esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Isolation and Characterization of the Major Polysaccharide from Grape Pomace
## 2 Materials and Methods
### Chemical Analysis of Purified GPP  
#### 2.6.1 UV-Vis Spectroscopy  
A total of 0.25 mg purified GPP was dissolved in 1.0 mL deionized water and examined with a DS-11 FX+ spectrophotometer (DeNovix, Wilmington, DE, USA). The light path was 1 cm, and the absorbance was recorded in the wavelength range of 190 nm to 600 nm with a step size of 1 nm.
## 2 Background
### Test Case Optimization
#### 2.2.1 Test Case minimization  
Test case minimization eliminates obsolete or redundant test cases from test suites. For example, if one test case identifies fault \(F_{1}\), and another identifies fault \(F_{2}\), they are combined into a new test case that identifies both faults \(F_{1}\) and \(F_{2}\). Such minimization not only reveals faults quickly but also ensures code coverage and reduces testing time.
# Design of Typhoon Detection Downcast Device Based on Four-Arm Helical Antenna Structure
## 2 Theoretical Studies  
### Four-Armed Helical Antenna Selection  
The receiving antenna is the key device of the satellite navigation system. The receiver antenna converts the received electromagnetic wave signals into electrical signals for subsequent analysis and calculation. The receiver antenna needs to meet the following requirements. First, the antenna must have the ability to receive incoming waves from the space of the upper hemisphere, especially the ability to receive electromagnetic waves with a low pitch angle; that is, the antenna should have good omnidirectionality. Secondly, the receiving antenna must have a circularly polarized radiation pattern. Unlike a linearly polarized antenna, which has constrained directional performance, a circularly polarized antenna can receive electromagnetic waves from any direction. Circularly polarized waves can also effectively resist rain and fog interference and reduce multipath reflections compared with linearly polarized waves [11].  
In the field of satellite navigation, the size and volume of traditional antennas must be relatively large in order to meet the needs of practical production and general use. The requirements for antennas are becoming more and more stringent, and antennas must be small and lightweight and have good circular polarization and wide-bandwidth electrical performance [12].  
The four-armed helical antenna was originally proposed by Prof. [PERSON]. [PERSON] in 1986. A four-armed spiral antenna generally consists of four identical parallel radiating metal arms, and the length of the radiating arms is an integer multiple of \(\lambda/4\). This antenna is generally a circularly polarized antenna. According to the principle of circularly polarized wave forming, the four arms of the the spiral antenna must flow through the amplitude of the same phase difference of \(90^{\circ}\) of the current. According to the phase difference between the feed port of the different directions of rotation, it can form a left-handed or right-handed circularly polarized wave. The radiation direction graph of this antenna is usually heart-shaped, so it has the characteristics of a wide beam and circular polarization and is suitable for use as a receiving antenna for satellite communications. In addition, the structure of the four-armed helical antenna can be printed with flexible material, making it easier to process. It also has high machining accuracy and results in a smaller antenna, which is preferable in engineering [13]. In conclusion, the four-armed helical antenna has high practical value and is worthy of further research.
## 3 Results and Discussion
### PSD Results
#### 3.1.2 Walnut PSD  
As noted earlier in this study, walnut is a highly rigid material that has been used in drilling fluids for decades. The analysis conducted on the walnut showed that the particle size range of the dry phase was from 3360 to 595 \(\upmu\)m. As shown in Figure 2, the overall performance of walnut with different carrier fluid systems showed that walnut performed with a very minimal size reduction in all fluid systems. Thus, the selection of the fluid type was not a critical issue that would influence the size degradation behavior of walnut.  
In Tables 5-7, a negative sign in the size degradation results represents the percentage of size reduction, and a positive sign represents the percentage of size expansion. Equation (1) was used to determine the average size degradation for each LCM with the different fluid systems.  
\[\text{Average size degradation }(\%)=\frac{\text{D}_{50}\ (\text{dry})-\text{D}_{50}\ (\text{wet})}{\text{D}_{50}\ (\text{dry})}\times 100 \tag{1}\]  
Figure 1: Dry PSD for walnut, graphite, and marble.  
Figure 2: Dry and wet PSD of walnut in the four carrier fluid systems with 10 and 30 min shearing intervals.  
