esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Data-Collection, Processing, and Display Software  
A very important and unique component of our system is the CALMIT Data Acquisition Program (CDAPTM), developed to support field spectral data collection by operating and collecting data from numerous types of hyperspectral field radiometers and ancillary instruments such as pyranometers, GPS, _etc._ An outstanding characteristic of the CDAP software is that it provides for immediate viewing of acquired spectra and ancillary data. Thus, field personnel can assess the quality of their data acquisitions, as well as crop status, before proceeding to other locations in a field.  
The program also performs post-processing on collected data to create tab-delimited files containing spectra, calculated reflectance, and ancillary instrument data that may be easily imported into spreadsheet software, such as Excel, or into special purpose software written in virtually any programming language. This facilitates the near-real time monitoring of key biophysical characteristics such as fraction of green vegetation (VF), fraction of absorbed photosynthetically active radiation (_f_APAR), green leaf area index (GLAI), and biomass, among others. To better understand this software, the following paragraphs describe the phases and modes of using CDAP.
## 5 Simulation Results and Discussions
### Tracking Control with Acoustic Localization
#### 5.1.2 Tracking Control Results without Prediction Biased Errors
Figure 8 shows the solver times of the proposed tracking controller. Mostly throughout the simulation, the solver times are between 10 ms to 20 ms, and are smaller than the system control sampling time \(T_{\text{s}}^{\text{c}}=0.5\) s, which indicates the potential of the proposed algorithm for real-time applications.
## 3 Results and Discussion
### Total Flavonoid Content  
Flavonoids are a single group of phenolic compounds with a high antioxidant capacity, extensively distributed in plants [26]. Coffee is considered one of the primary sources of dietary flavonoids in adults [33; 34]. In the espresso, the content of total flavonoids was significantly higher at the Medium and Moderately dark roasting compared to the Light-medium roasting (\(p<0.05\)). However, the total flavonoid content at the Very dark roasting was significantly lowered compared to the Medium and Moderately dark roasting (\(p<0.05\)). Total flavonoid content was the highest at the Light-medium roasting in the drip and significantly decreased as the roasting level increased (\(p<0.05\)). In our results, the reduction rate of total flavonoid content according to the roasting degree was greater than that of total phenolic content, which indicates instability of flavonoids with heat. In the espresso, the total phenolic content of the Very dark was 23% less than that of the Light-medium, and the total flavonoid content of the Very dark was 29.1% less compared to the Light-medium. In the drip, the total phenolic content of the Very dark was 30.2% less than that of the Light-medium, while the total flavonoid content of the Very dark was 71.4% less than that of the Light-medium. In a previous study, the content of total flavonoid in coffee beans was the highest when roasted at 191 \({}^{\circ}\)C, followed by green beans and coffee beans roasted at 202 \({}^{\circ}\)C, 220 \({}^{\circ}\)C, and 233 \({}^{\circ}\)C [35]. In the study of [PERSON] et al., the content of flavonoids was the highest in the light roasting, followed by green beans, Medium, dark, and very dark roasting [30]. Several factors affect the amount of bioactive compounds during the thermal procedure. The physical disruption of cellular constituents by thermal processing can enhance the release of antioxidants [36]. On the other hand, the amount of antioxidants can be reduced by hydrolytic enzymes activated thermal processing [36]. Therefore, it is estimated that the fluctuation in flavonoid content may be due to the difference in heating time required for the breakdown of cellular constituents to release the individual flavonoids and the activation of hydrolytic enzymes.
# Recent Advances in Lithiophilic Porous Framework toward Dendrite-Free Lithium Metal Anode
## 3 Non-Metal-Based Li Host
### Heteroatom Atom  
The doping of polar heteroatoms (such as N, S, B, and O) into the nonpolar nanostructured carbon is another approach to trap the Li-ions. The lithiophilic oxygen and nitrogen co-doped porous carbon granules (ONPCGs) was coated on Cu foil to achieve a high CE of 99.83% after 350 cycles at 2 mA cm\({}^{-2}\), 96.4% after 130 cycles at 20 mA cm\({}^{-2}\) and still stably work at a high capacity of 6 mAh cm\({}^{-2}\) for 115 cycles [93]. The channel-like pore structure in porous carbon with a high specific area of 2396 m\({}^{2}\) g\({}^{-1}\) containing oxygen and nitrogen functional groups lower the local electric field and enhances the lithiophilicity that leads to the uniform Li growth at an ultrahigh current density up to 30 mA cm\({}^{-2}\).  
