esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
## 2 Analysis Procedure of the Proposed Method  
Nonlinear THA of structures can well capture the frequency-/time-domain characteristics of ground motions and the elasto-plastic mechanical properties of structures--therefore, it is used to calculate the seismic responses of structures which forms the training set. The simulation framework proposed in this study mainly consists of two modules, which are sample preparation and machine learning method, as shown in Figure 1. The sample preparation module presents details of the structural system, ground motion records, and how each sample for model training is obtained. In the machine learning module, the BPNN and CNN are adopted as the specific machine learning methods, of which the model architecture and simulation results are given in Sections 4 and 5, respectively. The detailed simulation procedures are as follows:  
1. Establish the nonlinear finite element model of a structure, and perform nonlinear THA of the structure subjected for a large number of ground motion records. The structural response data (e.g., maximum displacement and acceleration), together with ground motion data, are collected as a training sample.
2. Process the training sample data according to the machine learning method adopted. For example, extract feature parameters from ground motion records or adjust the ground motion records to identical lengths.
3. Feed the machine learning model with the processed sample data and obtain the well-tuned machine learning model.
4. Apply the well-tuned machine learning model and assess the destructive power of a new ground motion record by predicting the EDP, such as the maximum displacement of a structure.  
Considering that the damage assessment methods and damage limits of different structures are different [4; 29; 30], the maximum displacement of a structure is adopted as the EDP to evaluate  
Figure 1: Analysis procedure of the proposed method.  
the earthquake destructive power to structures in this study. For other EDPs, such as maximum acceleration and maximum inter-story drift ratio, the method proposed in this study can also be adopted by retraining the machine learning model with training samples, including the adopted EDP.
# Current Trends on Green Wireless Sensor Networks
## 2 Energy Conservation
### Sleep/Wake-Up Schemes
#### 2.3.4 Topology Control  
When sensors are redundantly distributed to provide good coverage, it is possible to turn off some nodes while maintaining network operation and connectivity. Topology control protocols take advantage of redundancy to dynamically adapt the network topology based on application needs to minimize the number of active nodes. In fact, nodes that are not needed to ensure coverage or connectivity can be powered down to prolong the life of the network.  
This area of research has been maintaining a moderate (tens of works per year) but continuous activity during the last ten years. In 2013 two surveys on topology control techniques were published. [PERSON] et al. [117] provide an overview of topology control techniques, classifying them into two categories: network coverage issues (blanket, barrier and sweep coverage) and network connectivity issues (power management and power control). The survey of [PERSON] et al. [118] presents a comprehensive study of topology control techniques for extending the lifetime of WSNs, classifying them according to the energy conservation approach they adopt.  
[PERSON] and [PERSON] [119] published recently a new review of topology control algorithms found in the literature, classifying them into two categories (centralized and distributed algorithms).  
Next, we comment the most relevant research works published in recent years in topology control. [PERSON] et al. [120] propose the topology control protocol LBLATC, where every sensor has a learning automaton that chooses the most suitable transmission range using the reinforcement signal produced by neighboring nodes. As other typical problems related to WSNs, topology control can be also object of applying computational intelligence techniques. [PERSON] et al. [121] review the application of several computational intelligence methodologies based on fuzzy systems, neural networks, evolutionary computation, swarm intelligence, etc., to several problems in WSNs, and one of the addressed problems is topology control. [PERSON] et al. [122] propose a multihop topology control algorithm with double CH based on affinity propagation clustering (APDC-M). Exploiting the fact that combining topology control and network coding has more advantages than if we apply them separately, [PERSON] et al. [123] propose a topology control algorithm where in addition to the transmission power, the consumed energy for reception is also considered. [PERSON] and [PERSON] [124] also study an optimization and graph theory approach to propose a mathematical perspective for combining topology control with network coding.
