esa-sceva/satcom-chunk-collection huggingface dataset:
# RNN-Based Sequence to Sequence Decoder for Run-Length Limited Codes in Visible Light Communication
## 5 Conclusions  
In this paper, we adopted RNN technology to deal with the RLL decoding task for improving the reliability of the communication of UAVs embedded with VLC technology. Specifically, we transformed the RLL decoding task into a sequence-based problem that is suitable for the seq2 seq model, and proposed a novel seq2 seq decoder. Compared with the table look-up method, the well-behaved seq2 seq decoder can obtain a performance gain. Meanwhile, it can achieve MAP bit-error-rate performance and enhance the throughput of a VLC system by decoding multiple RLL-coded frames simultaneously. In addition, due to  
Figure 7: Scalability of seq2 seq decoder indicated by MEMAP with different number of parallel decoding frames.  
the restricted learning abilities of the decoder, we found there exists a _saturation frame_ and analyzed the upper limit of the scalability of our decoder, which provides a reference for practical VLC applications to select the number of parallel decoding frames.  
Writing--original draft, X.L.; Writing--review & editing, H.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key R&D Program of China under grant 2018 YFB2101300.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Materials and Methods
### Methods
#### 2.2.1 Calculation of Initial Influence Factors in the Grid Scale  
As we know, the accuracy of the spatial decomposition results of the demographic data is not only related to the decomposition model, the resolution, and the quality of the data source, but also to the cell size. Therefore, determining the appropriate cell size is the first step in the spatial decomposition of demographic data. According to a previous study [23], the cell's area should be close to 10% of the smallest subdistrict are in the overall study area. Hence, the cell size of 150 m was chosen for our study. Based on previous research experience, 24 initial influence factors were selected from two groups: natural factors and socio-economic factors. The 24 initial influence factors are shown in Table 1. The calculation methods for the factors in the unit of each cell were as follows. Road index is equal to the quotient of road length and cell's area. Land use index is equal to the ratio of each type of land use to total area of land use polygon. Night light intensity index is calculated as the average night light intensity in the cell. Elevation index is calculated as the average elevation of cell. Building area index denotes the ratio of the building area and  
Figure 2: Technology roadmap.  
cell's area. Fourteen types of POI index were calculated as the average density of each type of POI in the unit of each cell.
# Evolution towards Smart and Software-Defined Internet of Things
## 5 Evolution of IoT Architecture  
In this section, we shall traverse through the evolution of architectural models of IoT over the years and their advancements into the domains of SDN and NFV. The IoT concept was firstly introduced in 1982 when an Internet-connected cake machine was introduced at Carnegie Mellon University [71]. The term \"IoT\" was firstly coined by professor [PERSON] in the late 90s [72]. From 2000 to 2010, many electronics companies, such as LG, IBM,  
Bosh, etc., started working on the development of Internet-enabled electronic appliances. However, there has been a massive development in the field of IoT in the last decade.  
The evolution of IoT devices with respect to time (from 2010-2021) is shown in Figure 4. Due to the massive availability of funding and the introduction of new IoT companies, the development of IoT devices has been boosted since 2010. The total number of IoT devices exceeded 6.2 billion (Bn) in 2010, and financial strength, government initiative, and increased focus of large electronics companies in this field were the main reasons for such a massive amount of IoT devices being developed [72,73].  
The number of IoT devices reached 14.4 Bn in 2014. From 2010 to 2014, the development of radio frequency identification (RFID), low-cost sensors, surveillance and security devices, geo-location, and transportation management was a major focus of IoT companies. Security, mobility, inseparability, and QoS features were initially undervalued. However, these characteristics were improved over the next four years, with the number of IoT devices surpassing 34.8 Bn in 2018 [72]. Telemedicine, tele-education, remote sensing, ubiquitous positioning, remote monitoring, and controlling are all examples of IoT services that have been expanded. IoT companies' focus on improving security, mobility, and interoperability features in IoT devices has led to a global figure of 59.2 Bn IoT devices by 2021 [72]. In the following sub-sections, we explain the evolution of IoT networks in due course of time.  
Figure 4: IoT Evolution with timeline.
