esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Characteristic Absorption Peaks  
The infrared spectra of the CTBN, RAP and virgin bitumen samples are shown in Figure 3, where A indicates virgin bitumen, B indicates RAP, and C indicates CTBN. The peak at 966 cm\({}^{-1}\) is attributed to the bending vibration of C-H in the double bonds of butadiene (-CH=CH-). The peak at approximately 1468 cm\({}^{-1}\) corresponds to the bending vibration of the -CH\({}_{2}\) group, whereas the peaks at 2855 cm\({}^{-1}\) and 2926 cm\({}^{-1}\) are ascribed to the symmetric and antisymmetric vibrations, respectively, of -CH\({}_{2}\) groups [24; 30].  
In contrast to the characteristic absorption peaks of the virgin bitumen and RAP, the peaks at 1703 cm\({}^{-1}\) and 2238 cm\({}^{-1}\) represent the characteristic absorption peaks of CTBN. The peak at 2238 cm\({}^{-1}\) is due to nitrile stretching vibrations, whereas the peak at 1703 cm\({}^{-1}\) is ascribed to the stretch vibration of the carbonyl group; the infrared spectrum of CTBN coincides with its chemical equation [26]. Since carbonyl could also be produced during the aging process of bitumen, the peak at 2238 cm\({}^{-1}\) was chosen as the characteristic peak of CTBN to analyze the blending degree of virgin and RAP binders [24; 31].  
Figure 2: Illustration of the sampling of aggregate particles for Fourier transform infrared (FTIR) analysis.  
The chemical formula of CTBN and the absorbance peaks in Figure 3 indicate that CTBN does not possess a -CH\({}_{3}\) group, and the bond of the -CH\({}_{3}\) vibrations is steady during the aging process [26]; therefore, the characteristic absorption peaks of the -CH\({}_{3}\) group (at 2960 cm\({}^{-1}\), 1460 cm\({}^{-1}\) and 1375 cm\({}^{-1}\)) can be used to distinguish the bitumen from CTBN. Figure 4a displays the spectrum of RAP; the absorption peaks at 1460 cm\({}^{-1}\) and 1375 cm\({}^{-1}\) are obscured by the strong absorption peaks of the mineral powder and aggregate, indicating that the absorption peaks at 1460 cm\({}^{-1}\) and 1375 cm\({}^{-1}\) cannot be used as characteristic absorption peaks. Meanwhile, since the infrared spectrum of RAP (Figure 4b) exhibits a characteristic absorption peak at 2960 cm\({}^{-1}\), which is attributed to the antisymmetric vibrations of -CH\({}_{3}\) groups, the peak at 2960 cm\({}^{-1}\) was chosen as the characteristic absorption peak of the bitumen.
## 3 Dataset Analysis and Description
### FAERS Dataset Key Features  
The FAERS dataset contains detailed information about patients, drugs, and adverse events, which can be used to find new safety signals and track trends in adverse events. Researchers and healthcare professionals use the FAERS dataset to improve drug safety monitoring, inform regulatory decisions, and improve patient care. The following are some of the key features of the major categories of the FAERS dataset:  
1. Temporal Span: The data chronicle adverse drug events spanning from the year 1989 to 2017. This broad timespan allows for a deep historical analysis, uncovering patterns and trends across different eras of drug administration and healthcare practices.
2. Rich Textual Information: One of the salient features of the dataset is its incorporation of textual data, especially from active substance fields. This offers potential insights into the specific substances or drug combinations that might be associated with particular adverse events.
3. Demographic Data: The dataset is not just limited to events but also envelops demographic data. These data include the gender of the patient, which can be critical in understanding the differential effects of drugs across sexes, potentially aiding in gender-specific drug recommendations or precautions.
4. Qualification of Healthcare Provider: By including the qualification of the healthcare provider who reported the event, the dataset allows for nuanced analyses of the reliability and depth of the reported data. For instance, reports from specialized doctors might be weighed differently than those from general practitioners or nurses.
