esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Apple Fruit Processing and Juice Analysis  
Whole Gravenstein apples were rinsed and ground using an electric fruit mill, and then pressed into a juice with a hand press. Fruit was washed with dechlorinated tap water and ground and pressed at room temperature. One week prior to total sample processing, a small preliminary sample of Gravenstein apples was juiced and analyzed for tannin concentration (0.0467\(\%\)\(\pm\) 0.4), tittatable acidity (0.770\(\%\)\(\pm\) 0.008 malic acid equivalents), specific gravity (1.049 \(\pm\) 0.005), and pH (3.28 \(\pm\) 0.02), as these are pre-fermentation parameters important to the cider-making process.  
Specific gravity and pH were also measured before sampling the apple juice for HS-SPME-GC-MS analysis. All measurements were conducted in triplicate. The limit of detection (LOD) and limit of quantitation (LOQ) values were calculated based on the ASBC method for low-level detection [18]. LOD is defined as LOD = \(\overline{x}_{B}+3\sigma\) and LOQ is defined as LOQ = \(\overline{x}_{B}+10\sigma\) (\(\overline{x}_{B}\) = mean signal of replicate reagent blanks, N = 10; and \(\sigma\) = standard deviation of the blank). The reported precision of our method is one standard deviation of the three trials.  
Tannin concentration and tittatable acidity were determined by the well-established Lowenthal permanganate titration and the malic acid titration, respectively [4]. The Lowenthal permanganate titration was carried out by adding 1 g of indigo carmine (Millipore Sigma, Saint Louis, MO, USA, 85\(\%\)) and 50 mL concentrated sulfuric acid (Fisher Scientific, Waltham, MA, USA, 98.0\(\%\)) to 1000 mL of deionized water. A total of 5 mL of the indigo carmine solution and 1 mL of Gravenstein apple juice were then added to 150 mL of deionized water. An indicator blank was prepared by combining 5 mL indigo carmine solution and 150 mL of deionized water. These solutions were tittated with 0.005 M potassium permanganate (Millipore Sigma, ACS Reagent, \(\geq\)99.0\(\%\)) until a golden yellow end point was reached. The malic acid titration was performed by diluting 25 mL of Gravenstein apple juice to 100 mL with deionized water and titrating with 0.2 M NaOH (Fisher Scientific, \(\geq\)97.0\(\%\)) until a visible end point was reached. Phenol red indicator (Millipore Sigma, ACS reagent) was added to observe the color change. The concentration of malic acid equivalents was calculated according to the published expression by [PERSON] et al. [5].  
A Vernier PH-BTA pH probe was used throughout this study to measure the pH of juice and cider. Before each measurement, it was calibrated with buffers of known pH. Specific gravity was determined using an EasyDens portable density meter from [PERSON]. In addition to the juice analysis, both measurements were taken with every sample throughout fermentation.
# Design and Fabrication Technology of Metal Mirrors Based on Additive Manufacturing: A Review
## 2 Additive Manufacturing Technology for Metal Mirrors
### Additive Manufacturing Metal Mirror Fabricating Considerations  
#### 2.2.1 Material Type  
With the continuous development of AM technologies, printing materials have become increasingly abundant. At present, there are more than 300 kinds of materials for AM, mainly polymers, metals, ceramics, and biomaterials [43]. Metal materials that are frequently used are aluminum alloys, titanium alloys, nickel-based superalloys, stainless steel, and iron alloys. However, the representative materials are still Al alloy and Ti alloy for metal mirrors, which dominate 3D-printed metal mirrors. In 2015, Herzog fabricated AlSi\({}_{10}\)Mg and Ti6 Al4V mirror blanks using SLM and EBM [30], respectively. Nevertheless, after optical post-processing, the mirror surface of the final Ti mirror was far from the requirements. The details of this are further described in Section 3. Therefore, aluminum alloy is the material which more widely used to fabricate AM metal mirrors.  