## 6 Navigation and Interface  
### Position in the Map  
The navigation system is based on a combination of the mechanical wheel rolling data and data from a pair of safety scanners. The output signal from scanners consists of an array of points representing the measured distance from the obstacles located in the nearby environment. The safety scanner provides the measurement range of 0\({}^{\circ}\) to 270\({}^{\circ}\) with an incremental step of 0.25\({}^{\circ}\). All these points are transmitted in the form of polar coordinates. The safety scanners output data are shown in Figure 8.  
The first step in the safety scanner data processing is represented by the conversion of each output signal into a common polar coordinate system. The conversion results in 1080 points acquired via the front scanner and another 1080 points acquired with the rear scanner. However, there are overlapping parts in the field of view of both scanners, which can be observed in Figure 8 (left). The final output signal from both scanners has to be merged, in the range from 0\({}^{\circ}\) to 360\({}^{\circ}\), with an incremental step of 0.25. The final data consist of 1440 points needed to create a virtual 360\({}^{\circ}\) laser scan of the environment, Figure 8 (right). B&R PCL library [21] is used for the calculation of the virtual total angle laser scan. It transforms individual points into a point cloud object. The final laser scan with the required parameters is regenerated from that point cloud.  
The calculation of the robot's location within the virtual map is provided using [PERSON]'s Canonical Scan Matcher algorithm [22] and Monte Carlo localization [23]. [PERSON]'s Canonical Scan Matcher algorithm is based on the fusion of odometry from the wheels, together with the changes in the contours of the laser scan.  
The output signal coming from the laser odometry always represents only a delta offset from the previous position, which leads to integration error in the absolute position due to delta offset integration. Monte Carlo localization is used for elimination of the integration offset. It compares the  
Figure 8: The signals from both front and rear scanner (**left**), and the final output 360\({}^{\circ}\) environment scan (**right**).  
Figure 7: Experimental verification of the braking distance value.  
shape of the current laser scan and the virtual environment map, and, based on the comparison of the actual information of what the robot sees and the information from the map of what the robot should see, the integration error is suppressed.  
Global positioning accuracy has been verified by a set of measurements. Phollower was equipped with a chessboard pattern on its top and then navigated from randomly chosen locations to the certain measurement station. An area above this station was monitored by camera, and precise position (\(xy\) coordinates) was obtained by comparing the chessboard position with camera data each time the robot stopped. Evaluation of data obtained by this measurement showed average positioning accuracy error less than 1 cm.
## 5 Blasting Vibration Reduction Effect Analysis Using Electronic Detonators
### Comparison of Vibration Response between Non-Electric and Electronic Detonators
#### 5.2.3 The PPV on the Pipeline Profile under Two Detonation Methods  
A comparative analysis is conducted using the profile of the pipeline section closest to the blast source. The PPV of selected elements at regular intervals is extracted and shown in Figure 15.  
From Figure 15, the PPV of the closest profile caused by blasting using electronic detonators is much smaller than that caused by non-electric detonators. The PPV on the blasting side (0-180\({}^{\circ}\)) was significantly higher than that on the opposite side (180-360\({}^{\circ}\)). This is because the transmission distance of the explosive impact in a large-diameter pipeline is significantly longer than that in a small-diameter pipeline. Moreover, the hollow characteristics of the pipeline significantly attenuated these seismic waves during propagation.  
The distributions of PTS (the peak tensile stress) and displacement on the closest profile of the pipeline using the two blasting schemes just above the blasting source were extracted, as shown in Figure 16. From Figure 16, it can be observed that the PTS and displacement of the cross-section caused by electronic detonator blasting are much smaller than those caused by non-electric detonator blasting. The PTS of the pipeline was reduced by 48.72% by using electronic detonators with hole-by-hole blasting. Because the pipeline is tightly constrained by the surrounding rock, the maximum displacement of the pipeline under the action of tunnel blasting only reaches 0.4 mm. Therefore, this displacement standard is unsuitable for evaluating the vibration safety of pipelines subjected to tunnel blasting.  
Figure 14: The vibration response of the extracted elements at the bottom of the pipeline. (**a**–**c**) are \(x\)-, \(y\)-and \(z\)-directions.