The edge and high coordination sites in graphene and nitrogen-doped graphene with functional groups are suitable for Li adsorption. [PERSON] et al., presented nitrogen (N)-doped nanoporous graphene with a high lithium loading amount of 95.3 wt % [90]. The melted lithium can be infused into the nanopore channels within 1 min due to the doping of the lithiophilic functional group and capillary forces of nanopores. The N-doping offers well-distributed anchoring sites for uniform Li deposition.  
The N-doped graphene matrix is full of lithophilic functional groups, such as pyridinic, pyrrolic, and quaternary nitrogen atoms [91]. Figure 5d shows the fitted results of N 1s X-ray photoelectron spectroscopy (XPS) spectra with thee peaks- pyridinic nitrogen (pnN) pyrrolic nitrogen (prN) and quaternary nitrogen (qN). Figure 5e displays the BE of Li atom with Cu, graphene, and different functional groups of nitrogen-doped graphene. The higher binding energy of prN (4.46 eV) and pnN (4.26 eV), which increases the local charge density, provides strong Li affinity. The extra pair of electrons in pyrrolic or pyridinic nitrogen acts as an electron-rich donor with filled p orbitals behaving itself as a Lewis base site. Such a Lewis base site has a strong affinity to adsorb Lewis acidic Li-ions through acid-base interactions, which brings a lower nucleation overpotential.
# A Method of Detections' Fusion for GNSS Anti-Spoofing
## 2 The Proposed Method of Spoofing Detections' Fusion
### Valuation of the Belief Function for GNSS Spoofing Detections
\tag{10}\]  
\(f_{2}(T_{i},\gamma_{i})\) meets Equation (8), as well, and it is in a closed interval \([\,0,\,1\,]\). However, \(f_{2}(T_{i},\gamma_{i})\) descends quickly, and it makes a decision \(m_{i}(A)=0\) even if the anomaly is not obvious enough. The decision \(m_{i}(A)=0\) is strong, and it makes an irrevocable decision while all other detectors are meaningless. Figure 2 shows the curves of \(f_{1}(T_{i},\gamma_{i})\) and \(f_{2}(T_{i},\gamma_{i})\), where \(T_{i}/\gamma_{i}\) is plotted on the horizontal axis, and the red line is the threshold to determine \"\(A\)\" or \"C\". As mentioned previously, both \(f_{1}(T_{i},\gamma_{i})\) and \(f_{2}(T_{i},\gamma_{i})\) have weakness in practice. The linear combination of them would be applicable, and it can be given as follows:  
\[f(T_{i},\gamma_{i})=\alpha\cdot f_{1}(T_{i},\gamma_{i})+(1-\alpha)\cdot f_{2}( T_{i},\gamma_{i})\qquad\alpha\in[\,0,\,1\,] \tag{11}\]  
where \(\alpha\) is the weight of \(f_{1}(T_{i},\gamma_{i})\); it can be set according to the actual implementation.
