esa-sceva/satcom-chunk-collection huggingface dataset:
### Tocopherol Synthesis
Two spontaneous natural mutations in the _Or_ were recognized in _Brassica olercea_, which were responsible for the orange color of the inflorescences [64]. The overexpression of the _Or_ leads to an increase in carotenoid content in transgenic potatoes [65] and maize [66]. During fruit ripening, a signal transduction cascade in response to the plant hormone ethylene involves proteins of the ETHYLENE-INSENSITIVE 3/ETHYLENE-INSENSITIVE 3-LIKEs (EIN3/EILs) family. In tomato plants, CRISPR/Cas9 knockouts of _eil2_ were shown to produce yellow or orange fruits, in contrast to the red wild-type tomato fruits. Further analysis of the transcriptome and metabolome data of the ripe fruits of the mutant and the wild-type showed that TF EIL2 is involved in the accumulation of \(\beta\)-carotene through direct regulation of the expression of the SIERF.H30 and SIERF.G6 genes, which, in turn, are involved in the regulation of the \(\beta\)LCY gene in tomatoes (SILCYB2) [67]. Thylakoid lumen acidification under high light conditions activates violaxanthin de-epoxidase, resulting in zeaxanthin formation from violaxanthin through antheraxanthin [68]. The interconversion reaction, which is the epoxidation of zeaxanthin by zeaxanthin peroxidase localized on the stromal side of the thylakoid membrane, takes place under low light intensities [29]. Conversion of violaxanthin to neoxanthin is catalyzed by neoxanthin synthase (NXS). Fibrillins are lipid-associated proteins which are known as structural components of carotenoid sequestering in plant chromoplasts [69; 70]. Some fibrillins have the ability to bind proteins involved in the synthesis of plastid isoprenoids, thus contributing to their activity. Fibrillin 5 (FBN5) was found to be essential for PQ biosynthesis. FBN5 stimulates enzymatic activity of SPS1 and SPS2 through binding to its solanesyl moiety [71]. The same authors have shown that homozygous Arabidopsis mutants with the FBN5 encoding gene knocked out were seedling-lethal, and the growth rate of transgenic plants with low FBN5-B levels was slower than that of wild-type plants. FBN6 was shown to interact with PSY and increase its enzymatic activity [72].
## 3 Results
### Application Tests
#### 3.3.3 Validation Tests  
Once the verification process (mathematical and computational) succeeds, it is necessary to validate the code, i.e., assure that the output from the code to a given physical (electromagnetic in this case) corresponds (up to a certain acceptable error level) to the physical solution of the problem. In this context, we show an example of validation with an array of pyramidal horn antennas. The horn antenna, shown in Figure 9a, is designed to work at 10 GHz, with a WR90 waveguide as feed (with dimensions 22.86 \(\times\) 10.16 mm) and a straight section of 10.5 mm. To excite the problem, we have used the fundamental mode of the waveguide (transversal electric [35]) through \(\mathbf{\Phi}\) in (9). Note that no impressed currents \(\mathbf{J}\) or \(\mathbf{M}\) have been used to excite the problem. The pyramidal section has a height of 61.554 mm, whereas the size of the broader face is 40.131 \(\times\) 29.31 mm. We set up two different problems and we qualitatively compare them. On one of the problems, we use the approach in Section 2.2.3 (Figure 9b) to set up a 2 \(\times\) 2 grid. The unit cell is obtained by enclosing the horn antenna with an exterior box of dimensions 68.58 \(\times\) 50.8 \(\times\) 83 mm. On the boundary of the whole problem, we place an Absorbing Boundary Condition (ABC), i.e., we set the boundary as \(\Gamma_{\mathrm{C}}\) with \(\mathbf{\Phi}=0\). We use Perfect Electric Conductor for the structure of the horn, i.e., these faces belong to \(\Gamma_{\mathrm{D}}\). We focus here on the validation of the substructuring technique and, therefore, we compare the solution of this problem with an equivalent problem set up without using substructuring techniques at all. The equivalent problem consists of four horn antennas placed in a 2 \(\times\) 2 configuration and meshing at once the whole problem with the four antennas, where again we use an ABC on the outer boundary of the problem. We use a unit cell and the one Schur approach of Section 2.2.3 in Figure 9b, that we compare with the conventional FEM solution of the equivalent problem in Figure 9c. The differences between both approaches are not significant and they are due to the different meshes that are used (the division between unit cells forces us to have slightly different meshes even though we use the same size for the discretization elements). Moreover, no visible differences are observed between the different cells.  
