esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Flood Inundation During Typhoon Events Nasha and Haitang  
Nasha and Haitang were two successive typhoon events that occurred on 29-31 July 2017. These two events brought great disasters mainly to two provinces of China, Fujian and Taiwan. Figure 2 shows the daily variations of precipitation (GPM-derived, b1-b5), SR (CYGNSS-derived, c1-c5) and the inundated areas of Fujian and Taiwan (SR-derived, d1-d5) during and after the typhoon events. Since the available specular points of the daily CYGNSS data are sparse, the SR and inundated areas are gridded into 50 km resolution.  
In general, the precipitation and SR had consistent change patterns during the events. Note that the change of SR was consistent with that of typhoon rainfall, particularly on 30 July 2017, when there was extreme accumulated rainfall of up to 320 mm in south Taiwan. There are also inconsistencies  
Figure 2: Daily variations of precipitation (**b1–b5**), GPM-derived), SR (**c1–c5**), CYGNSS-derived), and inundated area (**d1–d5**), SR-derived)) of Fujian and Taiwan during Typhoons Nasha and Haitang. SR: Surface reflectivity.  
between SR and the precipitation, e.g., high SR values (Figure 2c4,c5 but low precipitation values (Figure 2b4,b5 over the west coast of Taiwan on 1 August and 2 August 2017. Vegetation cover (as shown in Figure 2a2) could attenuate the CYGNSS signals over the entire region to result in a relatively lower SNR than that of bare soil.
###### Abstract  
The paper proposes a real-time model for electric vehicles (EVs) controlled load charging. The proposed demand-side management (DSM) of EVs is implemented based on queuing analysis with a nonhomogeneous arrival rate and charging service periods dataset. An electric vehicle model is used which is based on a statistical survey to represent the uncontrolled demand of the EVs. A probability distribution for the time at which EVs are plugged and the corresponding value of the state of charges (SOCs) are considered. The preferences of individual EVs have been fully exploited through a set of instructions to fulfill the needs of the vehicles' owners. The designated preferences include the owner setting for both, charging price preferences (OPR), and the maximum estimated parking time duration (EPTD). The quasi-static time-series (QSTS) simulation is used to simulate real-time scenarios of the 24-h simulation period. The IEEE 123 nodes radial test feeder is analyzed with different daily load curves, EV charging scenarios, and wind power penetrations. The results show the effectiveness of the proposed DSM in avoiding excessive levels of charging with/without penetration of non-dispatchable wind power generation. The proposed DSM enables the EVs to charge with low tariff rates either at excessive renewable power generation or late evening hours with available committed bulk power plants and light loading conditions.  
controllable loads; intermittent demand load; peak shifting; electric vehicles (EVs); wind power; smart grids +
Footnote †: journal: Applied Sciences
## 5 Insights and Future Directions
### Cross-Layered Framework  
Contrary to strict layering approach of Open Systems Interconnection (OSI) model, the Cross-Layered (XL) approach allows interaction among various layers of communication protocol stack which may result into enhancing system performance [150; 151; 152]. Although the authors in [126] have reviewed network and cross layer routing protocols in WSNs while the authors in [102] have explored traditional/intelligent cross-layered routing protocols in UWSNs. However to the best of our understanding, there is a need to publish more routing-related surveys in WSNs that may focus on the synergies between network and other layers of _communication protocol stack_. Such XL routing surveys may eventually serve as a valuable resource for featuring the benefits of effective routing for solving various real world challenges in WSNs. They may also discuss and highlight the design challenges, methodologies and benefits of employing XL routing approaches in WSNs and may guide the researchers for successfully employing such XL routing approaches in their future research projects. However, XL approaches may suffer from various issues [153] and design intricacies [154] too, therefore XL oriented surveys should also discuss the inherent design complexities and relevant solutions for achieving high performance in WSNs.
## 6 Evaluation
### Evaluation Results
#### 6.3.2 Optimality of the Route  
We show the evaluation results in terms of the evaluation perspective (Eval. 3) in Section 6.1.3.  
We selected relay nodes and constructed routes in the high relay node density 5.27 model. This condition could easily meet the requirements. To meet the requirements with this relay node density, we set the \"relay function activation cut-off value\" at 50 for  
Figure 16: “Relay node ratio” for “the number of relay nodes” in “fewer adjacent nodes”.  
Figure 17: “Relay node ratio” for relay node density in “using MPR”.  