At present, the aluminum alloys used in manufacturing mirrors are mainly Al-Si alloy and Al-Mg-Si alloy, including AlSi\({}_{7}\)Mg\({}_{0.6}\), AlSi\({}_{10}\)Mg, AlSi\({}_{12}\), and AlSi\({}_{140}\). The addition of silicon improves the fluidity of aluminum and reduces its melting temperature. Adding magnesium to aluminum can not only improve its strength but also improve its strain-hardening ability. AlSi\({}_{10}\)Mg is the Al alloy which most intensively investigated, and the technology of printing AlSi\({}_{10}\)Mg by PBF is very mature. Table 4 provides the multiple parameters of PBFed AlSi\({}_{10}\)Mg and as-cast AlSi\({}_{10}\)Mg after the high pressure of die-cast (HPDC). The ultimate tensile strength and ductility values in additively manufactured AlSi\({}_{10}\)Mg are generally higher than or equal to as-cast and HPDC AlSi\({}_{10}\)Mg, which can be attributed to the fine microstructures seen in AM samples [28]. Therefore, the AlSi\({}_{10}\)Mg mirrors have excellent microstructure and mechanical properties. However, in engineering, the designers should consider the system requirements, prefabricating techniques, and post-treatment to make the best material choice.  
Noteworthily, the mismatch of CTE between AlSi\({}_{10}\)Mg and NiP is a problem for the use of AlSi\({}_{10}\)Mg for mirrors. The bimetallic bending effect is serious in the service environment with large temperature changes. The Institute of Fraunhofer (IOF) tested the AM mirror of AlSi\({}_{40}\), which can better improve temperature adaptability.
# RFID RSS Fingerprinting System for Wearable Human Activity Recognition
## 2 RSSI Model
### Proof of Concept  
The RSS fingerprinting method was performed through two distinct stages: the offline and the online stage. To illustrate the concept of RF-fingerprinting using the RSS of passive RFID, a key activity for all volunteer was considered. The offline mode is used to capture reference data sets for the known six patient positions. To achieve higher accuracy, two types of averaging were carried out, temporal averaging, where data samples were taken over a period of time and then averaged and spatial averaging, where several tags were mounted on different locations on the patients due to RSS variations. These averaging techniques help reduce the multipath effect on the power level of the received signal. In the offline mode, the dataset from the seven tag locations from six volunteers is highlighted in Figure 3. The sampled dataset is averaged to ameliorate fast fading effect. The sampled dataset was used to develop a reference vector of seven RSS datasets corresponding to each of the six patient positions. The sampled features in each dataset include a timestamp, RSSI and power (dBm) of the RFID tag. Table 2 highlights the averaged reference RSS datasets for the walking position (Walking_Ref) for the six volunteers.  
Table 2 highlights the computed RSS readings for walking for all six volunteers. It can be seen that the tag location_4 (i.e., back) has the highest RSS which indicates NLOS between the tags and the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Activity(Walking)** & **Head** & **Arm** & **Waist** & **Back** & **Thigh** & **Knee** & **Ankle** \\ \hline RSS (dBm) & \(-62.8\) & \(-64.3\) & \(-67.5\) & \(-70.9\) & \(-68.4\) & \(-66.8\) & \(-67.9\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Received signal strength (RSS) values for the Walking Ref position.  
Figure 3: RSS distribution for all seven tag locations on the patient body for walking position.  
RFID readers. Tag location_1 (head) readily possess the lowest RSS as there is increased LOS between the tags and the RFID reader, as shown in Figure 3.  
The maximum RSS variation for all tag locations is in the range of 8.1 dBm, which is indicative of the degree of NLOS and occlusion, especially from the furthest tag locations to the RFID readers. However, in the online mode, newly sampled RSS dataset for each for the six test patient positions was captured, analysed via MATLAB and compared with reference position database to validate the accuracy of the system to recognise each position in the testing dataset. The comparison technique employed is the Euclidean distance between testing and reference datasets. From existing works, it is established that the use of Euclidean distance is efficient in mapping an unknown characteristic to one of many stored known characteristics in RF-based systems [34]. Therefore, the Euclidean distance was used to quantitative map the unknown test position dataset with the most probable reference position dataset.
