esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2015**, _15_, 15952-15973; doi:10.3390/s150715952  
Sensors  
ISSN **1424-8220**  
www.mdpi.com/journal/sensors  
Article  
Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks  
[PERSON] 1, [PERSON] 2, [PERSON] 1 and [PERSON] 1,*  
1 School of Computer Science and Communication Engineering, Jiangsu University,  
Zhenjiang 212013, China; E-Mails: [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (C.L.)  
2 College of Computer Science, Sichuan University, Chengdu 610065, China;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]_ADDRESS];  
Tel.: +86-511-8898-6871.  
Academic Editor: [PERSON]  
Received: 10 May 2015 / Accepted: 26 June 2015 / Published: 3 July 2015  
With the rapid development of wireless communication technology, sensor technology, information acquisition and processing technology, sensor networks will finally have a deep influence on all aspects of people's lives. The battery resources of sensor nodes should be managed efficiently in order to prolong network lifetime in large-scale wireless sensor networks (LWSNs). Data aggregation represents an important method to remove redundancy as well as unnecessary data transmission and hence cut down the energy used in communication. As sensor nodes are deployed in hostile environments, the security of the sensitive information such as confidentiality and integrity should be considered. This paper proposes Fully homomorphic Encryption based Secure data Aggregation (FESA) in LWSNs which can protect end-to-end data confidentiality and support arbitrary aggregation operations over encrypted data. In addition, by utilizing message authentication codes (MACs), this scheme can also verify data integrity during data aggregation and forwarding processes so that false data can be detected as early as possible. Although the FHE increase the computation overhead due to its large public key size, simulation results show that it is implementable in LWSNs and performs well. Compared with other protocols, the transmitted data and network overhead are reduced in our scheme.  
keywords:wireless sensor networks; data aggregation; fully homomorphic encryption; data integrity; message authentication code +
Footnote †: journal: Journal of Information Theory
## 3 Theory of FE Fluctuation
\[\mathcal{K}_{x}(z)=\frac{\sqrt{2m^{*}}}{\hbar}\bigg{[}V(z)-E+\frac{\hbar^{2} }{2m^{*}}\kappa^{2}\bigg{]}^{\frac{1}{2}} \tag{8}\]  
\[\sigma_{x}(z)=\frac{\sqrt{2m^{*}}}{\hbar}\bigg{[}V(z)-E+\frac{\hbar^{2}}{2m^{* }}\kappa^{2}+\hbar\omega\bigg{]}^{\frac{1}{2}}. \tag{9}\]  
here, \(V(z)\) is the barrier potential and \(z\) is the coordinate of the axis perpendicular to the interfaces. The average of the current-current correlation in state \(i\) is expressed as:  
\[\langle\hat{I}^{*}(t)\hat{I}(0)\rangle=\frac{1}{N}\sum_{\mathbf{\xi}}\langle\mathbf{ \xi}\big{|}\hat{I}^{*}(t)\hat{I}(0)\big{|}\mathbf{\xi}\rangle. \tag{10}\]  
\(|I(\omega)|^{2}\) is the power spectrum of the fluctuation of the tunneling current for the FE mechanism. \[|I(\omega)|^{2}=\int_{-\infty}^{\infty}\langle\hat{I}^{*}(t)\hat{I}(0) \rangle e^{-i\omega t}dt \tag{11}\]  
By substituting Equations (6)-(9) into Equation (11), we finally obtain the following expression for the power spectrum of the FE current fluctuation:\[|I(\omega)|^{2}=\frac{e^{2}Am}{2\pi^{2}\hbar^{3}}\int dE\times\exp\biggl{(}-\int dz |\ell_{x}(z)|\biggr{)}\exp\biggl{(}-\int dz|\varphi_{x}(z)|\biggr{)}. \tag{12}\]  
where \(A\) is corresponding to the total area of the FE site, which electrons emit from the SWCNTs the cathode and \(m\) is the effective mass of an electron that moves parallel to the emission site in the source electrode, from which tunneling electrons are injected into the barrier. Equation (12) should be transformed into a more suitable form for an FE cathode using metallic SWCNTs. FE model was carried out in the Fowler-Nordheim tunneling regime with the barrier potential \(V(z)\), and a simplified form for the power spectrum is expressed as:  
\[|I(\omega)|^{2}\propto\frac{e^{3}A}{4\pi^{2}\hbar\phi_{B}}\cdot|E|^{2}\biggl{(} \frac{\hbar\omega}{e\phi_{B}}+1\biggr{)}^{-1}\times\exp\biggl{\{}-\frac{2\sqrt {2m}}{3\hbar e|E|}\biggl{(}(\hbar\omega)^{\frac{3}{2}}+(e\phi_{B})^{\frac{3}{ 2}}\biggr{)}\biggr{\}}. \tag{13}\]  
here, \(\phi_{B}\), as shown in Figure 3 is the barrier height potential, which is the difference from the vacuum level to the energy level of the bottom of the Fermi level in the case of metallic SWCNTs, and \(E\) is the field applied to the SWCNTs, which is enhanced by the tip shape. The enhancement factor \(\beta\) of \(E\) is calculated by simulation using the surface-charge method with a three-dimensional model of SWCNTs protruding from the ITO film [31; 32; 33], where:  
\[E=\beta E_{0}. \tag{14}\]  
there, \(E_{0}\) is the actual electrical field supplied between the cathode and anode electrodes. Moreover, field emission site \(A\) on the SWCNTs of the cathode was estimated from the comparison between the simulated and measured bright spots [31].
