esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application and Results
### CSE Corpus Building
#### 4.1.2 Dialogues Collection  
To perform the collection of dialogues [26] a cloud-based infrastructure was established to host all the required software services and custom scripts. More specifically, the infrastructure was deployed in a Cloudstack environment by provisioning virtual machines with discrete roles. A dissection of the infrastructure based on functionality is illustrated in Figure 2. The _Corpus Building_ section includes the _n Web Scrapers_ used, which host custom scripts that scan and collect text from different web sources. The Web Scrapers are the only members of the infrastructure that communicates with the Internet to locate dialogues. This section also hosts every server that stores information at all stages of the project. The _Raw Content_ server contains the scraped content before any preprocessing, while the _Database_ server and the _Corpus_ server contain selected information based on specific criteria and the final corpus, respectively. The _Processor_ server applies the custom scripts in different processing stages and stores the results in the appropriate target. Finally, the _Corpus Annotation_ section includes the workstations of the cybersecurity experts acting as annotators.  
\begin{table}
\begin{tabular}{c c} \hline
**No#** & **Web Sites/Forums** \\ \hline \hline
**Books** \\ \hline
13 & Advanced Persistent Threat Hacking: The Art and Science of Hacking-Tyler Wrightson \\ \hline
14 & [PERSON], [PERSON], and [PERSON], Social Engineering Penetration Testing: Executing Social Engineering Pen Tests, Assessments and Defense. Syngress, 2014 \\ \hline
15 & [PERSON], Unmasking the Social Engineer: The Human Element of Security. [PERSON] \& Sons, 2014 \\ \hline
16 & [PERSON], Hacking the Human: Social Engineering Techniques and Security Countermeasures. \\ \hline
17 & [PERSON] and [PERSON], The Art of Deception: Controlling the Human Element of Security. [PERSON] \& Sons, 2011. \\ \hline
18 & [PERSON], Ghost in the Wires: My Adventures as the World’s Most Wanted Hacker. Hachetite UK, 2011. \\ \hline
19 & [PERSON], No Tech Hacking: A Guide to Social Engineering, Dumpster Diving, and Shoulder Surfing. Syngress, 2011. \\ \hline
20 & [PERSON], Social Engineering: The Art of Human Hacking. [PERSON] \& Sons, 2010. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Infrastructure Setup.
# A Dual Stage Low Power Converter Driving for Piezoelectric Actuator Applied in Micro Mobile Robot
## 2 Driving Requirements and Strategies of Piezoelectric Actuator
### Driving Methods  
To avoid depolarization, the piezoelectric layers must be under unipolar driving (AC) signal condition in the polarization direction according to the characteristics of the piezoelectric actuator. Prior work about driving methods of piezoelectric actuator includes efforts by [PERSON], which made detailed analysis of the efficiency of several different configurations [27]. An available configuration of piezoelectric actuators is the \"bimorph,\" which consists of two compliant electrodes on the top and bottom surfaces bonded to a compliant elastomer film inserted in the middle. One of the driving methods for the bimorph is called \"alternating driving,\" which requires 2n drive stages per n bimorphs, as shown in Figure 3a. An optimized driving method, called \"simultaneous driving,\" allows the sharing of a high-voltage bias among multiple bimorphs, thereby requiring n drive stages and one bias per n bimorphs, as shown in Figure 3b. A DC bias boost stage is connected to the two compliant electrodes, whereas a suitable unipolar drive stage is applied to the central compliant elastomer film.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Static Parameters** & **Value** & **Dynamic Parameters** & **Mode 1** & **Mode 2** & **Mode 3** & **Mode 4** \\ \hline \(C_{0}\) & 24 nF & Ln & 580 H & 812 mH & 422 mH & 15 mH \\ \(R_{s}\) & 1\(\Omega\) & Cn & 3 nF & 2.85 nF & 1.9 nF & 3 nF \\ \(R_{p}\) & 10 M\(\Omega\) & Rn & 8.5 M\(\Omega\) & 7.5 M\(\Omega\) & 6.7 M\(\Omega\) & 5.2 M\(\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Equivalent parameters of the bimorph actuator.  
