esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Inter-Cellular Transport Is Required for Biosynthesis of TIAs  
Studies have revealed that TIA biosynthetic pathway in aerial organs of _C. roseus_ occurs in four cell types: internal phloem-associated parenchyma (IPAP), epidermal cells, laticifers and diioblasts [53; 54]. The IPAP cells exist in the periphery of stem pith or in traxylary on the upper part of the vascular bundles in leaves and are full of chloroplast with the plastid-located MEP pathway enzymes [53; 55]. MEP pathway, which is required for the development and function of chloroplast, happens mostly in young green tissues [56]. The early step of seco-iridoid pathway, geraniol conversion into 10-oxogeraniol via geraniol 10-hydroxylase (G10H) are co-localized in the IPAP cells of young tissues such as leaves and roots [11; 57]. Iridoid oxidase (IO), 7-deoxyloganetic acid glucosyltransferase (7-DLGT) and 7-deoxyloganic acid hydroxylase (DL7H) are also localized to the IPAP cells and their transcripts, which are four times more abundant in the whole leaf than in the epidermis, while loganic acid methyl-transferase (LAMT) and secologanin synthase (SLS) are preferentially expressed in the leaf epidermis. Loganic acid, as the intermediate, is assumed to move from the IPAP cells to the epidermis to convert into secologain under the catalyzing of LAMT and SLS [58; 59; 60; 61; 62]. Tryptamine and secologanin are condensed to form strictosidine in epidermal cell of young developing shoots and leaves, meanwhile, STR- and SGD- catalyzed central steps also happen in the epidermis (Figure 3) [63]. Strictosidine is the central precursor of TIAs, some of which are directly formed in epidermis, such as ajmaline and catharanthine. Ajmaline is mainly accumulated in root epidermis, while catharanthine is mainly secreted to the surface of leaves by the ATP Binding cassette (ABC) transporter CrTPT2 to transport it from the epidermis to the leaf surface in the wax exudates (Figure 3) [64]. As for vindoline, the late precursor desacetoxyvindoline for vindoline biosynthesis is formed in epidermis, the enzymes taberosine 16-hydroxylase 2 (T16H2), 16-hydroxytabersonine-16-O-methyltransferase (16 OMT), T3O/T3R and \(N\)-methyltransferaseerase (NMT) are preferentially localized in epidermis [31; 32]. However, Desacetoxyvindoline 4-hydroxylase (D4H) and acetyl-CoA:4-O-deacetylvindoline 4-\(O\)-acetyltransferase (DAT), catalyzing the last two steps are confirmed to localize idioblasts and laticifers of leaves, stems and flowers [65]. Therefore, the intermediates desacetoxyvindoline or other accessory products need to be transported from epidermis to idioblasts or laticifers for a two-step collaboration to form vindoline [66], but whether it is necessary or how vindoline is transported out of idioblasts and laticifers remains unknown. Whether some stages of the translocation are accomplished passively through the symplasm or are controlled by plasmodesmata-localized proteins is still an open question.
## 1 Introduction  
Intrusion detection systems (IDSs) aim to identify and isolate all types of intrusion inside a computer or communication system [1]. Anomaly and misuse detections are popular in the field of detection. Existing systems result in high false alarm in multiple anomalies interlaced together. Despite the sophisticated algorithms in the history of development, an effective method discovering mixed-type attacks is still needed for security administrators.  
Mixed-type attacks refer to the attacking pattern in which the suspicious packets are interlaced with normal or abnormal packets. Hackers often attack by sending abnormal packets, and some attacks can be identified by a single packet [1]. However, attackers might use useless or massive normal packets in a mixed mode in order to cheat autodetection algorithms [1]. Thus, a simple analysis of a single packet will fail to define the attack, and subsequent packet contents have to be analyzed through a specialized method [2]. In this study, we are no longer restricted to one single packet or analytical moment, we use finite-state machines (FSMs) to associate non-single packet attacks to internal tracking states. A state in the FSM can signal an attack. We apply parallelized FSMs to capture multiple suspicious attacks on the same time. Subsequently, an artificial immune system (AIS) is used to learn the condition of state transition from packet information and generate a complete state transition diagram.  