# Microencapsulation of Anthocyanins--Critical Review of Techniques and Wall Materials
## 2 Microencapsulation Techniques
### Physicochemical Methods
#### 2.3.4 Coacervation  
Most recently, due to the high loading capacity, low temperature operating requirements, reduced evaporation losses of volatile compounds and thermal degradation, andgreater competence in regulating the release rate of nutraceuticals, there is considerable interest in the coacervation technique among all the microencapsulation technologies.  
Coacervates are a spherical aggregate of organic macromolecules that form an inclusion that is held together by hydrophobic factors. Simple organic molecules may be selectively adsorbed from the surrounding medium due to the boundaries of the coacervates. The drop that forms the coacervate particle is frequently a liquid oil. The drops will have a shell of the hydrocolloid mixture surrounding them, which will set to form a solid or semipermeable shell under the right conditions. Since both entropy and enthalpy regulate the coacervate formation, a minimum temperature must be preserved [93]. Coacervation involves forming microscopic droplets of the coacervate phase in a stirred liquid phase, which typically results in an unstable colloidal dispersion with a tendency to coalescence. Crosslinking or mechanical gelation of the polymer in the coacervate process will retain and sustain droplets. Coacervation mechanisms are divided into two categories: basic coacervation and complex coacervation [94]. Simple coacervation is commonly used in medical technology to entrap drugs into microcapsules. There are various studies involving the application of coacervates for the microencapsulation of anthocyanins.  
In one of the studies, raspberry water extracts were microencapsulated using a double emulsion methodology prior to complex coacervation with gelatin and gum Arabic to reduce their anthocyanins' instability as water-soluble substances, especially under harsh processing and storage conditions. The findings demonstrated microcapsule thermostability [95]. The optimal condition microcapsules retained an intense red color over time, suggesting that the method used to conserve anthocyanins was successful. Regulated protein-polysaccharide interactions through complex coacervation may point to a way to improve their functional position as ingredients without requiring chemical or enzymatic changes.  
Another study involving coacervation was a study by [PERSON] et al. [96], who used blueberry- and purple-corn-derived anthocyanins to encapsulate particles based on hydrogels with alginate and pectin. A very high microencapsulation efficiency was demonstrated. At the same time, anthocyanins could be stored longer, thanks to this method.  
Like any method, coacervation also has some limitations, mainly related to the agglomeration of particles and particle size control. The second area of disadvantage is that, by their principle, the particles created by this method are very sensitive to pH and ionic strength, limiting their use in various matrices [70].
# Utilizing Different Machine Learning Techniques to Examine Speeding Violations
## 2 Materials and Methods  
The Fatality Analysis Reporting System (FARS) was used to collect information for this study for the years 2015-2019. The National Highway Traffic Safety Administration is responsible for maintaining this database system [29]. FARS is a United States census that provides the National Highway Traffic Safety Administration, Congress, and the American public with annual data on fatalities and injuries in motor vehicle traffic crashes. Specialists collect these data points from police reports, administrative files at the state level, and medical records. In addition, automated error checks and data monitoring ensure that data stay within acceptable ranges [30]. A total of 51,136 records were analyzed using the Waikato Environment for Knowledge Analysis (WEKA) 3.8.4 software [31]. Different variables related to the vehicle, driver, roadway, and environment have been investigated. Table 1 shows these factors and their categories. As shown in Figure 1, this study selected the top ten states in terms of crashes. These states are California, Florida, Georgia, Illinois, Michigan, North Carolina, Ohio, Pennsylvania, Tennessee, and Texas. CART, RF, and MLP are different machine learning algorithms used in this work. Classification and Regression Trees (CART) is a machine learning algorithm that explains how the target variable's values can be determined based on other data. It is a decision tree where each fork splits into a predictor variable, and each node ends with a prediction for the target variable. Random Forest (RF) is an ensemble learning-approach for classification, regression, and other tasks. It constructs many decision trees at training time and outputs the class, the mode of the classes (classification), or the mean estimation (regression) of the individual trees. The Multilayer Perceptron (MLP) is a feedforward ANN that generatesoutputs by passing input data via multiple layers of neurons. Next, what distinguishes the use of these algorithms will be explained in detail. CART is one of the machine learning techniques, consisting of nodes and branches similar to a tree. It starts with the root (parent) node, which contains all the data; this node starts splitting based on a selected variable according to splitting criteria or impurity func
