esa-sceva/satcom-chunk-collection huggingface dataset:
# The Current Development of Structural Health Monitoring for Bridges: A Review
## 5 Damage Identification Methods
### Bayesian Methods
In Bayesian computing methods, there is a highly important method to calculate the expectation of a posterior distribution, namely the Markov Chain Monte Carlo (MCMC) method. [PERSON] et al. [182] adopted the Metropolis-Hastings (MH) sampling of the MCMC method to solve the complex expressions in the damage assessment model. The applicability of this method to damage assessment was verified in the case analysis of a bridge hanger. [PERSON] et al. [183] introduced the PSO algorithm into the MH sampling method, and called it the MH-PSO hybrid MCMC sampling method. Numerical damage recognition results showed that the method had enhanced sampling efficiency and damage recognition ability. [PERSON] et al. [184] focused on damage detection in latticed shell structures. The proposed damage diagnosis indexes were analyzed by the MCMC method to obtain the frequency distribution histogram of the posterior probability. The finite element analysis results showed that this method provided a reliable tool for damage diagnosis under the premise of considering the uncertainty in the monitoring process. [PERSON] et al. [185] proposed an improved method for the MH algorithm to facilitate the rapid selection of proposal distribution in MCMC methods and to enhance computational efficiency. The proposed method employs an interchain communication mechanism among the simple population and utilizes a tuning-free strategy to simplify the algorithm. The numerical and experimental results demonstrate that the proposed method exhibits faster convergence than traditional algorithms, even when the population size is relatively small.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### Zinc-Based NPs
#### 4.4.4 Cytotoxic and Genotoxic Effects  
Cytotoxic and genotoxic impact of ZnO NPs (5 and 50 \(\upmu\)g/mL) causing oxidative stress in roots of _Allium cepa_ was reflected in lack of membrane integrity, reduced metabolic activity, ROS generation, chromosome aberration, DNA damage, cell-cycle arrest, and cell death in onion roots; considerably higher Zn levels were estimated in the cytoplasmic and nuclear fractions of _A. cepa_ roots. It was confirmed that, for the toxicity of ZnO NPs suspension beside Zn\({}^{2+}\) ions, predominantly ZnO NPs were responsible [241]. Similar effects were observed by [PERSON] et al. [242] who treated _A. cepa_ roots with ZnO NPs having a size of 85 nm, which deregulated the components of ROS antioxidant machinery causing DNA strand breaks and cell-cycle arrest at the G\({}_{2}\)/M checkpoint. Mitochondrial and chromosomal damage induced by oxidative stress in _A. cepa_ roots treated with Zn\({}^{2+}\) ions, bulk ZnO, and ZnO NPs (25 nm) was also reported by [PERSON] et al. [243]. ZnO NPs attachment and internalization, and a considerable increase in levels of thiobarbituric acid reactive substances and antioxidant enzymes in treated roots, were observed as well. Summarizing the adverse effects of ZnO NPs on onion roots, they can be considered as a clastogenic/genotoxic and cytotoxic agent [244]. Treatment of _A. cepa_ roots with 100 mg/L ZnO NPs reduced MI compared to the control (53.2% vs. 63.6%), which further decreased to 50% under UV irradiation. At application of 0.1 mg/L with ZnO NPs lagged metaphase and anaphase with multiple chromatin bridges in the root tip cells were recorded, although at doses 10 mg/L and 100 mg/L, respectively, both stickiness and diagonal anaphase were predominant. It was observed that, following exposure to ZnO NPs, the generation of ROS and MDA was accelerated [245].  
Elicitation of the production of hyoscyamine and scopolamine in HR of _Hyoscyamus reticulatus_ L. by ZnO NPs allows achieving 4.61- and 3.20-fold higher amounts than in the control. This is a promising method that can be considered to produce tropane alkaloids [67] (Table 2). Moreover, 3.80- and 13.36-fold higher amounts of hypericin and hyperforin compared to the control observed in cell suspension culture of _Hypericum perforatum_ L. using 100 mg/L ZnO NPs as an elicitor suggest this method as very encouraging [141] (Table 1).