\"randomly selecting\", and set \"the number of relay nodes\" at 1 for \"more adjacent nodes\" and at 4 for \"fewer adjacent nodes\".  
Relay Node Ratio  
(Eval. 2) As shown in Figure 18, for each method, the \"ratio of activate relay function\" is significantly reduced compared to \"all nodes\". The result indicates that an efficient network can be constructed.  
Total Distance  
(Eval. 3) Figure 19 shows the \"total distance\" of the routes constructed after selecting the relay nodes by each method. This should not be too long to avoid the risk of running out of battery power. In the \"more adjacent nodes\" and \"fewer adjacent nodes\", the \"total distance\" increased by almost 1 km compared to the \"all nodes\", since the \"relay node ratio\" decreased significantly. For all methods, the \"total distance\" increased when the number of relay nodes decreased. In particular, \"more adjacent nodes\" and \"fewer adjacent nodes\" confirmed that the distance became too large when the number of relay nodes was too small. On the other hand, \"using MPR\" resulted in a small reduction in the \"relay node ratio\", while it did not increase the \"total distance\" and did not result in worse routing.  
Figure 19: “Total distance” in each method.  
Figure 18: “Relay node ratio” in each method.  
Population-Dense Regions Distance  
(Eval. 3) Figure 20 shows the \"population-dense regions distance\" of the constructed routes. The \"all nodes\" did not construct any route through the population-dense regions. However, the \"more adjacent nodes\" and \"fewer adjacent nodes\" constructed routes that go through population-dense regions. Since \"more adjacent nodes\" gives the priority to the nodes that have more adjacent nodes for the relay nodes, the nodes in population-dense regions are highly selected as the relay nodes. Nodes in sparsely populated regions are less likely to be selected as the relay nodes. As a result, only large circuitous routes or short routes through population-dense regions can be constructed. The reason why the routes in \"fewer adjacent nodes\" pass through the population-dense regions node is that nodes connected to the relay node in the population-dense regions have been likely selected as source and destination nodes.  
Overall, the evaluation found that \"using MPR\" was the most appropriate method.
## 5 Simulation
### Performance Evaluation under Different Drop Ratios  
We change the drop rate of compromised nodes to different values and evaluate the performance of the approach under different drop rate values. A larger drop rate implies that more data packets will be dropped by compromised nodes. Therefore, the PIP is more likely to increase when the drop rate value increases. As illustrated in Figure 6(c), both SPDC and NRRP achieve a higher PIP value when we change the drop rate from 0.2 to 0.5.  
It can be seen that the performance of SPDC is better than NRRP when the drop rate value is large. The performance of SPDC at drop rate of 0.5 is close to that at 0.2. In the contrast, the overall PIP of NRRP increases a lot when the drop rate changes from 0.2 to 0.5. It means that the performance of NRRP is poor when the drop rate is high. With a large drop rate value, it is easier for SPDC to exclude compromised nodes from secure paths in the beginning, which results in a better performance. However, to NRRP, a large drop rate value just increase the number of data packets being dropped. Therefore, SPDC performs better than NRRP when the drop rate is high.
# Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence
###### Abstract  
Non-contact health care monitoring is a unique feature in the emerging 5G networks that is achieved by exploiting artificial intelligence (AI). The ratio of the number of health care problems and patients is increasing exponentially and creating burgeoning data. The integration of AI and Internet of things (IoT) systems enables us to increase the huge volume of data to be generated. The approach by which AI is applied to the IoT systems enhances the intelligence of the health care system. In post-surgery monitoring of the patient, timely consultation is essential before further loss. Unfortunately, even after the advice of the doctor to the patient, he/she may forget to perform the activity in the correct way, which may lead to complications in recovery. In this research, the idea is to design a non-contact sensing testbed using AI for the classification of post-surgery activities. Universal software-defined radio peripheral (USRP) is utilized to collect the data of spinal cord operated patients during weight lifting activity. The wireless channel state information (WCSI) is extracted by using orthogonal frequency division multiplexing (OFDM) technique. AI applies machine learning to classify the correct and wrong way of weight lifting activity that was considered for experimental analysis. The accuracy achieved by the proposed testbed by using a fine K-nearest neighbor (FKNN) algorithm is 99.6%.  
