esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite-Aided Consensus Protocol for Scalable Blockchains
## 2 Satellite-Aided Consensus Protocol
However, the corresponding services have to be ordered and purchased from satellite operators to generate and multicast random oracles from the space, which increases the maintenance cost. In fact, there is another tricky approach to obtain oracles. The random oracle can be generated by data packets multicast to the ground for other purposes such as satellite television (TV) and global positioning system (GPS). The consensus protocol gives a pre-defined rule to determine the specific packets (e.g., satellite sources and frequency bands) for oracle generation in each round. In this case, the satellite even do not realize that they participate in generating oracles and maintaining a blockchain, and it is thus unnecessary to purchase any satellite services. However, in this manner, the oracle randomness may be affected and the oracles are more likely to be manipulated by adversaries. Therefore, there exists a trade-off between security and cost in the oracle generation, which should be selected according to the purposes and requirements of blockchain. Note that it might be risky to directly select winners by random oracles in fear of Sybil attack-malicious users can create many fake identities to increase the winning probability. Hence, we can draw the principle from proof of stake (PoS). The oracle can be interpreted as an index in the list of all the already minted coins so that its owner is the winner of this round [13]. The winning probability is only related to the stake that miners hold and does not increase by creating multiple identities. Therefore, Sybil attacks can be prevented effectively, and satellites do not have to know or maintain a list of legitimate miners.
# Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network
## 4 Evaluation
### Performance Measurement  
It takes about 300 unit times for the platform to seek for station, and no more than 1400 unit times to complete global detection (shown in Figure 17). The platform is able to acquire about 40 node couples (shown in Figure 18), and the time it uses increases gradually as the scale of the target network becomes larger.  
Figure 16: The platform’s error measurement of node positioning in the near, middle and far communication range.  
Figure 17: The black curve is the time that the platform takes to complete all sampling, and the red curve represents the time it takes to find the station.  
Besides, another factor that affects modeling accuracy rate of the platform is the quantity of node couples captured. The more the node couples captured, the higher the modeling accuracy rate. A busier and larger target network brings higher platform accuracy. Thirdly, in consideration of the difference in node hardware and the environment and consumption of battery in target network, the topology of the target network changes at all times and irregularly. In the current stage, the platform is only applicable to target networks with a fixed network structure or at the preliminary stage of network with sufficient charge. At last, the platform is highly universal and in particular, brings good results for non-cooperative networks. The platform requires no tedious monitoring or network and is recyclable and reusable.
## 2 UAV Video Image Collection Program
### Case Study: NHERI Converging Design (Phase II) Shake Table Test Program
During the nine tests considered, the largest peak roof acceleration (1.32 _g_) and peak roof displacement (36.28 cm) were both in the Y-direction during MID 18. Although not presented herein, the modal properties of the building are determined using the Frequency Response Function (FRF) [30], as this serves to characterize the transfer function between the input signal on the shake table platen and the output signal on the test specimen (roof level). For this specimen, the fundamental periods for the first three modes of the building are determined from the peaks in the FRF as 1.14 s (X-flexural), 0.95 s (Y-flexural), and 0.71 s (torsional). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Earthquake**} & \multirow{2}{*}{**MID 1**} & \multirow{2}{*}{\
\begin{tabular}{c} **Input** \\ **Direction** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Intensity** \\ **Level** \\ \end{tabular} } & \multicolumn{2}{c}{**Achieved Peak Input Acceleration (PIA)**} \\ \cline{5-8}  & & & & **X [_g_]** & **Y [_g_]** & **Z [_g_]** \\ \hline
1994 Northridge, USA (Crustal 2) & 7 & & 67\% MCE\({}_{\text{R}}\) 3  & 0.38 & 0.52 & 0.45 \\
1994 Northridge, USA (Crustal 2) & 8 & XYZ & 100\% MCE\({}_{\text{R}}\) & 0.59 & 0.83 & 0.73 \\ _Station: Sun Valley—Roscope Blvd_ & 18 & & 110\% MCE\({}_{\text{R}}\) & 0.65 & 0.92 & 0.75 \\ \hline
2010 Ferndale, USA (Intraslab) & 12 & & 67\% MCE\({}_{\text{R}}\) & 0.38 & 0.41 & 0.75 \\ _Station: 89486_ & 15 & & 100\% MCE\({}_{\text{R}}\) & 0.59 & 0.63 & 1.16 \\ \hline
2010 Maule, Chile (Interface) & 13 & & 67\% MCE\({}_{\text{R}}\) & 0.42 & 0.34 & 0.02 \\ _Station: CSCH_ & 16 & & 100\% MCE\({}_{\text{R}}\) & 0.64 & 0.52 & 0.03 \\ \hline
2004 Niigata, Japan (Crustal) & 14 & & 68.9\% MCE\({}_{\text{R}}\) & 0.58 & 0.40 & 0.30 \\ _Station: NIGH11_ & 17 & & 100\% MCE\({}_{\text{R}}\) & 0.79 & 0.55 & 0.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Earthquake motion inputs used within the present UAV-based analysis. For additional details regarding the test protocol, scaling, and resulting achieved motions, see [PERSON] et al. [25].
# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 2 Primary Classifications of Electrical Energy Losses
### Technical Energy Losses
#### 2.1.3 Magnetic Losses Through Dielectric  
\[\Delta P_{3}=I^{2}\times\omega\times L\times\tan(\delta), \tag{6}\]  
where,  
* \(\omega\)--alternating current pulsation (1/s).
* L--cable inductance (Wb/A).
* tan (\(\delta\))--magnetizing loss tangent of the cable dielectric.  
In summary, the quantity of active energy losses that arise from the transmission and distribution of electricity through pipelines can be ascertained through calculation. This calculation encompasses the energy produced in generation plants and the energy delivered itself [9].  
Accordingly, the total technical energy loss can be attributed to a combination of factors that contribute to the generation, transmission, distribution, and delivery of electricity to the end user. This type of loss can be calculated based on the components of the electricity system. Therefore, estimates can be made for each component, and the number of technical losses in an analyzed electricity network can be calculated.
## 1 Introduction
Particularly in Europe, the high diversity of network configurations, Member States' (MS) policies towards residential PV installation, and different levels of PV penetration in the electricity mix, often make the lessons learned from the literature unclear. Electricity networks can be categorised into transmission and distribution. Even though both transmission and distribution activities are considered to be natural monopolies that need to be subject to regulatory supervision, their distinct characteristics make each of these activities rather unique. In particular, the fewer number of installations in the transmission networks allows having inventories of these networks and (simplified) models at the European level. On the contrary, in distribution, the number of installations is so large that it is not conceivable to have a single repository covering the full details of all of these networks. In Europe, there are 2400 distribution system operators (DSOs), connecting 260 million customers, operating 10 million km of lines, and supplying 2700 TWh of energy per year [21]. To model the effects of various PV penetrations across the wide spectrum of European Union (EU) distribution feeder architectures, a common approach is required. This opportunity is now available after the publication of several European reference networks (RN) [22]. European Representative Networks, even though not representing any country or region specifically, can serve as reference scenarios where technical, economic, and technological conclusions can be drawn as common interests. In this study, various levels of photovoltaic (PV) power injection levels are simulated on several typical distribution feeders of an urban low-voltage reference network. The main objective is to determine which levels of PV power injection create voltage unbalances, and under what conditions. This simulation is done using a real-time digital simulator (RTDS), and considers only steady-state voltage and current. It does not consider generation ramp rates, protection, and coordination. So far, there hasn't been a common approach to conduct such studies in what regards the network characteristics. Building upon the Joint Research Centre (JRC) report [22], this is the first study to use such RNMs as a common reference to develop a simulation work.
###### Abstract  
In this study, a static displacement monitoring program was developed to maintain the accurate performance of a Very Long Baseline Interferometry (VLBI) antenna by monitoring its structural stability. The monitoring program was designed to measure static displacement, among the many displacements of the antenna's main reflector, which can directly affect its performance. The program measures the position of a monitored object with mm-level accuracy through close-range photogrammetry that uses high-resolution Charge Coupled Device (CCD) cameras. The developed program will be used to evaluate the structural soundness of an antenna based on continuous displacement measurements, which can also be used as basic data for repair and reinforcement work in the future.  