###### Abstract  
This study evaluates the performance of large intelligent surface (LIS) technology in the context of a multi-user MIMO mobile communication system (Mu-MIMO) proposed for the sixth generation (6G). LIS employs digitally controlled reflectors to enhance Signal-to-Interference plus Noise Ratio (SINR) and establish line of sight (LoS) connectivity in non-LoS environments, improving transmission security. Analytical expressions are derived to assess LIS performance metrics, including distribution parameters, bit error probability, and secrecy outage probability, considering the presence of eavesdroppers and environmental fading. The study highlights the potential of LIS technology to enhance the confidentiality and reliability of digital communication systems in next-generation networks.  
large intelligent surfaces; reflecting surfaces; mobile communications; Nakagami-\(m\) fading; Mu-MIMO systems +
Footnote †: journal: _Sensors_**2024**, 24, 3332. [[https://doi.org/10.3390/s24113332](https://doi.org/10.3390/s24113332)]([https://doi.org/10.3390/s24113332](https://doi.org/10.3390/s24113332))
###### Abstract
[18] used two neural networks to replace the simulated tensile and bulging tests and simultaneously identified the elastic-plastic constitutive parameters of materials via multiple experiments. Further, [PERSON] et al. [20] employed a neural network, instead of a finite element program, to obtain the constitutive model parameters of aluminium foam. Thus, considering the existing research studies, it can be concluded that an ANN can not only significantly improve inverse efficiency but also maintain an accuracy as high as that of finite element calculations. The inverse identification of the residual stress field is a highly nonlinear problem. Using the ANN can remarkably reduce the calculation time. Considering this, an inverse method to generate a self-balanced residual stress field by replacing the finite element calculation with a convolutional neural network (CNN) [21] is proposed in the paper. By using this method, the residual stress field can be obtained from the residual stresses measured at a finite number of points. The CNN is a deep feedforward neural network that consists of convolution, pooling and fully connected layers. It exhibits good fault tolerance and generalization ability. Thus, it should be suitable for predicting complex residual stress distributions with a highly nonlinear relationship. As a deep convolutional network, the U-Net architecture has shown great potential in solving nonlinear problems [22]. For example, [PERSON] et al. [23] applied the U-Net to the nonlinear relationship between the contact force on an external surface and the finite element displacement of a three-dimensional cantilever beam as well as a liver model under a moving load on the external surface; their results showed that the network could simulate various geometric shapes and topological structures very quickly. [PERSON] et al. [24] applied the U-Net to model reduction technology and accurately predicted the stress, node force and displacement in relation to the plastic history of the model. Consequently, considering the complexity of the finite element programs used to calculate the residual stress of components, the U-Net was utilized in this work to replace the finite element calculation. The rest of this paper is structured as follows: The principles of the inverse method for residual stress field prediction are introduced in Section 2. In Section 3, the proposed inverse method is verified by simulated four-point bending experiments. Section 4 compares the performance of the CNN and FEMU methods and discusses their initial value stability. Finally, the results are summarized at the end of the paper.
## 6 Conclusions  
GNSS receivers cannot work and frequently lose lock under highly dynamic conditions due to the large Doppler frequency shift, which causes difficulties for the fast acquisition and re-acquisition of signals. The INS-aided acquisition can improve the acquisition performance by estimating and compensating the Doppler frequency shift and the code phase. This paper analyzes in depth the performance of GNSS signal acquisition aided by different grades of INS under highly dynamic conditions. In order to estimate and compensate the Doppler frequency shifts as well as the code phase, the error sources of the Doppler shift and code phase estimation are analyzed. Furthermore, the mathematical model of Doppler shift and code phase estimation errors contributed by the INS velocity  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **No INS Assisted** & **MEMS Grade** & **Civil Grade** & **Tactical Grade** & **Inertial Grade** \\ \hline Number of satellites & 12 & 12 & 12 & 12 & 12 \\ \hline Frequency range & \(\pm\)5 KHz & \(\pm\)500 Hz & \(\pm\)500 Hz & \(\pm\)500 Hz & \(\pm\)500 Hz \\ \hline Frequency search space & 500 Hz & 500 Hz & 500 Hz & 500 Hz & 500 Hz \\ \hline \multirow{2}{*}{Code phase range} & GPS:1023 & GPS:10 & GPS:10 & GPS:10 & GPS:10 \\  & BDS:2046 & BDS:20 & BDS:20 & BDS:20 & BDS:20 \\ \hline Code phase search space & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\ \hline \multirow{2}{*}{Search cells} & GPS: 42966 & GPS:60 & GPS:60 & GPS:60 & GPS:60 \\  & BDS: 85932 & BDS:120 & BDS:120 & BDS:120 \\ \hline Acquisition time & 12.87s & 1.85s & 1.85s & 1.85s & 1.85s \\ \hline \hline \end{tabular}
\end{table}
Table 7: Search parameters with different grades of INS.  
