esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Space Maglev Vibration Isolation Control System Modeling and Simulation
## 1 Introduction
The calculated relative position information is input into the relative position controller, which generates relative position control instructions and drives eight maglev mechanisms to generate corresponding control forces, achieving relative position control of the two modules. This control system achieves stable attitude control of the payload module while allowing the platform module to track the attitude of the payload module and maintain an appropriate relative position to avoid collision between the two modules, thereby achieving integrated control of the DFP satellite payload and satellite platform. [PERSON] et al. established a six DOF nonlinear dynamic model of a vibration isolation system based on [PERSON]'s method. However, the voice coil actuators were equipped with parallel mechanisms via flexure hinges, which have a great influence on the isolation performance. At present, a dynamic model of maglev vibration isolation systems with high precision and high fidelity is still not available to ensure vibration isolation performance [9; 10]. Based on DFP technology, this article solves the problems of satellite payload pointing accuracy and vibration isolation, achieving high-precision control of satellite payload attitude stability. By designing the satellite as a relatively independent and organically combined platform module and payload module, and using non-contact maglev technology to achieve dynamic and static isolation design between the two modules, the vibration and interference from the platform module, which has movable and flexible componentsinstalled, will not be transmitted to the payload module, thereby achieving vibration isolation effect in space. Simultaneously, a master-slave collaborative control strategy is adopted, featuring high-precision active control of the payload module and passive control of the support module that follows the payload module to meet high-performance payload requirements. Adopting a four-bar actuator configuration scheme, the actuator coil adopts a bidirectional four-channel structure, which has the function of simultaneously outputting bidirectional force values and high precision. A primary and backup redundant controller, using a one-to-four architecture was designed. The control board achieves high-speed and high-precision driving current control, voltage output, and output current feedback signal sampling through 16-bit high-precision DAC and ADC chips and power boards. Based on uniform magnetic field design, high-precision force control performance is ensured by adjusting current accuracy. Interdisciplinary joint simulation of electric, magnetic, and structural aspects was conducted on the magnetic levitation isolation system. By conducting physical testing and calibration, and designing a testing and calibration system, it has been proven that the system meets the design requirements, achieving high-precision current and driving force control technology.
# Arabidopsis Disulfide Reductase, Trx-h2, Functions as an RNA Chaperone under Cold Stress
## 3 Results
### Trx-h2 Binds to Nucleic Acids  
Next, we investigated how Trx-h2 confers enhanced cold tolerance in plants and microorganisms at the molecular level. Many studies have reported that the nucleic- and cytoplasmic-RNAs can be denatured at low temperature by their non-specific pairing to form misfolded hairpin structures [26]. Furthermore, resistance to cold shock is typically contributed by RNA chaperones, which exhibit several common characteristics. First, most of the RNA chaperones bind to nucleic acids including ssDNA and dsDNA. Second, RNA chaperones exhibit an anti-terminating mRNA transcription activity via their nucleic acid melting ability [29; 30]. Based on these data, we examined whether the Trx-h2 protein expressed in _E. coli_ interacts with M13 mp8 phage ssDNA and dsDNA, _Luc_ mRNA, and Arabidopsis total RNAs (as substrates) in vitro [26].  
Varying amounts of purified recombinant Trx-h2 were reacted with individual substrates in the reaction mixture containing binding buffer, and EMSAs were carried out using 0.8% agarose gels. Increasing amounts of Trx-h2 induced successive retardation of nucleic acid movement in agarose gels, indicating the binding of dsDNA, ssDNA, and total RNAs by Trx-h2 (Figure 5a-d) Additionally, excess of Trx-h3 (100 \(\mu\)g/\(\mu\)L; control) did not induce a shift in the position of nucleic acids, suggesting that Trx-h2 specifically interacts with ssDNA, dsDNA, and total RNAs. The results indicate that Trx-h2 binds to DNA and RNA, which is one of the peculiar properties of RNA chaperones.
