esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Digital Twin, from Concept to Architecture Model
### Digital Twin Models, Frameworks, and Architectures
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Digital Twin Capabilities** & **Technologies** & **Features** \\ \hline Data acquisition & IoT Sensors & Data format, enrichment, configuration \\ \hline Information access API & Monitoring, interactions, engaging, control of the physical devices & Information access and processing, upon request \\ \hline Deployment/simulation & Cloud Computing & Cloud-based DTs of manufacturing machines \\ \hline Information model & Virtual/Augmented Reality & Optimization, quality assurance, testing \\ \hline Security & Cyber-security & Role-based access control for authenticated users \\ \hline Interoperability & 5G, wireless connectivity & DT various information format \\ \hline Synchronization & DT replicas correlated with adjacent architectural tiers & Data transmission between architectural tiers \\ \hline \hline \end{tabular}
\end{table} 
Table 3: An overview of Digital Twin capabilities, technologies, and features. the offline behavior of the physical object. The characteristic of the Digital Twin is that the two entities influence each other in real time. The novelty introduced by the predictive Digital Twin is that the virtual model is a more complex one, including elements of data security and artificial intelligence, among others. One of the tools with a high degree of generality that can be used in the modelling and simulation of Digital Twins are represented by the Discrete Event Dynamic Systems formalism. It can be used by setting certain thresholds of the variables inside the model (whose values are tracked), thresholds that, once reached, become events that change the model. The advantages of the proposed reference model [23] are as follows. * The integration of different perspectives to achieve a complex framework regarding the use of the Digital Twin in Industry 4.0. * Addressing requirements according to production paradigms that make the transition from mass production to customized production. * Decomposition of a process into sub-processes, which can be approached and adapted with respect to the vertical axis. * Integration of relationships between different levels, which is achieved through the iterative and incremental process. In ref. [19], [PERSON] et al., highlighted that the cyber-layer of a cyber-physical system should integrate cloud technologies that ensure the scalability of storage, calculation, control, and communication capacities. Thus, the Digital Twin can continuously provide a feedback loop with a role in improving the services provided by physical systems. In addition, to achieve this objective, a reference architecture was proposed including operational modes, the fusion module for sensors at the physical level and the fusion
# PK-APF: Path-Keeping Algorithm for USVs Based on Artificial Potential Field
## 2 PK-APF Methodology
### Virtual Attractive Force  
In the practical environment, USVs will deviate from the planned path due to external disturbances, such as obstacle avoidance and the influence of wind and water waves. Virtual foot points provide attractive force calculated with the vertical distance between the current position of the USV and the original planned path (a line connecting the previous navigation point and the next navigation point). The size of the vertical segment determines the attractive force of the virtual foot point. The USV can realize high-precision path-keeping by the attractive force of virtual foot points, when it deviates from the original path.  
Virtual foot points provide attractive force, denoted as  
\[\begin{split}\prescript{v}{}{\mathbf{F}}_{att}=\left\{\begin{aligned} \mu_{u}\left(\frac{A\times x_{usv}+B\times y_{usv}}{\sqrt{A^{2}+B^{2}}}\right),r_{i}>r_{0}\\ \mu_{l}\left(\frac{A\times x_{usv}+B\times y_{usv}}{\sqrt{A^{2}+B^ {2}}}\right),r_{i}\leq r_{0}\end{aligned}\right.\end{split} \tag{4}\]  
\[A=\frac{y_{goal}-y_{start}}{x_{goal}-x_{start}}\;\;B=-1 \tag{5}\]  
where \([x_{usv}\;y_{usv}]^{T}\) is the current position of the USV, \([x_{goal}\;y_{goal}]^{T}\) is the desired goal point location, \([x_{start}\;y_{start}]^{T}\) is the desired start point location, \(r_{i}(\mathbf{p}_{usv})\) is the distance from the position of the \(i^{th}\) obstacles to the USV, and \(r_{0}\) indicates the influence range of repulsive  
Figure 1: Multiple obstacles provide repulsive force.  
force that arises from obstacles. A and B are the coefficients of the line from the start point to the goal point.  