Table 5 shows the detailed results of size degradation for walnut in all the tested carrier fluids. It was observed that the percentage average size degradation of walnut was a 4% reduction in the overall particle size distribution in all fluid systems. The 4% reduction is considered negligible for drilling fluid applications.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 7 Challenges/Future Directions of CR-LPWAN
### Upper Layer Challenges  
It is noted that CR is a major PHY layer solution to different communication networks including the LPWAN. Nevertheless, there are unique challenges that may limit the efficacy of CR-LPWAN systems, which we discuss as follows:
## 3 Saudi Dialect Identification Approach
### Character-Based Feature Extraction  
In this phase, the main aim was to convert each sentence in our dialect dataset into a character-based feature. As two different techniques were used for building the predicating models, each one had a different feature extraction method.  
For the deep learning, each sentence was represented by a numeric character sequence of vectors \(L\) in length, where \(L\) represented the maximum length of a sentence in our dataset, equal to 55. First, each character in the alphabet set was encoded in a one-hot representation of size \(1\times c\), where \(c\) was total number of characters in the alphabet set. Then, the sequence of characters for each sentence was transformed into a sequence of vectors.  
Two different alphabet sets were used in the experiments: the first set consisted of 30 characters (28 Arabic alphabet characters plus alhamza and the space), while the second set consisted of 37 characters, the same 30 in set one plus the 7 diacritics in the Arabic language, which are Tashdid, Fatha, Damma, Tanwin Damm, Kasra, Tanwin Kast, and Sukun.  
The experiment was run for each character set, where each sentence and its corresponding dialects were encoded into a sequence of vectors for the two fixed character sets, and a sequence of vectors of size 55 was obtained as the input layer.  
On the other hand, for classical machine learning, the TF-IDF was used to contract the character-based feature set from the dataset.  
TF-IDF combines two scores, the term frequency TF, which calculates the frequency of character n-grams in each sentence, and the inverse document frequency IDF, in order to reduce the weights of the character n-grams that are repeated frequently and increase the weights of character n-grams that are repeated very rarely. Therefore, TF-IDF is defined as follows:  
\[\text{TF-IDF(g, s, D)}=\text{TF(g, s)}\times\text{IDF(g, D)} \tag{1}\]  
Here, TF(g,s) calculates the number of times n-gram g appears in sentence s, and the IDF is defined as shown in Equation (2), where D is the total number of sentences in the dataset and df(g) is the number of sentences in which the character n-gram g appears in D.  
\[\text{IDF(g, D)}=\log\frac{|\text{D}|}{\text{df(g)}} \tag{2}\]
## 3 The Architecture of the Proposed E-BIOSARP  
The architecture of E-BIOSARP is based on BIOSARP but is further enhanced with a random generator based on a mathematical function [26], the same as that incorporated into SRTLD. The idea behind E-BIOSARP is that BIOSARP has better performance with respect to throughput with better network life, whereas the encryption mechanism of SRTLD provides communication security in terms of integrity and confidentiality. Under E-BIOSARP, the workflow of the encryption mechanism begins when a packet is to be forwarded to the next self-node. During forwarding, the BIOSARP routing mechanism, as shown in Figure 2, requests encryption from the Mathematical Encryption Function, which encrypts only some of the fields in the packet header (_Pkt_ID_, _D_ID_, and _S_ID_) rather than the payload or the complete packet, which has already been fragmented. The structures of the ready-to-receive (RTR) packet and the data packet header in E-BIOSARP are presented in [35]. As a result of encryption, if malicious nodes and/or adversaries capture a secure packet, they cannot read the packet's movement or its destination, which in turn ensures the privacy of its origin. The following assumptions were made while developing the encryption mechanism, which is based on a random key approach and dynamic mathematical computations:  
* Two master keys (_k1_ and _k_) generated with a pseudo-random function are injected into each sensor node while it is flashing its read-only memory (ROM). Here, _k1_ is a master key for the new node, and \(k\) is a master key for all nodes. * Before deployment, every node is set with a hard mathematical function and its reverse computation. The flow diagram of the encryption and decryption process is presented in [27]. In this paper, the state machine diagram is presented in Figure 2 and is explained in detail to elucidate every step and process at the hardware level. Whether the data arrive from the application layer or a data packet is received from a neighboring node, the BIOSARP routing state is called. If the data to be forwarded
