esa-sceva/satcom-chunk-collection huggingface dataset:
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 2 Families of Sequence-Specific Endoribonuclease TA Toxins
### MqsR  
_mqsAR_ loci are highly induced in biofilm and were originally classified as regulators of biofilm formation [73; 74]. Later, it was shown that MqsR overexpression inhibits protein synthesis and that it has a ribosome independent endoribonuclease activity [49]. The _E. coli_ MqsR recognition sequence is GCU and GCA [49]. Cleavage occurs at the 5\({}^{\prime}\) and/or 3\({}^{\prime}\) end of the G-residue. Despite exhibiting sequence-specific ribonuclelease activity independently of a ribosome, MqsR belongs to the same protein family as RelE and YoeB, which are the ribosome-dependent ribonucleases [39].  
MqsA, an antitoxin of MqsR, also belongs to a distinctive protein family from MazE [39; 75]. There are many differences between MqsA and MazE, with regard to the structure and the mechanism of inhibiting the toxin's activity. MqsA consists of a novel fold containing a zinc ion, although it merely serves structural role and not a catalytic role [39]. Unlike MazE, whose unstructured C-terminal tail is crucial for binding with the toxin to inhibit its activity, the structure of MqsA is not flexible [39; 76]. Furthermore, MqsA does not directly bind to the active site of the toxin to block catalysis. Binding with MqsA induces a substantial conformational change in MqsR so that it loses its activity. Additionally, when bound to its antitoxin, the active sites in two MqsR proteins face one another, making them inaccessible to the target mRNA.  
Despite its distinct phylogenetic origin and mechanism of toxin sequestration, MqsRA autoregulates its own transcription in a similar manner as other Type II TA systems [49; 76; 77]. The antitoxin or TA complex binds with DNA at the palindromic sequence and suppresses its own expression. MqsRA also regulates the expression of other genes as a global transcriptional regulator, which will be further discussed in the later sections.
# Air Entrainment and Air Demand in the Spillway Tunnel at the Jinping-I Dam
## 2 Project Description and Instrumentation  
### Project Description  
The Jinping-I Dam is located on the downstream of the Yalong River in southern China; its location and layout are shown in Figure 1. The total reservoir storage is 7.76 billion m\({}^{3}\), of which 63% is active. The dam is composed of several discharge structures, including: one controlled spillway with four gates on its crest, five intermediate orifice openings, two bottom orifice outlets, and one spillway tunnel on the right bank. The maximum discharging capability is 15,400 m\({}^{3}\)/s. Its power station, with an installed capacity of 3600 MW, produces an average annual energy generation of 16.6 billion kW-h. The objective of this project is to supply energy for expanding industrialization and urbanization, improve flood protection, and prevent erosion.  
The spillway tunnel is 1407 m long and possesses a structure pattern of \"dragon tail-down\" in the free flow tunnel (as shown in Figure 1). In order to reduce the length of spillway tunnel with high-velocity flow, 75% of the total head is concentrated in the \"dragon tail-down\" section, which only accounts for 25% of the total length. The free flow tunnel is with a horse-shaped cross section, the slope angle of its oblique line segment is 24.36\({}^{\circ}\), and the arc radius of its ogee segment is 300 m. A schematic diagram of the free flow tunnel is shown in Figure 2.  
Four aeration devices are employed in the \"dragon tail-down\" section to prevent cavitation damage (as shown in Figure 2). The first three aerators consist of ramps and offsets, and all of their offsets are 1.5 m high, with the heights of the ramps being 0.5, 0.4, and 0.2 m, respectively.  
Figure 1: Location and layout of the Jinping-I Dam.  
Figure 2: Schematic diagram of the free flow tunnel.  
The fourth aerator is a three-dimensional one, composed of sudden-narrowed sidewalls and convex ramp. Protection lengths of the four aerators are 81.0, 87.8, 128.6, and 115.6 m, respectively. Air ducts connecting to the top of the tunnel are arranged symmetrically on both sides of each aerator. The spillway tunnel of the Jinping-I Dam is constructed inside a mountain, so it is a relatively closed system; three air intake wells for air supply are built. Inlets of the air intake wells are exposed to the atmosphere and outlets of them are set on the roof of the spillway tunnel. The first air intake well is a circular section with a radius of 5.2 m, and the other two air intake wells gradually transform to square sections with the outlet cross section being 9 m wide and 4 m high.