## 2 Operational Conditions and Examples of Suspension System Damage
As is the case with corrosion, the tribological wear of the suspension system is influenced by the factors discussed at the beginning of this chapter. Figure 4: An example of tribological wear on one of the suspension system components. Figure 3: Examples of suspension system damage associated with vehicle operation. Operational conditions support the option for the development of fretting wear [25; 26], which becomes initiated when two basic conditions are met, i.e., the action of external forces and the occurrence of oscillatory tangential displacements of the mating components. The fretting wear may be demonstrated by corrosion traces at the surface of the components, the increase in surface roughness, and pits and microcracks in the top layer. As a consequence of fretting wear, top-layer cracks may come into being, which will develop deeper in the material. Over time, the network of cracks formed in the top layer will turn into fatigue wear and the destruction of the component.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 1 Introduction
In order to effectively analyze the non-stationary vibration signals, massive research efforts have been made in last two decades to develop various signal processing technologies, including wavelet transformation (WT), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), local mean decomposition (LMD), empirical wavelet transform (EWT), variational mode decomposition (VMD), stochastic resonance, sparse decomposition, etc. In addition, artificial intelligence-based methods are also attracting increasing attention. This paper intends to review the existing EFD-based methods and their applications in rotating machinery. For each EFD-based technique, we will review the applications to gears, rotors, bearings and others, respectively. This paper was written with the objective to serve as a guidemap for those who study EFD of rotating machinery. There are three reasons for such a review. First, a massive research effort has been made in last two decades to develop EFD techniques However, a comprehensive review on EFD techniques does not exist. Therefore, it is necessary to provide a thorough review on EFD methods and their applications in fault diagnosis of rotating machinery in this paper. The literature review will help other researchers, especially researchers new to the topic, to properly select appropriate EFD methods to diagnose early faults. Second, a full literature review is conducted for gears, bearings, rotors and other rotating machinery, respectively. Researchers who work on EFD of rotating machinery, can thus easily determine the research state-of-art in the use of advanced EFD methods. Our work will help them find proper methods for their specific applications and save them time. Third, although many techniques have been developed, there are still some issues that should be studied in-depth. The insights and experiences shared in this paper will benefit other researchers to further develop new EFD methods. The organization of the rest of this paper is as follows: Section 2 reviews early fault diagnosis approaches. Section 3 gives the applications of fault frequency detection (FFD) in early fault diagnosis of rotating machinery. Section 4 describes the applications of artificial intelligence (AI) in early fault diagnosis of rotating machinery. The conclusions are drawn in Section 5.
## 4 Proposed Protocol Procedure
### Authentication and Key-Exchange Phase
#### 4.2.1 Step 2
Identical values for these two parameters provide assurance to the server regarding the accuracy of the received information and the authorized identity of the node transmitting the data. Once the server has verified the identity and accuracy of the information, it proceeds to generate an anonymous value of its private key, referred to as SA. This value is obtained through the combination of the server's private key and random numbers c\({}_{\text{i}}\) and d\({}_{\text{i}}\). Generating the SA value allows the server to finalize the information for session-key generation. Subsequently, after creating the session key, the S\({}_{\text{4}}\) parameter is generated by it. In this process, the value of S\({}_{\text{4}}\) provides conditions so that the node can check the authenticity of the server's identity with the help of this parameter. The generation process for the SA, SK, and S\({}_{\text{4}}\) parameters is as follows. (5) \(\text{SA}=\text{(PVS}\text{E}\text{V}\text{E}\text{V}\text{E}\text{V}\text{E} \text{V}\text{E}\text{V}\text{E}\text{V}\text{E}\text{V}\text{E}\text{V} \text{E}\text{V}\text{E}\text{V}\textUltimately, the SA and S4 parameters are encrypted by the server with the acquired dynamic symmetric encryption key and then sent to the node.
