esa-sceva/satcom-chunk-collection huggingface dataset:
# Flavor Chemistry of Virgin Olive Oil: An Overview
## 3 Factors Affecting the Flavor of VOO
### Botanical, Agronomical and Technological Process  
#### 3.1.1 Variety and Ripening Degree  
The impact of botanical characteristics, agronomical practices and technological processing of the olives on olive oil quality has been extensively reported in the literature; therefore, the present section only reports some examples and general conclusions [20; 53]. The olive oil variety has a dramatic effect on the final flavor of VOO. Several studies have been performed over the last decades investigating oils obtained from single varieties of olives, to understand the effect of the genetic factors, the environmental factors (such as growing location and agronomic practices) and the processing factors [90; 91; 92; 93].  
For example, comparing typical Italian olive varieties and Tunisian ones, it was found that cv. Chetoui is characterized by a low content of C\({}_{6}\) aldehydes, responsible of the sensory notes \"green\" and \"fruity\" [94]. According to [PERSON] and coauthors [95], the major volatile compounds affected by the olive cultivars are the following ones: ethanol, 2-methyl propanol, pentanol, _cis_-2-penten-1-ol, _cis_-3-hexenol, and octanol.  
Different varieties might be different in their fatty acid composition and the resulting flavor because of different levels of enzymes, genetically defined, leading to different levels of volatile compounds. For example, the Spanish cultivars Arbequina, Hoijblanca and Picual show great differences in their phenolic composition and fatty acid composition [96].  
Important differences are observed also within the same variety depending on the drupe maturity degree or the growing location. It is well known that the harvesting moment is particularly important as the final VOO can be dramatically affected [19]. During the olive ripening, the aroma notes of \"fruity-grassy\" are significantly lower in oils obtained from greener, less ripe, olives. The sensory notes of bitter-pungent and other positive aromatic notes decrease with increasing ripening degree [97]. More ripe olives give rise to increased concentrations of 1-penten-3-ol and lower content of hexanal, _trans_-3-hexenol, _cis_-3-hexen-1-ol and _cis_-2-hexenol [98]. Other researchers reported that the content of trans_-2-hexenal as well as many other compounds, except _trans_-2-hexenol and hexyl acetate, decrease as maturity progressed [99]. Some triterpene alcohols have been be proposed as markers of fruit maturity. During maturity, the concentration of phenolic compounds decreases, with consequent decrease of the bitter-pungent notes [100].
## 3 Results and Discussion  
### Green Synthesis of AuNPs  
The green synthesis of nanoparticles for biomedical applications has received considerable attention due to the less harmful side effects of these compounds compared with those synthesized by chemical and physical methods [47]. The metabolites secreted by various biological entities, such as plants and microorganisms (i.e., bacteria, actinomycetes, fungi, yeast, and algae), are used as reducing and stabilizing agents to produce highly stable NPs. Plant extracts are preferred over other biological sources owing to their high metabolite production, avoiding the pathogenicity of microbes; direct one-step processing; and cost-effectiveness [7; 48]. Herein, the aqueous extract of _Z. officiale_ rhizome was used for the green synthesis of AuNPs. As reported previously, _Z. officiale_ rhizome provides an enriched source for various constituents, such as terpen, lipids, phenolic compounds, polysaccharides, organic acids, and volatile oils [49; 50]. These metabolites were used as biocatalyst to reduce metals and their oxide to produce nanoscale structures, followed by capping and stabilization (mediated by the extracted compounds). Therefore, we anticipated the high potentiality of the aqueous extract of _Z. officiale_ rhizome to form highly stable gold nanoparticles. In the current study, the appearance of purple color (upon direct addition of metal precursor to plant aqueous extract) indicated the successful formation of AuNPs. A purple color, which indicates the synthesis of AuNPs, was documented by [PERSON] et al. [51] after mixing the aqueous extracts of _Lippia ctiridora_, _Punica grantum_, _Salvia officinalis_, and _Pelargonium graveelons_ with HAuCl\({}_{4}\) solution. Environmental factors (especially pH) are crucial parameters affecting both the synthesis and the aggregation of synthesized nanoparticles. [PERSON] et al. [37] showed that alkaline conditions were necessary for the efficient synthesis of silver nanoparticles with wild ginger extract. They also demonstrated that the formation of AgNPs strongly depends on the molarity of silver and the type of ginger extract (aqueous extracts are more efficient than organic extracts, especially at low metal concentrations). The pH value is of critical importance in the biosynthesis of NPs. In the current study, pH values varying between 5 and 8 were applied during AuNP synthesis. A pH value close to 8 was selected as the most appropriate value to form a high-intensity purple color using the aqueous extract of _Z. officiale_. This phenomenon can be attributed to the activity of plant metabolites under alkaline conditions [52], contributing to an enhancement in the stability of AuNPs and preventing agglomeration or aggregation with aging [53].