## 2 Methods
Regarding intuitive exposure assessments, respondents had to respond as to how much they agree with four different statements on EMF exposure issues (1) \"No matter how low the EMF exposure is, there is still a risk due to the fact that even a minimal exposure may result in negative health impacts\"; (2) \"Man-made electromagnetic fields are more dangerous than natural ones\"; (3) \"The deployment of base stations in residential areas is not a mere technical question, but one that should respect the views of the concerned citizen\" and (4) \"No matter whether or not I am exposed to EMF radiation, base stations simply scare me.\" All of these statements were assessed on a 5-point Likert scale from 1 = \"Not at all\", to 5 = \"Absolutely\". For an improved measurement of RF EMF exposure perception, as well as affective or moral evaluations of particular exposure situations, picture-guided scenarios were used. Every item included a picture where the exposure situations were displayed. The questionnaire contained several exposure scenarios presented in randomized order; however, the focus of the present analysis is on mobile phones and base stations. The relevant exposure scenarios used were (1) a woman using a cell phone, and (2) living in the vicinity of an antenna on the rooftop of the building next door that could be seen from a window in an everyday situation. The following questions then had to be answered. For affective evaluation: \"Imagine you are the person depicted in the picture/living close to the building with the antennas, what kind of feelings about exposure would you have in this situation?\" on a 5-point Likert scale from 1 = \"Very positive\", to 5 = \"Very negative\". For the moral evaluation: \"In your opinion, does the situation depicted by the picture elicit any moral concerns about exposure?\" on a 5-point Likert scale from 1 = \"Not at all\", to 5= \"Yes absolutely\". The subjective exposure perception was operationalized by: \"In your opinion, how strong is the exposure to the person in the above picture/for a person living close to the building with antennas?\" on a 5-point Likert scale from 1 = \"Low\", to 5 = \"High\". Further descriptions of this approach can be found in our previous papers ([21,25]). Testing the effects of exposure reduction on the acceptance of RF EMF, subjects were asked about their willingness to accept a base station deployment close to their homes in fictional exposure reduction scenarios. The distance between their homes and a base station (in m) was used to operationalize the acceptance of a base station, evaluated in four scenarios: Scenario 1 (S1): 0% exposure reduction, S2: 30% exposure reduction, S3: 50% exposure reduction, and S4: 70% exposure reduction, compared to the current level. (Question: \"Roughly at what distance (m) would you accept a base station close to your home?\". \"...if the exposure was reduced by 30%?\", \"...if the exposure was reduced by 50%?\", \"...if the exposure was reduced by 70%?\"). Further details are provided elsewhere [21]. All analytical calculations were conducted using IBM, SPSS(r), V20 (IBM, Armonk, NY, USA).
## 2 Materials and Methods
### Methods  
#### 2.3.1 Overall Methodology  
The overall workflow of this paper is presented in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variables** & **Product** & **Spatial Resolution** & **Temporal Resolution** & **Period** & **Access Address** \\ \hline GPP & PML-V2 & 500 m & 8 days & 2000–2018 & [[https://data.tpdc.ac.cn](https://data.tpdc.ac.cn)]([https://data.tpdc.ac.cn](https://data.tpdc.ac.cn)) (accessed on 27 July 2023) \\ TEMP & & & & & \\ PRCP & ERA5 & 0.25\({}^{\circ}\) & hourly & 2000–2018 & [[https://modis.gifc.nasa.gov](https://modis.gifc.nasa.gov)]([https://modis.gifc.nasa.gov](https://modis.gifc.nasa.gov)) (accessed on 13 July 2023) \\ SR & & & & & \\ LUCC & Globe & & & & \\ Land30 & 30 m & yearly & 2000–2020 & [[http://www.globallandcover.com](http://www.globallandcover.com)]([http://www.globallandcover.com](http://www.globallandcover.com)) (accessed on 5 January 2022) \\ DEM & START & 1 km & yearly & - & [[http://www.globallandcover.com](http://www.globallandcover.com)]([http://www.globallandcover.com](http://www.globallandcover.com)) (accessed on 23 June 2023) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Remote-sensing data used in this study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Latitude (\({}^{\circ}\)N)** & **Longitude (\({}^{\circ}\)E)** & **Data Period (\({}^{\circ}\)E)** & **Annual Precipitation (mm)** & **Annual Temperature (\({}^{\circ}\)C)** & **Altitude (m)** & **Vegetation Type** \\ \hline Canghuishan (CBS) & 42.40 & 128.1 & 2003–2010 & 713 & 3.6 & 738 & Forest \\ Canglin (CL) & 44.59 & 123.51 & 2007–2010 & 470 & 4.9 & 171 & Grassland \\ Dangio (DX) & 30.50 & 91.07 & 2006–2010 & 450 & 1.3 & 295.7 & Grassland \\ Dlouch (DL) & 42.05 & 116.28 & 2007–2008 & 275 & 1.7 & 1350 & Grassland \\ Qanyanzhou (QYZ) & 26.74 & 115.06 & 2003–2010 & 1542 & 17.9 & 100 & Forest \\ Halei (HB) & 37.68 & 101.34 & 2003–2010 & 535 & \(-\)1.2 & 3190 & Bush \\ Yucheng (YC) & 36.83 & 116.57 & 2003–2005 & 582 & 13.1 & 28 & Cropland \\ Inner Mongolia (NMG) & 43.33 & 116.40 & 2004–2008 & 338 & 0.9 & 1200 & Grassland \\ Dame (DM) & 41.64 & 110.33 & 2015–2017 & 255.2 & 4.6 & 1407 & Grassland \\ Xiaolangid (CLD) & 35.03 & 112.47 & 2016–2017 & 642 & 13.4 & 410 & Forest \\ Xishuangbanna (OSBN) & 21.93 & 101.27 & 2003–2010 & 1493 & 21.8 & 750 & Forest \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information on the flux stations used in this study.