## 5 Conclusions  
During the analysis of the BER performances of different modulation techniques at various turbulence scenarios, i.e., weak, moderate and strong, it can be concluded that BPSK modulated links delivered an improved SNR gain of more than 10 dB over OOK links operated under similar conditions. Additionally, with the spatial diversity technique, BPSK modulated links witnessed further improvement in SNR gain to up to 25 dB over moderate and strong regimes. Furthermore, it can be concluded that among different modulation techniques investigated here, the BPSK FSO link combined with the \(2\times 2\) space diversity technique gives improved BER performance of 42% over others. The techniques used in the above analysis are simplex modulation techniques, which are very cost-effective. With the help of these techniques, we can integrate a set of devices into smart cities. With the considered link design parameters such as 155 Mbps of transmission speed, over 1 km of link range and moderate diversity order, the proposed link will be capable enough to support a hybrid communication model with last-mile capabilities. Moreover, for areas with rough and hostile terrain, where setting up RF infrastructure or digging fibers is very difficult, the proposed 1.2 km-long FSO links can be used in serial arrangement to reach out to the end-users. In the future, these modulation techniques can also be integrated with FEC techniques for further improvement in the links.  
Conceptualization, S.C., R.M.; Data curation, S.C., R.M.; Formal analysis, S.C., R.M.; Investigation, S.C. and R.M.; Methodology, S.C., R.M., L.K., G.S.G., and M.M.; Resources, M.M.; Visualization, L.K. and G.S.G.; Writing--original draft, S.C. and R.M.; Writing--review & editing, L.K., G.S.G., and M.M. All authors have read and agreed to the published version of the manuscript.  
Taif University Researchers Supporting Project number (TURSP-2020/10), Taif University, Taif, Saudi Arabia.  
We are very thankful for the support from Taif University with the Researchers Supporting Project (TURSP-2020/10).  
The authors declare no conflict of interest.
# Effect of Terahertz Antenna Radiation in Hypersonic Plasma Sheaths with Different Vehicle Shapes
###### Abstract  
Communication blackout caused by the plasma sheath surrounding hypersonic vehicles is a serious threat to the safety of hypersonic vehicles. Terahertz (THz) communication is considered to be a potential solution to the blackout problem. However, hypersonic plasma sheaths can affect not only electromagnetic wave propagation, but also the radiation performance of onboard antennae. Therefore, it is necessary to investigate the radiation performance of THz antennae in hypersonic plasma sheaths. In the present study, the impact of vehicle shapes (blunt-coned and sharp-coned vehicles) on plasma sheaths was investigated numerically. The antenna involved in the present study was a 0.14 THz array antenna. The antenna performance was compared with the aspects of mainlobe gain, mainlobe direction, and mainlobe width. The present study shows that both sharp-coned and blunt-coned plasma sheaths are inhomogeneous. Nevertheless, their structures are obviously different. Such differences yield different antenna performance in sharp-coned and blunt-coned plasma sheaths. Compared with sharp-coned plasma sheaths, blunt-coned plasma sheaths can refract antenna radiation direction, which can result in worsened communication quality in the expected direction. In addition, the phenomena are discussed in detail. Suggestions of vehicle shape design to guarantee communication quality in hypersonic flight missions are proposed.  
Keywords:plasma sheath; vehicle shape; antenna radiation performance +
Footnote †: journal: Applied
# A Survey of Graphene-to-Phoneme Conversion Methods
## 2 Grapheme-to-Phoneme Methods
### Methods Based on Deep Learning
#### 2.2.2 Cnn  
Convolutional neural network (CNN), due to its strong ability to extract internal representations between objects that have spatial relationships, is widely utilized in various fields. Besides LSTM models, research made efforts to develop G2P conversion models with CNN. Ref. [26] proposed a non-sequential greedy decoding (NSGD) model and combined fully convolutional encoder-decoder model with NSGD, compared with the recurrent neural networks, CNN can outperform in non-sequential tasks.  
LSTM sequentially reads inputs and outputs depending on the previous input, LSTM cannot parallelize these networks while applying CNN can reduce computational complexity by using a large receiving domain. Ref. [27] first applied CNN in G2P conversion and proposed a Seq2 Seq G2P model based on BiLSTM and CNN. The CNN layer of this method uses pixels as inputs for convolution operations, introduces residual connections to solve the problem of gradient vanishing, increases the number of network layers, and improves model performance.  