The difference between \(\mu_{u}\) and \(\mu_{l}\) ensures that the USV does not collide with obstacles when the virtual foot point provides attractive force to help it return to its original path. \(\mu_{u}\) means that the USV is outside the repulsive force influence range of the obstacle, with a significant coefficient to help quickly return to the original path. \(\mu_{l}\) represents when the USV fails within the repulsive force influence range of obstacles. In order to ensure that the USV does not collide with obstacles and maximize its approach to the original path, a smaller proportional coefficient is used.  
Figure 2 shows the direction of the resultant force. The resultant force \(\mathbf{F}_{res}\) is the summation of attractive force for goal point, second attractive force for virtual foot points, and repulsive force for obstacle shown in Equation (1). The USV deviated from the original planned path (start point pointing to the goal point) after reaching the repulsive force influence range. The attractive force of the goal point guides the USV towards the goal point, and the virtual attractive force of the virtual foot points helps the USV approach the original path to achieve high-precision path-keeping.
## 3 Results and Discussion
### Possible Degradation Mechanism  
Figure 11 is an illustration of the proposed degradation mechanism of MO using the OLNCs/TiO\({}_{2}\) nanocomposite as a photocatalyst. The mechanism is based on the literature for similar carbon-TiO\({}_{2}\) composites [31]. At first, the MO ions are adsorbed on the surface of the nanocomposite through the carbonyl groups [32]. This was followed by light irradiation and the photo generation of electrons (e\({}^{-}\)). As the e\({}^{-}\) migrates from the valence band (VB) to the conduction band (CB), holes (h\({}^{+}\)) are created in the VB. The TiO\({}_{2}\) will then donate an e\({}^{-}\) to the OLNCs, thus preventing the rapid recombination of e\({}^{-}\)/h\({}^{+}\). The e\({}^{-}\) reacts with O\({}_{2}\) to form oxide radicals (O2\({}^{-}\)) on the surface of the nanocomposite, simultaneously forming hydroxyl radicals (-OH) in the VB. Lastly, the respective radicals would degrade the MO.  
Figure 10: Regeneration of the TC-10 photocatalyst over five cycles of MO photocatalytic degradation.
## 2 Current Status of Development of Geophysical Exploration Techniques
### Exploration Techniques Based on Dielectric Constant
#### 2.3.4 Electromagnetic Wave CT Method  
The electromagnetic wave CT method is mainly a geophysical exploration method in which the electromagnetic wave propagation theory is applied to geological exploration. In this method, by observing the changes in the electromagnetic waves between boreholes, the location of underground anomalies and hidden dangers can be inferred. The studied spatial range is much wider than one wavelength. Therefore, unlike the induction field-based method, the electromagnetic wave CT method studies the radiation field or waves propagating in a lossy medium. The electromagnetic wave is transmitted from a point in the borehole antenna, and on encountering a geological body with different physical properties, wave transmission, reflection, refraction, and edge bypassing occur, and the wave is accompanied by energy attenuation due to absorption in the medium during propagation. These physical processes change the electromagnetic field distribution. By measuring this field change at a certain spatial location, the medium distribution can be determined. The electromagnetic wave CT method mainly uses electromagnetic waves through hidden parts. The reconstructed mathematical model is useful to reflect the integrity of the formation between boreholes.  
In the EM wave CT laminar analysis technique [PERSON] et al. (2018), the joint iterative reconstruction technique is applied for inversion, and a contour map of the EM wave CT absorption coefficient is obtained, which can better reveal subsurface hidden dangers and can be used when other physical exploration techniques, such as the electrical resistivity tomography method and shallow seismic reflection method, are inapplicable due to site limitations and interference factors. [PERSON] et al. [PERSON] et al. (2018) used the electromagnetic wave CT method for quality inspection and damage assessment of pile foundations, and the effectiveness and accuracy of the method were verified with engineering examples. [PERSON] et al. [PERSON] et al. (2018) determined the characteristics of underground and filled cavities on EM wave CT images and derived a formula to calculate the cavity diameter through simulation. [PERSON] et al. [PERSON] et al. (2018) combined engineering examples to reconstruct the distribution images of the relative absorption coefficients of a subsurface medium between two holes using the EM wave CT inversion calculation technique, and the effectiveness of the method was proven. The electromagnetic wave CT method is another lossy detection technique, which requires multiple pairs of boreholes with depths up to the bottom of the dam and must satisfy the condition that the leakage channel of the dam passes between the boreholes.