# Analysis of 5G Channel Access for Collaboration with TSN Concluding at a 5G Scheduling Mechanism
## 1 Introduction  
By offering guaranteed access to the resources of a network consisting of Ethernet bridges, Time-Sensitive Networking (TSN) plays an important role in many applications that demand reliable data links, especially in the field of industrial communication [1]. As the new 3 GPP standard for mobile communications, 5G is emerging in this application field, described by the 5G application profile ultra Reliable Low Latency Communications (uRLLC). As a highly configurable solution for wireless communication in both small and large areas, 5G has the potential to meet the tasks of many current and future commercial application areas, such as industrial automation and Vehicle to Everything (V2X) [2]. By entering this new application field, 5G comes into contact with technologies of wired industrial communication, such as TSN; a lack of compatibility between these technologies, however, limits exploitation of the full potential of both.  
By reaching an appropriate degree of compatibility, 5G could expand the range of use of TSN to the wireless area, opening up the application field of deterministic wireless communications. As 5G takes part in an ongoing standardization process, possible amendments can be further investigated to support the convergence of 5G and TSN. In this work, an analysis of the understanding of packet priorities regarding channel access in the respective technologies is carried out. On the basis of the identification of incompatible architectural conditions, a novel approach to allocating the resources of the 5G radio channel is derived.
## 4 Results and Discussion
### Diaphragm Movement and Stress Characteristics of the DSDC
#### 4.2.1 Diaphragm Deformation Under Different Pressure Conditions  
Figure 15 shows the FEM simulation results of the diaphragm deformation under varying pressure conditions. As the pressure acting on the diaphragm surface increased, the diaphragm deformation became progressively more pronounced, with deflection magnitudes increasing until the diaphragm fully conformed to the cavity geometry. As shown in Figure 15a, the first-stage diaphragm exhibited smoother volume (\(V_{mo}\)) variation with increasing pressure difference (\(\Delta p\)) compared to the second-stage diaphragm, requiring a higher pressure difference (\(\Delta p\)) to achieve maximum chamber volume (\(V_{mo}\)). Figure 15b,c shows that the radial diaphragm deflection followed similar trends with pressure difference for both stage diaphragm compressors. Moreover, the results indicated that the diaphragm center required significantly greater pressure difference to contact the cavity geometry compared to the edge regions, which revealed that insufficient oil-gas pressure difference could lead to residual clearance volume at the diaphragm center, thereby reducing the volumetric efficiency. Specifically, the first-stage diaphragm required a pressure difference of 85.58 kPa to fully conform to the cavity surface, while the second-stage diaphragm necessitated 75.49 kPa to achieve a similar contact.  
Figure 13: Experimental performance of the DSDC under various conditions. (**a**) Gas mass flowrate. (**b**) Volumetric efficiency.
# Production and Maintenance Planning for a Deteriorating System with Operation-Dependent Defectives
## 6 Sensitivity and Results Analysis
### Influence of the Pace of the Failure Rate  
We complement the sensitivity analysis with the analysis of the effect of the pace of deterioration of the failure rate. For this purpose, we examine the influence of the adjustment parameter \(\theta\), which accelerates the failure rate of the machine. For this parameter, we examine two different scenarios with values \(\theta\ =0.15\) and \(1\), as illustrated in Figure 14. From the results, we note that whenever the adjustment parameter has a moderate value of \(\theta\ =0.15\), the production thresholds have a smoother increment as the machine deteriorates. On increasing the adjustment parameter to \(\theta\ =1\), the production thresholds reach its maximum values more abruptly. Another more pragmatic reason for this pattern is because with the increment of the parameter \(\theta\), we are accelerating the pace of deterioration of the machine, and so the production thresholds increase earlier as protections against shortages and defectives.  