The immune algorithm or AIS is derived from imitating biological immune process with unique learning and memory features and superior identification capability in system activity log for detecting unknown attack modes [3; 4]. When a virus enters an organism, the immune system will perform a chain of reactions to kill the foreign enemy. AIS possesses specificity, discreteness, adaptability, and learning and memory functions; hence, it can detect more new attacks than conventional methods and retain effective protection [5; 6; 7; 8]. AIS is widely applied in various knowledge disciplines, particularly in the information technology sector. However, most previous applications in intrusion detection are restricted in static model analysis and lack adaptability to external environments. Although recent deep learning in neural networks produce excellent results in IDS [9], insufficient works concentrate on unknown types of attack. Immune system can well adapt to unknown attacks through its massive learning and adaptive capability [10; 11]. Additionally, an FSM is a mathematical model of computation with finite number of states at any given time, initial state, and input triggering state transition with a predefined transition probability [3; 12]. The advantage of this model is to keep tracking events effectively in a directed graph. Therefore, this model can be used to discern multiple attacks among massive mixed packets.
## 2 Project Overview and the Synchronous Propulsion and Assembly Method
### Synchronous Propulsion and Assembly Method  
Based on the above engineering requirements, the synchronous propulsion and assembly technology is proposed in the hope that it will reduce the impact of long tunnel distances on construction periods. The synchronous propulsion and assembly technology is mainly used in the straight-line section of Meifu Road Well to 3# air shaft construction. The synchronous propulsion and assembly technology is adopted under the condition of suitable geology, as shown in Figure 2.  
Synchronous propulsion and assembly method is realized by intelligently controlling the pressure of the shield propelling cylinder. The difference with respect to the traditional shield is that the shield machine of the synchronous propulsion and assembly method lengthens the length of the jacking cylinder and realizes the synchronous assembly of some segments by using the surplus of the stroke of the push cylinder. The construction efficiency of the synchronous propulsion and assembly method is directly proportional to the remaining distance of the jack, which can shorten the construction time of each ring segment by 60% at most. The shield machine used in this project has a margin time for the assembly of three segments. Therefore, the focus of this paper is also on the stress of the three segments in the synchronous propulsion and assembly mode. The specific operation methods of the synchronous propulsion and assembly method are as follows:  
1. The first segment is assembled, the jack acting on the first segment shrinks, and the thrust of the jack smoothly transitions to the thrust of the corresponding working condition;
2. The second segment is assembled, the jack acting on the first segment shrinks, and the thrust of the jack is attached to the thrust of the  
Figure 2: Schematic diagram of the synchronous propulsion and assembly method.  
2. The assembly of the first segment is completed; the jack acting on the first segment is advanced. The second segment is retracted corresponding to the jack, and the thrust of the jack smoothly transitions to the thrust of the corresponding working condition. The above working conditions are to be continuously cycled until the assembly of three segments is completed, as shown in Figure 3. During construction, the shield machine will switch to the synchronous propulsion and assembly mode. The contraction of the jack and the setting of the thrust will be automatically carried out by the shield machine. Different from traditional shield propulsion, in the synchronous propulsion and assembly mode, the jack propulsion force will be reasonably distributed to the non-assembled segments.
## 4 Results and Discussions
### Performance of the IoT-Based Sensor  
An IoT-based sensor consists of a sensor device and a client program to retrieve sensor data and send them to a cloud server. It is important to analyze the IoT-based sensor performance under various conditions. Performance metrics such as network delay and CPU and memory usage were utilized in this study. [PERSON] and [PERSON] proposed network delay as a metric to evaluate the sensor device performance [79], while [PERSON] et al. utilized CPU usage as a metric to evaluate IoT device capabilities in different scenarios [80]. In our study, network delay was defined as the average time between sending sensor data from by the source (sensor device) and successfully receiving the data at the destination (MongoDB). The second performance metric was the average CPU and memory usage of the client program under various scenarios.  
In this study, the client program was a python-based program running on an IoT-based sensor device that collected sensor data such as temperature, humidity, gyroscope, and accelerometer data. An IoT-based sensor with Linux Raspbian OS Jessie; 1 GB RAM was used for the experiment. Communication between the IoT-based sensor and cloud server was implemented via Wi-Fi. Figure 7a shows the network delay for different amounts of sensor data. The results show that the network delay  
Figure 6: The web-based real-time monitoring system.  
increases as the amount of sensor data sent by the sensor device increases. It takes approximately 50 s for the IoT-based sensor to send 1000 sensor data points at the same time. However, in a real-case implementation, it takes less than 0.02 s to send the sensor data, as we only set one sensor data point (temperature, humidity, gyroscope, and accelerometer data) to be sent every 5 s. In addition, Figure 7b shows the CPU and memory usage of the client program. Four different reading period scenarios were evaluated, in which the client program was reading and sending sensor data to the cloud server every 5, 10, 30, and 60 s. The results showed that the reading period has a very small effect on CPU or memory usage. Regarding the computational cost of the client program, it should be noted that the program used less than 3% CPU and 18 MB for all reading periods.