# Novel Generalized Three-Step Phase-Shifting Interferometry with a Slight-Tilt Reference
## 1 Introduction  
Phase-shifting interferometry (PSI) has been developing for decades and applied to many fields [1; 2; 3; 4; 5; 6]. In the development of PSI techniques, there is a transition from the traditional fixed phase shifts to random unknown phase shifts [7; 8]. In general, the phase shift of the reference beam introduced into the PSI is typically affected by environmental interference and phase shifter errors [9]. To further simplify the measurement procedures and avoid the negative effects of the environmental disturbances and phase shifter errors, [PERSON] et al. have introduced generalized phase-shifting interferometry (GPSI), where the phase shifts are generally arbitrary, unequal and unknown values, but can be extracted from several interferograms [10; 11]. [PERSON] et al. later have developed GPSI by using statistical distribution of the diffracted wave and normalized holograms [12; 13; 14]. To achieve further convenience, non-iterative methods [11; 15; 16; 17; 18] begin to replace the iterative methods, which always need a long computation time, and sometimes it is difficult to make the extracted values convergent. However, complicated equations and tedious operations in these phase shift extraction processes give rise to some inconvenience in the application of GPSI.  
In recent years, interference with a tilt reference [18; 19] has been proposed in digital holography for monitoring purposes. In order to improve the convenience and stability of the phase-shift extracting algorithm further in three-step PSI, we propose a simpler and more reliable method to extract the unknown phase shifts with a slightly tilted reference beam during the recording process.  
Three-step generalized phase-shifting interferometry (TGPSI) uses three interferograms to extract phase shifts and retrieve the original object wave. In GPSI research, TGPSI can retrieve the object wave stably by only three holograms without any additional measurements. In this paper, we propose the use of a slightly tilted reference so that the extraction of the phase shift can become simpler and more stable without iteration. We derive the theory behind the proposed method and carry out numerical simulations as well as optical experiments to verify our idea.
## 3 Iterative Learning Observer-Based Thruster Fault Reconstruction Approach
### Lor\({}^{2}\)Tc-Based Formation Configuration Maintenance  
Choosing parameters \(l=1\), \(\mu_{1}=1\times 10^{-4}\), and \(\beta_{1}=1\) yielded \(G_{2}=0.9999\) and \(G_{4}=-0.9999\). \(G_{1}=0.999\) and \(G_{3}=\mathrm{diag}\{0.999,0.999,0.999\}\) were obtained by choosing \(\gamma_{2}=0.0009\) and \(\gamma_{3}=0.0009\). In this subsection, both the simulation step and the learning interval \(\tau\) were taken as 0.001 s, and the initial values of desired state and actual tracking state were chosen to be \(\mathbf{q}_{0}(0)=[-15,\ -505,\ 20,\ -0.37875,\ 0.045,\ 0]^{T}\) and \(\mathbf{q}(0)=[0,\,0,\,0,\,0,\,0]^{T}\). Solving (34) and (35) yields  
\[\mathbf{P}_{1}=\left[\begin{array}{cccccc}0.0007&0&0&0.0063&0.0002&0\\ 0&0.0007&0&-0.0002&0.0063&0\\ 0&0&0.0007&0&0&0.0063\\ 0.0063&-0.0002&0&0.1127&0&0\\ 0.0002&0.0063&0&0&0.1126&0\\ 0&0&0.0063&0&0&0.1126\end{array}\right]\]  
\[K=\left[\begin{array}{cccccc}0.0063&-0.0002&0&0.1127&0&0\\ 0.0002&0.0063&0&0&0.1126&0\\ 0&0&0.0063&0&0&0.1126\end{array}\right]\]  
To illustrate the effectiveness and superiority of the LOF\({}^{2}\)TC-based approach, a simulation comparison between the proposed LOF\({}^{2}\)TC-based approach and the conventional output-feedback FTC (COF\({}^{2}\)TC) approach proposed in [30] is provided in detail. Further simulation results are provided in Figures 7-10. Figures 7 and 8 demonstrate the curves of relative position tracking error and relative velocity tracking error for satellite formation maintenance control using the proposed LOF\({}^{2}\)TC approach and the COF\({}^{2}\)TC approach. From Figure 7, it was also seen that the convergence curve of relative position tracking error deviated from the zero region at 1500 s in the \(x\)-axis, and had a fluctuation with small amplitude after 2000 s in the \(z\)-axis using the COF\({}^{2}\)TC approach, while the relative position tracking errors converged to the zero region with higher accuracy under the LOF\({}^{2}\)TC approach. Figure 9 illustrates the curves of three-dimensional space formation configuration maintenance with space perturbations and thruster faults. From Figures 7-9, it could be concluded that the LOF\({}^{2}\)TC approach could also have better formation maintenance performance than the COF\({}^{2}\)TC approach.  
the main reason the LOF\({}^{2}\)TC approach had a better formation maintenance performance than the COF\({}^{2}\)TC approach. Therefore, based on the designed ILO, the developed LOF\({}^{2}\)TC approach could achieve spacecraft formation maintenance with high accuracy under the space perturbations and thruster faults.  
