esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Social Media Applications and e-Government
### Benefits and Challenges of Social Media in Public Administration  
The use of social media by local governments offers significant benefits as well as challenges [43]. On the one hand, social media enhances transparency, promotes immediacy in communication and creates opportunities for participatory governance, allowing citizens to express opinions and make demands directly to authorities. Furthermore, the reduced need for intermediaries (e.g., media) reduces the cost of communication and strengthens citizens' sense of proximity and trust towards local authorities. Social media can function as powerful tools to enhance e-Government, provided that they are part of a clear digital governance strategy and accompanied by the necessary institutional, organizational and technological adjustments [45].  
However, there are also serious challenges, such as the lack of clear usage policies, insufficient staff to manage accounts, risks of privacy breaches and misinformation management. In addition, small local government departments often lack the technical skills and resources to fully exploit the interactive features of social media, resulting in them remaining trapped in a passive information model [45].
## 5 Conclusions  
The satellite navigation signal authentication technology will provide more credible PNT services. Based on summarizing and reviewing the existing satellite navigation signal authentication, this paper designs a service architecture for next-generation BDS authentication and analyzes the corresponding technical challenges. The main conclusions are as follows:  
1. Navigation signal authentication technology is a method used to improve the anti-spoofing ability of the GNSS on the system-side, which can solve the generated spoofing.
2. In the future, authentication services will become the GNSS standard to improve the credible service capabilities of the GNSS.
3. For the construction of the next-generation BDS, this paper designs a Beidou authentication service system integrating high, medium, and low constellations; standard positioning and augmentation services; and navigation and communication. It involves system security, key management, authentication mechanism, authentication performance evaluation and terminal processing.  
In summary, satellite navigation signal authentication is an emerging technology in the current GNSS development stage, which can provide users with more credible PNT services. During the gradual construction of the next-generation BDS in China, it is of great significance to seriously consider the \" assured and credible\" capabilities provided by navigation signal authentication and its application prospects and to identify and overcome corresponding key technologies.  
Conceptualization, X.C. and R.L.; methodology, X.C. and R.L.; investigation, X.C., R.L. and T.L.; resources, H.Y. and H.W.; writing--original draft preparation, X.C.; writing--review and editing, X.C., R.L., T.L., H.Y. and H.W.; visualization, X.C. and T.L.; supervision, T.L., H.Y. and H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Defence Science and Technology Innovation Special Zone of China (Grant No.: CX2022-04-03-02) and the Key Deployment Project of National Defense Science and Technology Innovation of Chinese Academy of Sciences (Grant No.: 2021-KJC-Y-0617).  
Not applicable.  
The authors declare no conflict of interest.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 5 Simulation Results Analysis
### Performance of Uplink Communications with No Hidden Nodes
#### 5.3.1 Goodput and Message Loss Rate  
Figure 8 shows goodput values obtained from simulations performed in the four scenarios, plotted as a function of network load. As can be seen, the goodput increases as network load does, although it is noted that higher load values imply lower goodput on scenarios with more devices on the network. This is because the more devices that are in the network, the higher the number of collisions for the same network load, as can be seen in the statistics obtained from the execution of the CSMA/CA algorithm shown in Table 3 (see Section 5.3.2 below).
## 2 Methods
### Algorithm Flow
#### 2.5.3 Levy Flight Based on Simulated Annealing Strategy  
A Levy flight perturbed population based on simulated annealing is introduced to enhance the diversity of the local solution space distribution. This strategy enables individuals to explore the global solution space with occasionally long trajectories and short random movements. During the exploration process of the population individuals, their corresponding local solution spaces also move within the solution space. The formula for the Levy flight in this paper is given by \(s=u/|v|^{1/\beta}\), where \(u\sim N\big{(}0,\sigma^{2}\big{)}\), \(v\sim N(0,\,1)\), and \(\sigma=\left\{\frac{\Gamma(1+\beta)\sin(\pi\beta/2)}{\beta\Gamma((1+\beta)/2) \cdot 2^{\beta/2}}\right\}^{1/\beta}\).  