# Acoustic Simulation for Performance Evaluation of Ultrasonic Ranging Systems
## 1 Introduction
In [20], Field II was successfully employed to evaluate the spatial coverage of a given transducer in terms of quantity and quality (e.g., amplitude and shape deformation) of the received signal. By means of this software, it is possible to calculate the acoustic pressure field at any point in the space by simulating transducers, signals, and wave propagation, including diffraction and attenuation, in combination with any desired processing technique. In addition, Field II allows us to freely change the shape and size of the transducer and the signal applied to the transducer to evaluate their effects in a trial-and-error design procedure. However, practical techniques for analyzing the effects of noise on the performance of an ultrasonic ranging system are still relatively unexplored. Additionally, it should be considered that acoustic noise varies according to the power level, type (e.g., continuous, impulsive, etc.) or specific power spectrum. In this work, the use of an acoustic simulation software is shown to evaluate the effects of the SNR variation on the overall ranging error of an example ranging technique quantitatively. The proposed approach provides the possibility to examine the acoustic field in time and space at each point of the region of interest as a function of the shape and size of the transducer and characteristics of signal emitted (e.g., bandwidth, shape, etc.). This allows us to easily test each algorithm dedicated to estimating the _TOA_ in various spatial positions and operating situations. The main novelty of this work lies in the innovative application of a simulator otherwise widely used to simulate and develop transducers and algorithms for medical ultrasounds, where the human body, mostly composed of water, is the typical means of propagation of acoustic waves, to evaluate an example of ranging system working in air. Useful information can be obtained from simulation results even if they are partially limited by the fact that phenomena caused by multipath propagation such as self-interference, typical of even a partially reflective environment, are not considered. A second limit is that the simulator calculates the propagation as if it occurred in free space without considering any obstacles and near-line-of-sight situations. The paper is structured as follows. Section 2 provides information about the simulation software and details about the simulation setup; Sections 3 and 4 show the simulation results and their discussion, respectively; and Section 5 draws the conclusions of the paper.
## 1 Introduction
It should be noted that the soil gas measurement was not a substitute for a conventional methodology, but served as a screening tool, allowing for the more effective use of conventional methods. Based on the soil gas screening, new groundwater sampling sites were selected for further exploration work to confirm these sources and identify sites intended for remediation. Although many studies and academic reports used soil gas screening to determine subsurface contamination by CLHCs and other VOCs, and to identify its main sources, they were mostly limited to pre-characterised source locations with the significant accumulation of contaminants (so-called hot spots) that were identified by a conventional groundwater sampling methodology. In this study, soil gas screening was used over a large area and at sites hundreds of meters away from the contamination source. Taking into account the advantages and disadvantages of soil gas screening, this method seems to be suitable for the studied area as the existing data showed that the groundwater contained contrasting concentrations of CLHCs spanning several orders of magnitude and, at the same time, the area has a relatively uniform rock environment with high permeability. In addition, the elucidation of the extent of groundwater contamination and its sources is of public interest due to permanent settlement in the south-western part of the study area, where residents are exposed to vapour intrusions from contaminated groundwater. In addition, the results of this study could be verified in other industrial areas with a similar type of contamination and geological settings. Therefore, the main objectives of this study could be summarised as follows: (i) to determine the extent of groundwater contamination with CLHCs using a conventional methodology and existing historical data at an industrial area (the former refrigerator manufacturer--Calex company, city of Zlate Moravce in western Slovakia); (ii) to apply soil gas screening to further refine the delineation of groundwater contamination and source identification, and to verify the suitability of its application by combining the results of soil gas screening with the results of a conventional methodology through spatial distribution map outputs and multivariate statistics; and last but not least, (iii) to assess human health risks from the exposure of residents to CLHCs in groundwater. This last objective is important because groundwater contamination with CLHCs has extended to the surrounding residential zones.