# Robust Face Recognition Based on a New Supervised Kernel Subspace Learning Method
## 4 Biometrics Application: Results and Analysis
### Experimental Results with the Sheffield Face Database  
The Sheffield Face Database (SH.F) [51] includes a total of 20 individuals, with 564 images in which each subject is illustrated, including poses ranging from profile to forehead views. The format of all images is PGM, image size is approximately 220 \(\times\) 220 pixels in 256-bit grayscale. Figure 2 illustrates a one-subject sample with different poses from the Sheffield face multi view. In our experimentation, every image was resized to 112 \(\times\) 92 pixels [52]. The maximal rate of recognition of each method and the related dimension implemented in the Sheffield database are illustrated in Table 1. The best performance result among other methods is assigned in bold-text. In addition, in all experiments due to the large number of implementations, it was decided to select some training and testing images that were more challenging for the classification task, in order to evaluate the performance of each technique in these critical areas. Thus, considering the small training sample size problem (SSS), first we selected a small number of training samples and then some large numbers to evaluate the performance of our SKLDNE method accurately. \(Tn=1,2,3,4,5,6,7,8,15,16,17\) were the training sample numbers selected from each class. According to Table 1, three main conclusions can be drawn. First, SKLDNE significantly outperformed other methods (PCA, KPCA, UDP, LPP, LDA, DNE, and LDNE) over an extensive range of dimensionality for all different numbers of training and testing images, whether the training sample size was large or small. As can be observed, when the training sample number was small, SKLDNE clearly behaved more efficiently than all other recognition techniques, which proves the robustness of this method in the case of the small training sample size problem (SSS). Secondly, it can be seen that the recognition rates of all implementations were better when more training samples are used. Third, as shown in Figure 3, SKLDNE provided the best results at the lowest number of dimension values compared to its competitors. In addition, when dimensionality increased to about 30, the recognition accuracy of each method first increased rapidly and then became stable. The differences between the recognition rate of SKLDNE and other methods were emphasized when the training sample number was very small. However, for the larger training sample, the mentioned differences in the classification rate increased. For instance, in the training number of 1, 2, 3, SKLDNE achieved much better results than others. Moreover, for \(Tn=17\), SKLDNE reached 100% recognition rate, while the results were
# Characterization of GNSS Signals Tracked by the iGMAS Network Considering Recent BDS-3 Satellites
## 3 Methodology and Results
### Carrier-To-Noise Density Ratio
[5] and [PERSON] et al. [13]. For compatible signals (B11/B3I) of BDS-2 and BDS-3, the B1I signals of BDS-3 are approximately 3 dB-Hz more powerful than those of BDS-2 for all three stations, while the B3I signals show almost the same C/N\({}_{0}\) values for BDS-2 and BDS-3. The new signals of BDS-3 share the same frequencies as some of GPS and Galileo signals. Figure 3 shows C/N\({}_{0}\) values of these signals for BDS-3, GPS, and Galileo observed at ABJA station. For BIC/L1/E1 signals, the C/N\({}_{0}\) values of GPS L1 signals are about 3 dB-Hz higher compared with Galileo E1, and about 6 dB-Hz higher compared with the BDS-3 BIC signal. For B2a/L5/E5a signals, the differences of their C/N\({}_{0}\) values are within 2 dB-Hz. The GPS L5 signal is more powerful than the BDS-3 B2a signal, and the Galileo E5a signal is less powerful than the BDS-3 B2a. For B2b/E5b signals, the C/N\({}_{0}\) values of the Galileo E5b signal are 1-2 dB-Hz higher than the BDS-3 B2b signal. There are also some differences for reported C/N\({}_{0}\) values between stations (ABJA, CHU1) with the same receiver and antenna, which is inevitable as this is related to the environment, although signal strength is more affected by the hardware of the satellite and receiver. In addition, the observed C/N\({}_{0}\)  
Figure 2: Mean C/N\({}_{0}\) values as a function of satellite elevation for all the available signals of GPS and Galileo (**a–c**) and BDS-2 and BDS-3 (**d–f**) at station CHU1 equipped with GNSS_GGR receiver (**a,d**); station CANB equipped with CETC-54-GMR-4011 receiver (**b,e**); and station ICUK equipped with CETC-54-GMR-4016 receiver (**c,f**), respectively. values could be affected by the tracking mode, and thus are not necessarily representative of the power level actually received from the satellites.