error and position error are derived in detail. In order to analyze the effects of the INS quality on the Doppler shift and code phase estimation accuracy, experiments are performed using different grades of INS under a highly dynamic scenario. Then, the INS-aided acquisition is simulated in a dual-mode software receiver. The simulation results show that the acquisition aided by INS can reduce the frequency and code phase search space under highly dynamic conditions. Moreover, the Doppler shift and code phase estimation errors increase as the quality of the INS decreases. The quantitative result is that the acquisition time can be shortened by about 1.85 s with the assistance of the selected INS. Compared with a high grade INS, the selected MEMS-grade INS provides the same improvement. It can be seen that the selected MEMS-grade INS can satisfy the demands of fast acquisition in the simulation scenario, and according to the theoretical analysis, experiments and simulations, a suitable INS can be chosen for the deeply coupled integration.  
[PERSON], [PERSON] and [PERSON] conceived the study idea and designed the experiments. [PERSON] and [PERSON] built the simulation platform and performed the experiments. [PERSON] wrote the paper. All authors read and approved the manuscript.  
The authors declare no conflict of interest.
# Blockchain-Enabled Secure and Interoperable Authentication Scheme for Metaverse Environments
## 6 Security Analysis
### Informal Security Analysis
* Lack of interoperability: Due to a lack of interoperability, an adversary may try to impersonate a user in different platforms with different identities. There is no uniformity in the identification of the user or avatar. The unique security feature provided by our scheme is interoperable identity. This will avoid the security issues caused by lack-of-identity interoperability. * Mutual authentication: If an adversary tries to fake authentication while communicating with other users or avatars, it will not be validated in our proposed scheme. Because of the Meta-ID, Metaverse server platforms are registered while enrolling the user. Also, session keys and timestamps are used while sharing messages. Consequently, the proposed system provides secure mutual authentication. * Stolen user- or avatar-information attack: The user information is protected by a Meta-ID and session key. Even if an adversary tries to steal user or avatar information, when it is being used somewhere else, session key will be invalid. Hence, our scheme protects users and avatars from stolen-information attacks. * Session-key-disclosure attack: In the framework proposed, the session key is maintained per session and is used in combination with a Meta-ID. If an adversary tries to disclose the session key, then it would not be possible to use against another Meta-ID. Thus, our scheme is not susceptible to session-key-disclosure attack. * Perfect forward secrecy: An adversary will not be able to perform a perfect forward secrecy attack on our proposed scheme. Even if the private key used in an individual session are stolen, then the attacker will not be obtain access to the Meta-ID for that user and device. Hence, an adversary will not be able to access data from other sessions. * Insider attack: In the insider attack, an adversary can impersonate an avatar, [PERSON], or a ser, [PERSON]. However, in our proposed scheme, without knowing session keys and a Meta-ID, it is difficult for any avatars or users to perform an impersonation attack. Hence, our scheme can prevent insider attacks.