VLBI; close-range photogrammetry; camera calibration; DLT; bundle adjustment +
Footnote †: journal: Applied Sciences  
_Article_  
A Static Displacement Monitoring System for VLBI  
Antenna Using Close-Range Photogrammetry
# Computer Vision and Image Processing Approaches for Corrosion Detection
## 4 Corrosion Detection Approaches
### Thermography  
Recent advancements in solid-state technology have allowed an improved infrared detector to be developed. This new infrared detector contributes to better resolutions and higher accuracies being obtained during corrosion monitoring, detection, and prediction. The cost of online monitoring with this type of sensor also has been considerably reduced. However, thermal images obtained from the infrared sensor can be affected by low signal strengths and noise. Consequently, image processing techniques and signal analysis are proposed to overcome these problems, especially to enhance the thermal images' quality.  
Infrared thermography (found in [42; 43; 44]) is a common computer vision and image processing technique for corrosion detection. This technique records the electromagnetic energy emitted by metallic materials. Then, corrosion patterns can be located from the thermal images. Other widely used thermography-based techniques include optical [45], laser [46], induction [47], and microwave [48] techniques. Each of these techniques have their own advantages and limitations in terms of energy, surface inspection, practicalities, source, etc. Recently, a new thermography technique was introduced to improve corrosion classification and detection. The photovoltaic (PV) electroluminescence module [49] was developed to detect cracks and corrosion on pipelines. The electroluminescence (EL) images were parsed from PV cells to machine learning algorithms such as CNN, random forest, and SVM. EL images were pre-processed with regression fitting, convex hull, thresholding, and filtering to create the planar index module. The results showed that CNN on EL images outperformed the other machine learning algorithms for pipeline corrosion degradation. Nevertheless, these methods required a proper sample for training, evaluation, and testing in order to increase the accuracy of corrosion prediction.
## 5 Quality Attribute Outcomes
### Data Integrity, Trustworthiness, Transparency, and Accountability  
A blockchain's immutable ledger ensures that once data is recorded, it cannot be altered, preserving the integrity of health and activity records. This significantly enhancestrustworthiness, as well as transparency and accountability. This feature is particularly valuable in applications where the authenticity of information is crucial.
# Low-Order Electrochemical State Estimation for Li-Ion Batteries
## 2 Methodology and Models
### State Estimation  
For the estimation process, a discrete version of the EKF with non-linear output function is considered [43; 57]. This basically consists of the following steps:  
(1) Prediction:  
\[\mathbf{\hat{\xi}}_{[k]} =\mathbf{A}_{d}\mathbf{\hat{\xi}}_{[k-1]}+\mathbf{B}_{d}\mathbf{u}_{[k-1]}, \mathbf{\hat{\xi}}_{[0]}=\mathbf{\hat{\xi}}_{0} \tag{37a}\] \[\hat{y}_{[k]} =\mathbf{\hat{\xi}}_{1,[k]}+U_{\text{OCP}}^{+}(SOC^{+}_{[k]})-U_{\text {OCP}}^{-}(SOC^{-}_{[k]})+R_{o}\cdot u_{1,[k]}\] (37b) \[\mathbf{\bar{P}}_{[k]} =\mathbf{A}_{d}\mathbf{P}_{[k-1]}\mathbf{A}_{d}^{T}+\mathbf{Q} \mathbf{P}_{[0]}=\mathbf{P}_{0} \tag{37c}\]  
(2) Kalman gain determination:  
\[\mathbf{L}_{[k]} =\mathbf{\bar{P}}_{[k]}\mathbf{H}_{[k]}^{T}R^{-1}, \mathbf{H}_{[k]} =\frac{\partial h(\xi,u)}{\partial\mathbf{\xi}}|_{\xi_{[k]}\mathbf{u}_{[k]}} \tag{37d}\]  
(3) Correction (innovation):  
\[\mathbf{\hat{\xi}}_{[k]} =\mathbf{\hat{\xi}}_{[k]}+\mathbf{L}_{[k]}(y_{[k]}-\hat{y}_{[k]}) \tag{37e}\]  
As the functions \(U_{\text{OCP}}^{\mp}(SOC^{\mp}[k])\) are known, the partial derivatives of \(h(\mathbf{\xi},\mathbf{u})\) are easily calculated and can be used for the computation of the dynamic gain \(L_{[k]}\). \(R\) and \(\mathbf{Q}\) are the covariance matrices of the measurement and process noise, respectively. For the initial values of the reduced stated of the concentration, the state transformation \(\hat{U}_{i}\) from (31) is used  
\[\hat{\xi}_{2,[0]}=(\boldsymbol{\Omega}_{C_{s}^{-}})^{\dagger}\cdot SOC_{0}^{-} \cdot\boldsymbol{1}(C_{s,max}^{-}) \tag{38}\]  
\[\hat{\xi}_{3,[0]}=(\boldsymbol{\hat{U}}_{C_{s}^{+}})^{\dagger}\cdot SOC_{0}^{+} \cdot\boldsymbol{1}(C_{s,max}^{+}) \tag{39}\]  
where \(\boldsymbol{1}\) represents a vector of ones of dimension \(n\).