Figure 8: Relative velocity tracking error for formation maintenance.  
Figure 7: Relative position tracking error for formation maintenance.
## 3 Research Methodology and Model Development
### Design Framework and DLA Development  
After a thorough analysis of the collected traffic data (the number of vehicles, vehicle categorization, etc.) and complete traffic demand analysis, it was determined that the selected corridor has great potential for implementing the DLA concept. Existing traffic control cannot meet peak traffic demand during morning and afternoon hours, and the full capacity of traffic network elements is not used. The new traffic regulation on the selected corridor consists of a DLA implementation designed to optimize lane utilization during peak hours and reallocate lane designation based on historically collected and analyzed traffic data. It is necessary to emphasize that this is the first implementation of dynamic traffic control in Croatia's traffic network.  
According to the presented simulation model, the existing situation consists of one eastbound lane and two westbound lanes. In the DLA scenario, two phases are defined:  
* The detection of increased traffic volume in westbound lanes;
* The dynamic reallocation of the center (middle) lane to operate bidirectionally, increasing capacity in the peak direction.  
A transitional phase between the normal operation and DLA change ensures safe lane reconfiguration. This phase includes the following:  
* Temporary closing of the center lane to empty it of vehicles already in the lane;
* Detecting the eventual presence of any remaining vehicles;
* The controlled reopening of the lane in the new direction once the transition is complete.  
This DLA concept depends on real-time traffic demands, particularly during peak periods, through controlled transitions of the central lane direction.
# Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes
## 1 Introduction
An improvement of KH applied to fault diagnosis with a Support Vector Machine to solve the power transformers' fault diagnosis problem based on the analysis of dispersed gases is presented in [38]. The SSA is a metaheuristic approach based on the behavior of flying squirrels which are a diversified nocturnal tree rodent group that are highly adjusted to gliding locomotion. The SSA mimics the dynamic food search behavior of flying squirrels and their effective form of locomotion, known as gliding, which is a very effective mechanism for traveling long distances. The algorithm has been recently proposed [39; 40]. Moreover, it has proven its good performance in some applications, for example in [41], is applied for the optimization of a backpropagation artificial neural network (BPNN) using a multi-objective method based on SSA to optimize the main parameters of a continuous galvanization process for advanced DP Steels. In [42], SSA is used to optimize a complex problem where combined heat and energy distribution for various regions is modeled integrating renewable energy sources. In [43], a hybrid algorithm based on the combination of SSA and the optimization of invasive weed is proposed. This algorithm is combined with the Support Vector Machine and the deterministic maximum likelihood algorithm to perform the classification of air quality levels. Recently, a Chaotic SSA variant for optimum programming of multiple tasks in an infrastructure cloud environment as service is reported in [44]. The main contribution of this paper consists of a fault detection system using a combination of SVDD with some optimization methods like SHO, KH, SSA, and the well-known PSO. The effectiveness of the used different meteheuristics, for the parameters optimization corresponding to the hyper-sphere as well as those associated with the Gaussian radial basis kernel used in SVDD, is compared. As it will be shown, promising results are obtained when the mentioned swarm intelligence algorithms are combined with SVDD and applied to fault detection in a real industrial problem. The rest of the paper is organized as follows: theoretical background is presented in Section 2, the proposed methodology for fault detection is described in Section 3, and this is followed by the industrial application section and finally some conclusions and future work are discussed in the last section.
## 5 Experimental Results
### Qualitative Experimental Results  
This section presents the qualitative experimental results of our method on the KITTI dataset, as shown in Figure 8. Most of the objects are well detected by our algorithm, which verifies the effectiveness of our method. At the same time, it is worth noting that some objects are missed or false positives appear due to reasons such as occlusion, truncation, and background clutter. Some objects are missed or the position estimation is inaccurate because they are far away from the camera. Solving these problems is the future optimization direction of our algorithm.