## 5 Conclusions  
This study analyzed the relationship between vegetation change and terrestrial carbon uptake in the Dongting Lake basin. The greening trend in the Dongting Lake basin was characterized by an increasing trend of average NDVI (\(y=0.0048x-9.069\), \(R^{2}=0.89\), \(p<0.01\)). As a result, the regional terrestrial carbon uptake also showed an overall upward trend, with the average annual NEP increasing by \(y=1.8915x+295.09\) (\(R^{2}=0.23\), \(p<0.05\)). This means that the greening trend of vegetation in the basin has enhanced the carbon uptake level of vegetation in the past two decades. Moreover, the forests played an important role in the carbon uptake, with a higher contribution (65.39%) than the grassland (26.21%) and cultivated land (8.40%), indicating that optimal forest management of the basin is necessary in the future. Furthermore, we found that the magnitude of carbon uptake decreased under all development scenarios in future, while the reduction is minor under the ecological protection scenario (7.83 \(\times\) 10\({}^{7}\) t C) when compared to the natural growth scenario (7.80 \(\times\) 10\({}^{7}\) t C) and the economic development priority scenario (7.78 \(\times\) 10\({}^{7}\) t C), suggesting that carbon sinks in subtropical regions may be mitigated by optimal land management in the future to prevent a decrease in magnitude.  
The results of this study can support the ecological governance of the Dongting Lake basin and present a scientifically supported supplemental foundation for comprehending how vegetation carbon uptake varies and for achieving the region's \"carbon peaking and carbon neutrality\" goals. It helped to capture the overall spatial pattern and temporal trend of the terrestrial carbon uptake through satellite observation. The ground observations can be used to explore the impact mechanism of the regional carbon uptake in future research. Furthermore, it needed to carry out spatial optimization simulations of national land for regional carbon neutrality targets, which may provide a scientific decision-making basis for regional ecosystem conservation and promote regional sustainable development.  
Conceptualization, H.F., B.Z. and S.W. ([PERSON]); Methodology, S.W. ([PERSON]) and H.F.; Writing--Original Draft Preparation, S.W. ([PERSON]); Writing--Review and Editing, H.F., S.W. ([PERSON]) and Z.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (Grant No. 42071378), the Nature Science Foundation of Hunan Province (Grant No. 2020J3045), and the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Mins-try of Natural Resources (Grant No. KF-2022-07-021).  
The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
# A Survey of Vehicle to Everything (V2X) Testing
## 5 V2X Testing Methods  
At present, V2X technology is in the exploratory stage. The traffic safety problems and information security problems brought about by its application have not yet been verified, so testing is an important part of V2X. Function testing, performance testing, and communication protocol conformance testing are mainly used to meet the testing requirements for latency and reliability. Security protocol consistency, gateway testing, penetration testing, and accelerated testing can find vulnerabilities and potential risks and its applications to ensure its security. After laboratory testing, V2X applications must undergo field testing before they can be used commercially. Field testing is mainly used to evaluate the performance of V2X applications in a real environment and to meet the performance and function requirements in a large-scale environment.