## 2 Time-Invariant Analog and Digital Filter Prototype  
In this paper, we use the well-known analog model of the oscillatory system. The considered system can be regarded as a second-order lowpass filter section. The transfer function describing the second order filter in the frequency domain is given, as follows:  
\[H(s)=\frac{k}{\omega_{0}^{-2}s^{2}+2\zeta\omega_{0}^{-1}s+1}, \tag{1}\]  
where \(k\) stands for the gain coefficient, \(\zeta\) for the damping ratio, and \(\omega_{0}\) for the natural frequency of the filter. By using the well-known bilinear transform, in the form of:  
\[s\to\frac{2}{t_{s}}\frac{z-1}{z+1}, \tag{2}\]  
where \(t_{s}\) stands for the sampling time, one can obtain a digital oscillatory model with the entangled gain, damping ratio, and natural frequency parameters. The obtained transfer function of the discretized filter is as follows:  
\[H(z)=\frac{kt_{s}^{2}(z^{2}+2z+1)}{[4\omega_{0}^{-2}+4\zeta\omega_{0}^{-1}t_{ s}+t_{s}^{2}]z^{2}+[-8\omega_{0}^{-2}+2t_{s}^{2}]z+4\omega_{0}^{-2}-4\zeta \omega_{0}^{-1}t_{s}+t_{s}^{2}}. \tag{3}\]  
Figure 1 presents the step responses of exemplary digital and analog second-order oscillatory systems, proving the correctness of the proposed discretization process. In this paper, the second-order Chebyshev type-I filter, with passband peak-to-peak ripple \(R_{p}=3\) dB and normalized cutoff frequency \(\omega_{c}=1\) rad/s, is considered as a study case.  
Figure 2a,b present the Bode diagrams of both the analog and digital filters.
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 4 Simulation Results and Discussions
### Number of Iterations Analysis  
In this section, we examine the influence of the iteration number on the presented localization approaches based on PSO and chicken swarm optimization (PSODV-Hop, DV-HopCSO, W-DV-HopCSO, and HW-DV-HopCSO) considering the same four distinct types of complex network topologies. In this configuration, the number of nodes remained constant at 200, the percentage of anchor nodes was set to 15%, and the communication range was 30 m, while the number of iteration was varied from 1 to 50. For CSO, the number of roosters was fixed at 8, the number of chicks at 1, and the number of hens at 15. Additionally, the particle velocity \(Vmax\) and learning coefficients \(C_{1}\) and \(C_{2}\) for PSO were fixed at 10, 1.5, and 2, respectively, and the swarm size for both PSO and CSO was fixed at 20. Table 2 provides a summary of the parameters used for this simulation.  
The results depicted in Figure 9 reveal that the HW-DV-Hop algorithm based on CSO is faster in terms of convergence speed in all four network topologies, in contrast to PSODV-Hop. The HW-DV-HopCSO algorithm demonstrates the robustness of using CSO for localization. It maintains a stable localization error compared to PSODV-Hop when increasing the maximum number of iterations, with the latter exhibiting an unstable localization error across different simulation scenarios and presented types of networks.