# Combined MvdXML and Semantic Technologies for Green Construction Code Checking
## 6 Conclusions and Perspectives  
Although professionals in the AEC domain realize the importance of green construction, there is no efficient and accurate evaluation method for green construction. Traditional green construction evaluation is usually completed by manual inspection, with low efficiency and high error rate. Therefore, construction industry practitioners put forward urgent requirements for a more efficient and convenient approach to conduct green construction code checking.  
Based on the particularity of green construction standards and the limitation of existing code-checking approaches, this paper combines mvdXML technology and semantic web technology to organize, store, and re-use green construction knowledge. In our approach, BIM, logical rule, ontology, and semantic web query language are integrated to conduct green construction code checking. These techniques are used for automated information transformation and extension, knowledge base construction, logical inference, and information query. The automation is facilitated by semantic-based and logic-based representations, which is general and flexible. The approach is composed of five main parts: data source, knowledge base, rule base, JESS inference, and query.  
In this paper, code checking of green construction standards was classified into four classes based on the difficulty level to meet the requirements of the clauses, and the particularity of green construction code checking was analyzed. Based on the above classification and analysis, several types of green construction rules were checked. The proposed BIM-based code-checking approach for green construction was validated by a case study.  
The proposed approach not only focuses on quantitative requirements, but also supports the checking of other types of requirements, such as checking based on semantic information of BIM and checking based on project criteria, owner requirement, etc.
# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
###### Abstract  
Amid the accelerating pace of global urbanization, the volume of municipal solid garbage has surged dramatically, thereby demanding more efficient and precise garbage management technologies. In this paper, we introduce a novel garbage classification approach that leverages a dynamic graph neural network based on multimodal feature fusion. Specifically, the proposed method employs an enhanced Residual Network Attention Module (RNAM) network to capture deep semantic features and utilizes CIELAB color (LAB) histograms to extract color distribution characteristics, achieving a complementary integration of multimodal information. An adaptive K-nearest neighbor algorithm is utilized to construct the dynamic graph structure, while the incorporation of a multi-head attention layer within the graph neural network facilitates the efficient aggregation of both local and global features. This design significantly enhances the model's ability to discriminate among various garbage categories. Experimental evaluations reveal that on our self-curated KCHO dataset, all performance metrics approach 1.00, and the overall classification accuracy reaches an impressive 99.33%, surpassing existing mainstream models. Moreover, on the public TrashNet dataset, the proposed method demonstrates equally outstanding classification performance and robustness, achieving an overall accuracy of 99.49%. Additionally, hyper-parameter studies indicate that the model attains optimal performance with a learning rate of 2 \(\times\) 10\({}^{-4}\), a dropout rate of 0.3, an initial neighbor count of 20, and 8 attention heads.  
multimodal feature fusion; adaptive K-nearest neighbor algorithm; dynamic graph neural network; garbage classification 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., pp. 1-10. External Links: Document Cited by: SS1.
# Fault Reconstruction for a Giant Satellite Swarm Based on Hybrid Multi-Objective Optimization
###### Abstract  
To perform indicator selection and verification for the on-orbit fault reconstruction of a giant satellite swarm, a hybrid multi-objective fault reconstruction algorithm is proposed and then verified by Monte Carlo analysis. First, according to the on-orbit failure analysis of the satellite swarm, several optimization indicators, such as the health state of the satellite swarm, the total energy consumption of reconstruction, and the balance of fuel consumption, are proposed. Then, a hybrid multi-objective fitness function is constructed, and a hybrid multi-objective genetic algorithm is used to optimize the objective function to obtain the optimal reconstruction strategy. Finally, the algorithm is statistically verified by Monte Carlo analysis. The simulation results not only show the algorithm's validity but also reveal the relationship between the number of satellite faults and the health of the satellite swarm. From this, the maximum number of faulty satellites allowed in the giant satellite swarm is calculated, which is significant for assessing the swarm's health.  