Artificial intelligence (AI); fine K-nearest neighbor (FKNN); IoT; orthogonal frequency division multiplexing (OFDM); universal software-defined radio peripheral (USRP); wireless channel state information (WCSI) +
Footnote †: journal: Applied Sciences
# Blockchain-Based Lightweight Trust Management in Mobile Ad-Hoc Networks
## 3 Proposed Solution
### Blockchain-Based Lightweight Trust Management in MANETs
#### 3.2.2 Phase 2--Consensus Algorithm (Delegated Proof-of-Trust)  
Calculated trust information is included in blocks such that every node in a network can consistently access the information simultaneously. In a MANET environment, because there is no centralized party that handles blockchain processes, the consensus process must be performed by routing nodes in a distributed manner. Delegated proof-of-trust (DPoT) is introduced to compensate for the downsides of other consensus mechanisms by considering MANET characteristics. For example, proof-of-work (PoW), which uses computationally complex operations to perform validation, is not a suitable choice when energy consumption is a primary concern. Although another candidate mechanism called proof-of-stake (PoS) works well in resource-constrained environments, the \"nothing-at-stake\" problem cannot be ignored. Because this algorithm uses predefined tokens (stakes) to identify validators, it is impractical to apply it to a temporary ad hoc network. Furthermore,  
Figure 6: Demonstration of Phase 1.  
its biggest limitation is that it gives the richest nodes in the network get the greatest chance to become validators, meaning it lacks fairness. Therefore, we propose DPoT as a new consensus algorithm for dynamic and resource-hungry environments.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation
### Data Sharing Phase  
During data sharing, the DU must undergo identity verification and validate the legitimacy of the request. The data sharing phase can be roughly divided into two scenarios:  
* Patients query their own EMR and then authorize healthcare service providers to view them.
* Other DUs, such as medical staff from healthcare units or researchers from research institutions, query the system for medical data available for sharing based on their specific needs.  
In the first scenario, patients can query their encrypted medical record number \(C_{N_{EMR}}\) in the attribution channel by decrypting \(N_{EMR}=De_{sk_{i}}(C_{N_{EMR}})\) and then calculating the hash value \(N_{EMR_{hash}}=Hash(N_{EMR})\).  
Subsequently, they call the chaincode in the data channel to retrieve \(TreatmentData\). This enables patients to access all of their medical record information, which they can then authorize others to view. To protect patient privacy from being compromised, this method of query can only be initiated by the patients themselves. Under this storage method, it is impossible for anyone to retrieve \(C_{N_{EMR}}\) by querying \(N_{EMR_{hash}}\) in the data channel, thus ensuring the protection of patient privacy.  
In the second scenario, DUs can execute a conditional query to obtain the \(N_{EMR_{hash}}\) associated with the medical data that they wish to access. The system can then invoke the chaincode on the data channel to retrieve \(TreatmentData\). Next, they can initiate sharing requests with the medical institutions that hold the data.
# Review of Communication Technologies for Electric Vehicle Charging Management and Coordination
## 2 Vehicular Radio Access Technologies
The authors in [46] also compare the different characteristics of Wi-Fi to those of cellular and DSRC networks and conclude that the shorter coverage range and lower mobility support of Wi-Fi compared to UMTS, LTE, and DSRC make it difficult to be utilized for information exchange during dynamic charging of mobile EVs [46]. This is because the small Wi-Fi coverage range shortens the dwell-time, and the access point discovery process in the short EV crossing time presents a significant challenge [38]. Furthermore, since Wi-Fi operates in the unlicensed 2.4 and 5 GHz bands, a vehicular communication network based on Wi-Fi is more prone to interference, which compromises the security and privacy of the data exchange process. Another comparative study between DSRC and 4G-LTE for V2V and V2I communications is presented in [49] based on field experiments, without particularly addressing dynamic charging requirements. The reported results reveal that the round trip time (RTT) of a traffic text message broadcast using DSRC is around 10 milliseconds over a range of vehicle traveling velocities between 30 and 120 km/h. For LTE, on the other hand, the RTT is slightly lower than 100 milliseconds at 30 km/h and increases to around 150 milliseconds at 120 km/h. This means that despite the broad coverage and higher data rates offered by LTE, DSRC offers significantly lower latency and is more suitable for safety-related applications, despite the additional costs of implementation of its roadside units (RSUs). Using LTE for vehicular communications has much lower deployment costs as it utilizes the existing 4G cellular network infrastructure. Accordingly, by analyzing the results of the aforementioned studies, it can be concluded that short-range wireless communication technologies, such as Wi-Fi, can only be utilized for stationary EV charging systems, although their limited throughput and questionable security significantly impact their reliability and QoS. They are, however, unsuitable for EV DWC applications due to their coverage and capacity limitations and their weak mobility support. On the other hand, the use of other wide-range RATs, such as DSRC and 4G/5G cellular networks for EV charging applications, needs to be further studied and compared to draw realistic conclusions on their capability to enable and support wireless EV charging systems, particularly in dynamic charging modes.