# Biosynthesis of Natural Rubber: Current State and Perspectives
## 3 Omics Analyses Provide New Insights into Natural Rubber Biosynthesis
### Transcriptome Analysis
brasiliensis_ PR107 treated with ethephon revealed that the upregulation of the key genes in the glycolytic pathway and Calvin cycle, instead of rubber biosynthesis, might be responsible for ethylene-induced latex production in rubber trees [112]. Multiple genes involved in stress responses and secondary metabolites biosynthesis exhibit high expression levels in _E. ulmoides_[5]. Libraries of stem tissues were established and major latex protein (MLP)-encoding and metallothione-encoding genes were found to be the large parts which may have a protective role and required for homeostasis in stem tissues [6]. MEP and MVA pathway genes and rubber particle membrane protein-encoding genes were identified in _E. ulmoides_. Among them, five _trans_-isoprenyl diphosphate synthase (EuTIDS/EuFPS)-encoding genes were obtained with EuTIDS2 and 4 could complement FPP synthase-deficient in yeast while EuTIDS1, 3 and 5 were supposed to be the long-chain _trans_-polyprenyl diphosphate synthases [6]. For _P. argentatum_, because rubber synthesis is associated with the moderately cold night temperatures, transcriptome of bark tissue of the cold-acclimated guayule was investigated [64]. Stress-related protein-encoding genes were found to be the vast majority of ESTs, while transcripts-encoding proteins associated with rubber particles (such as CPTs and SRPPs) or the isoprenoid pathway enzymes that make the precursors for rubber biosynthesis (MVA enzymes and FPS) were not enriched in the cold-acclimated guayule, indicating post-translational modifications instead of gene expression control is more important for regulating enzymatic activity of the rubber transferase complex in guayule [64]. In _T. kok-saghyz_, MVA pathway key genes such as _TkHMGR1_ and _TkHMGR2_ and rubber elongation-related genes (_TkCPT1_, _TkCPT2_, _TkCPT1_, and _TkSRPPs_) have high expression levels in latex and roots, suggesting that these genes may play critical roles in natural rubber biosynthesis [11]. [PERSON] et al. reported the comparative transcriptome analysis of high and low rubber-yielding _T. kok-saghyz_ genotypes and found 21,036 SNPs and 158 significantly differentially expressed transcripts [113]. However, the rubber biosynthesis-related genes, such as _CPTs_, _SRPPs_ and _REFs_, were not present in the differentially expressed transcripts, implying that other regulatory mechanisms or factors may be involved in rubber yield regulation in _T. kok-saghyz_. [PERSON] et al. performed the transcriptome analysis of MeJA-induced _T. kok-saghyz_ seedling and found expression of _HMGR_, _FPS_, _IDI_, _GGPS_, _REF_/_SRPP_ and certain transcription factors-encoding genes were induced by MeJA treatment [114]. _HMGR_ expression was upregulated in _DREB_-overexpressing lines, suggesting JA regulates secondary metabolism and rubber biosynthesis through the interaction of JA-related TF with the genes required for rubber biosynthesis [114].
# A Comprehensive Review of Computer Vision in Sports:
## 6 GPU-Based Work Stations and Embedded Platforms
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **GPU-Based** & **Embedded** & **Problem Statement** & **Performance Measures** & **Result** \\ \hline
[29] & NVidia Titan X & - & Event classifications in basketball videos. & Average Accuracy & 58.10\% \\ \hline
[38] & NVIDIA GTX & - & \
\begin{tabular}{c} Basketball trajectory \\ prediction based on \\ real data and \\ generating new \\ trajectory samples. \\ \end{tabular} & Measured in terms of \\  & 960 & - &
\begin{tabular}{c} AUC \\ \end{tabular} & 91.00\% \\ \hline
[282] & - & FPGA & Recognizing swimming &
\begin{tabular}{c} The result shows the \\ three-level \\ identification system \\ in Average, \\ Minimum and \\ Maximum offset. \\ \end{tabular} & 4.14\%, 2.16\%, 5.77\%. \\ \hline
[198] & - & - & - & Recall and Specificity & 85\% and 96.6\% \\ \hline
[33] & NVidia &
\begin{tabular}{c} Tracking ball \\ movements and \\ classification of players \\ in a basketball game \\ \end{tabular} & Precision and Recall & 74.3\% and 89.8\% \\ \hline
[282] & \multirow{4}{*}{FPGA} & Ball detection and \\  & &
\begin{tabular}{c} tracking to reconstruct \\ trajectories in \\ basketball. \\ \end{tabular} & Accuracy & \textgreater{}90\% \\ \hline
[43] & NVidia GTX & - &