giant satellite swarm; fault reconstruction; health state; genetic algorithm; Monte Carlo simulation +
Footnote †: journal: Applied Sciences  
0
Footnote 0: Copyright** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 6 Numerical Results
### Outage Probability  
Figure 6 presents the outage probability with different numbers of relay nodes, where \(Q=15\) dB and SNR = 22.5 dB. From it, we can find that the outage probability decreases slowly as the number of relay nodes increases. At the same time, corresponding to Figure 3, when the number of relay nodes increases to five, the influence of the relay node's number on the outage probability becomes weak. At the same time, when compared with BRS, the AB achieves an adaptive transmission to obtain a lower outage probability. At the same time, the EEAB introduces the adaptive transmission and OPA to obtain a lower outage probability. Furthermore, with the adaptive transmission, SWIPT, and OPA, the outage probability can be further decreased, and the EEAB-SWIPT obtains the lowest outage probability. For example, when the relay node's number is eight, the outage probabilities under BRS, AB, EEAB, and EEAB-SWIPT are 0.0.086, 0.055, 0.050, and 0.026, respectively.  
Figure 7 presents the outage probability with different SNRs, where \(Q=15\) dB and the number of relay nodes is 6. From it, we can find that the outage probability decreases with the SNR increases. At the same time, the EEAB-SWIPT also obtains the lowest outage probability. For example, when SNR is 25 dB, the outage probabilities under NC, BRS, AB, EEAB, and EEAB-SWIPT are 0.082, 0.056, 0.035, 0.030, and 0.006, respectively. With Figures 6 and 7, it can be found the designed EEAB-SWIPT transmission strategy effectively reduces the network outage probability.  
Figure 8 demonstrates the convergence of our proposed TIPT determining algorithm, where \(P_{P}=10\) W, \(\eta=0.6\), and \(\rho=0.5\). In the figure, we select three different initial intervals, i.e., [0, 60], [10, 50], and [20, 40], to simulate its convergence behavior. From it, we can find that regardless of the initial interval, with almost nine iterations, the total transmit power \(P_{T}\) stabilizes at around 39.7 W. This means that after nine iterations, the \(P_{T}\), which lets the transmission rate of BRT be equal to that of BDT, can be found. It also shows the convergence of our proposed TIPT determining algorithm and the effectiveness of our proposed adaptive transmission strategy.  
Figure 5: The transmission rate vs. the PS factor.  
Figure 8: The convergence of the TTPT determining algorithm.  
Figure 6: The outage probability vs. the number of relay nodes.  
Figure 7: The outage probability vs. the SNR.
## 4 Quantization of CSI Feedback  
In FDD, the terminal estimates the downlink CSI and feeds it back to the APs and CPU. To reduce the overhead of feedback, the estimated channel information is quantized before feedback.  