# Server Operation and Virtualization to Save Energy and Cost in Future Sustainable Computing
## 2 Background
### NFV (Network Function Virtualization) and Trend of Virtualization  
Since the current telecommunication network consists of the dedicated equipment of network equipment manufacturers, it has to be remade in order to apply new services and technologies. Thus, the telecommunication service provider was burdened with excessive cost and time consumption for introduction, for instance. Such disadvantages can be reduced if NFV (Network Function Virtualization) is used. As such, telecommunication service providers grew interested in it [10; 11].  
NFV involves separating hardware and software on various NEs (pieces of Network Equipment) on the network by applying the virtualization concept. The separation between hardware and software was made possible by using a hypervisor. Hardware induces the reduction of investment cost by using a compatible IT server. Software can be operated since it is mounted on the virtualized environment regardless of hardware and OS, thereby enabling easing the dependency on hardware. Aside from this effect, it is a compatible piece of hardware [11; 12].  
Therefore, NFV is a technology that virtualizes the main necessary functions of the network at the compatible server with high performance computing power instead of expensive equipment. It is an execution of the existing Physical Network Function (PNF) by means of virtualization at the VM server or compatible equipment. It does not need to purchase expensive equipment. By installing an application that performs the networking function on the general server, however, it enables the construction of inexpensive infrastructure. The L4-L7 middle box service functions of telecommunication service providers, which are sold at a high price considering the dense combination of specific hardware can be realized as a cheap compatible service through application. Since NFV provides a network function to the server through application operation, it does not need to install new equipment and reduces the purchasing cost and operating cost. Moreover, since it realizes network infrastructure through software, network function improvement through software upgrade is made possible, including central concentrated control [13; 14].  
Meanwhile, since the existing network equipment provides one Physical Network Function (PNF) at one physical network equipment, there is no competition on the hardware resource. If multiple hypervisors or applications are installed on one server, however, hypervisors or applications compete for limited computing resources.  
Due to resource competition, there may be a Virtualized Network Function (VNF) placed in the lower priority and which cannot be allocated with computing resources sufficiently. At this point, there is an imbalance of network resource use between the final user allocated with plenty of resources and the user using VNP in high priority. Accordingly, there is an unintended service quality difference or a traffic processing priority between users. Figure 1 shows vision of network function virtualization.
# An Effective Blockchain-Based Defense Model for Organizations against Vishing Attacks
## 3 Preventing Social Engineering Attacks Using Security Principle
### Defenses Based on Split Knowledge Principle
#### 3.1.1 Smishing  
Smishing is a mashup of the words SMS and phishing. Smishing is a type of cyberattack in which the attacker deceives the victim into disclosing sensitive information or handing over money. The cybercriminals dupe the victim into thinking the message came from a well-known person or group. They frequently ask the victim to click on websites or contact a specific number to verify, update, or reactivate their account [24]. We distinguish another SE attack that uses instant messages named spinning. Spimmers typically employ bots to gather instant message IDs and send spam to victims. A bot is an application that performs automated activities over the network.  
For cybercriminals, bank smishing is the most profitable sort of attack. Cybercriminals prey on the victim's fear of having their bank accounts compromised. They usually send the victim a text message purporting to be from the bank, warning them about a large financial transfer or a new payee being introduced. When the target responds and follows the instructions, the attackers collect sensitive information such as bank account login credentials and other personal information. The attacker can quickly access your bank account once they obtain your credentials. As a business owner, you must educate your personnel on how to recognize smishing and other sorts of attacks. The victim's psychological situation, on the other hand, puts training provisions in jeopardy.  
When the split knowledge concept is employed, the victim will lack the necessary information to satisfy the attacker's objectives, rendering their attacks ineffective. Split knowledge will ensure that the victim does not possess the complete information to execute the transaction desired by the requester (possible hacker).