## 4 Cost of Energy Results and Discussion
### System Levelized Cost of Energy
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Hamburg**} & \multicolumn{2}{c}{**Murcia**} \\ \hline \multirow{2}{*}{**Levelized Cost**} & \multirow{2}{*}{\begin{tabular}{c} **Involved Cost (TC\({}_{i}\))** \\ **Components (i)** \\ **(Table A7 Appendix E)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Near** \\ **Future** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mid** \\ **Century** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Near** \\ **Future** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mid** \\ **Century** \\ \end{tabular} } \\ \hline \(\text{LCoE}_{e,S}\) [\(\ell\)/MWh] & S1 & 68 & 31.7 & 37.6 & 17.5 \\ \(\text{LCoE}_{e,W}\) [\(\ell\)/MWh] & W1 & 23.5 & 16 & 26.5 & 18.2 \\ \(\text{LCoE}_{t,S}\) [\(\ell\)/kg H\({}_{2}\)] & S1–6 & 13.7 & 2.9 & 6.5 & 1.5 \\ \(\text{LCoE}_{t,W}\) [\(\ell\)/kg H\({}_{2}\)] & W1–6 & 2.3 & 1.2 & 2.7 & 1.4 \\ \(\text{LCoE}_{t,W,S}\) [\(\ell\)/kg H\({}_{2}\)] & W1–6 and S1–6 & 2.7 & 1.7 & 3 & 1.5 \\ \multicolumn{2}{2}{2}{2} & \multirow{2}{*}{\
\begin{tabular}{c} **System levelized cost** \\ **parameter** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **W1–6, S1–6 (surplus),** \\ **TT1 and2,** **S1 Bland2,** **HFS1–4,** \\ **W1–6,** S1–6 (surplus),** \\ **S1C0E\({}_{e,V2G}\) [\(\ell\)/MWh]** & **TT1 and2,** **SHS1 and2,** **HFS1–4,** \\ **FCEV1 and2** \\ \(\text{SLCoE}_{e}\) [\(\ell\)/MWh] & **TT1 and2,** **SHS1 and2,** **HFS1–4,** \\ **FCEV1 and2** \\ \(\text{SLCoE}_{e}\) [\(\ell\)/MWh] & **TT1 and2,** **SHS1 and2,** **HFS1–4,** \\ **FCEV1 and2** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Levelized (LCoE) and system levelized cost of energy (SLCoE) parameters for Hamburg and Murcia in the Near Future and Mid Century scenarios.
# Feature-Attended Federated LSTM for Anomaly Detection in the Financial Internet of Things +
## 3 Our Proposed Architecture
### Correlation-Based Federated Learning  
The correlation-based federated learning includes two parts, cooperative training and aggregation optimization. In the cooperative training part, each detection node first uses local traffic data for model training to obtain local parameters and then uploads model parameters to the parameter aggregation node for federated optimization. Then, the model is trained in the cooperative scheme according to the model parameters updated by the parameter aggregation node, and the model parameters of each detection node are updated by the parameter aggregation node according to the weight of parameter aggregation so as to provide the next round of training for each detection node. Aggregation optimization is mentioned to find a trade-off between using global data features and local data features, and a parameter aggregation weight optimization algorithm is proposed based on the above cooperative process. The local parameter weight and global parameter weight of each detection node were determined by feature extraction and correlation degree and weight calculation.  
In this way, the global data feature is indirectly used to improve the problem of insufficient data through the coordination of the parameter aggregation node to each detection node. By updating the model parameters of detection nodes in a weighted way, the global data features can be utilized while the local data features can be appropriately retained to improve the detection accuracy and adaptability of the model to different financial scenarios.
## 6 Limitation of NIR-HSI Spectroscopy  
Despite the importance of hyperspectral imaging techniques, it has certain limitations in its applications. NIR-HIS has a vast amount of data, including redundant information that poses challenges during data processing and computational analysis; such massive data require enough storage space for the computer, which adds to the cost of accessories. Second, like conventional spectroscopy, the accuracy of NIR-HSI, an indirect technique, depends on the standard of the reference values; hence, the prediction accuracies depend on the reliability of the wet laboratory analysis. Third, since the strength of imaging resides in its capacity to discern spatial heterogeneity in models, hyperspectral imaging is inappropriate for homogeneous materials such as liquid samples. In addition, providing samples with a high water content, such as fresh foods, results in a strong absorption band in a particular spectral area and obstructs the processing of spectra. Fourth, multicollinearity is another known limitation of hyperspectral imaging. In addition, image pre-processing and modeling could be time-consuming and affected by interferences from instrumental noise and other external factors, such as the ambient condition of the instrument room, which are sometimes challenging to control.
