esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Operator
#### 3.4.5 Frameworks for Operators in Industry 4.0  
The concept of Industry 4.0 appeared to provide cohesion to different visions regarding the future of manufacturing, connected by Key Enabling Technologies (KET). Alongside the development of such technologies, recent research has focused on theoretical frameworks to conceptualize the use of the KET and its impact on human operators. [PERSON] et al. [221] studied how to evaluate the Human-Robot Collaboration in terms of safety, trust and operator experience; [PERSON] et al. [222] looked into the future Industry 4.0 interaction between operator and workstation, composed of three subsystems: observation, analysis and reaction.  
The key role of operators in the era of the 4 th Industrial Revolution has been identified by numerous authors, coining the term _Operator 4.0_[223]. Industry 4.0 technologies should support operators in their tasks, either by directly helping them or by providing meaningful information to assembly system design engineers. [PERSON] et al. developed a theoretical human-centered framework for Operator 4.0 using digital twin-based simulation, and real-time human data capture can be used to provide insights on operator ergonomics and mental workload [69]. In a similar way, [PERSON] et al. propose a method of measuring the wellbeing and performance of operators at assembly stations using electro-dermal activity [88]. Industrial IoT is another technology that can be used for capturing human and machinery data for understanding human impact on Industry 4.0 assembly systems [65]. Understanding the operator's information needs is vital for the design of smart assembly factories [57].  
**In conclusion**, new operator-centered frameworks are appearing to conceptualize the role of people in the 4 th Industrial Revolution era. The key role of operators has been identified by numerous authors, coining the term operator 4.0 [223]. Industry 4.0 technologies should support operators in their tasks, either by directly helping them or by providing meaningful information to assembly system design engineers.
# Multi-Objective Gate Allocation Problem Based on Multi-Commodity Network Flow Model
## 5 Numerical Study
### Results Analysis
#### 5.2.3 Analyzing the Optimal Allocation of Gates  
Based on the criterion of minimum idle time variance, the final weight combination is set to \(\alpha=0.2,\beta=0.6,\gamma=0.2\). Its optimized objective values for gate cost, taxi time, and walking distance are 4.2, 494,155.2, and 19,500,658, respectively. As shown in Figure 5, the gate allocation results are visualized using a Gantt diagram, where the dark blue represents the occupied time of each flight, and light blue represents the buffer time. Correspondingly, the distribution of the number of flights assigned to each gate is provided in Figure 6.  
Overall, all gates are fully utilized and there are no idle gates during the entire time period. Although remote gates have the highest cost in terms of walking distance, taxi time, and gate size, they still cannot avoid being used. In our final results, each of the two remote gates (i.e., G19 and G20) served one flight. As mentioned earlier, during the time period of 15:00-18:00, a large number of flights were piled up, resulting in all contact gates being  
Figure 4: Variance of idle time under different clusters.  
Figure 5: Gantt diagram for gate allocation results.  
occupied. This phenomenon can be clearly seen from Figure 5. Additionally, among all contact gates, G1 has the most flights allocated, with a total of seven flights. This is because it has absolute advantages in taxi time and walking distance among small-size gates. In contrast, G16 and G18 are allocated the fewest flights due to their larger taxi time and the larger proportion of taxi cost in this weight combination.  
In addition, we also counted the relative utilization rate and absolute utilization rate of each gate, as shown in Figure 7. On the whole, the utilization rate of the contact gate is relatively high, except for the remote gate. Additionally, the absolute utilization rate of most of them is above 40%, while the relative utilization rate is above 55%. Similar to the distribution of the number of flights, G1 has the highest absolute utilization and relative utilization, while G16 and G18 have the lowest. From the above conclusions, it can be found that the established multi-objective optimization model for gate allocation can effectively balance the interests of multiple parties and obtain a higher utilization rate. On the premise of ensuring relevant interests, by considering the robustness of the allocation results, the number of flights and the idle time at each gate are also balanced, thereby reducing the difficulty of airport management.  
Figure 6: Number of flights allocated for each gate.  
Figure 7: Utilization rate for each gate.
# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 5 Conclusions
In the future, more suitable sampling methods or retrieval algorithms will be considered to further improve the model's retrieval accuracy for heavy rain and torrential rain. Conceptualization, [PERSON] ([PERSON]) and Y.B. ; methodology, [PERSON] ([PERSON]) and Y.B. ; software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]); formal analysis, [PERSON] ([PERSON]) and G.P.P. ; data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, Y.B., Q.L., G.P.P., [PERSON] ([PERSON]) and F.W. ; visualization, [PERSON] ([PERSON]); supervision, [PERSON]; funding acquisition, Q.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China (2022 YFC3004102), the Natural Science Foundation of China (U2242212), the Fengyun Application Pioneering Project (FY-APP-2022.0603), the Fengyun Application Pioneering Project (2022) Xu Jianmin Meteorological Satellite Innovation Center Project (FY-APP-ZX-2022.0208), the Water Science and Technology Project of Jiangsu Province (2023022) and the Shanghai Aerospace Science and Technology Innovation Foundation (AST2021-032). YY-4B data are available free of charge upon registration at the FENGYUN Satellite Data Center ([[https://satellite.nsmc.org.cn/](https://satellite.nsmc.org.cn/)]([https://satellite.nsmc.org.cn/](https://satellite.nsmc.org.cn/)) (accessed on 3 August 2023)). GPM IMERG data are available at the Precipitation Data Directory ([[https://gpm.nasa.gov/data/directory](https://gpm.nasa.gov/data/directory)]([https://gpm.nasa.gov/data/directory](https://gpm.nasa.gov/data/directory)) (accessed on 9 October 2023)) upon registration. All other data will be made available upon request. The authors would like to thank the China National Satellite Meteorological Center and Global Precipitation Measurement website, which are freely accessible to the public. Also, the authors would like to sincerely thank Python for offering powerful analysis computational tools such as scikit-learn. The authors would like to thank the editor and anonymous reviewers for their constructive comments and suggestions for improving the manuscript. The authors declare no conflicts of interest.
## 3 Preliminaries
### Basic Block Extraction  
Considering the security and performance overhead, we choose basic blocks (BBs) as the granularity of the proposed integrity-monitoring architecture. The BB is defined as a fine-grained code fragment which only contains instructions that will be executed sequentially [12]. To avoid modifying the ISA, the jump instructions are used to divide BBs. In general, the start address of each BB is the target address of the previous jump instruction, and the end address is the address of the next jump instruction. Without a memory management unit, the BB uniquely corresponds to a start address, which means that the corresponding BB can be found by searching for the start address.  
Different instruction sets have different types of jump instructions. To obtain the internal details of the embedded processor core, we select the open-source embedded processor OpenRISC1200 (OR1200) for research, which is a 32-bit scalar RISC processor with a Harvard micro architecture [11]. In OR1200 ISA, there is a clock cycle gap from the execution of a jump instruction to the code running within the jump target address, which is called the delay slot. Therefore, the actual start address and end address of BBs need to consider the delay slot. The jump instruction types of OR1200 ISA are shown in Table 1. The BB extraction in OR1200 ISA is shown in Figure 1.  
In Table 1, there are mainly three types of jump instructions in the OR1200 processor instruction set, namely, direct jump (I,j and I,jal), branch jump (I.bf and I.brl), and indirect jump (I.jr and I.jalr). There is one target address for direct jump, which is the effective address (EA). There are two target addresses for branch jumps. According to the flag being 1 or 0, the target address may be an EA, or it may be the instruction address immediately following the end address of the current BB, which is the end address plus 0x4. For example, in Figure 1, the jump instruction contained in BB1 is I.bf 2020. When the flag is 1, the target address of I.bf is EA, which is 0x2020. When the flag is 0, the target address of I.bf is the end address of BB1 plus 0x4, that is, 0x202c + 0x4 = 0x2030. Due to the start address of BB being the target address of the previous jump instruction, the start address of BB2 is 0x2020, and the start address of BB3 is 0x2030. The jump target address of an indirect jump instruction is related to the register value used in the instruction. The interrupt return instruction (l.refe) is a special jump instruction. Since the proposed method does not involve interruption, l.refe is not considered.