# An Optimization Routing Algorithm for Green Communication in Underground Mines
## 2 Related Work  
The wireless sensor network (WSN) in underground mining is composed of a large number of sensor nodes that are deployed on miners or in other places. Thus, the WSN plays an important role in production and can save lives [6]. Simply summarizing the results of the paper [7], many instruments and equipment were invented by CRISO for various underground environments, e.g., high resolution, sub-surface radar for underground object and structure detection. In our research, the WSN is mainly used underground to achieve localization and other functions. Although WSN techniques are widely used in many fields, the WSN of green communication faces many challenges, especially in underground mining [8]. [PERSON] proposed a channel model for underground mine to reduce the energy consumption, noise, and electromagnetic interference [9]. Thus, green communication is significant for each network node to work long hours, and effective to maintain functionality, and achieve a long lifespan and better connectivity of the network [10].  
The WSN in underground mining is used to collect information from sensor nodes that are deployed on miners and mobile equipment and at various locations that require supervision [11; 12]. Numerous mobile sensor nodes are used in underground mining, and they are all powered by batteries. Some researchers have proposed many routing protocols for energy-efficiency in WSNs [13; 14]. Therefore, green communication has extensive applications. Many previous studies have investigated green communication, the DCMST algorithm, and GAs to reduce network energy consumption.
# A Vehicle Velocity Prediction Method with Kinematic Segment Recognition
## 4 Prediction and Analysis
### Parameter Setting and Training
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Driving Condition** & **Prediction** & \(\mathbf{R^{2}}\) & \(\mathbf{RMSE}\) & \(\mathbf{MAE}\) & \(\mathbf{Time}\) \\
**Type** & **Methods** & **\(\mathbf{R^{2}}\)** & **(km/h)** & **(km/h)** & **Horizon (s)** \\ \hline \multirow{3}{*}{Type 1} & LSTM & 0.9723 & 1.9662 & 1.5097 & 2 \\  & PSO-LSTM & 0.97456 & 1.8845 & 1.404 & 2 \\  & QPSO-LSTM & 0.97583 & 1.8367 & 1.3522 & 2 \\ \hline \multirow{3}{*}{Type 2} & LSTM & 0.98785 & 1.8531 & 1.288 & 2 \\  & PSO-LSTM & 0.99161 & 1.6076 & 1.0857 & 2 \\  & QPSO-LSTM & 0.99188 & 1.5153 & 1.002 & 2 \\ \hline \multirow{3}{*}{Type 3} & LSTM & 0.99134 & 2.8681 & 2.0725 & 2 \\  & PSO-LSTM & 0.99199 & 2.7587 & 2.0203 & 2 \\  & QPSO-LSTM & 0.99481 & 2.2208 & 1.8171 & 2 \\ \hline \multirow{3}{*}{Type 1} & LSTM & 0.79538 & 6.3759 & 3.4762 & 5 \\  & PSO-LSTM & 0.87641 & 4.1451 & 2.9673 & 5 \\  & QPSO-LSTM & 0.90036 & 4.0226 & 2.1233 & 5 \\ \hline \multirow{3}{*}{Type 2} & LSTM & 0.94099 & 4.2559 & 2.9106 & 5 \\  & PSO-LSTM & 0.94736 & 4.022 & 2.8344 & 5 \\  & QPSO-LSTM & 0.95043 & 3.9041 & 2.67 & 5 \\ \hline \multirow{3}{*}{Type 3} & LSTM & 0.9722 & 5.738 & 3.9431 & 5 \\  & PSO-LSTM & 0.96385 & 4.7484 & 3.2997 & 5 \\  & QPSO-LSTM & 0.96473 & 4.6913 & 3.2972 & 5 \\ \hline \multirow{3}{*}{Type 1} & LSTM & 0.32817 & 9.6329 & 7.0982 & 10 \\  & PSO-LSTM & \(-\)0.035857 & 11.9613 & 8.3113 & 10 \\  & QPSO-LSTM & 0.22749 & 10.3295 & 6.9411 & 10 \\ \hline \multirow{3}{*}{Type 2} & LSTM & 0.78341 & 8.1622 & 5.7281 & 10 \\  & PSO-LSTM & 0.74334 & 8.8853 & 5.7359 & 10 \\  & QPSO-LSTM & 0.80565 & 7.7318 & 5.2808 & 10 \\ \hline \multirow{3}{*}{Type 3} & LSTM & 0.87092 & 8.9326 & 5.7073 & 10 \\  & PSO-LSTM & 0.8788 & 8.6566 & 5.6616 & 10 \\ \cline{1-1}  & QPSO-LSTM & 0.8655 & 9.1182 & 5.5855 & 10 \\ \hline \end{tabular}
\end{table} 
Table 7: The evaluation indices of the results obtained by the different methods with different time horizons. Figure 14: Prediction results of driving condition type 3 by different methods with 2 s time horizon.