## 4 GKL Algorithm of Active Suspension
### Simulation Analysis
#### 4.3.4 Comfort Mode  
The optimal solution of weighting coefficients of comfort mode is as follows: \(q_{1}=10973\), \(q_{2}=4511\), \(q_{3}=1\). The comparison curves of time domain response of passive suspension and comfort mode are shown in Figure 14a-c, and the RMS values of each performance index are shown in Table 15.  
From Table 15, it can be known that no matter how the road excitation changes, the comfort mode of active suspension designed by the GKL algorithm has the most significant improvement in BA. In the comfort mode, the optimization rates of BA and DTD are near 40% and 20%, respectively. Compared with the passive suspension and the active suspension based on GA-LQR, the comfort mode can effectively improve the comfort of the vehicle. This mode is suitable for soft drivers who pay more attention to the body's vibration response.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Road Class**} & \multirow{2}{*}{**Suspension Type**} & \multicolumn{4}{c}{**RMS Values**} \\ \cline{3-5}  & & **BA** & **SWS** & **DTD** \\ \hline \multirow{3}{*}{A} & Passive & 0.3377 & 0.0034 & 0.0011 \\  & GKL (Comprehensive mode) & 0.2714 & 0.0024 & 0.0011 \\  & Improvement/\% & \(-19.64\%\) & \(-29.24\%\) & \(-3.92\%\) \\ \hline \multirow{3}{*}{B} & Passive & 0.6755 & 0.0068 & 0.0023 \\  & Improvement/\% & \(-19.76\%\) & \(-32.67\%\) & \(-3.97\%\) \\ \hline \multirow{3}{*}{C} & Passive & 1.3526 & 0.0135 & 0.0045 \\  & Improvement/\% & \(-19.96\%\) & \(-29.24\%\) & \(-3.92\%\) \\ \hline \multirow{3}{*}{D} & Passive & 2.7168 & 0.0271 & 0.0093 \\  & Improvement/\% & \(-19.40\%\) & \(-28.11\%\) & \(-4.04\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 14: RMS values and improvement rate of performance indexes for comprehensive mode.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Road Class**} & \multirow{2}{*}{**Suspension Type**} & \multicolumn{4}{c}{**RMS Values**} \\ \cline{3-5}  & & **BA** & **SWS** & **DTD** \\ \hline \multirow{3}{*}{A} & Passive & 0.3377 & 0.0034 & 0.0011 \\  & GKL (Comprehensive mode) & 0.2714 & 0.0024 & 0.0011 \\  & Improvement/\% & \(-19.64\%\) & \(-29.24\%\) & \(-3.92\%\) \\ \hline \multirow{3}{*}{B} & Passive & 0.6755 & 0.0068 & 0.0023 \\  & GKL (Comprehensive mode) & 0.5420 & 0.0045 & 0.0022 \\  & Improvement/\% & \(-19.76\%\) & \(-32.67\%\) & \(-3.97\%\) \\ \hline \multirow{3}{*}{C} & Passive & 1.3526 & 0.0135 & 0.0045 \\  & GKL (Comprehensive mode) & 1.0827 & 0.0096 & 0.0043 \\  & Improvement/\% & \(-19.96\%\) & \(-29.24\%\) & \(-3.92\%\) \\ \hline \multirow{3}{*}{D} & Passive & 2.7168 & 0.0271 & 0.0093 \\  & Improvement/\% & \(-19.40\%\) & \(-28.11\%\) & \(-4.04\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 15: RMS values and improvement rate of performance indexes for comfort mode.
# Mixed Method for Isogeometric Analysis of Coupled Flow and Deformation in Poroelastic Media
## 3 Isogeometric Analysis  
### Introduction  
Since its first introduction by [PERSON] et al. [44], isogeometric analysis (IGA) has been successfully applied to several areas of engineering mechanics problems. The fundamental aim for the introduction of IGA was the idea of bridging the gap between finite element analysis (FEA) and computer-aided design (CAD). The main concept behind the method is the application of the same basis functions used in CAD for performing finite element analysis. In the process of its application to various engineering problems, IGA has shown advantages over the conventional finite element method, for instance the ease of performing finite element analysis using higher order polynomials.  
We briefly present the fundamentals behind B-Splines and Non-Uniform Rational B-Splines (NURBS) in the next section and highlight the features of IGA that are important in our context.