## 2 Modified Case Study System  
The double end transmission link model as described in Figure 1 is utilized to implement the developed technique. The transmission link is connected between Buses 1 and 2. Sources 1 and 2 represent the large area utility grids. Buses 1 and 2 are considered as the sending and receiving end buses, respectively. Table 1 presents the various system parameters utilized in this study [3]. The proposed technique can be incorporated into the relay, which is located on the side of Bus 1 of the transmission link to trip the link at the moment of fault incidence. The developed technique can be implemented at the sending terminal of the transmission line. However, for the ring main feeders, it can be utilized at both terminals. The recoded current signals are processed by utilizing the developed technique to generate the trip command signal to the circuit breaker for the outage of the transmission line during the fault event.
## 5 Application of the Developed Approach
The factors of safety for both the designed support system (\(F_{sup}\)) and the surrounding rock mass, considered as the natural support component (\(F_{sm}\)), can be derived from the following equations:  
\[F_{sup}=\frac{p_{3}}{p_{a_{sup}}} \tag{23}\]  
\[F_{sm}=\frac{p_{a_{sup}}}{p_{a_{eq}}} \tag{24}\]  
In cases of competent and fair rock masses (classes I and II), the ground-support equilibrium point was located on the yielding part of the SCC curve. This is in contrast to weak rock masses, where the ground-support equilibrium point was situated on the 2 nd elastic deformation part of the SCC curve. This indicates that the maximum yielding capacity of the support was reached, due to the large value of the ground displacement. Consequently, the steel sets are no longer able to slide, thus acting like a rigid element. As a result, the factor of the safety of the roadway support (\(F_{sup}=2.00\)) for a weak rock mass was significantly lower than in case of a fair (\(F_{sup}=4.17\)) or competent rock mass (\(F_{sup}=5.31\)). Meanwhile the factor of the safety of the surrounding rock mass component was greater in the case of the weak rock mass (\(F_{sm}=2.54\)) than in cases of a fair (\(F_{sm}=1.32\)) or competent rock mass (\(F_{sm}=1.33\)). In all analyzed cases, the support capacity at the yielding part of the SCC curve exceeded the static load exerted on the steel arches at a certain value of ground displacement. This observation, connected with the calculated values of the factors of safety, suggests that the designed support system effectively ensures roadway stability--thereby mitigating ground failure resulting from the loss of confinement and the eventual collapse of the fractured rock mass. Table 2.0 SCC curve coupled with GRC curve for a fair rock mass (class II). After reaching the equilibrium point between the rock mass and yielding steel support, a more rigid and durable support may be employed, such as a thin layer of shotcrete or a thick layer of in situ-made concrete. Embedding steel sets in shotcrete or concrete may yield some additional advantages:  
* creation of a favorable, triaxial state of stress on the roadway boundary,
* protection of steel arches from corrosion,
* shielding the rock mass from the influence of water inflow and the mine's atmosphere.