## 4 Applications of Piezoelectric Materials in Analytical Chemistry
### Trace Metal Analysis
This is because adding new structures to sensors, like nanostructures, can significantly increase their effectiveness in terms of selectivity, sensitivity, portability, and multiplexed detection [130]. Due to their availability, chemical stability, and high-temperature impedance, quartz crystals have become the most often used type of natural and synthetic materials that demonstrate the piezoelectric effect [131]. The self-generating, flexibility, high frequency, huge dielectric constant, and ease of use are some of the benefits of piezoelectric transducers. The quartz crystal microbalance could be utilized under a variety of conditions, including liquid conditions, gas sensors, and in vacuums. It is helpful for monitoring the record of the rate of vacuum-based thin-film deposition systems. A flow-type chemical sensor was investigated by [PERSON] et al. [132] in 2011 for the purpose of detecting trace metals in the aqueous solutions, Figure 27. The 9 MHz AT-cut quartz crystal resonator used in the sensor's construction enables the surface chelation of metal ions. The use of new methods has made it possible to isolate gold electrodes with surface modifications that are highly capable of tracing metal ions and complexing them. By complexing with the functional groups included in polymers, these polymers grafted quartz crystal microbalance sensors may effectively adsorb trace metals from solution, such as lead, chrome, cadmium, and copper, in a range between 0.01 ppm-1000 ppm of concentration. Similarly, [PERSON] et al. [133], in 2018, investigated a novel method for the quartz crystal microbalance nano-sensors for the detection of Hg (II) ions in wastewater. In this method, the N-methacryloyl-(I)-cysteine (MAC) and the Hg\({}^{2+}\) ions were transformed in the pre-complex, which was subsequently changed on the nano-sensor chips to produce pHEMAC polymers and pHEMAC-Hg (II) ions. According to the estimates, the detection limit is about 0.21 \(\times\) 10\({}^{-9}\) M. The developed Hg (II) ions imprinted nano-sensors exhibit  
Figure 27: Chemical sensor developed by [PERSON] et al. for the detection of trace metals, schematic diagram reproduced with permission from [132], Elsevier, 2011. Figure 26: Numerous methodologies utilized for the detection of trace metals reproduced with permission from [130], Elsevier, 2021.  
great sensitivity and selectivity for the detection of Hg (II) ions from wastewater. This approach outperforms others in terms of speed, sensitivity, and affordability.
## 7 Result Analysis  
This section covers the deployment of sensor node and gateway in a real-time environment, and we will also present the sensors data that recorded on the cloud server and also a comparison of the previous studies with proposed studies is mention in detail. To evaluate the coverage of the LoRa, we deployed our sensor nodes in the bins that are located in our university. The gateway is placed at 320 m from the sensor nodes. In this study, we designed the bins for evaluating the filling levels of the bins. LoRa-based sensor node is deployed in the four bins with HC-SR 04 ultrasonic sensor ([[https://www.sparkfun.com/products/15569](https://www.sparkfun.com/products/15569)]([https://www.sparkfun.com/products/15569](https://www.sparkfun.com/products/15569)) (accessed on 7 March 2021)) with the sensor that measures the level in the bins. LoRa network is deployed in the sensor nodes according to the architecture that is proposed in Figure 4. Sensor nodes are connected with a gateway for receiving the status of the bins via the 433 MHz LoRa module. The gateway is placed at a distance of 320 m from the bins. The gateway is effectively receiving the data from the sensor node and it is logging in to the cloud server through ESP 8266 Wi-Fi module. Figure 13 illustrates the deployment of the sensor node and gateway in the real-time environment. The status of the bins that are recorded in the cloud server with graphical representation is presented in Figure 14. Distinct color representation in the graph provides the % of waste-filled in the bins. The green color represents that the bin is filled 50% and the red color represents that the bin is filled 100%. Orange and blue color represents that bins are 75% filled. With this result, we conclude that the proposed architecture based on LoRa can provide real-time data of the bins on the cloud server. Figure 14 presents that bin '2' and bin '4' are having constant waste for the prescribed time format.  
