. The concentration of polyphenols was quantified as mg L\({}^{-1}\) of GA equivalent (GAE). 50 \(\upmu\)L of the wine was introduced into 50 mL volumetric flasks, then 2.5 mL of Folin-Ciocalteu reagent diluted with demineralized water (1:2 \(v/v\)) was added followed by addition of 5 mL Na\({}_{2}\)CO\({}_{3}\) (20% water solution). Mixtures were vigorously shaken and filled up with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variety**} & \multicolumn{1}{c}{**Sample**} & \multirow{2}{*}{**Wine Type**} & \multirow{2}{*}{**Wine Region**} & \multirow{2}{*}{**Winery**} \\  & **Abbreviation** & & & \\ \hline Chardonnay & CH 1 & dry & Juznoslovenska & Wine Manufacturer A \\ Chardonnay & CH 4 & dry & Stredoslovenska & Wine Manufacturer B \\ Chardonnay & CH 7 & dry & Malokarpatska & Wine Manufacturer C \\ Chardonnay & CH 10 & dry & Nitrianska & Wine Manufacturer D \\ Pinot Blanc & PB 2 & dry & Juznoslovenska & Wine Manufacturer A \\ Pinot Blanc & PB 5 & dry & Stredoslovenska & Wine Manufacturer B \\ Pinot Blanc & PB 8 & semi-dry & Malokarpatska & Wine Manufacturer C \\ Pinot Blanc & PB 11 & semi-dry & Nitrianska & Wine Manufacturer D \\ Pinot Gris & PG 3 & dry & Juznoslovenska & Wine Manufacturer A \\ Pinot Gris & PG 6 & dry & Stredoslovenska & Wine Manufacturer B \\ Pinot Gris & PG 9 & semi-dry & Malokarpatska & Wine Manufacturer C \\ Pinot Gris & PG 12 & dry & Nitrianska & Wine Manufacturer D \\ \hline \hline \end{tabular} Note: CH—chardonnay, PG—pinot gris, PB—pinot Blanc, numbers are in relation to wine regions: 1-3 3 \(\upmu\)moslovenska, 4–6 Strednoslovenska, 7–9 Malokarpatska, 10–12 Nitrianska wine region.  
\end{table}
Table 1: Wine samples.  
demineralized water to 50 mL volume. The blue-coloured complex was developed in 2 h at laboratory temperature. Each wine sample was used for preparation of triplicates. Measurement was performed with use of the UV/Vis spectrophotometer at 765 nm.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ASK & amplitude-shift keying \\ AMI & a priori mutual information \\ APM & a posteriori mutual information \\ AWGN & additive white Gaussian noise \\ BER & bit-error rate \\ BICM & bit-interleaved coded modulation \\ BSC & binary symmetric channel \\ CN & check nodes \\ DM & distribution matcher \\ EMI & extrinsic mutual information \\ GS & geometric shaping \\ JSCC & joint source-channel coding \\ JSCM & joint source-channel coded modulation \\ LDPC & low-density parity-check \\ MI & mutual information \\ PAS & probabilistic amplitude shaping \\ PS & probabilistic shaping \\ PEXIT & protograph extrinsic information transfer \\ PEG & progressive edge growth \\ QAM & quadrature amplitude modulation \\ SNR & signal-to-noise ratio \\ VN & variable node \\ \end{tabular}
# Biomass Dynamics in a Fragment of Brazilian Tropical Forest (Caatinga) over Consecutive Dry Years
## 2 Materials and Methods
### Climate Variables  
A time series of daily rainfall and temperature data for 2008 to 2018 was used. The data were obtained from the Meteorological Database for Teaching and Research (BDMEP/INMET) [24]. The annual climate normal (1974-2019) was calculated for the series of data available in the INMET system, in addition to consecutive dry days (CDDs) during the rainy season (December to May) from 2008 to 2018. A dry spell is considered as the occurrence of five CDDs with a rainfall of \(<\)2 mm [25]. The dry spells were later separated into classes: C1 (5-10 CDDs), C2 (11-20 CDDs) and C3 (\(>\)20 CDDs).  