Figure 9: Pyramidal horn in 2 \(\times\) 2 configuration. The subfigure (**a**) represents the definition of the model, (**b**) is the result without using unit cells, and (**c**) is the output of the code using one unit cell in a 2 \(\times\) 2 configuration.
# The Role of Fungi in Food Production and Processing
## 1 Introduction
### Overview of the Significance of Fungi in the Context of Food Microbiology and Processing
Throughout the past few centuries, numerous scientists have contributed to a vast body of knowledge about fungi, underscoring the significance of comprehending their role in food production. This review provides insights from previous work to accelerate fungal research in food production and to highlight the research advances in the following six major topics: 1. Exploration of the diverse fungal species present in various food products; 2. Methodology for fungal identification and characterization in food samples; 3. Fungi as food spoilage agents; 4. Mycotoxins and food safety; 5. Fungal fermentation; and 6. Current trends and future directions.
## 5 Simulation Setting and Results
### Total Successfully Received Packets  
Figure13 indicates the comparison of total received information packets at the upper sink nodes. The ratio of information packet secured by the sink node of the CoEERD is higher than the EERD, CoDBR, and ODBR. The reason for this achievement is that the CoEERD scheme uses the weighting function parameters: highest residual energy, lowest BER and lowest distance which ensures the accuracy of the maximum information at the final DSN. The CoDBR scheme uses two RLNs during the cooperation with the DSN and considers only the depth or the selection of RLNs and DSN. The contribution of maximum nodes in CoDBR during the routing consumes maximum energy and retains the burden on the close-to-surface nodes, which die quickly and result in data loss. The ODBR is a non-cooperative scheme which loses maximum information in information proceeding. The EERD is also non-cooperative and considers the robust function parameters for the choice of data advancer node, which increases the possibility of the maximum packets received at the final DSN.
## 4 Multi-Objective Optimization Design
### Constraints and Limitations  
* Dimensional constraint. According to the analysis in Section 3.2, the constraints on the design parameters are as follows: \[\left\{\begin{array}{c}2\;\mathrm{mm}\leq r\leq 5\;\mathrm{mm}\\ 10\;\mathrm{mm}\leq b\leq 20\;\mathrm{mm}\\ 0.2\;\mathrm{mm}\leq t\leq 0.5\;\mathrm{mm}\end{array}\right.\] (18)
* Maximum stress constraint. CMs, which rely on material deformation to achieve motion, should typically ensure that the maximum stress (\(\sigma_{\mathrm{max}}\)) endured by the material during maximum displacement remains below the yield stress (\(\sigma_{y}\)) of the material. This is crucial for guaranteeing the material's fatigue life. In the case of this optical angle nano-adjustment mechanism, \(\sigma_{\mathrm{max}}\) is observed at the flexible hinge at the maximum output angle. The calculation formula for \(\sigma_{\mathrm{max}}\) is provided below [9; 32]: \[\left\{\begin{array}{c}\sigma_{\mathrm{max}}=\frac{E(1+\eta)^{\frac{9}{20} }}{\eta^{2}f(\eta)}\varphi_{\mathrm{max}}\leq\frac{\sigma_{y}}{\mathrm{SF}}= [\sigma]\end{array}\right.\] (19) where \(\eta=t/2r\) is a dimensionless coefficient; \(\mathrm{SF}\) is the safety factor, and where \(f(\eta)\) is as follows: \[\left\{\begin{array}{c}f(\eta)=\frac{1}{2\eta+\eta^{2}}[\frac{3+4\eta+\eta^ {2}}{(1+\eta)(2\eta+\eta^{2})}+\frac{6(1+\eta)}{(2\eta+\eta^{2})\mathrm{15}} \;\mathrm{tan}^{-1}(\frac{2+\eta}{\eta})^{0.5}]\end{array}\right.\] (20)
# No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
## 1 Introduction
### Related Work  
In recent decades, a lot of NR-IQA methods have been published in the literature. NR-IQA models can be divided into two categories, such as distortion-specific and general methods. Specifically, distortion-specific methods assume a certain distortion type (e.g., blur [6] or JPEG compression noise [7]) present in the image, while general algorithms do not make any assumptions about the noise or distortion type present in the image. Early NR-IQA methods mainly fell into the distortion-specific category. On the other hand, general NR-IQA algorithms can be divided into three groups, i.e., natural scene statistics (NSS)-based, HVS-based, and learning-based ones.