Figure 2: Impedance characteristic of one layer of bimorph.  
The simultaneous driving architecture exhibits the advantage of using fewer components to reduce the size and mass of the converter when multiple bimorphs are required in a system [12].  
When the AC voltage is operating in the positive half cycle, the upper layer of the bimorph produces less contraction than the bottom layer, which causes the bending actuator to exhibit downward displacement. The effect of material contraction on the other half of the cycle is the opposite, which causes the piezoelectric bimorph to achieve an upward displacement. The simultaneous driving process is illustrated in Figure 3b.
# Model Updating of a Freight Wagon Based on Dynamic Tests under Different Loading Scenarios
###### Abstract  
This article presents an efficient methodology for the calibration of a numerical model of a _Sgnss_ freight railway wagon based on experimental modal parameters, namely natural frequencies and mode shapes. Dynamic tests were performed for two distinct static loading configurations, are weight and current operational overload, under demanding test conditions, particularly during an unloading operation of the train and without disturbing its tight operational schedule. These conditions impose restrictions to the tests, especially regarding the test duration, sensor positioning and system excitation. The experimental setups involve the use of several high-sensitivity accelerometers strategically distributed along with the vehicle platform and bogies in the vertical direction. The modal identification was performed with the application of the enhanced frequency-domain decomposition (EFDD) method, allowing the estimation of 10 natural frequencies and mode shapes associated with structural movements of the wagon platform, which in some cases are coupled with rigid body movements. A detailed 3D FE model of the freight wagon was developed including the platform, bogies, wheelsets, primary suspensions and wheel-rail interface. The model calibration was performed sequentially, first with the unloaded wagon model and then with the loaded wagon model, resorting to an iterative method based on a genetic algorithm. The calibration process allowed the dominant of the optimal values of eight numerical parameters, including a double estimation of the vertical stiffness of the primary suspensions under the unloaded and loaded static configurations. The results demonstrate that the primary suspensions present an elastic/almost elastic behaviour. The comparison of experimental and numerical responses before and after calibration revealed significant improvements in the numerical models and a very good correlation between the experimental and numerical responses after calibration.  
freight wagon; FE model; dynamic tests; model updating; genetic algorithm +
Footnote †: journal: Applied Sciences
# Investigation of Impact Behavior of STS304L Steel Plate Under Cryogenic Temperature
###### Abstract  
STS304L is widely used in liquefied natural gas cargo containment systems for cryogenic liquefied gas storage because of its excellent mechanical properties at low temperatures. However, unpredictable sloshing impacts can induce excessive plastic deformation, leading to phase transformation from austenite to martensite. This study investigated the impact resistance of STS304L under cryogenic conditions through drop-weight impact tests. Temperature sensitivity was analyzed using electron backscatter diffraction to quantify plastic deformation and phase fraction. The results showed that, as the temperature decreased, the energy absorption and stiffness increased, whereas the plastic deformation remained relatively constant. Energy absorption increased by 59.63% at \(-100\)\({}^{\circ}\)C and 68.80% at \(-193\)\({}^{\circ}\)C compared with that at 20 \({}^{\circ}\)C. The martensite fraction, measured at the end of the hemispherical impact region, increased from 19.26% at 20 \({}^{\circ}\)C to 77.85% at \(-100\)\({}^{\circ}\)C and 96.87% at \(-193\)\({}^{\circ}\)C, indicating significant strain-induced martensitic transformation at cryogenic temperatures.  