The Maximum Cumulative Water Deficit (MCWD) was calculated following the methodology described in [PERSON] et al. [26]. The MCWD is an annual estimate of the water deficit that considers both the duration and intensity of the dry season based solely on climate variables, i.e., the properties of the soil were not considered. The water year starting in December, at the start of the rainy season, was used to calculate the MCWD, as the beginning of the water year does not necessarily follow the calendar year [27]. Calculation of the water deficit used the potential evapotranspiration (PET), estimated as a function of temperature based on the Thorntwaite equation; whereas, [PERSON] et al. [26] determined a value derived from the mean evapotranspiration in different locations and seasons in the Amazon, of 100 mm mo\({}^{-1}\).
## 3 Fiber-Reinforced Concrete (FRC)  
Regardless of numerous benefits, concrete has some weaknesses including low energy-absorption capacity and low tensile resistance resulting in spalling, cracking, and lower lifespan of the structures [76; 77]. Recently, using macro fibers as concrete reinforcement has become prevalent to present a solution for enhancing the mechanical properties of the OPC concrete [78; 79; 80; 5]. The use of fiber in concrete matrix has less or no impact on concrete pre-cracking behavior. However, fibers improve post-cracking response, control the brittle fracture process, provide strength, and offer post-cracking toughness through the advantage of reliable deformation behavior and post-cracking strength [81; 82; 83]. Furthermore, incorporating fibers in cementitious substances has become important to some extent, because of reducing the shrinkage cracking, which is able to enhance the material durability. The preventing of shrinkage cracks may contribute to diminishing the material permeability [84]. When considering the structural behavior of a material, it is important to consider both strength and toughness together. The capacity of the material to absorb energy in the plastic range is considered to be the toughness index [83]. The propagation of the crack is inhibited by adding fibers to the concrete matrix, resulting in improvement of energy-absorption capacity [85; 86; 87]. The range of the enhancement in the mechanical properties and toughness index of concrete mixture depends on the fiber length and its amount [88; 89; 90; 91].  
Toughness index of FRC is increased by reduction in fiber cross-sectional area. Higher specific surface area is achieved by reduction in the diameter of the fibers, leading to higher contact areas between fibers and the matrix. Moreover, it causes a significant increment in FRC energy absorption compared to plain concrete [92]. In the mechanisms of energy-absorption in FRC, de-bonding and fiber pull-out are features related to the fiber surface area. Therefore, fiber-specific surface (FSS), the fiber content (FC), and the reinforcement area are relevant parameters to be investigated to find their effect on FRC properties. The implication is that the length of individual fibers influences computing the reinforcement area. To calculate these parameters, many analytical expressions are mentioned in reference [81].  
[PERSON] et al. stated that the splitting tensile, compressive, and flexural strengths reached their maximum with 0.36% fiber volume fraction in comparison to plain concrete. The concrete strengths started to decrease due to high-volume fiber interface with the cohesiveness of the concrete matrix causing difficulty in concrete compaction lowering its workability [93].  
According to past scientific findings, short fiber reinforcement in concrete could enhance the properties of plain concrete in the appropriate fiber volume fraction. Presently, regarding financial problems and environmental concerns, natural fibers are fascinating for industrial applications. Therefore, the natural (bio) fiber-reinforced concrete (NFRC) is an attractive subject for further research.
## 2 Results and Discussion
### Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)
#### 2.5.3 Variable Importance in Projection (VIP) Analysis
However, due to their volatility, efficient encapsulation is necessary to minimize loss. The size and porosity of the microcapsules also play a significant role; larger microcapsules are better at encapsulating more material and reducing the release rate of volatile organic compounds. This is particularly important for highly volatile esters and siloxanes, which are prone to rapid evaporation. Overall, the choice of wall material is critical in determining the effectiveness of aroma compound retention, as it must provide a balance of hydrophilic and hydrophobic interactions, barrier properties, and encapsulation efficiency to preserve the desired aroma profile.