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 3 Visual-Based Positioning System
### Transmission Line Detection  
Transmission lines detector uses the geometric design and uniformity of transmission lines to establish the power wires in each image. At this stage, the purpose is to find a set with three or more straight lines that fulfill the parallelism and equidistance of transmission lines. Using the parameters \(\rho\) (the distance between the line and the origin) and \(\tan\left(\theta\right)\) (slope) that are provided by Hough transform, it is possible to prove these conditions. However, topographic relief and camera tilt disturb the equidistant line recognition process, as shown in Figure 5a. These negative events are corrected using an image orthorectification process.  
The image orthorectification process corrects the adverse effects using the geometric relation between different angles and known distances; this relation is displayed in Figure 5b, where each parameter is described as follows:  
* \(\alpha_{1}\) and \(\alpha_{2}\) are the angles between the real points of power-lines and focal point.
* \(\gamma\) is the draft angle of the camera around \(x\)-axis.
* \(\psi\) is the field of view (FOV).  
* \(d_{1}\) and \(d_{2}\) are the distorted distances measured in pixels between power-line in the center and power-line left and right, respectively.
* \(d\), \(d^{\prime}{}_{1}\) and \(d^{\prime}{}_{2}\) are the real distances measured in pixels between transmission line phases, when the camera plane is parallel to power-lines plane.  
Using geometry, it is possible to solve this problem and to compute the distance \(d^{\prime}{}_{1}\) as a function of \(d_{1}\):  
\[d^{\prime}{}_{1}=d_{1}\times\left(\frac{\cos\left(\gamma\right)}{\tan\left( \gamma-\alpha_{1}\right)}+\sin\left(\gamma\right)\right) \tag{2}\]  
and \(d^{\prime}{}_{2}\) as function of \(d_{2}\):  
\[d^{\prime}{}_{2}=d_{2}\times\left(\frac{\cos\left(\gamma\right)}{\tan\left( \gamma+\alpha_{2}\right)}+\sin\left(\gamma\right)\right) \tag{3}\]  
The system acquires several images at fixed height \(h_{ref}\) above the transmission lines to determine the relation between image units and world units. In this work, power wires were manually extracted in each image, and the separation between them was directly measured in the image. The estimated reference distance \(\hat{d}\) is determined using a consistent estimator of the mean. This will help to determine the measurement of the separation between power wires as well as the drone position, as shown in the next section.
# Depth-Extrapolation-Based True-Amplitude Full-Wave-Equation Migration from Topography
###### Abstract  
The lack of an initial condition is one of the major challenges in full-wave-equation depth extrapolation. This initial condition is the vertical partial derivative of the surface wavefield and cannot be provided by the conventional seismic acquisition system. The traditional solution is to use the wavefield value of the surface to calculate the vertical partial derivative by assuming that the surface velocity is constant. However, for seismic exploration on land, the surface velocity is often not uniform. To solve this problem, we propose a new method for calculating the vertical partial derivative from the surface wavefield without making any assumptions about the surface conditions. Based on the calculated derivative, we implemented a depth-extrapolation-based full-wave-equation migration from topography using the direct downward continuation. We tested the imaging performance of our proposed method with several experiments. The results of the Marmousi model experiment show that our proposed method is superior to the conventional reverse time migration (RTM) algorithm in terms of imaging accuracy and amplitude-preserving performance at medium and deep depths. In the Canadian Footballs model experiment, we proved that our method can still accurately image complex structures and maintain amplitude under topographic scenario.  
image processing; true-amplitude migration; numerical solutions; petroleum industry