## 3 Materials and Methods
### Sequence of Events  
#### 3.3.1 Stowed to Inject Position  
In the _stowed_ position, depicted in Figure 7 and phase I of Figure 2, the anchor assembly is completely enclosed within the anchor sleeve, the anchor spike is fully retracted, the anchor plug is flush with the bottom of the hull, and the AUV may maneuver normally, free of external protrusions. At the start of the anchoring sequence, the anchor spike (6) is extended through the spike well (19) at the bottom of the anchor plug (16) into the _inject_ position, as depicted in phase II of Figure 2, while the vehicle descends. An internal view of this phase transition is depicted in Figure 8.  
The protrusion of the anchor spike (6) enables the vehicle to leverage downward momentum to aid in the penetration of the seabed surface while simultaneously lowering the vehicle's center of gravity slightly and promoting a vertical orientation due to the increased righting moment caused by the increased vertical separation of the center of gravity from the center of buoyancy. The increased righting moment is especially helpful in the case of spherical AUVs, as proper vertical orientation further ensures that the bottom of the sphere will come into contact with the seabed first, and the anchor spike will be driven into the sediment more efficiently.  
To position the anchor spike (6) into the _inject_ position, the tether spool actuator (7) briefly tensions the tether (3), compressing the anchor spring (13) and releasing the tensioner  
Figure 8: RAATM phase transition from _stowed_ (**left**) to _inject_ (**right**) position.  
latch; it then reverses to allow the spool to unwind freely and enable the anchor spring to expand against the anchor sleeve cap (14) and compress the anchor cap coupler (12) firmly onto the anchor cap (17). The vertical spur gear gears (10) are then rotated via electrical actuators adjacent to the anchor plug (16). The vertical spur gears (10) interface with the vertical outer splines of the anchor cap coupler (12) through slots in the anchor sleeve. The outer splines of the anchor cap coupler slide freely along the vertical spur gears as they rotate, transferring torque to the anchor cap (17) and spike (6), which rotates and extends the spike through the spike well (19). As the anchor spike is extended out the bottom of the anchor plug, the lower face of the anchor cap (17) contacts the wire root ring (21), causing it to be in position to be compressed by the anchor cap (17) when the anchor spike (6) is screwed further into the anchor plug (16) during the next phase of deployment. The anchor spike (6) is held in position by corresponding inner \"threads\" (not depicted) of the spike well (19) semi-rigidly, enabling sufficient resistance for impact and penetration of the surface of the seabed during the _inject_ phase, while the wire root array (18) remains retracted within the anchor plug (16).
# Systematic Mapping Study on Remote Sensing in Agriculture
## 2 Research Methodology  
The bibliographic review carried out in the present work has taken the form of a systematic mapping study [84], with the purpose of providing an overview of the field of remote sensing in agriculture (RSA) to identify the quantity and channels of the papers published, the type of research that is currently being done, and the results available in the literature. Systematic mapping studies follow a well-established methodology [85], consisting of the following main steps; (i) study planning by determining the mapping questions, the source databases, and the search string; (ii) searching for the relevant papers in the predefined databases; (iii) defining a classification scheme of the papers; (iv) mapping the selected papers; and (v) extracting the main findings, implications, and limitations of the study. All these steps are described in the following sections.
# Humans and Automation: Augmenting Security Operation Centers
## 4 Discussion  
Throughout the results and findings, it was repeatedly shown that automation can be implemented to varying degrees. In addition to that, our findings in the SOC automation implications on analysts' section revealed that varying levels of automation can be applied (i.e., only to provide recommendations or to action-specific responses autonomously). It is worth distinguishing the difference between these two concepts: degrees of automation and levels of automation:  
1. Degree of Automation: The number of processes that a SOC automates. For example, SOCs with high degrees of automation will have automated processes throughout the incident response lifecycle. SOCs with low degrees of automation may only automate the detection of alerts. This can also be described as the breadth of automation.
2. Level of Automation: The level of autonomy that automated SOC processes possess. For example, high levels of automation in the response phase will recommend and implement response actions, with the analyst's option to intercede if necessary. Conversely, lower levels of automation in the response process may only provide analysts with several alternative response strategies. This can also be described as the depth of automation.  
Therefore, a high degree of automation does not necessarily equate to high levels of automation. This is supported by [66] who define automation's breadth as the number of tasks and contexts that automation can handle and define automation's depth as the level of improvement that automation can achieve on specific tasks.