## 2 Related Work
### Traffic Monitoring Based on Traffic Conditions  
The authors of [10] explained an intelligent transmission-control system employing cloud perspective and ML methods. The graphics of its next vehicular intersection are captured and saved in the cloud database. The concentration and vehicle characteristics are identified using the cloud image API. The condition is also returned to the subsequent traffic signal. The prior traffic signal, which is now the current signal, will track the progress of the next traffic signal and then proceed with the activity predicated on the conditions. In order to increase the safety and efficiency of ITM, the authors of [11] demonstrated that these methods can help us to anticipate traffic performance, automated traffic-signal management, driveway identification, and recognition of nearby objects/vehicles. Various researchers are working on intelligent transport systems, but better traffic management is still challenging [12]. The authors of [13] demonstrated that significant traffic surveillance frameworks transform smart urban areas. Many studies have been conducted regarding intelligent traffic-control systems centered on the IoT approach.  
The authors of [14] demonstrated that automatic traffic detecting is central to urban planning services and infrastructure. Intelligent connectivity sensor nodes estimate traffic flow, anticipate traffic jams, and adaptively control traffic movement. When performed correctly, this creates a level of consciousness that allows for more effective recourses and infrastructure usage. In another study [15], traffic flow, usage, and average density broadly utilized vehicular congestion assessments, most of which were acquired from pictures and videos recorded by computer vision applications. The authors of [16] proposed an IoT-enabled control system to gather, operate, and accumulate authentic traffic patterns for such a set of circumstances. Their primary goal was to deliver essential traffic information on road congestion and unexpected traffic collisions through the highway signaling system and increase the range of motion. The authors of [17] incorporated a framework to explore the effectiveness of the traffic model. The experimental findings demonstrate excellent vehicle detection and tracking precision and relatively low inaccuracy in highway occupancy prediction.
# Internet of Things and Long-Range-Based Smart Lampposts for Illuminating Smart Cities
## 2 Related Works and Overview of IoT
### Overview of IoT
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Parameters** & **Zigbee** & **BLE** & **6 LoWPAN** & **LoRa** & **Sigfox** & **NB-IoT** & **GSM/GPRS** & **Wi-Fi** \\ \hline
**Network** & **LAN** & **PAN** & **PAN** & **WAN** & **WAN** & **MAN** & **LAN** & **LAN** \\ \hline
**IEEE Standard** & 802.15.4 & 802.15.1 & 802.15.4 & 802.15.4 & 802.15.4g & 802.15.4g & NA & NA & 802.11a, b,g,n \\ \hline
**Frequency Band** & 868/915 MHz and 2.4 GHz & 2.4 GHz & 868/915 MHz and 2.4 GHz & 868/915 MHz and 2.4 GHz & 868/915 MHz & Licensed MHz & 850–1900 MHz & 2.4/5 GHz \\ \hline
**Range** & (10–50) m & 10 m & (10–50) m & 5 km (Urban), 20 km & 10 Km (Urban), 40 km (Rural) & (5–30) km & 100 m \\ \hline
**Network Topology** & Star, P2P, mesh, tree, & Star, bus & Star, mesh & Star of stars & Star & & Cellular system & Point-to-hub \\ \hline
**Power Consumption** & Low & Low & Low & Low & Low & High & High \\ \hline
**Company** & Zigbee Alliance & \begin{tabular}{c} Bluetooth \\ Special \\ Interest \\ Group \\ \end{tabular} & \begin{tabular}{c} Internet \\ Engineering \\ Task Force \\ \end{tabular} & \begin{tabular}{c} Semtech \\ Engineering \\ Task Force \\ \end{tabular} & \begin{tabular}{c} Sigfox \\ alliance \\ \end{tabular} & \begin{tabular}{c} 3 GPP \\ Company \\ \end{tabular} & \
\begin{tabular}{c} GSM \\ Association. \\ \end{tabular} &
\begin{tabular}{c} Wi-Fi \\ Alliance \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Technical Specifications of wireless communication technologies [41]. Figure 2: Four components of IoT.