\[\hat{g}_{mk}^{q}=Q(\delta_{mk}). \tag{26}\]  
According to the [PERSON] theorem, the quantized output is decomposed into a desired signal component and an uncorrelated distortion [26] as follows:  
\[\begin{split}\hat{g}_{mk}^{q}&=F_{mk}\delta_{mk}+ \epsilon_{mk}\\ &=F_{mk}(g_{mk}-\tilde{g}_{mk})+\epsilon_{mk}\\ &=F_{mk}g_{mk}+\epsilon_{mk}^{\prime},\end{split} \tag{27}\]  
where \(F_{mk}\) is obtained from the linear MMSE estimation, \(F_{mk}=(1-\varsigma_{mk})\), and \(\varsigma_{mk}=\frac{\mathbb{E}[(\delta_{mk}-\hat{g}_{mk}^{q})^{2}]}{\mathbb{E }[\hat{g}_{mk}^{2}]}\) is the distortion factor. The power of the non-Gaussian effective noise \(\epsilon_{mk}^{\prime}\) is as follows:  
\[\mathbb{E}[\epsilon_{mk}^{\prime}(\epsilon_{mk}^{\prime})^{*}]=(1-\gamma_{mk} )\gamma_{mk}\mathbb{E}[\hat{g}_{mk}^{2}]+(1-\gamma_{mk})^{2}\mathbb{E}[\hat{g }_{mk}^{2}]. \tag{28}\]  
The number of quantization bits, \(b\), is at least 1. As \(b\) increases, the quantization error \(\varsigma\) decreases, and there is less loss of channel information due to quantization. Under the high-resolution assumption, i.e., more than 4 bits, approximately the following distortion exists:  
\[\varsigma\approx\frac{\pi\sqrt{3}}{2}2^{-2b}. \tag{29}\]With CB precoding, the \(m\)th AP transmits the signal  
\[x_{m}^{q}=\sqrt{\rho_{ap}}\sum_{i=1}^{K}\sqrt{\eta_{mi}}(\hat{g}_{mi}^{q})^{*}s_{ i}, \tag{30}\]  
and the signal received by the \(k\)th terminal is  
\[y_{k}=\sum_{m=1}^{M}g_{mk}x_{m}^{q}+w_{k}=DS_{k}^{q}+UI_{kk^{\prime}}^{q}+ES^{q}, \tag{31}\]  
where  
\[DS_{k}^{q}=\sqrt{\rho_{ap}}\sum_{m=1}^{M}\sqrt{\eta_{mk}}\hat{g}_{mk}^{q}(\hat{ g}_{mk}^{q})^{*}s_{k}, \tag{32}\]  
\[UI_{kk^{\prime}}^{q}=\sqrt{\rho_{ap}}\sum_{i\
eq k}^{K}\sum_{m=1}^{M}\sqrt{\eta _{mi}}\hat{g}_{mk}^{q}(\hat{g}_{mi}^{q})^{*}s_{i}, \tag{33}\]  
\[ES^{q}=\sqrt{\rho_{ap}}\sum_{i=1}^{K}\sum_{m=1}^{M}\sqrt{\eta_{mi}}\hat{e}_{mi }^{\prime}(\hat{g}_{mi}^{q})^{*}s_{i}. \tag{34}\]  
The signal-to-interference-plus-noise-ratio (SINR) of the \(k\)th terminal is  
\[\text{SINR}_{k}^{q}=\frac{\rho_{ap}\left(\sum_{m=1}^{M}\sqrt{\eta_{mk}}\hat{g }_{mk}^{q}(\hat{g}_{mk}^{q})^{*}\right)^{2}}{\rho_{ap}\sum_{i\
eq k}^{K}\left( \sum_{m=1}^{M}\sqrt{\eta_{mi}}\hat{g}_{mk}^{q}(\hat{g}_{mi}^{q})^{*}\right)^{2 }+\rho_{ap}\sum_{i=1}^{K}\sum_{m=1}^{M}\eta_{mi}\hat{g}_{mi}^{q}(\hat{g}_{mi}^{ q})^{*}\mathbb{E}[\hat{e}_{mk}^{\prime}(\hat{e}_{mk}^{\prime})^{*}]+1}. \tag{35}\]  
The quantization of the estimated channel vector introduces additional quantization error into the channel estimation error. Therefore, more channel error is introduced into the available downlink rate in FDD cell-free massive MIMO after CSI quantization, leading to available downlink rate degradation. At the same time, more quantization bits increase the CSI feedback overhead, which is proportional to the number of quantization bits.
## 3 BBC Code Design and Optimization
### BBC Design Method
Additionally, the generation principle of the gold sequence results in fewer code families than that of the Weil sequence. Therefore, the Weil sequence is selected as the pseudocode sequence for the subsequent BBC design. The specific design process of the BBC sequence is shown in Figure 3.  