## 3 Discussion
For example, the main VOCs in 'Gesang Lv' were linalool, cyanophyllene, oxidized linalool, and pinene, the main VOCs in 'Gesang Hong' included macrophylene D, methyl decanoate, hexol, and pinene, and the main VOCs in 'Gesang Huang' were linalool, cyanophyllene, methyl decanoate, and germacrene [40]. The volatile components of the flowers within the genus _Chimonanthus_ were mainly terpenes and esters, and terpenoids and aromatic hydrocarbons were the main VOCs in rose [41]. A total of 270 volatile organic compounds were identified using HS-SPME in conjunction with GC-MS. Esters, hydrocarbons, terpenoids, and alcohols were the dominant aroma components in three _C. oleifera_ petals in terms of quantity and proportion. Phenylethyl alcohol was the main aroma component, widely existing in plant essential oils, which is an important aroma compound, with rose, fruits, and honey flavor [42]. Here, phenylethyl alcohol was the main VOCs in the white petals, which was in accordance with the results that phenylethyl alcohol was the unique compound of 'Fragrant cloud' varieties and phenylethyl alcohol was the highest aroma components in _Rosa. odorata var odorata_[43]. The hotrienol and alpha-terpineol were detected with a high content in candy-pink petals. In accordance with our results, [PERSON] et al. (2016) identified hotrienol and alpha-terpineol as the important aroma compounds in red freesia flower [44]. [PERSON] et al. (2020) detected as main aroma constituent butanoic acid, 2-methyl-, 2-methylbutyl ester in _Areca catechu_ flower [45], in which we detected a high accumulation of butanoic acid, 2-methyl-, 2-methylbutyl ester in the dark-red _C. oleifera_ petals. Different volatile organic compounds and release amount were together contributed to the unique aroma of _C. oleifera_ petals, which not only increased the ornamental value, but also had important significance for the commercial development of aromatic _C. oleifera_ petals. In summary, phenylethyl alcohol, hotrienol, alpha-terpineol, and butanoic acid, 2-methyl-, 2-methylbutyl ester were generally present in white, candy-pink, and dark-red _C. oleifera_ petals, which mainly contributed to the perfumery value. Our understanding of the floral scent traits of _C. oleifera_ petals with different colors is limited. A comprehensive knowledge of the coloring substances and volatile compounds in _C. oleifera_ petals is the first step in cultivating colorful and aroma varieties. Esters, alcohols and terpenoids are ubiquitous in floral volatiles. Functional studies of genes involved in the biosynthesis of terpenoids would be very prospective. Finally, with the support of biotechnology tools, metabolic engineering methods for flower color and fragrance-related products could be realized. Therefore, our research not only would be helpful to provide comprehensive information about the characteristic components of flower color and fragrance in _C. oleifera_ petals with different colors, but also lay a fundamental reference for cultivating excellent _C. oleifera_ varieties with specific flower color and fragrance.
# Thermogravimetry Applicability in Compost and Composing Research: A Review
## 3 Results and Discussion
### Thermogravimetry in Compost Pyrolysis
#### 3.4.2 Thermogravimetry in Compost Hydrogen Production  
On the other hand, [PERSON] et al. [74] studied the pyrolysis of compost to obtain hydrogen. A significant production of H\({}_{2}\) at temperatures higher than 300 \({}^{\circ}\)C has taken place. This was mainly due to dehydrogenation reactions of the compounds present in the compost. The high presence of alkaline and alkaline-earth metals apparently catalyzed the process since the hydrogen was obtained at lower temperatures than those observed in biomass (200-450 \({}^{\circ}\)C). In this sense, they demonstrated that a significant concentration of Ca could promote the production of H\({}_{2}\), while the presence of K has been associated with the production of CH\({}_{4}\). In this context, thermogravimetric analysis combined with mass spectrometry (TGA-MS) to study the thermal behavior of pyrolysis of compost and biomass mixtures has been studied [86; 90; 91]. The decomposition of the compost mixture took place mainly from 209 to 373 \({}^{\circ}\)C, with a total weight loss of 23%. In this case, the main gaseous products generated during pyrolysis were H\({}_{2}\)O, CO and CO\({}_{2}\). For this reason, this study concluded that the pyrolysis of these composts did not seem to be a promising process due to the low values of superior calorific value and biochar. In another study, [PERSON] et al. [92] and [PERSON] et al. [93] analyzed the thermal degradation behavior, calorific value and gas spectrometry during pyrolysis among compost from food waste. They concluded that these composts can be used as a promising alternative fuel. Moreover, when the compounds formed during pyrolysis (TGA-MS) have been studied, the maximum emission peak was obtained at temperatures around 350 \({}^{\circ}\)C for most compounds, being the most abundant CO, H\({}_{2}\)O and CO\({}_{2}\). It is remarkable the production of H\({}_{2}\) at high temperatures (>450 \({}^{\circ}\)C, peak extended up to 1000 \({}^{\circ}\)C). It was hypothesized, based on experimental data, that H\({}_{2}\) production took place through both the reverse of the methanation reaction and the steam reforming reaction [94; 95]. The slow production rate of H\({}_{2}\) was correlated with the slow carbonization process of the char, evidencing char thermal cracking and dehydrogenation reactions. The presence of Ca in the residue content could promote the H\({}_{2}\) production via steam reforming reactions, whereas the presence of K was associated to the production of CH\({}_{4}\).