# Modelling the Defect Processes of Materials for Energy Applications
## 2 Methodology
### Thermodynamic Methodology
Differentiating Equation (7) with respect to \(T\) or \(P\), we obtain the isobaric temperature derivative (\(\frac{\partial B_{A_{1-x}B_{x}}}{\partial T}|_{P}\)) and the isothermal pressure derivative (\(\frac{\partial B_{A_{1-x}B_{x}}}{\partial P}|_{T}\)) of the bulk modulus of the alloy:  
\[ \begin{array}{c}\frac{\partial B_{A_{1-x}B_{x}}(x)}{\partial T}|_{P}=\frac{ 1+x(\mu-1)}{1+x\left(\frac{\mu}{\lambda}-1\right)}\frac{\partial B_{A}}{ \partial T}|_{P}\\ +B_{A}\frac{(1-x)\mu(\beta_{Ge}-\beta_{Si})\left[1-\frac{x}{\lambda}(1-\mu) \right]-x\frac{\mu}{\lambda}(1-x+x\mu)\left(\frac{1}{B_{A}}\frac{\partial B_{A }}{\partial T}|_{P}-\frac{1}{B_{B}}\frac{\partial B}{\partial T}|_{P}\right) }{\left(1-x+x\frac{\mu}{\lambda}\right)^{2}}\end{array}  \tag{8}\]  
and  
\[ \begin{array}{c}\frac{\partial B_{A_{1-x}B_{x}}(x)}{\partial P}|_{T}=\\ \frac{1+x(\mu-1)}{1+x\left(\frac{\mu}{\lambda}-1\right)}\frac{\partial B_{A}} {\partial P}|_{T}-x\frac{\mu}{\lambda}\frac{(1-x)(1-\lambda)\left(1-\frac{1} {\lambda}\right)+(1-x+x\mu)\left(\frac{\partial B}{\partial T}|_{T}-\frac{1} {\lambda}\frac{\partial B_{B}}{\partial T}|_{T}\right)}{\left(1-x+x\frac{\mu }{\lambda}\right)^{2}}\end{array}  \tag{9}\]  
The volumetric coefficient of thermal expansion of the alloy, \(\beta_{A_{1-x}B_{x}}\), is given by [37]:  
\[ \begin{array}{c}\beta_{A_{1-x}B_{x}}(x,T)=\beta_{A}\frac{1+x(\mu-1)\left(1+ \frac{\frac{\beta}{\beta T}\left(\frac{\partial B}{\partial T}|_{T}\right)}{ \beta_{A}\left(\frac{\partial B}{\partial T}|_{T}\right)}\right)}{1+x(\mu-1)} \end{array}  \tag{10}\]  
The \(T\) derivative of the quantity \(\frac{\partial B}{\partial P}|_{T}\) in Equation (10) can be estimated through the approximation, \(\frac{\partial B/\partial P|_{T}\times\partial B^{S}}{\partial P|_{T}+2T \beta_{T}}\), where \(B^{S}\) is the adiabatic bulk modulus and \(\gamma\) is the Gruneisen constant [37]. Considering that the T dependencies of \(\frac{\partial B^{S}}{\partial P}\) and \(\gamma\) are practically negligible, \(\frac{\partial}{\partial T}\left(\frac{\partial B_{A}}{\partial P}|_{T}\right)\) can be approximated with \(2\gamma\left(\beta+T\left(\frac{\partial\beta}{\partial T}\right)|_{P}\right)\), which is tractable as it contains terms that can be accessed [37]. These relations provide a way to calculate the elastic and expansion properties, and \(c^{act}\) as a function of concentration. At \(T=0\) K, \(g^{act}\) is replaced by \(h_{o}^{act}\), and thus, \(c^{act}\) is equal to \(h_{o}^{act}/B_{o}\Omega_{o}\)[37]. With available experimental diffusion data for a temperature range, the mean value method is also sufficient to calculate \(c^{act}\)[50]. Employing Equation (2) in logarithmic form,  
\[lnD=lnln\,\left(fga_{0}^{2}\
u\right)-c^{act}\frac{B\Omega}{k_{B}T} \tag{11}\]  
This infers the linear dependence of the experimentally determined \(lnD\) with the calculated \(\frac{B\Omega}{k_{B}T}\) using the \(cB\Omega\) model for a single diffusion mechanism. Trivially, \(c^{act}\) is the slope of the corresponding linear fitting.
## 8 Conclusions  
To the best of our knowledge, this paper is the first attempt to minimize the total time employed by an AGV in completing a series of tasks under a dynamic scenario in which disruptions in the form of unexpected or random delay times might occur while the AGV is moving along defined pathways. We have presented a reactive heuristic, which is combined with a dynamic matrix of expected delays. This matrix is built using historical data to estimate the delay associated with a given circuit at any point in time. Additionally, we study the creation of the permutation of tasks for the proposed reactive heuristic and compare it to three other different approaches.  