The G2P conversion of convolutional neural networks based on residual connections (mid part in Figure 7) has high PER (5.84%) and WER (29.74%), but the model has fewer parameters. The encoder convolutional neural network and decoder Bi LSTM (right part in Figure 7) model with residual connections achieved the best PER (4.81%) and WER (25.13%), with PER better than previous solutions on both CMU and NetTalk datasets, and WER very close to the results obtained in [28].
## 2 PHM: Reference Methodologies, Frameworks, and Architectures
### Fault Diagnosis Assessment Function
#### 2.2.1 Anomaly Detection  
According to [56], anomaly detection algorithms for streaming applications should have the following characteristics: (1) they have to identify the state of the current data point before the next data point is available; (2) they have to learn continuously without storing the entire data stream; (3) they have to adopt an unsupervised approach; (4) they have to deal with the concept drift problem, which means having the ability to adapt model structures if a data point cannot be considered a true anomaly; (5) they have to identify an anomaly as early as possible; (6) they should minimize false positives and false negatives. In [57], online data-driven anomaly detection is introduced, named ODDAD (Online Data-Driven Anomaly Detection). This algorithm adopts a sliding window method based on the Mahalanobis Distance and on a pre-determined threshold to decide whether the current data point is anomalous. In [58], a one-class classification model named TEDA (Typicality and Eccentricity Data Analytics) is introduced, which assumes to have the label of data related to the normal condition. The algorithm uses the concepts of typicality and eccentricity. Typicality is related to the similarity of a particular data sample to an entire data set in the sense of spatial proximity, while eccentricity reflects how different a data sample is from the data group. The TEDA algorithm is fully unsupervised and does not rely on any assumption on data distribution; in addition, it is recursive, fast, and has low computational complexity. Typicality and eccentricity are calculated and updated as a new data point arrives by resorting to a distance measure. However, this requires the definition of a certain threshold to assign the label 'normal' or 'faulty' to a data point.
## 4 Results  
The behavior of Mg-SiC nanocomposites during compaction was examined utilizing the MDPC model in FEM simulation to study the impact of submicron and nano-size SiC reinforcement particles. Figure 5a illustrates the distribution of equivalent pressure stress for Mg-10% SiC composite mixtures containing both submicron and nano reinforcement particles at applied pressures of 100, 300, 500, and 700 Mpa. According to these results, inhomogeneous pressure distribution within the samples was predicted under different applied pressures, despite the uniformly applied isostatic pressure. The larger equivalent pressure, indicating harder compressibility, is visible in the Mg-SiC nanocomposite containing nanoparticles reinforcement.  
The relative density distributions in M10S\(\upmu\) and M10 Sn composites are shown in Figure 5b after CIP simulation under applied pressure values of 100, 300, 500, and 700 Mpa.  
As shown in Figure 5b, different relative density distributions were predicted within M10S\(\upmu\) and M10 Sn composites after CIP simulation under 100, 300, 500, and 700 MPa pressure values. The relative density was estimated as described in Section 3.3, and under the same pressure, M10S\(\upmu\) indicated a higher magnitude of relative density compared to M10 Sn. Correspondingly, the higher magnitudes of relative density were predicted as 82.36%, 87.6%, 90.84%, and 92.81% for M10S\(\upmu\) and as 79.50%, 84.75%, 87.46%, and 88.67% for M10 Sn.  
Figure 4a compares the FEM and experimental approaches for determining relative density under 100, 300, 500, and 700 MPa uniform isostatic pressure for M10S\(\upmu\) and M10 Sn.  
The computational and experimental outcomes are in good agreement, and M10S\(\upmu\) exhibits the maximum magnitude of relative density under different pressures. The element volume (EVOL) in certain pressure ranges was also calculated in Figure 4b to investigate the density distribution after CIP. Consequently, the maximum EVOL values computed for M10S\(\upmu\) are considerably higher than those for M10 Sn, with values of 28.46 and 18.95 estimated within the 690.5 to 717 MPa pressure ranges for M10S\(\upmu\) and M10 Sn, respectively. This outcome provides evidence for a higher homogeneous density distribution of the Mg-SiC composite containing submicron particles. Figure 4c shows the volumetric plastic strain changes with pressure for M10 Sn and M10S\(\upmu\). The volume reduction percentage under 700 MPa for computational and experimental outcomes was compared in Figure 4d. It can be observed that M10S\(\upmu\) exhibits 2.13% and 2.81% enhancement in volume reduction percentage for FEM and experimental results, respectively, compared to the values for M10 Sn.
# Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges
## 4 Prediction Models for Thermal Conductivity and Other Properties of the NFs
### Thermal Conductivity Machine Learning-Based Models  
In recent years, many machine learning and statistical models has been developed to predict the thermal conductivity and other thermal properties of materials and nanofluids with high accuracy and robustness. In fact, properties such as thermal conductivity [85], density [86], and viscosity [87; 88] of nanofluids, glass transition temperature of polymers [89; 90] and also the decomposition onset temperature of lubricant additives [91] have been estimated precisely with machine learning-based models. Those models are fast, stable, and low-cost tools to predict the thermal properties on a wide range of industrial applications, particularly in electronic devices, heat sinks, heat exchangers, renewable energy [92], and automotive industries. Accordingly, there are also many research works whose predictions were based in machine learning algorithms, as can be seen in Table 2, which summarizes representative papers using machine learning prediction models published in recent years.  
The most common machine learning data-driven modeling can be provided by one of these four fundamental models: ANN (Artificial Neural Network), CART (Category and Regression Tree), RF (Random Forest), and SVM (Support Vector Machine). The chart presented in Figure 6 shows these fundamental methods and their possible combinations like, for instance, GA (Genetic Algorithm) and PSO (Particle Sworn Optimization). Figure 7 illustrates the structure of an ANN estimation model, which is the most commonly used model in the research field.
## 4 Discussion  
In the intricate field of path planning, the improved Fast Marching Tree (FMT*) algorithm introduced in our paper emerges as a notably efficient alternative to both optimization-based and learning-based methodologies. Its principal advantage lies in the elimination of the necessity for either a pre-constructed mathematical model, which demands extensive prior knowledge, or a data-intensive model that requires substantial train  
Figure 14: The robotic arm executes the trajectory planning generated on the PC.  
Figure 13: Visualization of the end-effector trajectory generated by the improved FMT* using RVIZ.  
ing. This distinctive characteristic markedly reduces the computational resource footprint, enhancing the algorithm's ease of deployment and broadening its application spectrum.  
When scrutinized alongside analogous sampling-based path-planning methods, specifically, the standard FMT, Rapidly Exploring Random Tree (RRT), and RRT-connect algorithms. Our improved FMT* algorithm demonstrates a clear superiority in generating more concise paths, as quantitatively evidenced in Table 1. This outcome signifies not only an optimization of the path's length but also an indirect contribution to the efficiency and safety of robotic operations. It is noteworthy, however, that the improved FMT* does not boost the fastest computation times among its peers. Yet, this slight concession in speed is counterbalanced by its unique capability for singularity avoidance, a critical feature absent in its counterparts that justifies the marginal increase in computational effort.  
The algorithm's efficacy, particularly in singularity avoidance, was rigorously validated through simulation experiments employing a 6-degree-of-freedom robotic arm model in MATLAB. The results, illustrated in Figures 10 and 11, unequivocally demonstrate how our algorithm significantly elevates the robotic arm's motion performance by skillfully navigating away from singularities. Furthermore, real-world applicability tests conducted on the AUBO-I5 robotic arm, as depicted in Figures 12 and 13, confirmed the algorithm's practical effectiveness. The smooth avoidance of obstacles and singular regions, coupled with successful target point acquisition, attests to the robustness and real-world viability of the improved FMT* algorithm.  
This discussion affirms the improved FMT* algorithm's status as a highly effective, computationally efficient solution for robotic arm trajectory planning, outperforming traditional models in both theoretical and practical arenas. Its ability to reduce path length while incorporating singularity avoidance into its computational framework represents a significant leap forward, paving the way for future advancements in robot arm trajectory planning.