## 4 Solution Algorithms
### A Branch-and-Bound Algorithm Combined with Enhanced Lower Bound Rules  
In this subsection, we present a customized branch-and-bound algorithm for the model M1; the flowchart of the algorithm is shown in Figure 15.  
The algorithm first generates the initial temporary lower bound \(lb\) by calling Algorithm 1 and obtains the initial upper bound \(ub\) using the rule described in Section 3.2, then the initial solution can be marked as the one in the root node. A best-lower-bound-first search strategy is adopted in the algorithm, that is, when executing the branching process on the pending node of the enumeration tree, we always choose the node with the smallest lower bound value. To implement this strategy, we use a priority queue to store the search nodes and they will be sorted by the lower bound value. In the initialization stage of the algorithm, the root node is placed in an empty priority queue. For large-scale instances, it is difficult to enumerate all nodes in a limited computational time. Therefore, we also define the solution gap tolerance \(\eta\) and the maximum number of enumerating iterations \(n\) as the terminal conditions. The algorithm starts selecting nodes from the priority queue and executes the branch-and-bound procedure until the queue becomes empty. We choose the top one in the priority queue each iteration, update the best global lower bound \(LB\), and calculate the gap between \(LB\) and \(UB\). Then, the algorithm checks if the termination conditions are met, i.e., whether the gap is less than or equal to \(\eta\) or the number of iterations exceeds \(n\). In the branching process, we assign the active station \(j\) of the current node to the DC and accordingly create child nodes for every feasible assignment of station \(j\) to each DC. In this process, the proposed Algorithm 1 is called to calculate the temporary lower bound \(lb\) and the temporary upper bound \(ub\) for each child node. For children nodes, if their temporary lower bounds are greater than UB they will be pruned, otherwise, they will be pushed into the priority queue. The detailed algorithm execution process is summarized in Algorithm 2, and Figure 15 provides the flow chart of the customized branch-and-bound algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**GLB for Standard QAP**} & \multicolumn{2}{c}{**Lower Bound Estimation in**} \\ \hline \multirow{2}{*}{First stage decision} & problem type & LAPs & GLAPs \\ \cline{2-4}  & cost & fixed cost independent of flows & the product of distances and flows \\ \cline{2-4}  & problem type & LAPs & GLAPs \\ \cline{2-4}  & cost & fixed cost independent of flows & the product of distances and flows \\ \hline \multirow{2}{*}{Cost backward propagation} & Optimal solutions of the LAPs in & Optimal solutions of the GLAPs \\ \cline{2-4}  & second stage decision & in second stage decision \\ \hline \hline \end{tabular}
\end{table} 
Table 10: A comparison of obtaining lower bounds in QAP and QSAP-C.  
Figure 15: Flowchart of the customized branch-and-bound algorithm.
# Intrusion Detection Model Based on Improved Transformer
## 3 Materials and Methods
### Loss Function  
Focal loss [30] is a loss function that was originally proposed for object detection tasks with highly imbalanced datasets. In this paper, it is applied to handle the highly imbalanced intrusion detection dataset. The focal loss function adjusts the weights of positive and negative samples to enable the model to prioritize difficult-to-classify samples, typically those belonging to the minority class in imbalanced datasets. This helps to alleviate the issue of data imbalance and improves the model's ability to accurately classify both positive and negative samples. The application of the focal loss function enables the model to better handle the data imbalance and improve its classification ability.