## 8 Conclusions
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline  & **Level of Complexity** & **Reproducibility** & **Design Space Exploration** & **Robustness** & **Scalability** \\ \hline Convergence to optimum value & **MR:** an increase in complexity & **MR:** an increase in complexity can an at values & **MR:** exploration is required to move from initial point towards optimum value & **WR:** compromise optimality for stability \\ \hline Low computational time & **MR:** some complexity (e.g., surrogates, use of parallel approaches) aims at finding solutions faster & **SR:** exploring many solutions increases run time & **WR:** checking for robustness requires time \\ \hline Generation of new design alternatives & **WR:** complexity in the solution creation process aims at creating diverse solutions (e.g., mutation and recombination operators) & **SR:** exploration of the design space is required to find alternative designs & **WR:** having different high quality solutions increases the probability of obtaining a high quality robust solution \\ \hline \hline Base of use & **MR:** increased complexity requires more effort in setting up the algorithms (e.g., more parameters have to be set) & & & \\ \hline Algorithm customization & **MR:** expert knowledge is required to decide how the algorithms complexity transfers to different problems & & & \\ \hline Applicability to whole product range & & & & \\ \hline Low effort for integrating algorithm operational system & **MR:** some types of resources, requires additional resources, tools, and software & & & \\ \hline Comprehensibility & **WR:** some complexity, i.e., using approximate values, can hinder comprehensibility & **WR:** a low reproducibility hinders comprehensibility \\ \hline \end{tabular}
\end{table} 
Table 14: Part 2 of relationships between end-user requirements and comparison criteria for the Decision Matrix. The used relationships are: **SR**, strong relationship; **MR**, moderate relationship; **WR**, weak relationship.
# Game Difficulty Prediction Based on Facial Cues and Game Performance
## 4 Experimental Results
### Comparison with Different Data Modalities on the Three-Class Task  
From the above results on the two-class and three-class tasks, we observe that the latter task is more difficult. Therefore, we used the three-class task to conduct an extra experiment to verify the differences in classification performance from different input data modalities. Since MLP demonstrated better performance on these tasks, we used it as the classifier.  
First, we compare four types of single input data: FE, GD, HP, and GP, where FE indicates the intensity of facial expressions, GD denotes gaze directions, HP is stands for head poses, and GP refers to game performance. We only adjusted the number of neurons in the input layer and kept other parameters unchanged. Rows 1-4 in Figure 9 show the results of MLP with different single inputs. We find that GP is more accurate than the other data modalities. Therefore, in the next step, we use GP as the baseline feature and integrate it with other modalities. Then, three multiple fusion datasets were compared: GP + FE, GP + FP + GD, and GP + FE + GD + HP. Rows 5-7 in Figure 9 report the results of MLP with different multiple inputs. We observe that the prediction accuracy increases as the feature dimension rises. Fusing these four modalities achieves the best results across all four metrics. The experimental results demonstrate the effectiveness of combining multiple data modalities to predict game difficulties.  
Figure 8: Confusion matrices of MLP, AB, and GB on the three-class classification task.  
Figure 7: Performance comparison on the 3-class classification task.
# Designing Parallel Adaptive Laplacian Smoothing for Improving Tetrahedral Mesh Quality on the GPU
## 6 Conclusions  
In this paper, by exploiting the parallelism features of the GPU, we specifically designed a parallel adaptive Laplacian smoothing algorithm for improving the quality of large-scale tetrahedral mesh models. In the proposed parallel adaptive Laplacian smoothing, we added a judgment of tetrahedral mesh quality in the Laplacian smoothing process. The adaptive algorithm avoids the shortcoming of the ordinary Laplacian algorithm to create potential invalid elements in the concave area. In this paper, we conducted five groups of comparative experimental tests and compared and analyzed the operational performance of the two iteration methods and two data layouts. Moreover, we analyzed the efficiency of the iteration of a kernel with a single block and a kernel with multiple blocks. We found that (1) the proposed algorithm is up to 23 times faster than serial algorithms; (2) the accuracy of the tetrahedral mesh is improved after the proposed adaptive Laplacian smoothing; and (3) the proposed adaptive Laplacian mesh smoothing has broader applicability than ordinary Laplacian smoothing. This indicates that the proposed parallel algorithm can be applied to improve the mesh quality in large-scale and complicated application scenarios.  