## 4 Results--Emotional Response Predicted Values
All the relationships explored in this study could be further investigated in future research in order to better analyze their meaning from a psychological perspective. Figure 2 summarizes findings from the regression models, providing evidence to the importance of each video and emotion for the prediction of individual traits. For example, we can observe that videos number 14, 9, and 2 were those that triggered the most useful emotional responses. Among emotions, fear and happiness were those most used to make predictions, with fear being particularly relevant for the DOSPERT traits. Predicting Personality and Morals Using Machine Learning  
While correlations and regressions showed promising results, we wanted to complete our analysis to explore non-linear relationships and the possibility of making predictions by using machining learning and considering a test sample (a subset of observations) not used for model training. In particular, we binned the continuous scores of our dependent variables into three classes in order to understand if values were high, medium, or low. Subsequently, we used a gradient boosting approach to make predictions, namely, Xgboost [35]. We trained our models using 10-Fold Cross Validation and the SMOTE technique [36] in order to treat unbalanced classes. ADASYN was also used as an alternative to SMOTE [37], in the cases where this led to improved forecasts. In Table 7, we present the results of these forecasting exercises, made on 10% of observations that were held out for testing prediction accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Predictor/Dependent** & **Neuroticism** & **Extraversion** & **Openness to Experience** & **Agreeableness** & **Consciousness** \\ \hline Angry 2 & & & & 4.922 *** & & \\ Angry 7 & 0.665 * & & & & \\ Disgusted 4 & & 0.297 * & & & \\ Disgusted 11 & & & & 0.547 ** & \\ Happy 1 & & & & & \(-\)0.067 ** \\ Happy 8 & & 0.070* & & & 0.149 *** \\ Happy 9 & & & & & \(-\)0.104 ** \\ Happy 13 & \(-\)0.398 * & & & & \\ Happy 15 & 0.226 ** & & & & \\ Neutral 1 & & & 0.038 * & & \\ Neutral 10 & & & & 0.062 * & \\ Surprised 7 & & & \(-\)2.735 ** & & \\ Surprised 9 & & 0.499 ** & & & \\ Surprised 11 & & & 0.352 * & & \\ Surprised 14 & & \(-\)3.049 ** & & & \\ Constant & 0.518 *** & 0.659 *** & 0.582 *** & 0.591 *** & 0.700 *** \\ \hline Adjusted R2 & 0.184 & 0.263 & 0.257 & 0.131 & 0.198 \\ N & 80 & 80 & 80 & 80 & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Regression models for the Big Five personality traits.
## 3 System Design of the Intelligent System
### Wireless Communication and Networking  
Taking advantage of low price, good stability and linearity, excellent corrosion resistance and developed technology, the electrical resistance based strain gauge is used as the major sensing probe. The strain amplifying module amplifies, filters, and demodulates the strain gauge data, followed by data collection using DSP. A F28335 DSP microchip made by YanXu Company, Nanjing, China, is used in this study. The interface between DSP module and wireless transmission module is a serial communication. The wireless transmitter adopts STR-30 micro-power wireless transfer-block, in view of its advantages of remote transmitting power, long transmission distance (within the range of sight distance, antenna height \(>\)1.5 m, reliable transmission distance \(>\)800 m), low Bit Error Rate (BER), small-size, and low weight. Point-to-point wireless serial communication can help the wireless module work firmly and exactly, as shown in Figure 3.
## 7 Fault Location Method Based on Similarity of Voltage Signals
### Fault Analysis Based on Symmetrical Components  
Voltage vectors \(u_{a}\) and \(u_{b}\) and, current vectors \(i_{a}\) and \(i_{b}\) are shown in Figure 10. These vectors are resolved into their components i.e., zero sequence and positive sequence components and are represented:  
\[\left\{\begin{array}{l}u_{a}=\ u_{a0}+u_{a1}\\ u_{b}=\ u_{b0}+u_{b1}\end{array}\right. \tag{53}\]  
\[\left\{\begin{array}{l}u_{b0}=\ u_{a0}\\ u_{b1}=\ u_{a1}\end{array}\right. \tag{54}\]  
Figure 9: Fault location algorithm based on voltage similarity signals.  