A close examination of Figure 15 shows that the repair/major maintenance policy also is significantly affected by the variation of the pace of the failure intensity. To have a better understanding about such influence, we analyze four different scenarios with values = 0.25, 0.50, 0.75 and 1.00. From the maintenance traces presented in Figure 15, we realized that when the adjustment parameter is low, for instances \(\theta\ =0.25\) and 0.50 it implies that the system experiences less frequent failures, and so major maintenance is less recommended. There are more frequent failures when the parameter is set to a higher value such as \(\theta\ =0.75\) and \(1\), where more major maintenance is conducted. From this pattern, we can draw the inference that with higher values of the parameter \(\theta\), we accelerate the pace of deterioration of the machine, and this promotes the conduction of more major maintenance to rapidly mitigate the effects of deterioration.  
Figure 14: Sensitivity of the pace of the failure rate on the production policy. (**a**) \(\theta\ =0.15\); (**b**) \(\theta\ =1\).  
Figure 15: Sensitivity of the pace of the failure rate on the repair/major maintenance policy.  
Throughout the discussion of the sensitivity analysis, we observe that the obtained joint control policy is significantly influenced by the deterioration of the machine. Furthermore, this sensitivity analysis allows us to state that the structure of the proposed control policy is consistent, and that such policy is well characterized by the control parameters \(Z_{i}(\cdot)\) and \(T_{m}(\cdot)\). At considering simultaneously in our integrated model, production planning and the repair/switching strategies, we seek to operate more efficiently the manufacturing system.
# Graph Theory Approach to the Vulnerability of Transportation Networks
## 2 Materials and Methods
### Selected Domination Concepts in Graph Theory
The cardinality of a minimum edge-dominating set is called the edge-domination number of a graph \(G\) and is denoted by \(\gamma^{\prime}(G)\). Furthermore, the weighted edge-domination number extends the possibility of their application to searching for the critical edge sets in graphs represented a transportation network. The weighted edge-domination number \(\gamma^{\prime}_{w}(G)\) of edge-weighted graph \((G,w_{e})\) is defined as the smallest weight \(w(X)=\sum_{e\in X}w_{e}(e)\) of all edges in edge-dominating set \(X\subseteq E(G)\)[36]. Another interesting concept is introduced by [PERSON] et al. in [37]. They examined a question concerning the vulnerability of the communications network under link failure. They supposed a situation where someone does not know which nodes in the network act as transmitters but does know that the set of such nodes can be built as a minimum dominating set in the related graph. Thus, they asked about the fewest number of communication links that he must serve so that at least one additional transmitter would be required so that communication with all sites is possible. In this way, they introduced the new parameter called the bondage number of a graph. It is defined in the following way. The bondage number of nonempty graph \(G\) is the minimum cardinality among all sets of edges \(E\) for which \(\gamma(G-E)>\gamma(G)\)[37; 38]. Thus, the bondage number of graph \(G\) describes the smallest number of edges whose removal from \(G\) leads to a graph with a domination number larger than that of \(G\). Because the above definition of bondage number is not enough for transportation network analysis, in [31], the author defined the bondage-connected number \(b_{c}(G)\) of nonempty graph G. It means the minimum cardinality among all sets of edges \(E\) for which \(\gamma(G-E)>\gamma(G)\) and graph \(G-E\) is connected. Finally, one more concept refers to the domination in graphs, so-called edge subdivision. This operation is defined as follows [28; 29; 30; 32; 39]. For a graph \(G=(V,E)\), subdivision of the edge \(e=uv\in E\) with vertex \(x\) leads to a graph with vertex set \(V\cup\{x\}\) and edge set \((E\ \{uv\})\cup\{ux,xv\}\). Let \(G_{e,t}\) denote a graph obtained from \(G\) by subdivision of the edge \(e\) with \(t\) vertices (instead of edge \(e=uv\) we put a path \((u,x_{1},x_{2},...,x_{t},v)\)). For \(t=1\) we write \(G_{e}\). This approach corresponds with the domination subdivision number \(\xi(G)\) or \(sd(G)\) of a graph \(G\). It is the minimum number of edges that must be subdivided (where each edge can be subdivided at most once) to increase the domination number.