Keywords:STS304L; drop-weight impact test; cryogenic temperature; mechanical behavior; phase transformation +
Footnote †: journal: Applied sciences
# Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm
## 3 An adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm
### Weighted Dynamic Mutation Strategy  
The core idea of the DE algorithm lies in how to balance global exploration capabilities and local development capabilities. The standard DE algorithm uses the mutation strategy of Equation (2). Individuals that are mutated are randomly selected individuals in the current population. This increases the diversity of the mutated individuals and improves the overall exploration ability, but the randomness largely influences the direction of evolution, which may lead to the evolution of the population deviating from the optimal value, as well as a reduction in the convergence speed of the algorithm. Equation (3) makes use of the optimal individual as the base of variation, which ensures the optimization direction of evolution, improves the local development capability and accelerates the convergence speed of the algorithm. But the optimal individuals are too single, which greatly reduces the diversity of the population, and makes the algorithm easy to fall into local optimum. In order to better balance the algorithm's global exploration and local development capabilities, we improved Equation (3) and weightily combined it with Equation (2) to propose a new weighted dynamic mutation strategy:  
\[\begin{array}{l}t_{i,j}(g)=\mu[x_{1,j}(g)+F\cdot(x_{\tau,2,j}(g)-x_{\tau,3,j }(g))]+\\ (1-\mu)[x_{\tau,1,j}(g)+F\cdot(rand\cdot x_{best}(g)-x_{\tau,1,j}(g))]\\ \mu=\exp(\sqrt{g})\end{array} \tag{11}\]  
Among them, \(\mu\in[0,1]\), \(r1,r2,r3\in[1,2,\ldots,NP]\) and \(r1\
eq r2\
eq r3\
eq i\), while _rand_ is the random number evenly distributed in the interval [0,1] and \(x_{best}(g)\) is the optimal solution of the g-th generation population.  
From Equation (11), we can see that the weighted operator \(\mu\) is a monotonically decreasing function. After weighted combination, the early stage of mutation operation focuses on the overall exploration, and the later stage focuses on local development, making the algorithm better balanced in global exploration and local development, which is not only conducive to the diversity of the population, but also increases the convergence speed of the algorithm. The improvement of Equation (3) mainly considers the singleness of the population optimal solution. Using a random selection of the optimal solution and performing a difference with any individual of the population can increase the diversity of the population, and can randomly adjust the evolutionary direction of the population, making the population evolve towards the optimal direction.
## 9 Results and Discussion
### Battery Life Estimation Based on the Sleep/Wake Scheme  
The application of the sleep/wake scheme verifies that this approach can improve the power consumption of the proposed WAS to 0.731 (sensor node), 0.967 (router node), and 1.86 mA (main router node). Consequently, battery life can be extended to 148 (sensor node), 112 (router node), and 58 (main router node) days by using two rechargeable Li-ion battery with 7.4 V/2600 mAh. The battery life of the sensor node and the router/ main router nodes is 5.6 days and 6 days in the traditional operation, respectively. The battery life for a particular energy of the battery, which relies on Equation (11) at the current usage of the sensor, router, and main router nodes, is presented in the relationship in Figure 14. The figure also shows the improvement in the current drained by the nodes of the WAS when the sleep/wake scheme is considered.
# An Image-Processing Method for Extracting Kinematic Characteristics of Droplets during Pulsed GMAW
## 5 Conclusions  
(1) A monitoring scheme of pulsed GMAW based on a high-speed camera and laser backlight was proposed. The interference of the high-brightness arc was weakened by using a narrow band filter and ND filter. Finally, the droplet and translucent arc could both be observed clearly.  
(2) An effective image-processing algorithm was proposed to extract the edge and region of the droplet. It is unable to remove the interference of the arc and obtain a high-quality image segmentation effect using only the threshold method. However, by comparison, the interference of the arc can be removed, and the droplet can be extracted precisely using the proposed image-processing method.  
(3) During the period with an undetached droplet, the problem of recognizing the droplet was solved by precisely cutting the droplet at the minimum diameter of the neck. Thus, the centroid coordinates of the undetached and detached droplets could both be calculated accurately, which broadened the scope of obtaining the effective information of the droplet.  
(4) Based on the proposed algorithm, a series of kinematic parameters of the droplet can be obtained, such as velocity, acceleration, size, momentum, and so on. This gives a simple and feasible method to investigate the kinematic characteristics and mathematic descriptions of the droplet flight trajectory. This paper also provides a new possible application for developing a precise automatic welding system.  
Methodology, P.Z.; software, P.Z. and J.W.; investigation, P.Z., T.C., and Y.T.; writing--original draft preparation, P.Z.; writing--review and editing, S.X. and P.Z.; supervision, S.X.; project administration, S.X.; funding acquisition, S.X.  