Using Equations (53) and (54), following expression is achieved:  
\[\left[\begin{array}{c}u_{a}\\ u_{b}\end{array}\right]=\left[\begin{array}{cc}1&1\\ 1&-1\end{array}\right]\left[\begin{array}{c}u_{a0}\\ u_{a1}\end{array}\right] \tag{55}\]  
and symmetrical components are expressed as:  
\[\left[\begin{array}{c}u_{a0}\\ u_{a1}\end{array}\right]=\frac{1}{\sqrt{2}}\left[\begin{array}{cc}1&1\\ 1&-1\end{array}\right]\left[\begin{array}{c}u_{a}\\ u_{b}\end{array}\right] \tag{56}\]  
Because of bipolar HVdc systems and its working under normal conditions, nominal rated value of voltage is given by:  
\[\left\{\begin{array}{c}u_{a}=\ U_{dc-}\\ u_{b}=\ U_{dc+}\end{array}\right. \tag{57}\]  
Voltages are assumed to be constant for steady state operation unless rectifier is at constant \(\alpha\) (firing angle) mode and inverter is at constant \(\gamma\) (extinction angle) mode. However, pre-fault condition at fault location is given by:  
\[\left\{\begin{array}{c}u_{a1}=\ \sqrt{2}U_{dc-}\\ u_{b1}=\ \sqrt{2}U_{dc+}\\ u_{a0}=0\\ u_{b0}=0\end{array}\right. \tag{58}\]  
When a fault occurs in a dc grid, fault network is converted into symmetrical two-phase system according to the superposition theorem as shown in Figure 9.  
Figure 10: Bipolar voltage source converter (VSC) high-voltage direct current (HVdc) station with specified voltage and current.  
where \(Z_{0\,\Sigma}\) and \(Z_{1\,\Sigma}\) are zero and positive sequence Thevenin's impedance at the fault point. Faults are classified as pole to ground fault, pole to pole fault, and double pole to ground fault in HVdc systems. These fault scenarios are analyzed based on zero and positive sequence components of TWs.
## 4 Results and Discussion
### Influence of the Geometrical Parameters
#### 4.3.2 Effect of Coil Diameter (d)  
In this section, the effect of coil diameter is investigated. Accordingly, two cases including case C and case D are considered for numerical simulation. The geometrical parameters of these cases (coils and shell) are listed in Tables 3 and 4. Two different coil diameters including 0.016 and 0.022 m and two calculated helix diameters as free parameters (in order to keep constant the heat transfer area) including 0.48 and 0.25 m are selected for cases C and D, respectively. The height of spiral coil and the coil pitch are considered 0.36 and 0.09 m, respectively. The dimensions of the shell are kept constant as listed in Table 4. The configurations of the two investigated cases (C and D) are illustrated in Figure 11. The liquid fraction (LF) of two cases of C and D are depicted in Figure 14. The LF is drawn for 280 min. These cases are compared with together in four point including 66.67, 133.34, 200, and 266.67 min.  
As illustrated in Figure 14, at time = 66.67 min, case C exhibits the best performance with a LF improvement of 57.1% compared to case D. At time = 133.34 min (resp. 200 min), case C has a better thermal performance in comparison with case D with a LF improvement of 41.4% (resp. 30.2%). Totally, after 266.67 min, the LF of case C is significantly higher than for case D (+31%). The liquid fraction and temperature contours for these two cases at different time are illustrated in Figure 15. Because of the geometry of the coil utilized in case C, the used coil covers both top and bottom regions of the shell (Figure 15). Also, generally, the melting rate of the shell bottom region is lower than the top region. Therefore, case C has a better melting rate than case D. At time = 66.67 min, bottom region of the shell covers by the case C, so the LF of the case C is more than case D (about 57.1%). Generally, the melting rate of the top region of the shell is independent of the coil geometry and covering the bottom region of  
Figure 14: Temporal evolution of the liquid fraction (LF) for cases C and D.  
the shell has a significant effect on the LF improvement. Therefore, the trend of melting rate until the time = 266.67 min is the same. By considering the time = 266.67 min, the effect of covering the bottom region of the shell on LF improvement is shown clearly (Figure 15). Therefore, the LF significantly improves for case C about 31.0% compared to the case D.  
The effect of coil geometry on temperature distribution is shown clearly in Figure 15 especially after time = 200 min. It can be seen clearly that the temperature significantly increases in the domain of case C in comparison with the case D. At time = 266.67 min, the red region appears which shows the effect of covering the bottom region of the shell in the melting process by coil geometry.
# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 2 IoT Tide Gauge Network Design
### Enclosures and Cabling  
Various types of enclosures were tested during the field experiments (see Section 3): Experiment 1 used an inexpensive PVC wastewater pipe with waterproof sealed end caps; in Experiment 2, a Blue Robotics 7.62-cm (3-inch) enclosure was used with the standard end caps.  