## 5 Analysis of the Forecasting Results
### Multi-Step Forecasting  
Multi-step forecasting is an effective way to test the accuracy of the forecasting method. Therefore, this paper employed multistep forecasting on the proposed hybrid model. The experimental results are used to verify the accuracy of the proposed approach. The results of the multi-step forecasting are presented in Table 6. Figures 5-7 show the output values of different methods using multi-step forecasting. In 1-step ahead forecasting, the MAPE values of the BP, ARIMA, RBF, and the proposed model are 5.46%, 5.22%, 4.83%, and 3.07%, respectively. In 2-step ahead forecasting, the MAPE values of these models are 6.70%, 6.42%, 5.97%, and 4.55%. Additionally, in 3-step ahead forecasting, the MAPE values of the four models are 7.83%, 7.15%, 6.92%, and 5.46%, which indicates that the proposed hybrid model achieves a better performance than other conventional models.  
When comparing the results of three-step forecasting with each other, one-step forecasting clearly performs better than the others.  
**Remark 4**.: _These results can be summarized as follows: The proposed hybrid approach obtains higher accuracy compared with the traditional models used in our experiments in both single step and multi-step forecasting. Therefore, the proposed hybrid approach is valid._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **WI** & **E\({}_{\text{NS}}\)** & **E\({}_{\text{LM}}\)** & **WI** & **E\({}_{\text{NS}}\)** & **E\({}_{\text{LM}}\)** \\ \hline \multicolumn{6}{c}{Wind turbine 1} \\ \hline BPNN & 0.9410 & 0.7822 & 0.5403 & 0.9708 & 0.8850 & 0.6806 \\ RBFNN & 0.8926 & 0.6017 & 0.4630 & 0.9429 & 0.7751 & 0.6128 \\ ARIMA & 0.9476 & 0.8137 & 0.6512 & 0.9755 & 0.9024 & 0.7148 \\ EEMDCSSOVM & 0.9838 & 0.9332 & 0.7600 & 0.9918 & 0.9670 & 0.8268 \\ \hline \multicolumn{6}{c}{Wind turbine 2} \\ \hline BPNN & 0.9731 & 0.8889 & 0.6962 & 0.9412 & 0.7628 & 0.5877 \\ RBFNN & 0.9595 & 0.8320 & 0.6422 & 0.7035 & 0.3339 & 0.3248 \\ ARIMA & 0.9752 & 0.8933 & 0.7017 & 0.9503 & 0.7937 & 0.6128 \\ EEMDCSSOVM & 0.9929 & 0.9713 & 0.8388 & 0.9839 & 0.9350 & 0.7565 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the value of WI, E\({}_{\text{NS}}\) and E\({}_{\text{LM}}\) between the proposed model and some traditional models (BP RBF ARIMA).
## 2 Results
### Evaluation of Each VOC for Their Fumigant Activity against Meloidogyne hapla  
The fumigation activity of VOCs was evaluated by recording the mortality rate of the J2s and the egg hatching rate at the same distance from different concentrations of VOCs. The results showed that the 3-methyl-1-butanol, 5-methyl-2-hexanone, 2-heptanone, 2,5-dimethylpyrazine, and 6-methyl-2-heptanone all displayed fumigation activity against J2s, and the effect was more significant with increasing concentrations. Of these, 3-methyl-1-butanol showed particularly significant effects. When the concentration was 10 \(\mu\)L/mL, the mortality rate of 3-methyl-1-butanol to J2s was 99.7% (Figure 4). Additionally, through continuous observation, all VOCs at different concentrations had significant inhibitory effects on egg hatching in the first 4 days of treatment. The 3-methyl-1-butanol and 2-heptanone significantly reduced the hatchability of eggs at the concentration of 10 \(\mu\)L/mL, and the hatching rate was 20.8% and 13.5% after 7 days of observation, which was 32.3% and 55% lower than that of the control (Figure 5). The above results indicate that these VOCs have volatile diffusivity and can generally inhibit the hatching of eggs and have fumigation effects on J2s.  
Figure 4: Fumigant effects of six VOCs on second stage juveniles at different concentrations. Different lowercase letters indicate significant differences among treatments (\(p\) < 0.05); bars indicate the standard error of the means (\(n\) = 3).  
Figure 5: Impact of fumigation by six VOCs at different concentrations on egg hatching of root-knot nematodes after 1, 2, 3, 4, 5, 6, and 7 days of incubation. Different lowercase letters indicate significant differences among treatments (\(p<0.05\)); bars indicate the standard error of the means (\(n=3\)).