# _Synechococcus elongatus_ PCC 7942 as a Platform for Bioproduction of Omega-3 Fatty Acids
## 1 Introduction
The initial condensation reaction is catalyzed by FabH (\(\beta\)-ketoacyl-ACP synthase III), reducing acetyl-CoA and malonyl-ACP to form acetoacetyl-ACP. AccABCD, FabD, and FabH are enzymes that perform reactions in the initiation step. The next reaction in the elongation cycle is catalyzed by FabG (\(\beta\)-ketoacyl-ACP reductase), which reduces acetoacetyl-ACP to form \(\beta\)-hydroxyacil-ACP, which is dehydrated by FabZ/FabA (\(\beta\)-hydroxyacyl-ACP dehydratase) to produce _trans_-2-enoyl-ACP. The final step in the cycle is catalyzed by FabI (_trans_-2-enoyl-ACP reductase). The resulting fatty acyl-ACP can be elongated further by FabF/FabB (\(\beta\)-ketoacyl-ACP synthase II/I) or if the fatty acyl-ACP contains 16 or 18 carbon atoms (C16\(\cdot\)0-ACP or C18:0-ACP, respectively) can be unsaturated by the action of desaturases. FabF, FabG, FabZ, and FabI are enzymes that perform reactions in the elongation cycle. In the desaturation step, four consecutive desaturases (DesC or \(\Delta\)9, DesA or \(\Delta\)12, DesB or \(\Delta\)15, and DesD or \(\Delta\)6) catalyzed the conversion of stearic acid (SA) into stearidonic acid (SDA). OA, oleic acid; LA, linoleic acid; GLA, \(\gamma\)-linolenic acid; ALA, \(\alpha\)-linolenic acid. The limited information available about the function and regulation of fab genes [35] complicates engineering efforts to improve FA production in cyanobacteria. To address this issue, this work identifies the function of two enzymes (FabG and FabZ) involved in the elongation cycle of the FA synthesis pathway (Figure 1) by overexpressing the corresponding genes. FA elongation represents the major pathway defining the chain length of saturated and unsaturated FAs and, consequently, determining the yield of omega-3 FAs in cells. Moreover, the overexpression of endogenous _fabG_ and _fabZ_ in combination with DesAB desaturases of _Synechococcus_ sp. PCC 7002 (hereafter, Ss7002) in ALA production was investigated, showing a negative impact on the accumulation of omega-3 FAs in _Synechococcus elongatus_ PCC 7942 (hereafter, Se7942). On the other hand, SDA was successfully produced in Se7942 when using desaturase DesD of _Synechocystis_ sp. PCC 6803 (hereafter, Se6803). We establish that longer post-induction times were necessary to obtain higher yields of SDA.
## 2 Methods
### Surface Electromyography (EMG)  
Muscle activity was measured in bilateral lower limbs using EMG electrodes attached to the thigh (vastus lateralis obliquus [VLO] and biceps femoris [BF]) and lower leg (tibialis anterior [TA] and gastrocnemius medialis [GCM]). The same person obtained all EMG measurements. The skin was shaved, cleaned, and rubbed with an abrasive gel to ensure good signal conduction. Eight self-adhesive Ag/AgCl dual-snap electrodes (10 mm diameter each and 20 mm inter-electrode distance, Noraxon Inc., Scottsdale, AZ, USA) were placed according to SENIAM (surface EMG for a non-invasive assessment of muscles) recommendations [23]. An assistant observed the quality of the EMG signals throughout and immediately eliminated sources of movement artifacts (owing to interference with the harness, contacting cables, or dropping electrodes). The EMG signal was recorded (sampling rate: 1500 Hz) with a wireless TeleMyo DTS system (Noraxon Inc., Scottsdale, AZ, USA; common-mode rejection ratio >100 dB; 10 Hz first-order high-pass hardware filter) and the corresponding software applications MyoResearch 3 and myoMUSCLE (Noraxon Inc., Scottsdale, AZ, USA). The system was time-synchronized with a custom device (16 Mhz clock speed) paired via Bluetooth v2.0 with Walkbot_G to trigger a start signal in the EMG software (Figure 1). The EMG set up is shown in Figure 2.  
Figure 1: Schematic system configuration for synchronization between TeleMyoDTS and WALKBOT_G.