## 2 Description of the Proposed Method
### Frequency-Power Droop Coefficient Determination Method  
In the optimization problem described in the previous section, the forecasted load profile is used to calculate the optimal operating points. However, load characteristics are different from the forecasted values, because there are uncertainties in the load forecasting procedure. Furthermore, uncertainty increases with the integration of bulk renewable energies [36]. In this situation, the outputs of multiple HVDCs form new operating points following droop characteristics, the coefficient of which is generally proportional to the capacities of the converters [37]. The conventional method for determining droop coefficients is simple, but the coefficients cannot create a more efficient solution. Therefore, a droop coefficient design method based on an optimization problem is required to operate MIDC systems more economically. We propose a statistical method to obtain optimal droop coefficients in this section.
# Novel Motion Sickness Minimization Control via Fuzzy-PID Controller for Autonomous Vehicle
## 2 Methodology
### The Configuration of Fuzzy-PID Controller  
The fuzzy-PID controller is formed by a FLC with four scaling factors, \(K_{e}\), \(K_{d}\), \(\beta\), \(\alpha\) connected in series. \(K_{e}\) and \(K_{d}\) normalize the inputs to the range in which the inputs membership function (MF) are defined. In addition, \(\beta\) and \(\alpha\) normalize the output of the FLC to an applicable value [30]. The first input of the controller is head roll error, \(e_{H}\) which is obtained from the difference of the desired/driver's head roll response, \(\theta_{HD}\) and actual/passenger's head roll response, \(\theta_{HP}\). The other input of the controller is the derivative of head roll error, \(\dot{e}_{H}\). The output of the fuzzy-PID is as follow,  
\[\delta_{c}=\alpha U+\beta\int Udt \tag{9}\]  
where \(U\) is the FLC output. The relation between input and output variables can be given as:  
\[U=A+PE+D\dot{E} \tag{10}\]  
where \(E=K_{e}e_{H}\) and \(\dot{E}=K_{d}\dot{e}_{H}\). Therefore, from (9) and (10), the fuzzy-PID controller output is:  
\[\delta_{c}=\alpha A+\beta At+\alpha K_{e}Pe_{H}+\beta K_{d}D\dot{e}_{H}+\beta K _{e}P\int e_{H}dt+\alpha K_{d}D\dot{e}_{H} \tag{11}\]  
Here, the FLC is designed based on the heuristic approach. It has two inputs and an output MF. Each of the MFs has a triangular and trapezoidal membership as shown in Figure 8. The input variables are decomposed into three varied fuzzy linguistic levels: Negative (N), Zero (Z) and Positive (P). Table 2 describes the control rules which are designed in IF-THEN structure.  
Particle swarm optimization (PSO) is utilized to select the scaling factors gain. The PSO fitness function is set to be the root-mean-square (RMS) value of lateral acceleration. The goal is to find a set  
Figure 8: Membership function (MF) of input-output in fuzzy-PID, (**a**) MF of input \(e_{H}\), (**b**) MF of input \(\dot{e}_{H}\) and (**c**) MF of output \(U\).  
of optimal values of the scaling factors by minimizing the fitness function. The fitness function can be formulated as:  
\[Fitness\ function=f(K_{e},K_{d},\beta,\alpha)=\sqrt{\frac{\sum\left[a_{y}(t)\right]^{2 }}{N}} \tag{12}\]  
Table 3 tabulates the parameter of PSO. The parameter selection is mainly based on previous study [31]. After the optimization process, the gain value of the scaling factors is determined as, \(K_{e}=1.3,\ K_{d}=1.43,\ \beta=0.9\) and \(\alpha=-0.1\).
## 3 Results and Discussion
### Influence of Different Wave Amplitudes  
The \"Downstream\" propagation direction is therefore selected as the base to study the influence of traveling wave amplitude, the fixed TWW velocity ratio \(c/U_{\infty}\) is taken as 2.0, and the wave number per quarter circle is taken as 4. The aerodynamic coefficients and vortex shedding pattern after activating TWW are chosen to access the control effectiveness.  