In the first experiment, a PVC pipe (with 5.08 cm (2 inches) diameter and 35 cm length) housed the main electronics and antenna, and a smaller PVC pipe (4 cm diameter with 15 cm length) held the in-water depth pressure sensor and some ancillary components (e.g., an I\({}^{2}\)C differential converter). The two separate parts were connected using a waterproof ethernet cable that was sealed at both ends using epoxy resin. I\({}^{2}\)C sensors are limited to using cable lengths of up to one meter; when using much longer cables, such as the 10-m cable (necessary because of the large tidal range), two I\({}^{2}\)C differential converters must be incorporated at each end of the cable run.  
In the second experiment, a Blue Robotics 5.08 cm (2 inches) diameter enclosure contained all the electronic components, antenna and batteries.
## 1 Introduction
In [23] the results of a measurement campaign performed during one week in 20 classrooms of four primary schools in Scotland were presented. Indoor temperature, relative humidity and CO\({}_{2}\) concentration were measured for in each classroom (one sensor per room), which were either naturally (manual operation of windows) or mechanically ventilated. The ventilation rate was computed from the CO\({}_{2}\) measurements and it was concluded to be higher in mechanically ventilated classrooms. In [24] the authors analysed the airborne infection risk of COVID-19 in 20 naturally ventilated university classrooms. For doing so, CO\({}_{2}\) concentrations were monitored in seven classrooms. The measured data was also complemented with previous experimental campaigns reported in the literature. Based on a box model and the Wells-Riley equation, the authors estimated the infection risk in each classroom for a one-hour exposure time under different conditions such as activity level or use of masks. In this paper we focus on an Uruguayan naturally ventilated public school building. We quantitatively assess the ventilation rate and the transmission risk by measuring CO\({}_{2}\) concentrations and using a box model and a Wells-Riley approach. We present a novel and remarkably simple strategy for adjusting a CO\({}_{2}\) mass balance and compare risk results via a Rudnick-Milton [25] and a Gammaitoni-Nucci approach [26; 27]. Finally, we show that periodic natural ventilation is a valuable and extremely efficient strategy for reducing airborne transmission risk, which does not require major building modifications.
# The Application of Blockchain in Social Media: A Systematic Literature Review
## 3 Social Media Basics
### Existing Blockchain-Based Social Media Platforms  
Apart directly using blockchains in existing popular social media, there are already blockchain-based social media platforms such as Society2, Peepeth, Sapien, and Steemit [39].  
* Society2 is a social media architecture that is decentralized according to the client, management of information, and speech. Members have options such as exchanging information, and the platform handles their status, conversation, and contacts [40].
* Peepeth consists of Ethereum and IPFS from the database and the Peepeth frontend. Peepeth's posting, liking, following, and other behaviors need to pay gas fees to be packaged on the blockchain. With no company controlling the data, anyone can build a frontend that reads and writes smart contracts, saving the content to the blockchain [41].
* Sapien is another platform based on Ethereum; it has the goal of using a proof of value consensus protocol to return control of the social media experience to users by creating a truly autonomous social network environment that compensates content creators and combats bogus information. It provides users with a speech structure and promotes community enthusiasm and participation [42].
* Steemit is a decentralized social media structure that promises to develop groups and improve social interactions by awarding members with cryptocurrency for their content based on the number of positive reviews they receive. The platform provides users with structured news and analysis, appropriate replies to personalized inquiries, and a stable cryptocurrency pegged to the US dollar, among other things [43].
# Driver Fatigue Detection Based on Residual Channel Attention Network and Head Pose Estimation
## 3 Materials and Methods  
This study mainly contains three aspects: facial state recognition, head pose estimation, and fatigue assessment. Firstly, this study uses Retinaface to detect face and mark the facial bounding box, eye regions, and the mouth region. Then the eye regions and the mouth are sent to judge the state by the RCAN. Next, this study uses the facial landmark generated by Retinaface and then uses the EPnP algorithm to estimate the head pose. Finally, fatigue is judged by PERCLOS and POM and the head pose parameter. The overall structure of this paper is shown in Figure 1.