## 1 Introduction
In a dynamic and complex environment, there are many uncertain factors in the SN, such as the topological structure and order, production, and inventory parameters for different enterprises in case of disruption [14; 15; 16]. The research on complex networks and agent-based models provides help to solve this problem [17; 18]. The outer synchronization problems for different network structures have been researched [19; 20; 21]. [PERSON] studied outer synchronization problems for uncertain parameters [22]. Later on, scholars investigated the various combinations of factors. Outer synchronization problems with different network structures and uncertain parameters have been studied [23; 24; 25]. Numerous technologies related to outer synchronization have been developed, for example pinning control [26; 27] and impulse control [12; 28]. The results of these important studies have laid a good foundation for further research studies about the practical application of outer synchronization. In the meantime, however, the question of how to design an effective outer synchronization controller considering the uncertainty of the SN is pressing and challenging issue. In practice, connections between nodes may break due to interruption. Therefore, it is necessary to analyze the impact of topology on the resilient recovery of supply networks. In fact, the nodes within the SN may become disconnected sometimes due to the disruption. Therefore, it is necessary to analyze the resilient recovery of the SN with the dynamic topology [29]. We found that many links are redundant for resilient recovery. Identifying and removing the redundant links for resilient recovery in the SN is one of the meaningful aspects in an application; analyzing resilient recovery strategies is another. The control efficiency is affected by not only resilient recovery effectiveness, but also cost control. In this paper, a control efficiency measurement method based on cost analysis is presented, which can be very helpful when controlling decision-making. Based on the above discussions and previous studies [30], this paper aims at discussing outer synchronization-based resilient recovery measures with memory and control. The framework of this study is shown in Figure 1. The rest of the paper is organized as follows. In Section 2, we introduce a resilient recovery measurement based on the outer synchronization. In Section 3, the impulsive controller and the updated laws are designed. In Section 4, an algorithm is adopted to identify the network backbone for resilient recovery and a measurement method of control efficiency is proposed considering cost analysis. In Section 5, the effectiveness of the method is verified by a case study. In the final section, a brief conclusion is given.
## 4 Numerical Results and Discussion
### Training Weights in SVM  
A series of tests was carried by training the SVM with different weights matrices \(K_{m}\) of misclassification costs. To isolate the effect of \(K_{m}\) over planning, two matrices were chosen aiming at holding the same accuracy: one lowering the false discovering rate (FDR) at the expense of the false negative rate (FNR) and the other one doing exactly the opposite, which was chosen such that the achieved accuracy would be the same in both cases.  
The two confusion matrices are reported in Figure 6. In the first case, the misclassification cost was 0.01 for the true class versus 1 for the false class. In the second case, the misclassification cost was 0.12 for the false class versus 1 for the true class. The measured accuracy came out to 96.4% and 96.5%, respectively.  
Figure 5: The confusion matrix of the SVM used, with an accuracy of 98.3%.  
Figure 6: (**a**) Confusion matrix for planning with higher FDR. (**b**) Confusion matrix for planning with higher FNR.  
The \"higher FDR\" case resulted in the benchmark trajectory in the exploration of 182 nodes and the solution of 4389 LMI problems. Conversely, the \"higher FNR\" case resulted in the exploration of 350 nodes while requiring solving 14,611 LMI; thus, the FNR appears to be the critical parameter in classifier training for trajectory planning.
# Phase Behavior and Rheological Properties of AES/CAPB/H\({}_{2}\)O Ternary System
## 3 Results and Discussion  
### Preliminary Identification of Phases  
Polarizing optical microscopy (POM) is a simple and easy method to investigate the lyotropic liquid crystal phases of surfactant mixed systems, because different liquid crystal phases can show anisotropy (e.g., lamellar phase and hexagonal phase) or isotropy (e.g., micellar phase and cubic phase) under polarized light [28; 29; 30]. For example, the lamellar phase usually shows oily-streak POM texture, and the hexagonal phase usually shows irregular POM texture. Therefore, POM can be used to preliminarily determine the phase of each AES/CAPB/H\({}_{2}\)O sample.  