\begin{tabular}{c} Analyzing the behavior \\ of the player. \\ \end{tabular} & Accuracy & 83\% \\ \hline
[283] & - & FPGA & \begin{tabular}{c} Detecting the \\ movement of the ball in \\ basketball. \\ \end{tabular} & \
\begin{tabular}{c} Average rate vs \\ Frame range \\ \end{tabular} &
\begin{tabular}{c} Varies from \\ 12 to 100\% \\ for different \\ frame \\ \end{tabular} \\ \hline
[215] & NVidia GTX & - & \
\begin{tabular}{c} Individual player \\ tracking in sports \\ events. \\ \end{tabular} &
\begin{tabular}{c} Achieved an Area \\ Under Curve (AUC) \\ \end{tabular} & 66\% \\ \hline
[88] & NVidia & - & Action recognition in \\
[88] & GeForce GTX & - &
\begin{tabular}{c} soccer \\ \end{tabular} & 52.80\% \\ \hline
[284] & - & FPGA &
\begin{tabular}{c} Movement \\ classification in \\ basketball based on \\ Virtual Reality \\ Technology to improve \\ basketball coaching. \\ \end{tabular} & Accuracy & 93.50\% \\ \hline
[62] & NVidia GTX & - &
\begin{tabular}{c} Detection and tracking \\ of the ball in soccer \\ videos. \\ \end{tabular} & Accuracy & 87.45\% \\ \hline
[285] & - & FPGA &
\begin{tabular}{c} Action recognition \\ based on arm \\ movement in \\ basketball. \\ \end{tabular} & Accuracy & 92.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance of various studies on hardware platforms.
# Effect of Preheating Treatment before Defatting on the Flavor Quality of Skim Milk
## 2 Results and Discussion
### PCA and CA of the Volatile Compounds in the RSM, PSM, and USM  
Volatile compounds were the important indicator of the flavor quality of skim milk. In our previous study, we did not evaluate the effect of volatile flavor compounds on the flavor quality of skim milk [20]. In this study, the effects of volatile compounds on the flavor of different skim milk samples were further investigated based on the GC-MS data. PCA and CA were used to express the correlation between volatile compounds and the three samples. The results in Figure 5a indicate that the first factor (PC1) presented a positive correlation with hexanoic acid, octanoic acid, dodecane, tetradecane, 2-nonanone, 2-undecanone, 2-ethyl-1-hexanol, and tetrahydro-6-pentyl-2H-pyran-2-one (6-pentyloxan-2-one). The volatiles negatively correlated to PC1 included naphthalene, 2-methylnaphthalene, nonanal, decanal, 2-heptanone, and nonanoic acid. It could deduce that the acids, alkanes, ketones, naphthalene and aldehydes significantly distinguish the flavor of the three skim milk samples. Hexanoic acid, octanoic acid, naphthalene, 2-methylnaphthalene, dodecane, and tetradecane were positively correlated to PC2, whereas nonanal, decanal, 2-nonanone, 2-undecanone, 2-heptanone, nonanoic acid, 2-ethyl-1-hexanol, and tetrahydro-6-pentyl-2H-pyran-2-one were negatively correlated. It showed that the acids, naphthalene, ketones and aldehydes might distinguish the flavor of the three skim milk samples. From Table 2, we can observe that acids, ketones, and alkanes were mainly found in the PSM and USM, and naphthalene was only found in the RSM. This may be the main reason for the change in aroma and flavor of the skim milk after the preheating treatment.  
As shown in Figure 5b, cluster analysis was applied to show the comparability and dissimilarity of the volatile flavor compounds of the three skim milk samples according to the Euclidean distance [36]. The results showed that five clusters were identified, indicating that volatile flavor compounds could provide a better alternative for difference analysis of the three skim milk samples. According to the principle of cluster analysis, the greater the Euclidean distance, the greater the difference between the samples. Therefore, the acids and ketones may be key flavor compounds that differentiate the three  
Figure 4: Heat map explains the different concentrations of the volatiles in the three kinds of skim milk samples. (Red represents a high concentration of compounds; white represents a medium concentration of compounds; blue represents a low concentration of compounds.)skim milk samples. Notably, the concentrations of acids and ketones in the USM were significantly higher than the other two samples (\(p<0.05\), Table 2). In addition, the aldehydes and alkanes had an influence on the flavor difference of the three samples, whereas the other compounds appeared to have no effect on the flavor difference. It could be attributed to the difference in concentrations and the perception threshold.