# Priority-Driven Resource Allocation with Reuse for Platooning in 5G Vehicular Network
## 5 Results and Analysis
### Simulation Configuration and Performance Metrics
Therefore, it is essential to analyze how effectively resources are allocated to general vehicles as the number of platoon vehicles increases. As mentioned Section 4, the following performance metrics were used to evaluate the performance of the proposed algorithm in the simulation: \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Bandwidth & 10 MHz \\ Resource Block Bandwidth & 180 kHz \\ Resource Block Time Slot & 1 ms \\ PC5 Frequency Band & 5.9 GHz \\ Resource Allocation Interval (TTI) & 1 ms \\ Maximum number of re-request (\(n_{\text{max}}\)) & 3 \\ Transmission Power (\(P_{0}\)) & 23 dBm \\ Antenna Gain & 3 dBm \\ Noise Power (\(N_{0}\)) & \(-110\) dBm \\ Path Loss Factor (\(\beta\)) & 4 \\ Threshold SINR (\(\gamma_{\text{min}}\)) & 4.3 dB \\ Number of vehicles per Platoon & 5 vehicles \\ Simulation time & 60 s \\ Modulation and Coding Scheme (MCS) & 5 \\ Pathloss model & (3 GPP-TR 36.873) [50] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters in Simu5G and SUMO. * Average Allocation Rate (AAR): the rate of the number of vehicles successfully allocated resources to the total number of vehicles requesting resources [51]. * Average delay in general vehicles: the average time taken for a packet transmitted by a general vehicle to reach its destination. * Average delay in platoon vehicles: the average time taken for packets transmitted by a platoon vehicle to reach the next platoon vehicle. The AAR measures how reliably resources are allocated to general vehicles and is used to assess the first term of the objective function, which aims to improve resource utilization. In V2X applications, average delay is a critical performance metric directly linked to meeting real-time communication requirements. Delays in the resource allocation process can result from resource re-request procedures and allocation failures. Under congested conditions, the priority-based algorithm allocates fewer resources to general vehicles than to platoon vehicles, consequently increasing the delay experienced by general vehicles during resource allocation. To evaluate the impact of these conditions, we calculate the delay experienced by general vehicles during the resource allocation process, which is used to assess the second term of the objective function focused on minimizing communication delays. Furthermore, the average delay in platoon vehicles is evaluated to determine how effectively the priority-based allocation algorithm supports their communications. At this point, since CAM is transmitted to a random nearby vehicle each time, it is difficult to accurately analyze the delay time. Therefore, the delay time of general vehicles was evaluated based on the BSMs transmitted to the RSU. Additionally, the delay of platoon vehicles was used as an index to evaluate how effectively the proposed resource allocation algorithm meets the communication requirements of platoon vehicles.
# Linomycin-Induced Transcriptional Alterations in the Green Alga _Raphidocelis subcapitata_
## 3 Results
### Analysis of DEGs  
Based on BLAST results, a total of 13,383 (100%), 13,347 (99.73%), 7511 (56.12%), and 3680 (27.49%) genes, respectively, were matched in the Ensembl, UniProtID, GO, or KEGG databases. The correlation coefficient (R\({}^{2}\)) in each group was beyond 0.93 as found by conducting Pearson's correlation analysis (Figure 2a), which showed a strong correlation. In line with this, a PCA of our 12 samples represented that the triplicate samples in each treatment group clustered together (Figure 2b); gene expressions in low and medium groups were not separated from those in control groups. With the increase in LIN concentration, a clear separation appeared, suggesting that high exposure of LIN may give rise to adverse effects, which is demonstrated by the significant inhibition of algal growth.  
Figure 1: A 7-day growth inhibition plot of _R. subcapitata_. Asterisk (*) demonstrates a significant difference (\(p<0.05\)) in control and LIN dosing groups. Data expressed as mean \(\pm\) SD of three parallel measurements (n = 3).  
Figure 2: _Cont._  
Figure 2: Transcriptic analysis of LIN dosing groups in alga _R. subcapitata_ on day 7. (**a**) Correlation test of DEGs in each group. (**b**) PCA of FPKM profiles of DEGs after adding ROX. (**c**) A heatmap of centered and scaled FPKM values of DEGs in all dosing groups of _R. subcapitata_. (**d**) Venn diagram of the distribution of DEGs in each group. C: control group; L: low treatment group; M: medium treatment group; H: high treatment group.  
The LIN exposure sample was compared with the control to identify DEGs, and NGS screening identified 21 (21 up- and 0 downregulated), 92 (64 up- and 28 downregulated) in low and medium groups, respectively. High groups represented a significantly differentially expressed compared with the low exposure (low and medium) groups, which identified 2514 (663 up- and 1851 downregulated) genes. (Figure 2c,d and Figure S1). To verify the accuracy of RNA-seq analysis, two DEGs involved in porphyrin and chlorophyll metabolism pathways were detected by qRT-PCR. The results of this experiment are represented by linear regression curves of fold change levels determined by qRT-qPCR and NGS data, and therefore conform to the reliability of NGS analysis (Figure S2). For instance, genes _hemE_ and _cdo1_ demonstrated a moderate to high correlation estimated by qRT-PCR analysis with the \(p\) value = 0.0044 and R\({}^{2}\) = 0.57, and \(p\) value \(<\) 0.0001 and R\({}^{2}\) = 0.86, respectively.