1. Input parameters to select the order for generating PRN codes;
2. Generate a specified order of bipolar Weil sequence families based on the instructions;
3. Sequentially feed the pseudo-random sequences from the code family into the bit-balanced code design process until completion;
4. Group the input pseudo-random code sequences by every \(n_{code}\) chips (\(n_{code}=f_{chip}/f_{data}\), where \(f_{data}\) is the data rate, representing the number of PRN codes corresponding to the combination window);
5. To minimize the disruption to the characteristics of the original pseudo-random code sequence, perform a preliminary screening of the PRN sequence groups segmented by \(n_{code}\). The preliminary screening condition is that the absolute value of the sum of PRN chips in each group must be less than or equal to a variable \(T_{0}(T_{0}=\lfloor n_{code}/10\rfloor)\). If the condition is met, proceed to the next step; otherwise, discard the sequence and return to step 3; \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**PRN**} & \multirow{2}{*}{**Status**} & \multirow{2}{*}{**Code**} & \multirow{2}{*}{**Max** **Cross-**} & \multirow{2}{*}{**Remarks**} \\  & & & **Euclidean** & **Euclidean** \\ \hline m & Original & 1023 & \(-60.20\) & \(-20.38\) \\ (Polynomial coefficients (10, 3) and (10, 5, 4, 2)) & \(-1\) or \(1\) & 1024 & \(-23.30\) & \(-21.03\) \\ \hline gold & Original & 1023 & \(-24.21\) & \(-23.94\) \\ (m-preferred pair polynomial coefficients (10, 3) and (10, 8, 3, 2)) & \(-1\) or \(1\) & 1024 & \(-21.12\) & \(-22.25\) \\ \hline \multirow{2}{*}{Weil} & Original & 1031 & \(-24.22\) & \(-24.37\) \\  & Cyclic truncation & 1024 & \(-23.82\) & \(-23.95\) \\ \hline \hline \end{tabular}  
* \({}^{1}\)\(E_{corr}\) represents the correlation value, and \(N\) represents the length of the PRN sequence.  
\end{table} 
Table 1: Comparison of the correlations of the three sequences. Figure 2: The maximum sidelobes of Autocorrelation and Cross-correlation for the three types of sequences. For PRN sequences that meet the conditions, mark the indices of chips in each group that can be flipped;  
Based on the sum \(\pm T_{0}\) of the PRN chips in the group, randomly flip \(|T_{0}|/2\) of the marked chips to make the sum equal to 0;  
Reassemble the PRN sequences flipped by the group in step 7 into a new ranging PRN sequence in the order of their group numbers. This sequence is the bit-balanced code sequence, which is added to Set 1, and then returned to step 3.
# Photo-Fermentative Bacteria Used for Hydrogen Production
## 2 Photo-Fertanative Bacteria for Hydrogen Production
### Metabolic Pathway of Photo-Fementative Bacteria  
Hydrogen metabolism is of great importance in maintaining the stability and performance of various microbial biotopes at the ecosystem level. Hydrogen has been used as an electron donor for reductive dehalogenation by several microbes and for the presence of hydrogenase enzymes [23]. There are certain key factors associated with the metabolic pathways of these microbes that enhance hydrogen production. These include light absorption, as these bacteria possess photosynthetic pigments that allow light energy to be absorbed from the environment, followed by the incorporation of organic substrates, such as sugars and volatile fatty acids, which are then catabolized through metabolic pathways.  
This light energy enables the reduction of protons to produce molecular hydrogen while carbon fixation takes place for bacterial growth. _R.__palustris_, which has been extensively studied for hydrogen production, assimilates carbon dioxide into organic compounds via the Calvin-Benson-Bassham cycle, which contributes to bacterial growth and its biomass production, although this cycle is not the primary route of hydrogen production in this species [24]. The photosystem of PNSB is not very efficient at splitting water, so no oxygen is involved, making it suitable for hydrogen production. Protons are pumped into the periplasmic space, as electrons move through the electron transport chain to ferredoxin, which delivers these electrons to the nitrogenase enzyme to reduce molecular nitrogen to ammonia, creating a proton gradient or proton motive force. This is followed by the hydrogenase enzyme present in these bacteria, which catalyzes the reversible reaction of proton reduction to form molecular hydrogen, which accumulates in the periplasmic space. In the absence of nitrogen, nitrogenase functions similarly to hydrogenase, catalyzing the reduction of protons with the electrons derived from ferredoxin.  
The processes of hydrogen production and efflux generate ATP as a form of energy through a process called chemiosmotic ATP synthesis, which is used by the cells for various metabolic processes [25]. A general outline of the metabolic pathway followed by these photo-fermentative bacteria is presented in Figure 3, and a detailed description of the complete process is given in Figure 4.  
Figure 2: Bacteria mostly studied for photo-fermentative hydrogen production.