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## Appendix A Study Design Choices  
_Resource Selection for FHIR Conversion_  
We carefully selected a subset of FHIR resource types for the LLMs to use in converting input text. The chosen resource types were Condition, Observation, MedicationStatement, Procedure, AllergyIntolerance, Encounter, Immunization, and EpisodeOfCare.  
This decision was based on their collective ability to comprehensively represent the clinical complexities inherent in most possible scenarios. This curated set encompassed a broad spectrum of healthcare activities, from diagnosis and treatment to patient history and follow-up. By focusing on a small set, we established a robust foundation to ensure that the LLMs had the correct tools not only to accurately model the majority of clinical scenarios in these specialties but also to capture all details due to their intrinsic complexity.  
The absence of resources specifically for imaging is justified by the fact that the primary focus of this study was on extracting information from textual reports. Furthermore, the selected set of resources was sufficient for representing the key elements of mammography and dermatology reports. The findings within dermatology and mammography reports were captured using the Condition resource, while instances related to imaging observations were recorded using the Observation resource. Procedures such as biopsies or surgical interventions were documented using the Procedure resource. Medications prescribed for treatment were captured using the MedicationStatement resource, and patient allergies and intolerances were recorded using the AllergyIntolerance resource. Encounters was used to document patient visits and the services provided, while immunizations were recorded using the Immunization resource.  
This selection allowed us to ensure that the LLMs followed our guidelines, using the most relevant and comprehensive set of FHIR resources for the given clinical contexts.
## 4 Results of Probabilistic Stability and Performance Assessment
### Inverter Power Level Impact on System Stability and Performance
#### 4.2.1 Damping Factor Analysis  
Two different behaviors for solid and weak grids can be seen from Figure 8a. The damping factor was not affected by the inverter power level in the strong grid. In contrast, it was highly influenced by the power level in the weak grids. As discussed in the previous subsection and as verified by participation factor analysis in Table 3, for the strong grids (higher SCRs), the critical mode was initiated by an AVC due to its lower bandwidth among different control loops. Moreover, grid currents and other control loops did not have much impact. However, under weak grids (lower SCRs), the inner current controller (CC) and outer control loops (PLL, DVC, AVC, and active power balance (APB)) affected the damping of the critical mode at lower and higher output powers, respectively.
## 5 Risk Assessment
### Risk Assessment Result Validation  
Compared with the risk assessment results of previous studies, our results have certain similarities. For example, [PERSON] (2017) used the statistical analysis method to evaluate the risk of land subsidence in Shanghai and concluded that the spatial characteristics of the land subsidence risk in Shanghai are as follows: in terms of the overall composition of the three-level system, that is, from the central city to the higher-risk area, some suburbs are medium-risk, and other suburbs are lower- or low-risk. There are some differences between the results of this previous study and the results of our study due to selecting different evaluation index factors. However, most of the regional levels are the same.  
Figure 16: Distribution of land use types in Shanghai city during the past 20 years; (**a–c**) are the land types in 2000, 2010, and 2020, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Land Type** & **2000** & **2010** & **2020** & **Change** \\ \hline Cultivated land & 3118.52 & 2456.50 & 2032.91 & \(\downarrow\) \\ Grassland & 1.67 & 6.17 & 69.24 & \(\uparrow\) \\ Forest land & 13.68 & 13.96 & 72.23 & \(\uparrow\) \\ Water body & 192.49 & 189.96 & 222.54 & \(\uparrow\) \\ Construction land & 1320.90 & 1981.31 & 2245.17 & \(\uparrow\) \\ Unused land & 0.81 & 0.18 & 5.99 & \(\uparrow\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Land use change area/km\({}^{2}\). The up arrow in column of “change” represents increase and the down arrow marks decrease.  
Figure 17: Distribution map of hazard (**a**) and vulnerability assessment (**b**) of land subsidence in Shanghai city.  
Figure 18: The distribution map of land subsidence risk in Shanghai.  