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 3 Mobile Platforms
### Ground-Based Platforms
#### 3.1.3. Wheeled Robots  
[PERSON] et al. [44] delivered a brief review of robotics in NPP, referring the use of robots with wheels (Figure 1a) or tracks or a combination of both (Figure 1b), with additional features, like stairs climbing ability and the integration of manipulators for inspection and maintenance. They concluded that the development of specific robots for reactor operations was not pre-planned; instead, it was a response to some need, thus reducing their efficiency for these tasks. The authors of Reference [18] also referred that most professional UGVs (no legged systems) available commercially are related to bomb disposal field and are normally propelled by tracks, run on batteries, and have manipulators for explosive device handling. A more extensive review of terrestrial robots for nuclear environments can be found in Reference [45]. A more recent example of the design and manufacture of a specific tracked robot for a nuclear accident scenario was described by [PERSON] et al. [46] (Figure 1c).  
[PERSON] et al. [48] reported some tests of mobile robots inside a reactor building to assess the structural damage and dose levels in an emergency scenario. For that, the Quince robot was used, already with some capabilities for disaster scenarios (e.g., traverse bumps and stairs), and then retrofitted in order to respond to some specific issues: hardware reliability, communication (hybrid solution--wireless and long cable), and radiation hardness of the electronic components. Other features introduced into the robot were: a  
Figure 1: Examples of unmanned ground vehicles (UGVs) types: (**a**) UGV with wheels and a manipulator. ©2010 IEEE. Reprinted, with permission, from Reference [47]. (**b**) UGV with wheels and four tracks. ©2010 IEEE. Reprinted, with permission, from Reference [47]. (**c**) UGV with tracks. ©2014 IEEE. Reprinted, with permission, from Reference [46].  
radiation sensor, a 3D laser range scanner, and a simple 2 degree-of-freedom manipulator. The system was later used in six real missions at the damaged reactor building of FDNPP.  
[PERSON] et al. [49] made a review of the current rescue robots (2014-2018).
# Traffic Signal Time Optimization Based on Deep Q-Network
## 3 Problem Formulation
### Proposed DQN-Based Traffic Signal Control
In addition, if the green signal is distributed when considering only a capacity maximization, the green signal can be distributed in favor of only certain lanes, which can cause problems to other lanes. However, considering the standard deviation of the waiting time can address problems that may arise when distributing signals for maximum capacity. Therefore, the standard deviation of the waiting time between lanes should be kept small. In addition, the standard deviation of the waiting time is considered as a parameter rather than the waiting time because of the specificity of the data used in learning. Figure 6 shows an increase or decrease between the average waiting time and the standard deviation of the waiting time over the time at an intersection. At approximately 30, 60, and 90 time units, the waiting time for a total of three times suddenly decreases. This is because the vehicle exits the intersection with the green light. The average waiting time has a large range of changes when the vehicle exits the intersection. The instantaneous value of the change is recognized as a significant value that affects the learning. Thus, the value of the standard deviation of the waiting time is more stable than the average waiting time when a sudden change occurs. In other words, sudden change can be prevented from being reflected in the learning. Therefore, we consider the standard deviation of the waiting time and the capacity as parameters to maximize the capacity at intersections with an efficient green signal distribution. \[r_{t}=\alpha*\tau-\beta*wt_{std}, \tag{2}\]  
To maximize the performance of an intersection dealing with many vehicles, we configure the reward function with two parameters, i.e., the capacity (\(\tau\)) and the standard deviation of the waiting time (\(wt_{std}\)). \(\alpha\) is the adaptive weighting factor, which depends on the traffic load at the intersection. It ranges between 0 and 1. \(\beta\) denotes \(1-\alpha\). The reward function is defined as (2). Figure 6: Waiting time over overall time. Figure 7 is a data flow chart representing the data and the process flow of the proposed model. Moreover, it shows an interaction between an environment and the agent. The environment represents several intersections. The perceived information from the target intersection, such as the waiting time of the vehicle at the intersection and the traffic load of the exit lane, is sent to the agent. Next, the agent learns from the received information and determines an action for the current state to maximize the reward. The action is sent to the environment. The action represents the time length of the green light on the lane that will move next. The performance of the traffic light control at the intersection is calculated and reflected in the learning of the agent.