## 3 Results and Discussion
### Color Determination  
Food color is a major factor that affects the quality and acceptance of food. It can also be used to predict the chemical and microbiological changes in food. In our case, the effect of sterilization on the color of the product was significant. In addition, the amount of yellow index and lightness decreased compared to the those of the unsterilized products. Enzymatic browning (catalyzed by PPO) and non-enzymatic browning (Maillard reactions) are also important factors regarding the color changing in the browning of the samples [49]. According to [PERSON] et al. [50], sterilization, as a method used for baby puree thermal treatments, causes structural, taste and color defects. In the current study, we used a vertical autoclave for the sterilization process, and all values, L*, a* and b*, were decreased after the thermal treatment. Consumers prefer infant food with no artificial colors, revealing that over time, consumers became more aware of what they consume [4; 51; 52; 53]. Therefore, the products with a lower color intensity did not affect the acceptability as long as the color did not represent a quality defect. For the fresh puree, which was not \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Puree Sample**} & \multirow{2}{*}{**Vitamin C (mg/100 g)**} & **Polyphenols Content** & **Antioxidant Capacity (\(\mu\)m** \\  & & **(mg GAE/100 g)** & **TE/g FM)** \\ \hline
1 & 2.11 \(\pm\) 0.1 & 73.67 \(\pm\) 1.67 \({}^{\text{bc}}\) & 0.62 \(\pm\) 0.47 \({}^{\text{f}}\) \\ \hline
2 & 2.91 \(\pm\) 0.3 & 64.36 \(\pm\) 0.2 \({}^{\text{de}}\) & 0.64 \(\pm\) 0.31 \({}^{\text{e}}\) \\ \hline
3 & 3.27 \(\pm\) 0.1 & 65.63 \(\pm\) 0.1 \({}^{\text{de}}\) & 0.88 \(\pm\) 0.70 \({}^{\text{b}}\) \\ \hline
4 & 2.58 \(\pm\) 0.1 & 61.9 \(\pm\) 4.88 \({}^{\text{e}}\) & 0.67 \(\pm\) 0.31 \({}^{\text{d}}\) \\ \hline
5 & 3.5 \(\pm\) 0.2 & 69.588 \(\pm\) 0.59 \({}^{\text{cd}}\) & 0.67 \(\pm\) 0.47 \({}^{\text{d}}\) \\ \hline
6 & 2.8 \(\pm\) 0.1 & 59.91 \(\pm\) 0.1 \({}^{\text{e}}\) & 0.81 \(\pm\) 0.39 \({}^{\text{c}}\) \\ \hline
7 & 3.27 \(\pm\) 0.2 & 70.492 \(\pm\) 0.62 \({}^{\text{bcd}}\) & 0.51 \(\pm\) 0.15 \({}^{\text{s}}\) \\ \hline
8 & 3.28 \(\pm\) 0.3 & 77.32 \(\pm\) 1.95 \({}^{\text{b}}\) & 0.91 \(\pm\) 0.31 \({}^{\text{a}}\) \\ \hline
9 & 4.21 \(\pm\) 0.3 & 86.31 \(\pm\) 0.59 \({}^{\text{a}}\) & 0.81 \(\pm\) 0.78 \({}^{\text{c}}\) \\ \hline
10 & 2.57 \(\pm\) 0.2 & 64.12 \(\pm\) 1.03 \({}^{\text{de}}\) & 0.81 \(\pm\) 0.78 \({}^{\text{c}}\) \\ \hline \hline \end{tabular} \({}^{\text{a}}\)8—mean values in columns indicated with various letters are significantly different. Puree sample column represents the apple variety used in order to obtain the puree: 1—Champion, 2—Elstar, 3—Baujade, 4—[PERSON], 5—[PERSON], 6—Path, 7—[PERSON], 8—Juliana, 9—[PERSON], 10—[PERSON].  
\end{table} 
Table 4: Vit. C, TPC and AC determination in unsterilized product.