This work was funded by the National Natural Science Foundation of China, grant No. 51675269 and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).  
The authors declare no conflict of interest.
## 2 Overview of Autonomous Vehicles (AVs)
### Emotional Intelligence in Autonomous Vehicles  
As we approach an era of fully automated vehicles, emotional intelligence has emerged as a vital area of exploration. Emotional intelligence, defined as the capacity to comprehend, manage, and respond appropriately to emotions in oneself and others, is a characteristic typically attributed to humans. However, the integration of emotional intelligence into autonomous vehicles is becoming increasingly feasible with the rapid advancements in artificial intelligence (AI) and machine learning [19].  
Autonomous vehicles, through sensor technologies, AI, and sophisticated algorithms, already demonstrate cognitive intelligence in terms of navigating complex environments, making split-second decisions, and communicating with other vehicles or infrastructure. Nevertheless, to fully understand and interact with their human passengers, these vehicles also need to possess emotional intelligence. This intersection between autonomous vehicles and emotional intelligence could transform the passenger experience, safety, and public acceptance of these vehicles [20]. Research is underway to develop systems that can identify, comprehend, and react to the emotional states of passengers. For instance, if the vehicle detects signs of passenger anxiety during a high-speed drive, it could respond by slowing down or by providing reassuring communication about the journey's safety. Furthermore, it could tailor the in-vehicle environment, adjusting elements like lighting, temperature, or music to help calm the passenger [21].  
The integration of emotional intelligence in AVs can also enhance safety. A vehicle that can recognize a passenger's fatigue or medical distress could take preventative actions such as slowing down, stopping, or even calling for medical assistance [22]. Moreover, emotional intelligence in AVs could promote public acceptance. As these vehicles become more attuned to human emotions and responsive in a human-like manner, they may help mitigate the uncertainty or discomfort associated with relinquishing control to a machine. It is important to note that the integration of emotional intelligence into autonomous vehicles raises several challenges and ethical considerations, including privacy, data security, and the risk of over-reliance on technology. Nevertheless, it represents an exciting frontier in theevolution of autonomous vehicles, potentially contributing to a safer, more personalized, and more human-like autonomous driving experience [23].
## 2 Experimental  
### General Characterization  
FTIR spectra were obtained using a Bruker TENSOR 27 spectrometer. Samples were mixed with KBr at a 1:100 mass ratio and pressed into pellets under 10 MPa of pressure. Spectra were collected in the range of 4000-400 cm\({}^{-1}\) with 32 scans per measurement. Powder X-ray diffraction (XRD) patterns were recorded on a Rigaku Ultima IV diffractometer with Cu K\(\alpha\) radiation (\(\lambda\) = 1.5418 A) operating at 40 kV and 40 mA. Data acquisition covered the 2\(\uptheta\) range of 20-70\({}^{\circ}\) with a step size of 0.02\({}^{\circ}\). Raman spectra were acquired using a Renishaw inVia confocal Raman spectrometer with 514.5 nm of Ar\({}^{+}\) laser excitation. Transmission electron microscopy (TEM) images were acquired using a JEOL JEM-2100 Fmicroscope operating at 200 kV. XPS measurements were performed on a Thermo Scientific K-Alpha spectrometer using monochromatic Al K\(\alpha\) radiation (1486.6 eV) under an ultrahigh vacuum (\(<\)5 \(\times\) 10\({}^{-9}\) mbar), with pass energies of 20 eV for high-resolution scans and 100 eV for survey spectra. All binding energies were referenced to the adventitious carbon C\({}_{18}\) peak at 284.8 eV. The surface area and pore distribution were measured using a Micromeritics ASAP 2020 via BET method with N\({}_{2}\) adsorption at 77 K. The samples were degassed at 200 \({}^{\circ}\)C for 12 h before analysis. The BET calculations used a P/P\({}_{0}\) range of 0.05-0.30, while the pore size distribution was determined using the non-localized density functional theory (NLDFT) method. Thermogravimetric analysis with differential thermogravimetry (TGA/DTG) was performed using a Shimadzu DTG-60H simultaneous thermal analyzer under flowing air (30 mL/min). The specimens (4.225 mg) loaded in alumina crucibles underwent heating from ambient temperature to 1000 \({}^{\circ}\)C at a constant rate of 10 \({}^{\circ}\)C/min. Both mass variation profiles and derivative thermogravimetric signals were synchronously recorded with a mass resolution of \(\pm\)0.1 ug. The magnetic measurements were assessed by a Lake Shore 7404 in the range from \(-\)30 kOe to + 30 kOe at room temperature. Platinum was analyzed using ICP-OES (PerkinElmer Optima 2100 DV, radial mode) with 0.7 L/min of Ar flow, 1.3 kW of RF power, and a 5 s integration time for Pt lines at 214.423 nm and 265.945 nm.