Figure 12: Time to live (TTL) of sensor node.  
Figure 14: Bin status on the cloud server.  
Figure 13: Sensor node deployed in the bins.  
of the proposed architecture with the customized sensor node and gateway with cloud server is the significant achievement in the results.
## 4 Numerical Investigation
### Variable Damping Coefficient Configuration  
For the fairness of the comparison and consistency with the optimisation procedure followed for the case of passive TMD control, the selection of the variable damping coefficients for the case of the semi-active and hybrid controlled SDOF systems is performed via examination of the rms acceleration response of the system at steady state. To this end, an investigation of the acceleration response for maximum damping ratios (\(\zeta_{\text{max}}=c_{\text{max}}/2m_{d}\omega_{n}\)) ranging from 1% to 100% of the critical damping is carried out, the results of which are presented in Figures 5 and 6. With reference to Figure 5a, for the STMD-equipped system, at higher damping ratios, the acceleration response of the structural mass reduces and the distance between the side lobes increases. On the contrary, for the SHMD-equipped system (Figure 5b), it can be observed that at low damping ratios, it has a performance inferior to its STMD-equipped counterpart. Nevertheless, as the damping ratios increase beyond the value of 0.3, the performance of the SHMD-equipped system drastically improves, reducing the acceleration response while at the same time pushing the side-lobes of the response further apart. The comparison of the two systems as a function of the damping ratio is shown in Figure 5c which presents the difference of the acceleration responses of the two systems (i.e., \(\ddot{x}_{shmd}(\omega)-\ddot{x}_{stmd}(\omega)\)). Owing to the selected sign convention, negative values in Figure 5c indicate performance gains of the SHMD over the STMD system, while positive values indicate performance loss. For clarity, the two-dimensional acceleration response of the STMD and SHMD controlled systems for maximum damping ratios of 0.3, 0.5, 0.75 and 1 is presented in Figure 6. The average response of the systems over the wider range of frequencies is captured by the area under the response curves as illustrated in the same figure.  
For the case of the SDOF, the performance of the novel hybrid configuration is investigated at the average maximum damping ratio of 0.5. Similar to the passive (TMD) optimisation procedure, when practical constraints are applied such as force and stroke saturation limits (for the case of the MDOF system), further numerical optimisation is carried out and appropriate values of maximum damping coefficients are selected. For the fairness of the comparison, the SDOF the STMD configuration is also designed with a maximum damping ratio of 0.5.  
Figure 5: Acceleration response of (**a**) STMD; and (**b**) SHMD; and their (**c**) difference at different damping ratios. (Units of acceleration response in m\({}^{2}\)/s\({}^{3}\)/rad).  
Figure 6: Acceleration response at steady state for damping ratios of (**a**) 0.3; (**b**) 0.5; (**c**) 0.75; and (**d**) 1. Shaded area illustrates average system response.
## 5 Conclusions  
This paper uses a combination of chart-based analysis of erosion and sedimentation evolution and sediment sample analysis to study the effect of the Hanjiang and Rongjiang Rivers on the sedimentation pattern of the Hanjiang underwater delta. The following conclusions are drawn:  
1. The significant reduction in sediment fluxes from the Hanjiang and Rongjiang Rivers into the sea led to the transformation of the underwater delta area in the outer channel of Shantou Port from a depositional state in 1980-1989 to an erosional state in 1989-2016, which was positively correlated with the significant reduction in the sediment fluxes from the Hanjiang and Rongjiang Rivers into the sea.
2. The significant reduction in the sediment flux into the sea from the Hanjiang River generally reduced the deposition rate of the underwater delta. In the area close to the estuary, the change in the deposition rate of the columnar samples (ZZY51, ZZY55, and ZZY04) is more distinct than that far from the estuary (ZZY01).
3. The significant reduction in the sediment flux into the sea from the Hanjiang River led to a transformation from deposition to erosion of the underwater delta area near the estuary.  