The steps of the Levy flight based on the simulated annealing strategy are as follows:  
Step 1: Set an initial temperature \(T\) to a sufficiently high value.  
Step 2: Consider the original solution of the individuals as \(S_{1}\) and the new solution after Levy flight perturbation as \(S_{2}\). Calculate the difference \(\mathrm{d}f=f\left(S_{2}\right)-f\left(S_{1}\right)\) for each individual in the population, where \(f\) represents the object function.  
Step 3: If \(\mathrm{d}f<0\), accept the new solution \(S_{2}\).  
Step 4: If \(\mathrm{d}f\geq 0\), generate a random number \(R\) uniformly distributed in the interval (0, 1) and calculate the acceptance probability \(\exp(-\mathrm{d}f/\mathrm{T})\) for \(S_{2}\). If \(\exp(-\mathrm{d}f/\mathrm{T})>\mathrm{R}\), accept \(S_{2}\) as the new solution; otherwise, keep the solution unchanged as \(S_{1}\).  
Step 5: Reduce the global temperature by setting \(T=q\times T\), where \(q<1\).  
As the DE algorithm progresses through iterations, the global temperature gradually decreases, resulting in a reduced probability of accepting inferior solutions. When the temperature reaches a certain threshold, the search process terminates.
# A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller
## 5 Implementation and Evaluation
### Model Detection Performance  
We use Shannon entropy (\(\alpha\) = 1) to statistic attribute features and compare the performance of 4 common machine learning algorithms and HMM-R algorithms with the standard of RP, FA and DR when the L-DDoS attack ratio is 0.01. As can be seen from Table 2, the HMM-R algorithm increases the true positive rate by at least 2% and the false positive rate by at least 5% or even 20% compared to the other 4 machine learning algorithms. It is because the state transfer matrix and the observation probability transfer matrix in the HMM-R algorithm are in the form of probability to relate the potential association between data packets in the data center network. Since the HMM-R algorithm obtains the best model parameters at different orders, different k-means clustering results and observation sequence lengths are set. Table 3 shows the result of the k-mean clustering and the length of the observation sequences in each sequence for multi-rate attack streams at the attack ratio of 0.01. The different clustering results show that the hidden state and the state of the observed sequence are different. The classification of Renyi entropy clustering is more, indicating that it is more fine-grained in the classification. Table 4 shows the performance of HMM-R at different attack ratios. With the increase of L-DDoS attack traffic, the gap between normal traffic and attack traffic is increasing. The probability that the attack traffic is mistaken for normal traffic is getting smaller and smaller. Therefore, the false negative rate gradually decreases and the true positive rate increases. At any attack ratio, the HMM-R detection performance is superior to other algorithms in both the true positive and the false positive because HMM-R through the state transfer matrix to consider the L-DDoS attack state in a variety of rates adaptively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Detection Algorithm** & **RP** & **FA** & **DR** \\ \hline KNN & 0.8990 & 0.2530 & 0.0 \\ SVM & 0.8990 & 0.2409 & 0.007 \\ SOM & 0.9250 & 0.0636 & 0.0834 \\ BP & 0.9230 & 0.1315 & 0.0 \\
**HMM-R** & **0.9461** & **0.01** & **0.08** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of algorithms at attack ratio of 0.01.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Order of Renyi Entropy** & **K** & **L** \\ \hline \(\alpha=1\) & 10 & 13 \\ \(\alpha=3\) & 11 & 13 \\ \(\alpha=5\) & 10 & 13 \\ \(\alpha=8\) & 11 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of HMM-R at attack ratio of 0.01.
## 5 Results
### Simulation Verification with VISSIM  
In order to verify the reliability of the BP neural network model for the delay analysis, the VISSIM simulation model is constructed for the above four intersection entrance lanes. The actual traffic data are input into the simulation model under the conditions of inside and outside left-turn lanes, respectively. The delay results obtained through the simulation analysis are shown in the simulation values in Table 5, and the delay comparison is shown in Figure 8.  