## 4 Analysis of the Results
### Bearing Capacity of Dam Body with Hydraulic Fracturing  
#### 4.3.1 Overloading Capacity of Dam Body with Hydraulic Fracturing  
Due to the brittleness and variability of the crack propagation [21] in the dam, the displacement curve along the river exhibits great fluctuation. At a crack depth of 2 m, the overload safety factors at heights of 0 m and 2 m from the foundation are 2.4 and 2.8, respectively, as opposed to the ones not considering hydraulic fracturing whose overload safety factors are both 3.1, as shown in Figure 17, resulting in the difference of the dam's overload capacity of 22.6 % and 9.7 % and closer to the reality. It is more beneficial to determine the failure mode of the dam body by the overload coefficient of the crack initiation. It can be seen in Figure 18 that if the overload coefficient of crack initiation is greater than that of the plastic model in Section 3.1, plastic failure would appear superior  
Figure 16: Safety coefficient of stability against sliding on the foundation.  
Figure 15: Strength reduction safety factor of dam body.  
to hydraulic fracturing failure, making the dam features a plastic failure, as shown by the cracks that are 2 m deep, 7 m deep, or 9 m deep relative to the foundation. If the overload coefficients of initiation are less than those of the plastic model in Section 3.1, this indicates that the hydraulic fracturing failure takes precedence over the plastic failure, and the dam will undergo hydraulic fracturing failure.
# Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review
## 7 Conclusions  
The study of PHM has evolved considerably over the last two decades, representing a confluence of interdisciplinary research efforts in engineering, computer science, and advanced technologies. The convergence of these fields has broadened the scope of PHM and presented new challenges and opportunities in the quest for more robust and efficient systems. Integrating deep learning techniques holds promise for enhancing the predictive capabilities of PHM, facilitating more accurate and timely prognostic assessments. Similarly, advancements in sensing technology offer the potential to enable real-time monitoring and analysis, thereby bolstering the overall effectiveness of PHM applications. The integration of augmented reality and digital twin technologies stands to revolutionize the landscape of PHM, offering immersive visualization and enhanced simulation capabilities. This trend underscores the growing recognition of the transformative potential of digital twins and augmented reality in redefining the paradigms of PHM. While acknowledging the strides made in PHM research, addressing the challenges and limitations that impede the seamless integration of new technologies is imperative.  
In conclusion, the evolution of PHM, in tandem with advancements in deep learning, sensing technology, augmented reality, and digital twin methodologies, has ushered in a new era of possibilities. As we navigate the frontiers of PHM research and development, it is incumbent upon the scholarly community to forge ahead in addressing the challenges and shortcomings while charting a course toward realizing more sophisticated and efficacious PHM systems.  
Conceptualization, W.L.; methodology, W.L.; validation, Z.F. and J.F.; investigation, W.L.; data curation, W.L.; writing--original draft preparation, W.L.; writing--review and editing, Z.F. and J.F.; visualization, W.L.; supervision, L.S.; funding acquisition, Z.F. and J.F.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by Elevator Typical Failure and Accident Prevention and Control Multi-dimensional Twinning Co-Intelligence Technology and Application Demonstration, grant number 2023 YFC3081800; Funding for Centralized University Talent Projects, grant number buctrc202026; Science and Technology Program of the State Administration for Market Regulation, grant number 2022 MK204.  
Each author is thanked for their contribution to this study.  
The authors declare no conflicts of interest.