Conceptualization, [PERSON] ([PERSON]), C.H., X.L. and J.Y.; Methodology, [PERSON] ([PERSON]), [PERSON] ([PERSON]), X.W., R.W., J.Y. and Y.Z.; Software, [PERSON] ([PERSON]), [PERSON] ([PERSON]), X.W., C.H., R.W., X.L. and Y.Z.; Validation, [PERSON] ([PERSON]); Formal analysis, [PERSON] ([PERSON]), [PERSON] ([PERSON]), X.W., C.H., R.W., [PERSON] and [PERSON]; Investigation, [PERSON] ([PERSON]) and [PERSON]; Resources, [PERSON] ([PERSON]) and [PERSON]; Data curation, [PERSON] ([PERSON]), [PERSON] ([PERSON]), [PERSON] and Y.Z.; Writing--original draft, [PERSON] ([PERSON]), [PERSON] ([PERSON]), [PERSON], [PERSON], [PERSON] and [PERSON]; Writing--review & editing, [PERSON] ([PERSON]), [PERSON], [PERSON], [PERSON], [PERSON] and Y.Z.; Visualization, [PERSON] ([PERSON]) and [PERSON]; Supervision, [PERSON], [PERSON] and [PERSON]; Project administration, [PERSON], [PERSON] and R.W.; Funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
Comprehensive Geological Survey of Chaoshan Coastal Zone, grant number DD20208013; Comprehensive Survey of Natural Resources in Huizhou-Shanwei Coastal Zone, grant number DD20230415.  
Chaoshan Coastal Zone, grant number DD20208013; Huizhou-Shanwei Coastal Zone, grant number DD202030415.  
The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
# Hierarchical Gated Recurrent Unit with Semantic Attention for Event Prediction
## 3 Our Proposal
### Hierarchical Semantic Attention
#### 3.3.2 Attention Layer  
For current event link \(L=\{E_{1},E_{2},\cdots,E_{M}\}\), the attention layer mainly fuses the time-based event information and the semantic background of current event link by the semantic selective attention mechanism, and finally generates the current event link embedding. In the past, the RNN-based approach only focused on the time series of event links. Less attention has been paid to the impact of event semantic background on the event evolution direction. We consider that the influence of different semantics on the evolution direction of events is discrepant. Therefore, we propose a semantic selective attention mechanism, which is a method for calculating attention weight by the semantic background. Under the semantic background of current event link, we apply the semantic selective attention mechanism to calculate impact weights of current events on prediction. This can pay more attention to events that have a significant impact on the development direction of event links. The attention layer calculates the influence score of events in the current event link by the selective attention mechanism, and weights event embeddings to get event link embedding \(l\). Influence score \(\alpha_{i}\) of event \(E_{i}\) is defined as follows:  
\[\alpha_{i}=\frac{\exp{(u_{i})}}{\sum_{m}\exp{(u_{m})}} \tag{13}\]  
where \(m\) stands for the number of events in event link \(L\). \(u_{i}\) is a scoring function, which scores the degree of matching between event embedding \(e_{i}\) and event link semantic embedding \(s\). We chose the bilinear form as follows:  
\[u_{i}=e_{i}Ws \tag{14}\]  
where \(W\) is a weight matrix. Finally, the influence score is used to evaluate the effect of current events on event prediction under semantics \(s\) and compute the event link embedding \(l\). The process is as follows:  
\[l=\Sigma_{i=1}^{m}\alpha_{i}e_{i} \tag{15}\]  
Figure 3: (**a**) Example sub-ALN. Solid lines are words in the event link, and dotted lines are first-order neighbors. (**b**) Unrolled one timestep. (**c**) Parameter binding of adjacency matrix. The parameter is the weight of corresponding edge in sub-ALN.  
where \(l\) is the event link embedding under semantics \(s\). The event link finally is embedded by weighting event embeddings with influence score \(\alpha\).