Data analytics techniques are used to estimate the AGV interruption for each determined pathway during determined time spans. White Gaussian noise is also employed to artificially generate scenarios with circuit delays. Different levels of dynamism (low, medium, and high) are considered in creating the circuit delays. Computational evaluations were performed with four groups of small, medium, large and very large instances and ANOVA and Tukey tests were used to analyze results. These results show that the FIFO approach works worse than all the other ones, while the mean gap provided by the reactive approach is always is higher in absolute terms. The gaps increase as the level of dynamism grows, which confirms the usefulness of the proposed reactive approach in a dynamic environment as the one considered here for the AGVs.  
For future works, more than one AGV can be considered. In addition, machine learning models can be used to predict delays from historical data. Finally, simulation-optimization methods such as simheuristic [34] may also be explored.  
Conceptualization, A.A.J. and S.H.; methodology, X.A.M.; software, X.A.M.; validation, L.C. and M.P.; writing--original draft preparation, X.A.M., L.C. and M.P.; writing--review and editing, S.H. and A.A.J.; project administration, S.H. All authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by the Spanish Ministry of Industry, Commerce and Tourism (AEI-010500-2021b-54), the EU Comission (HORIZON-CL4-2021-TWIN-TRANSITION-01-07, 101057294 AIDEAS), and the Generalitat Valenciana (PROMETEO/2021/065).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pair of Approaches** & **Difference** & **Lower** & **Upper** & **P Adjusted** \\ \hline Reactive—Non-Reactive & \(-2.01\) & \(-2.6075\) & \(-1.4125\) & \(0.00000\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Tukey multiple comparisons of means (95% family-wise confidence level).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Df** & **Sum Sq** & **Mean Sq** & **F Value** & **Pr (\textgreater{}F)** \\ \hline Approach & 1 & 24.24 & 24.24 & 50.37 & 0.0000 \\ \hline Level of Dynamism & 2 & 7.88 & 3.94 & 8.19 & 0.00324 \\ \hline Instance & 3 & 4.56 & 1.52 & 3.16 & 0.05181 \\ \hline Residuals & 17 & 8.18 & 0.48 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results from the ANOVA analysis.
# At the Nexus of Blockchain Technology, the Circular Economy, and Product Deletion
## 3 Framework and Propositions
### Micro: Circular Economy at the Organizational and Consumer Level
#### Consumer Knowledge and Behavior  
A large fraction of consumers expects companies to be sustainable [66]. Autonomous motivations, which are ideally embedded into humans' sense of self, contain intrinsic and extrinsic motivations that provide energy to individuals to actively pursue the goal of environmental protection or other goals of sustainability [67]. Intrinsic motivators may refer to inherent enjoyment that may drive consumers to purchase green products or adopt environmental-friendly behaviors such as returning products, repairing materials, reducing wastes, and recycling efforts. Reducing costs and meeting environmental regulations can be extrinsic factors that direct consumers to adopt circular economy and sustainability initiatives [68; 69]. Similar to what we discussed in organizational value and knowledge, blockchains can play an important role in building environmental knowledge that fuels the intrinsic and extrinsic motivations in consumers.  
Consumers can track product life cycle information, form knowledge, and adjust their behavior based on the available real-time information. Companies can also address proper reverse logistics strategies by using blockchain information regarding consumer demands, actions, loyalty, and post-purchase behavior. Being aware and confident of certain green product characteristics can help motivate purchase behavior for sustainable products. The transparency and traceability of blockchain technology can greatly improve this confidence; circular economy characteristics such as recycled materials can increase the confidence of an environmentally sustainable product.  
Blockchain can be used to incentivize products returns to the supply chain. For instance, those consumers who return products at the end of the life cycle can be rewarded by cryptocurrency tokens. This can stimulate the circularity of products and provide integrated information about the performance of the returning programs and funding management for these programs. The incentive systems also help identify those products with low returning rates which can be candidates for deletion, because they do not provide value for circular economy purposes. Traceability of information allows companies to identify products and materials that are collected by poor people and informal markets for recycling purposes and secondary markets. Deleting those products may conflict with moral values that should be considered in product deletion decision making.  
In each case, we provided some examples of how the nexus can work together at multiple levels of CE. These activities and characteristics are exemplary. Many additional and emergent issues, as well as broader categories, also exist.