The lift and drag coefficient time histories for given \(\hat{A}/D\) of 0.01, 0.02, 0.03, 0.04, and 0.05 are shown in Figure 10. The TWW is activated at \(t~{}=~{}0\) throughout the following calculation process. The mean lift coefficient decreases slightly, and the fluctuating lift coefficient remains almost the same. With an increase of wave amplitude, the fluctuating drag coefficient increases significantly and the mean drag coefficient decreases to negative values. It can be found that the lift coefficient is less affected by wave amplitude, but wave amplitude has significant influence on drag coefficient, as shown in Figure 10.  
Figure 11 shows the lift and drag coefficient characteristics under different wave amplitudes, including the mean and RMS values of lift and drag coefficients. Form Figure 11a, with the increase of \(\hat{A}/D\), \(\overline{C}_{l}\) decreases slightly, which is smaller than the standard circular cylinder result. Overall, there is less difference in \(\overline{C}_{l}\) compared to that in standard cylinder.  
When amplitude ratio \(\hat{A}/D~{}=~{}0.02\), \(C_{l}^{\prime}\) dramatically decreases to \(0.005\) and reaches the minimum, which drops by \(99.48\%\) from that of standard circular cylinder. When \(\hat{A}/D\) increases from \(0.02\) to \(0.05\), \(C_{l}^{\prime}\) shows a slightly increasing trend. \(C_{l}^{\prime}\) equals to \(0.012\) at \(\hat{A}/D~{}=~{}0.05\), which dropped by \(98.7\%\) from that of standard circular cylinder. In Figure 11b, with the increase of \(\hat{A}/D\), \(\overline{C}_{d}\) decreases from \(-0.010\) to \(-0.456\), and \(C_{d}^{\prime}\) increases from \(0.085\) to \(0.385\), which is almost \(4.7\) times the standard circular cylinder result. It's easily observed that different wave amplitudes have little impact on \(C_{l}^{\prime}\) and \(\overline{C}_{l}\). However, \(C_{d}^{\prime}\) and \(\overline{C}_{d}\) change obviously as \(\hat{A}/D\) increases from \(0.01\) to \(0.05\). To make TWW perform an ideal functionality in eliminating the aerodynamic forces, \(\hat{A}/D~{}=~{}0.02\) should be chosen, which can eliminate lift without extra significant drag oscillation.  
Figure 8: The vorticity contours of TWW cylinder with “Upstream” propagation. (**a**) \(t\) = 2.16 s. (**b**) \(t\) = 2.31 s. (**c**) \(t\) = 2.46 s. (**d**) \(t\) = 2.61 s. (**e**) \(t\) = 2.97 s.
# Towards Secure Searchable Electronic Health Records Using Consortium Blockchain
## 3 System Model
### System Model Design
#### 3.3.2 Phase 2: Data Storage  
In this step, firstly the doctor produces the encrypted patient's personal information and his/her EHRs with the patient's identity and sent it to the private blockchain which works as a medical server. Secondly, the doctor encrypts the index with a public key encryption using the ECDH algorithm and stores it in the smart contract-based consortium blockchain, then requests the keyword set from the key server and then encrypts the keyword set and the patient private key. Secondly, the doctor sends the encrypted EHRs to the private blockchain and the secure index (encrypted keyword and patient's private key) is stored in the smart contract-based consortium Ethereum blockchain.  
In this phase, the doctor first logs in to the Ethereum blockchain with the patient's address. Secondly, the encrypted patient information and his/her EHR with their identity are stored in a private blockchain. Thirdly, he encrypts the secure index (encrypted keyword and patient's private key) with the doctor's public key using the public-key encryption algorithm, which is the ECDH algorithm in this case, and stores it in the smart contract-based consortium Ethereum blockchain. This phase runs the following algorithm:  
* _PEKS_ (_Pk_, \(\omega\), _IDpi_ ): Given the patient public key _Pkpi_, select a keyword set \(\omega\) = (\(\omega_{1}\), \(\omega_{2}\),..., \(\omega_{n}\)) as input and return it to the patient identity _IDpi_ as a pointer for the EHR's location in the private blockchain;
* _EncryptEHR_ (_m_, _Pkpi_): It takes the patient's public key _Pkpi_ and plaintext \(m\) as an input and returns ciphertext _Cm_;
* _Encryptindex_(\(\omega\),_Sk,Pk_): It takes the keyword set and the patient's private key and encrypts it with the patient's public key then returns the index ciphertext.