It can be seen from Figure 5 that the delay in the inside left-turn lanes of entrances 1 and 2 is lower than the outside delay, and the delay of the outside left-turn lanes of entrances 3 and 4 is lower than the inside delay in the BP neural network model and VISSIM simulation model. The results show that entrance roads 1 and 2 are suitable for the conventional inside left-turn lane scheme, while entrance roads 3 and 4 are more suitable for the outside left-turn lane scheme. Additionally, the result is consistent with the realistic scheme. Moreover, Figure 6 also shows that the calculated values of the BP neural network model of each group of entrance delays are in good agreement with VISSIM simulation values.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Entrance Road**} & \multicolumn{2}{c}{**Inside Delay(s)**} & \multicolumn{2}{c}{**Outside Delay(s)**} \\ \cline{2-5}  & **Model Value** & **Simulation Value** & **Model Value** & **Simulation Value** \\ \hline
1 & 40.6 & 39.4 & 48.7 & 50.7 \\ \hline
2 & 60.3 & 58.7 & 73.6 & 71.5 \\ \hline
3 & 86.6 & 88.7 & 59.0 & 59.6 \\ \hline
4 & 98.5 & 96.5 & 70.9 & 71.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The model and simulation values of delay in inside and outside left-turn lanes.  
Figure 7: Relationship between mean square error and training times.
## 4 Prediction based on LSTM
### Prediction Combining with Multiple-Wave Data  
In order to explore the relationship between waves and ship motions, the effect of multiple-wave elevation data as input characteristics on the roll motion prediction is studied. Based on three sets of wave elevation data at 1 m, 3 m, and 5 m from the ship's side, three different wave elevation data schemes and roll data are combined as input features for roll motion prediction. The three wave elevation input forms are based on the wave elevation data at 1 m and 3 m, at 3 m, and 5 m and at 1 m, 3 m, and 5 m. The comparison between the time series of the ship roll predicted based on different input schemes and the sample value is shown in Figures 9-11. The roll prediction error is summarized in Table 5.  
Figure 8: Time series of roll prediction combining wave data 5 m from ship.  
Figure 9: Time series of roll prediction combining wave data 1 m and 3 m from ship.  
height of 1 m from the ship, the prediction accuracy decreases with a small advance prediction step (\(n\leq 30\)), but the prediction accuracy increases significantly in case of a large advance prediction step (\(n\geq 40\)). Compared with the prediction scheme with the input wave height of 3 m from the ship, the prediction accuracy based on the two sets of wave input has greatly improved even if the number of advance prediction step is small. With the increase in the number of prediction steps, the prediction accuracy is equivalent to both. When the input scheme is combined with the wave data at 1 m, 3 m, and 5 m from the ship, the stability of the prediction results is improved to a certain extent compared with that based on the two sets of wave input, especially when the advance prediction step is smaller. The prediction accuracy is improved significantly. The number of advance step for the ship roll prediction can reach 150 by combining the three sets of the wave input scheme. Because the wave elevations at different locations have different optimal advance prediction durations, the LSTM neural network could extract the optimal weights of the multiple wave elevations. When the different advance prediction durations are used, the stability of the prediction could be improved as a whole.
## 4 Implementation and Discussion
### Performance Analysis
#### 4.2.5 Cost  
Distributed data storing, verification, and transaction increase the use of internet and power (ethernet and WiFi) [76]. Alternatively, PoF is comparatively cost-effective. Moreover, the cost of PoF is CAPEX (capital expenditure), whereas it is OPEX (operational expenditure) for the existing system [77]. Initial system setup covers most of the expenditure of PoF, whereas other studies [37; 39; 40] cost both during setup and use. Additionally, PoF saves extra cost caused for mishandling of EHRs [10; 11].
## 2 Study Area and Data
### Sentinel-1 Imagery  
Sentinel-1 imagery was employed in this study as a remote-sensing data source. Sentinel-1 is a two-satellite constellation developed by the European Space Agency. The dual-polarization C-band SAR instrument carried by the Sentinel-1 satellite is capable of all-day all-weather ground imaging. The stable and fast revisit cycle combined with its excellent ground imaging capability makes it a reliable data source for crop identification based on time-series remote sensing data. The Sentinel-1 data used in this study were processed using Google Earth Engine. Each Sentinel-1 image includes vertical transmit/vertical receive + vertical transmit/horizontal receive (VV + VH) polarization data preprocessed to the backscatter coefficient [20].  