## 2 Materials and Methods
### Design Proposal of the Measurement Test Rig  
For the implementation of the load test rig, a prototype design was developed from a structural perspective, which assumes the placement of four independent functional units on a common base structure (Figure 3). The arrangement of the individual units was designed to ensure their independent operation while guaranteeing maximum operator safety during the monitoring of the correct cycle of activities in the gear reducer operations.  
The goal was to design the structure of a testing device that enables the measurement of multiple static and dynamic characteristics through a single combined measurement station. The design was conceived to minimize the risk of result distortion caused by the versatility of the used measurement system. This was reflected in the selection of appropriate types of measurement sensors, as well as data collection and evaluation units, with an emphasis on their scalability for future expansion or changes in the measured parameters.  
The proposed design also allows for the testing of various sizes and types of gear reducers used in robotic drives. Sufficient system rigidity was considered to eliminate undesirable influences on measurement accuracy. The design solution provides flexibility for testing gear reducers from different manufacturers, differing in both external geometry and internal configuration.
## 3 Biological Activities
### Antifungal Activity
After 24 h (log phase) of biofilm formation, the components were added for preformed biofilm. The biofilm inhibitory concentration value for citral was determined to be 64 mg/mL, while thymol inhibited biofilm formation at a lower concentration of 32 mg/mL [86]. Interestingly, as an antifungal agent, citral targets the cell membrane, whereas thymol targets the cell wall. These were evident by the differential expression of ERG11/CYT450 and CNB1 and sorbitol protection assay against citral and thymol. Exogenous ergosterol binding assay showed that citral does not bind directly to ergosterol. Although both citral and thymol exert similar antifungal activity, different action on the cell wall and membranes depicts their diverse mode of action [86]. The administration of citral on _C. tropicalis_ biofilm leads to a fungicidal effect. Furthermore, the differential expression of proteome has revealed twenty-five proteins in _C. tropicalis_ biofilm, which were differentially expressed in the presence of citral. Among these, amino acid biosynthesis (_Metop_, _Gln1p_, _Pha2p_), nucleotide biosynthesis (_Xpt1p_), carbohydrate metabolism (_Eno1p_, _Fba1p_, _Gpm1p_), sterol biosynthesis (_Mvd1p/Erg19p_, _Hem13p_), energy metabolism (_Dmm1p_, _Coalp_, Ndk1p, Atp2p, Atp4p, Hts1p), oxidative stress (_Hda2p_, _Gre22p_, _Tsa1p_, _Pst2p_, _Sod2p_), and biofilm-specific (_Adh1p_, _Ape1p_, _Gsp1p_) proteins were identified [87]. The overexpression of oxidative stress-related proteins indicates the response of biofilm cells in combating oxidative stress during citral treatment. Moreover, the upregulation of _Adh1p_ is of particular interest because it subsidizes biofilm inhibition through ethanol production as a cellular response. The augmented expression of Mvd1p/Erg19p signifies the effect of citral on ergosterol biosynthesis. The presence of citral has shown an increment in hexosamine and ergosterol components in the extracellular matrix of _C. tropicalis_ biofilm. Hence, it is indicated that the cellular response towards citral acts through multifactorial processes [87]. In the same study, the influence of citral and thymol on _C. tropicalis_ and its biofilms was compared with the expression levels of certain antifungal tolerance genes. The antifungal and antibiofilm activities of both were studied using XTT reduction assay, field emission scanning electron microscope (FE-SEM), confocal laser scanning microscope (CLSM), and real-time reverse transcription polymerase chain reaction (RT-PCR) analysis. Citral and thymol have damaged the cells with distorted surfaces and less viability. Quantitative real-time PCR analysis showed augmented expression of the cell membrane biosynthesis genes, including ERG11/CYT450 against citral and the cell wall-related tolerance genes involving _CNB1_ against thymol, thus depicting their differential mode of actions [86].