Meanwhile, the Shanghai Provisions on the Administration of Geological Hazard Risk Assessment, published on 29 June 2023 [59], indicated a zoning map of geological hazard risk assessment units, the results of which had obvious similarities with Figure 18, indicating that the results of this paper are reliable.
# Channel Estimation Algorithm Based on Parrot Optimizer in 5G Communication Systems
## 1 Introduction
Based on this, the present study proposes a PO-based channel estimation technique and validates its effectiveness in specific 5G channels. Specifically, this research employs the Parrot Optimizer for channel estimation, exploring its potential application without prior channel statistical information. Compared to traditional MMSE algorithms and deep learning methods, the Parrot Optimizer demonstrates greater flexibility in practical deployments, particularly without the need for complex training processes. The specific contributions of this paper are as follows:  
1. This paper proposes a Parrot Optimizer based channel estimation scheme (Parrot Optimizer-Channel Estimation, PO-CE), designed for pilot-based channel estimation to enhance OFDM system performance under frequency-selective fading conditions. The PO-CE scheme leverages the global search capability of the Parrot Optimizer to optimize the channel estimation process, improving system robustness and accuracy;
2. Extensive simulations were conducted to evaluate the performance of the PO-CE scheme in terms of BER and MSE, comparing it with traditional LS and MMSE methods. The experiments, based on the 5G New Radio (NR) TDL-300 fading channel model, covered various modulation schemes (such as QPSK, 16 QAM, 64 QAM, and 256 QAM) and multiple pilot configurations (1/3, 1/6, 1/9, and 1/12). Simulation analysis confirmed the effectiveness of the PO-CE scheme under different channel conditions and system configurations. The experimental results indicate that the PO-CE channel estimation scheme outperforms the traditional LS algorithm in all scenarios regarding BER and MSE metrics and achieves performance comparable to the MMSE algorithm. Specifically, the PO-CE scheme exhibits high channel estimation accuracy and low error rates across different modulation schemes and pilot configurations, demonstrating its promising application potential in practical 5G communication systems. This research highlights the application prospects of the Parrot Optimizer in 5G channel estimation and its feasibility as an effective optimization tool, providing new insights and technical support for future optimization algorithm-based channel estimation methods.
## 6 Conclusions and Future Research  
This paper presented a set of the most recent routing and scheduling algorithms for the WirelessHART technology. Through the discussions of routing topics, we can verify that this is a rich field, in which the variety of the algorithms and solutions is growing along with the number of applications where WirelessHART is applied. For example: monitoring applications can use mesh networks in order to improve their reliability through the use of spatial redundancy (multiple paths), and control applications can use temporal redundancy (multiple copies of the transmitted data). For other applications, there is also the possibility of using a hybrid solution.  
As indications for future research in this area, we can suggest studies of the metrics used so as to include new characteristics (e.g., robustness), using the ideas from the weighted functions of the routing algorithms in the decision making for the scheduling algorithms and developing an automatic adaptation of the function's weights based on the network life cycle. Based on the fact that the majority of the scheduling algorithms presented use dedicated slots for scheduling, we can also propose for future study the usage of shared slots in scheduling (as in [92]) in order to improve reliability. Furthermore, it is worth noting that the heterogeneous traffic issue presented in [99] has still not been fully explored in the literature, and thus, is an interesting topic for future works. Another possible future study that we can point to would be about the influence on the network's behavior of the concentration of the transmission in specific regions of the superframe.  
A point that could be more explored in the new technologies presented is the matter of their scalability. The performance of bigger networks is more affected by the overhead of the routing and scheduling algorithms; thus, scalability is a key issue when choosing algorithms for a WirelessHART network.  
One difficulty encountered was how to compare the different technology implementation methods, even though all of them used simulations as a validation method. One possible question is whether it is possible to compare those simulations fairly and how to do so. One possible solution may be a simulation tool that would submit all of the protocols to a standard test that would present the same conditions for all of the tested protocols.  
Finally, as the standard does not define routing and scheduling algorithms and noting the plurality of solutions presented, we can conclude that the development of optimal solutions for the diverse scenarios of the IWSN will demand more time despite the efforts made so far.