The Sentinel-1 images used in Regions A (Mississippi Alluvial Plain) and B (Sacramento Valley) were acquired in 2019. The images used in Region C (Shuangcheng District) were acquired from 2017 to 2020. The images used in Regions D (Songnen Plain), E (Liaohe Plain), and F (Sanjiang Plain) were obtained from 2017. Considering the phenological period of rice in the study area, all Sentinel-1 images acquired from 1 April to 2 November of each year were used. Although the revisit period of Sentinel-1 is 12 days, it is actually not possible to obtain Sentinel-1 data every 12 days in some areas due to missing data. Therefore, Sentinel-1 24-day composite time-series SAR data were generated based on the average of all Sentinel-1 images acquired during each 24-day period from 1 April to 2 November to ensure the consistency of time-series SAR data and avoid missing data (Figure 2). The final time-series SAR data contained nine 24-day-average composite SAR images; each image included VV and VH bands. The preprocessed Sentinel-1 images were used as input data for training the deep learning-based crop recognition model and crop mapping.  
Figure 1: Location of the study areas and samples. (**a**) Regions A, B, C, D, E and F are located in the Mississippi Aluvial Plain, Sacramento Valley, Shuangcheng District, Songnen Plain, Liaohe Plain, and Sanjiang Plain, respectively. Location of ground survey points and validation samples in the Songnen Plain (**b**), Liaohe Plain (**c**), and Sanjiang Plain (**d**). Photos of ground survey sites for rice (**e**), maize (**f**), soybean (**g**), and wetland (**h**). The arrows in (**a**) indicate the direction of model transfer. Other validation samples in (**b–d**) include non-rice samples such as maize, soybeans, wetlands, etc.
# Distributed Joint Source-Channel Coding Using Quasi-Uniform Systematic Polar Codes
## 4 Proposed DJSCC Scheme and Performance Limit  
In this section, we propose a DJSCC scheme based on the previous construction of two polar-like codes, and prove its asymptotic optimality.
# Fast and Reliable Burst Data Transmission for Backscatter Communications
## 3 Challenge and Motivation
### Challenge 1: Variable Energy per Transmission  
As shown in Figure 1, the CRFID node operates in a series of charging and discharging processes, which cannot work continuously due to too little energy. The device collects energy and charges a small energy store during a short period of sleep time and then wakes up and discharges to send packets.  
Figure 1: Energy-harvesting process.  
Why is the energy available in each discharge cycle difficult to predict? First, if the energy-harvesting condition is too low, the efficiency of storing energy into the capacitor is low [24]. Therefore, the maximum energy that can be accumulated depends on the current harvest condition. Second, the RF energy collected by the node depends on how much energy the reader outputs. When the reader is communicating, the harvest rate of each node is constantly changing. Third, the node needs to use an analog-to-digital conversion (ADC) to measure the energy level. Each ADC operation consumes 327 \(\mu\)J on the WISP platform, which is equivalent to the energy budget for transmitting 27-bit data. This cost is too much on a micro-powered platform.  
Therefore, we need to adjust the length of the transmitted data according to the current energy environment. When the energy level is low, we need to shorten the frame length to adapt, but this usually reduces the goodput which is affected by the overhead of each transmission, including preamble, header, and hardware. In order to optimize goodput at the same time, it is important to transfer the data as much as possible given the available energy. Therefore, the problem faced by the node is that it needs to shorten its transmission frame under poor harvest conditions and to scale up to increase goodput when condition permits.
## 6 Conclusions  
The setup of a GDD and LTF is shown here to be very sensitive to MMW radiation. By adding the long-pass filter, the noise level in the output from the GDD is reduced. Thus, a higher value of SNR is obtained. By using the data from Figure 12 and Equation (4), the NEP can be calculated for both incidence with and without long-pass filter, where the average value of the output frequency noise with filter is \(\Delta f_{n}=0.9\) Hz, and without filter is \(\Delta f_{n}=2.75\) Hz. NEP is \(2.15\times 10^{-11}\ W/\sqrt{\rm Hz}\) (with filter) and \(4.12\times 10^{-11}\ W/\sqrt{\rm Hz}\) (without filter). The GDD and LTF setup can be used as a pixel in a focal plane array (FPA), such that the pixel readout can be performed using a digital system.