Conceptualization, N.X., L.X., G.M., Y.S.; Methodology, N.X., L.X., G.M., Y.S.; Writing--Original Draft Preparation, N.X., G.M.; Writing--Review & Editing, N.X., G.M., L.X., Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was jointly supported by the National Natural Science Foundation of China (Grant No. 11602235), the Fundamental Research Funds for China Central Universities (2652018091), and Major Program of Science and Technology of Xinjiang Production and Construction Corps (2020 AA002).  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
The authors would like to thank the editor and the reviewers for their contributions.  
The authors declare no conflict of interest.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 6 Conclusions  
Energy conservation is one of the prime requirements in protocol designing of UWSNs because of limited resources. Energy holes, created by random distribution or death of nodes, increase the data packet's drop rate and decrease the network lifetime. In this paper, we have proposed FLMPC-One and FLMPC-Two in order to achieve reliability and energy efficiency. Both schemes direct multiple copies of a data through various data routes towards the surface gateways where they are combined to generate an original data packet. Deep water faces less noises; thus, both proposed schemes (FLMPC-One and FLMPC-Two) have not generated the binary tree from cross nodes deployed in deep water. This is only done to reduce number of duplicate packets at the destination in order to save nodes battery and prolong the network operations. However, in shallow water, the noise increases, therefore cross nodes generate binary tree and direct multiple copies towards the destination which ensured the reliable delivery of data packets. FLMPC-One and FLMPC-Two avoid the void hole problem through a  
Figure 13: Required Packer Error Ratio.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline
**Protocol** & **Achievements** & **Figure** & **Compromised Parameter** & **Figure** \\ \hline LMPC & Reliability and delay & Figure 10 & Energy consumption & Figure 11a,b \\  & & active nodes and PRR & Figure 9, Figure 12 \\ \hline FLMPC-One & Reliability, energy consumption & Figure 11a,b & Delay & Figure 10 \\  & active nodes and PRR & Figure 9, Figure 12 & & \\ \hline \end{tabular}
\end{table}
Table 5: Performance Tradeoffs in Proposed and Compared Schemes.  
proactive approach and find an alternative route towards the destination making sure that the selected path has less noises. The simulation results show that our proposed schemes achieve energy efficiency, high PRR and reduce the number of active nodes which increases the network lifetime by paying the cost of delay.  
The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group NO (RG-1438-034).  
[PERSON], [PERSON], [PERSON] and [PERSON] proposed and implemented (validated) the main idea. [PERSON] and [PERSON] wrote, organized and refined rest of the manuscript.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Scanning Electron Microscope Imaging (SEM) and Energy-Dispersive X-ray (EDX) Analysis  
The sample surfaces were first sputter-coated with gold/palladium particles (Leica EM ACE600C, Leica Microsystems Inc., Concord, ON, Canada) to form a film of approximately 6 nm. The surfaces were then examined with an SEM device (Hitachi Regulus 8230 FE-SEM, Ibaraki, Japan) at \(\times\)5000 magnification, and images were recorded. The device was operated at 5 kV. Subsequently, the representation of elemental presence in the composites was analyzed using energy-dispersive X-ray spectroscopy (EDX) (X-Max 20, Oxford Instruments, Abingdon, UK).
## 3 Case Study
### Data Preprocessing
#### 3.2.1 Data Segmentation  
In tunnel construction, the length of one ring is not completed in a single excavation. One ring is usually cut into several segments due to the capacity of slag discharge and grouting. When the programmable logic controller (PLC) stores the effective drilling cycles, it also records a large amount of invalid data that are not related to the tunneling process, as shown in Figure 7a. The first step of data preprocessing was to eliminate invalid data and filter out valid data.  