Representative samples were selected to show the typical POM textures, with their corresponding components summarized in Table 1. As shown in Figure 3a, the bright oily-streaks texture was clearly observed for sample \(\alpha\). It indicates the formation of lamellar phase (L\(\alpha\)) in sample \(\alpha\)[31]. When the proportion of AES (70%) decreased from 87.5 wt.% to 60 wt.%, the bright irregular non-geometrical texture corresponding to the hexagonal phase (H\({}_{1}\)) was observed (Figure 3b), indicating the occurrence of phase transition. The lamellar phase (sample \(\alpha\)) has been transformed to the hexagonal phase (sample \(\beta\)). When the proportion of AES (70%) was further reduced to 12 wt.% (sample \(\gamma\)), a typical micellar solution (L\({}_{1}\)) was obtained, and a dark field was observed.  
Interestingly, when the proportion of CAPB (38%) was increased from 10 wt.% (sample \(\gamma\)) to 20 wt.% (sample \(\delta\)), the POM image also showed a dark field (Figure 3c), but the viscosity increased significantly from 0.02 Pa\(\cdot\)s (sample \(\gamma\)) to 29.88 Pa\(\cdot\)s (sample \(\delta\)). The dark field means that sample \(\delta\) is isotropic, which may be the micellar phase or the cubic phase. However, the cubic phase has been reported to be very viscous and stiff [8], usually with a viscosity higher than 200 Pa\(\cdot\)s [12], even reaching \(10^{3}\) Pa\(\cdot\)s [9; 32]. Importantly, the cubic phase can be easily identified by small angle X-ray scattering spectra with a specific ratio of Bragg peaks, whereas the micellar phase usually only exhibits a signal broad scattering peak because the short-range order of their mesoscopic structure is not sufficient to define a crystal lattice [33]. Thus, small angle X-ray scattering can further identify whether the sample with the dark field belongs to the micellar phase or the cubic phase.  
For sample \(\varepsilon\) with higher AES and CAPB concentrations than sample \(\delta\), some weak irregular textures were observed by POM (Figure 3d), and its viscosity further increased to 47.28 Pa-s. Although the POM texture of sample \(\varepsilon\) showed some characteristics of hexagonal phase, its viscosity was significantly lower than the hexagonal phase (e.g., sample \(\beta\)). Therefore, in order to confirm their phases, all samples were further analyzed using small angle X-ray scattering.
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 3 Energy-Efficient Coverage Control Using Memetic Algorithm
### Network Reconfiguration Mechanism  
In actual situations, the events monitored by the sensor network may be relatively static or dynamic. For example, in the investigation of mineral resources on the seabed, events are stationary. Conversely, in marine water-quality monitoring, incidents to be monitored, i.e., pollutants, may drift along with the movement of seawater; in a dynamically changing situation, the sensor network and the events to be monitored are likely to be constantly changing. However, it costs too much for a node to update the event information every moment to perform intelligent processing or tracking. Therefore, it is necessary to reanalyze the node coverage after a certain period. We divide each round into decision-making and operational phases. The decision-making phase reevaluates the monitoring of events. In the operation phase, all nodes follow the ESACC plan according to the evaluation results in the decision-making phase until the end of the round. Figure 5 shows the flowchart of the network-reconfiguration mechanism.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Environmental Monitoring and Smart Farming
#### 3.2.6 \"The Smaller the Better\" Sensor Node  
The node proposed by [25] was designed for the long-range monitoring of environmental parameters such as temperature and humidity. Regarding the processor and memory, the CMWX1 ZZABZ-078 chip by ABZ Murata that integrates a microcontroller (STM32L082 CZ), a low-power long-range (LoRa) module (SX1276) and an impedance matching line on a single system-on-chip was utilized by the authors. The STM32 micro-controller is a Cortex M0+ type containing 192 kB flash memory, 20 kB SRAM and 6 kB EEPROM.  
The LoRa module capable of transmitting at 868 MHz and 915 MHz was chosen for communication. The module's output power is around 14 dBm. Furthermore, the nodes are equipped with an on-board temperature sensor, humidity sensor and a pluviometer for measuring rain precipitation. Power is supplied by energy harvesters (solar panel, Peltier module). The harnessed energy is stored by a supercapacitor and/or a battery. An energy manager chip (SPV1050) and a chip for monitoring the battery current and voltage are also incorporated in the nodes. The LoRa transceiver consumes 0.5 uA in the sleep state and 41 mA at 14 dBm transmission power while the MCU consumes 2.8 uA in the sleep state and 9.5 mA in the active state.