## 3 Problem Formulation
### Weights Definition of Connection Lines  
During the process of energy transfer and conversion, the efficiencies of energy routing ports are different. Therefore, to some extent, the conversion efficiency affects the energy output of source, which affects the operation and environmental costs and line loss of the system as a consequence. Thus, the efficiencies of ports should be taken into account in the process of weights' definition of connection lines.  
In this paper, the weight of a connection line is composed of two parts, one part consists of operating cost, environmental cost, and conversion efficiency, while the other part contains energy loss on the connection line. The weight \(W_{1,i-j}\) is defined as follows:  
\[W_{1,i-j}=\left(1-\eta_{ix}^{p}\right)C_{cost}^{O}+\left(1-\eta_{ix}^{H} \right)C_{\cos}^{E}, \tag{15}\]  
where \(x\) stands for the numbers of output ports such as \(a,b,c,\cdots\). \(i\) represents the number of the ERC, thus \(\eta_{ix}^{p}\) and \(\eta_{ix}^{H}\) means the electrical and thermal conversion efficiency of the \(xth\) ports in the \(iith\) ERC, respectively. According to Equation (15), the connection line \(l_{i-j}\) is given a smaller weight when it is connected to a port with higher conversion efficiency.  
The weight \(W_{2,i-j}\) is defined as follows:  
\[W_{2,i-j}=C_{i-j}^{loss}. \tag{16}\]  
According to Equations (15) and (16), the weight of \(l_{i-j}\) can be depicted as:  
\[W_{i-j}=m_{1}W_{1,i-j}+m_{2}W_{2,i-j}, \tag{17}\]  
where \(m_{1}\) and \(m_{2}\) are parameters to balance the order of magnitude between \(W_{1,i-j}\) and \(W_{2,i-j}\).  
To summarize, the weight of a connection line reflects its energy transmission efficiency. The larger the weight is, the more energy is wasted during the transmission process. Thus, in order to schedule an optimal energy routing path with lower power loss and less operating and environmental cost, a path with a smaller weight is preferred.
## 4 Data and Examples
### Parameter Prediction and State Analysis
#### 4.3.1 Training and Prediction  
1. Training and prediction of traffic parameters based on the LSTM method.  
In this section, we first select the LSTM method to predict the multivariate traffic parameters. The time series of the six parameters shown in Table 4 was studied, where the vehicle speed, headway, and upstream travel time were selected as independent variables for the prediction model, and the total travel time was taken as the dependent variable. Using the learning and predicting ability of LSTM, the model was trained using the first 460 time slices as the training set and the last 40 time slices as the testing set, with 0.990 (MAE), 0.007 (MAPE), and 2.971 (RMSE). The training fitting and prediction results of the multivariate traffic parameters were obtained as shown in Figure 8.  
2. Training and prediction of traffic parameters based on the LSTM method with phase space reconstruction.  
Figure 8: (**a**). Training results from LSTM for travel time parameters. (**b**) Prediction results from LSTM for travel time parameters.  
In this section, phase space reconstruction technology is combined with the LSTM method to predict multivariate traffic parameters. For the time series of six parameters, the vehicle speed, headway, and upstream travel time were selected as independent variables for the prediction model, and the total travel time was taken as the dependent variable. Using the learning and predicting ability of LSTM, the reconstructed multivariate variable time slices were used as the training set, and the last 40 time slices were used as the testing set, with 3.806 (MAE), 0.043 (MAPE), and 5.386 (RMSE). The training fitting and prediction results of the reconstructed multivariate traffic variables were obtained as shown in Figure 9.