## 4 Discussion
### Effect of Heat Treatment on Tuning the Stiffness and Damping Capacity of NiTi WMD  
#### 4.3.1 Softening versus Stiffening  
As mentioned previously in Sections 3.2.3 and 3.2.4, a trade-off exists between the stiffness and damping capacity of the NiTi WMD when adjusting heat treatment temperature and time. This can be attributed to a competitive relationship between the hardening effect and softening effect caused by the heat treatment. Softening is the dominant effect when the heat treatment temperature is lower than 200 \({}^{\circ}\)C or the heat treatment time is shorter than 30 min. At this stage improving the heat treatment temperature simply lowers the resonant frequency and increases the loss factor. However, the hardening effect outperforms the softening effect once the heat treatment temperature is higher than 200 \({}^{\circ}\)C or the heat treatment time is longer than 30 min. At this stage improving the heat treatment temperature only leads to radical stiffening, as reflected by the increasing resonant frequency.
## 3 Method
### The Predictive Model and the Global Aesthetic Score
#### 3.2.3 The Global Aesthetic Decision Boundary  
Support vector machine (SVM) is a supervised learning model for analyzing data in classification and regression analysis, which is a classical binary classification machine learning algorithm. Given the data and labels, SVM can learn a hyperplane called the decision boundary that separates the attributes as much as possible. A linear problem is the initial core problem that SVM aims to solve. For a given dataset with corresponding labels, \(D=\{(x_{1},y_{1}),(x_{2},y_{2}),...,(x_{n},y_{n})\), \(x_{i}\in R^{n}\) is the target data of the learning decision surface, and \(y_{i}\) and \(x_{i}\) are the one-to-one label value. SVM learns a hyperplane, denoted as \(w^{T}x+b=0\), where (\(w\)) is a weight vector, (\(x\)) is an input vector, and (\(b\)) is a bias term. The hyperplane \(w^{T}x+b=1\) minimizes the distance between positive samples, while the hyperplane \(w^{T}x+b=-1\) minimizes the distance between negative samples. The problem solved by SVM is to find a hyperplane that maximizes the margin between the two sides while minimizing the probability of misclassification. The formula is shown below:  
\[\gamma=\frac{2}{\|\omega\|} \tag{7}\]  
\[\begin{split}&\arg\underset{\omega,b}{min}\frac{1}{2}\|\omega\|^{2}\\ & s.t.y_{i}(\omega^{\top}x_{i}+b)\geq 1,i=1,2,...,m.\end{split} \tag{8}\]  
where \(\omega\) and \(b\) are the parameters of the hyperplane, and \(\gamma\) is the distance between hyperplane \(w^{T}x+b=1\) and hyperplane \(w^{T}x+b=-1\).  
This algorithm utilizes SVM to learn decoupled representations of aesthetic styles from the hidden space of the generative model. Aesthetic SVM decision faces are, respectively, learned from the \(Z\) and \(W\) latent spaces of the generative model that is based on \(I_{origin}\) and \(P_{NIMA}\). The 2000 sheets with the highest NIMA score and the 2000 sheets with the lowest NIMA score were selected as the aesthetic decision faces corresponding to the training of positive and negative samples. Examples of the positive and negative samples of the partially generated model are shown in Figure 4.  
Finally, a total of 22 aesthetic decision faces were trained in \(Z\) and \(W\) of 11 generated models, and the classification accuracy of decision faces is shown in Table 1. The average accuracy of all decision faces based on \(W\) latent space training is higher than that on \(Z\) latent space, and the average accuracy improves by 9.3%. The accuracy pairs, such as seen in Figure 5. The average accuracy of the decision surface based on \(W\) latent space training reached 0.997.  
Figure 4: The example of positive and negative samples for SVM training.