In a drilling cycle, EPB relies on the propulsion system and the cutterhead system to roll, cut, and spall the soil layer continuously. The whole process can be summarized into free running stage Figure 7b-1, loading stage Figure 7b-4, boring stage Figure 7b-2, and unloading stage Figure 7b-3. These stages have the following typical characteristics:  
* There is a clear loading section with a group of ascending rotation speeds and advance rates set by the machine operators.
* There is a clear shut-down period at which the rotation speed and advance rate are set by the machine operators to zero, accompanied by the sudden drawdown of the values of cutterhead thrust and torque to zeros.
* The hydraulic cylinder limits the maximum possible advancement of a complete boring cycle, which is 1.5 m for the machine currently under investigation.
* After the unloading period, there is a reasonably long period of time with zero records of advance rate. Other readings of the mechanical parameters are also virtually zero. This indicates that the machine is silent, and the machine operators are doing the  
Figure 6: Histogram of SMA parameters: (**a**) horizontal deviation of the shield tail (HDST); (**b**) vertical deviation of the shield tail (VDST); (**c**) horizontal deviation of the shield head (HDSH); (**d**) vertical deviation of the shield head (VDSH).  
necessary preparatory work for the next drilling cycle, such as slagging and segment assembly.  
Based on the above characteristics, an algorithm for automatic segmentation of EPB data was developed, and the segmentation effect is shown in Figure 7b. More segmentation results can be found at [[https://github.com/ChenZuyuIWHR/TBM-processing](https://github.com/ChenZuyuIWHR/TBM-processing)]([https://github.com/ChenZuyuIWHR/TBM-processing](https://github.com/ChenZuyuIWHR/TBM-processing)). (accessed on 14 April 2021). It is worth noting that the advance rate at the free-running and unloading stage was zero. Therefore, the subsequent AI algorithms only used the data of the loading and boring stage.
# Fractal Cardioid Slot Antenna for Super Wideband Applications
## 4 Conclusions  
The original design of a broadband slot antenna is shown, which is suitable for mass and cheap production and can be used either as a separate device or integrated with other subdevices of a telecommunication system in the frequency range of 1.8-30 GHz (16:1). A parametric analysis was performed to obtain the widest possible impedance bandwidth in order to avoid a lossy matching circuit between the antenna and detector in energy harvesting systems. Simulation results show that, by using fractal geometry and the cardioid shape, the desired impedance matching, gain and efficiency are all achieved in ultra-wideband frequency ranges.  
The antenna has small dimensions at only 35 mm \(\times\) 47 mm (0.21\(\lambda\)\(\times\) 0.285\(\lambda\)) and exhibits a maximum BDR (bandwidth dimension ratio) of 3062 compared to the ones published in the literature so far. A larger BDR indicates that the designed antenna is smaller in dimension and wider in bandwidth. Simulated and measured results show that the antenna has a reflection coefficient, S11, below \(-\)10 dB in the entire 1.8 GHz to 30 GHz frequency range. The antenna exhibits gains up to 5 dBi. The measured radiation patterns on frequencies 1.8, 2.2, 2.4, 3.4, 5.8 and 10 GHz are presented.  
Figure 13: Measured and simulated radiation patterns in the E-plane and H-plane.  
The antenna is uniplanar and does not require any complicated photolithography procedures during fabrication. It is realized on a cheap FR-4 substrate and is fed by a CPW line; thus, it is very suitable for integration with diodes and transistors. This is very important since it is primarily intended for the broadband harvesting of electromagnetic energy and its conversion in direct currents for biasing low-power sensor networks. Antenna impedances have very small variations from 28 to 87 \(\Omega\), which is very convenient for use in rectennas without matching circuits. In addition to this basic purpose, the antenna can be used as a transceiver antenna for 3G, LTE, 5G, WLAN, RFID, Bluetooth, ISM, satellite communication and radars.  
Conceptualization, L.L. and B.J.; formal analysis, L.L., B.J., A.J. and V.R.; validation, L.L. and M.R.; writing--original draft preparation, L.L., B.J., A.J. and V.R.; writing--review and editing, L.L. and B.J. 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.