## 4 Experiments and Analysis
### Experimental Results
#### 4.2.1 Long-Distance Maritime Target
Specifically, in the central part of the tower, the alternating bright and dark areas form a regular pattern that displays the arrangement of the bricks and their depth variations. In contrast, Figure 12b exhibits lower brightness, with the depth information appearing smoother, and almost no distinct details are visible. The depth information shown is relatively even, and whereas the overall outline of the tower can still be observed, the surface details are significantly reduced compared to Figure 12a. The brick texture is almostly invisible, and the surface presents a smooth, curved effect. Figure 13a illustrates the surface details of the tower, particularly the arrangement of the bricks. Overall, the surface texture of the tower is clear, especially in the middle area, where a regular layered structure is visible. The top region of the image exhibits more drastic color variations, indicating significant target surface depth changes. In contrast, Figure 13b exhibits minimal undulation, and the detailed representation is inferior to Figure 13a. Most notably, the true depth information at the top of the image is almost completely lost.
## 3 Results
### Comparison to Prior Work  
We compared our study with prior similar works, and we summarize our findings in Table 31. Prior works were similar in several ways but differed in other ways from our work, limiting a fair comparison. Both [PERSON] et al. and [PERSON] et al. utilized data in which intoxication was simulated using drunk buster goggles, but they combined data collected from both a smartphone and a smartwatch. As we collect data only from a smartphone, we compare our results to the results they achieved on only their smartphone dataset. Our results from classifying all domain features outperformed prior work by [PERSON] et al. (2018) with a 22.9% increase in accuracy. There were other notable differences. [PERSON] et al. utilized a deep learning model, while our work utilized traditional machine learning on handcrafted features. Additionally, while our dataset was balanced, [PERSON] et al. utilized a highly imbalanced dataset, but did not report their AUC or F1-score, a more useful metric for imbalanced datasets. Our study, on the other hand, did not overfit to our small dataset and reported an impressive AUC score of 0.928. While [PERSON] et al. achieved 89.45% accuracy using skew, kurtosis, gait velocity, residual step time, band power, XZ sway, XY sway, YZ sway, and sway volume features, primarily from gait and postural sway domains, our work explored a broader set of 27 features spanning time, frequency, statistical, wavelet, and information-theoretic domains, as shown in Table 1. Despite using a smaller dataset, our model achieved 84.90% accuracy, demonstrating that our more diverse feature set captures complex patterns that should generalize well to real-world variability. Also, with the expanded number of features across different domains and the utilization of 10-fold cross-validation, our work achieves a stronger subject-level generalization compared to previous works that utilize fewer predictive features.
# A Fuzzy-Logic-Based Load Balancing Scheme for a Satellite-Terrestrial Integrated Network
###### Abstract  
With the development of communication systems, users are becoming more widely distributed and require higher speed networks. A satellite-terrestrial integrated network could provide seamless coverage for these users. In previous studies of load balancing, initial access and load balancing are decided on based on signal reception and are performed reactively after the overloading occurs, which may not work well in satellite-terrestrial integrated networks. Therefore, this paper proposes a fuzzy-logic-based load balancing scheme. In this scheme, a fuzzy evaluation metric to pre-evaluate the user's impact on overload is presented. The fuzzy logic system is constructed based on adaptive neuro fuzzy system, which takes the user's signal reception, speed and data requirement as inputs. Then, the fuzzy-logic- and reinforcement-learning-based access is proposed to give an access decision for all users in the network to prevent overloading. Due to the large dimensions of action space, the reinforcement learning model is trained by the proposed fuzzy, deep, deterministic policy gradient. Next, the fuzzy-logic-based offloading algorithm is proposed to balance load after overloading. A simulation platform is established to evaluate the performance. Simulation results indicate that the proposed scheme can ensure load balance for a longer time than base line schemes while ensuring data rate of users.  
load balancing; fuzzy logic; reinforcement learning; satellite-terrestrial network Article
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
###### Abstract  
In order to address the challenge posed by existing anti-jamming methods (including intelligent anti-jamming techniques) that struggle to counter high-speed reactive jamming in complex jamming environments, we have developed a novel approach that involves leveraging intelligent jamming in such environments rather than merely attempting to evade jamming. Unlike existing anti-jamming techniques that extract energy from jamming signals as a power source, our proposed method can use intelligent reactive jamming signals as a positive factor in frequency detection. To be precise, we have designed an intelligent multi-sequence frequency hopping communication framework (IMSFH), which includes two stages: communication and training. Firstly, during the synchronous sequence transmission, supervised learning is used in the training stage to obtain the rules of reactive jamming through neural networks. In the communication stage, IMSFH using narrowband reception utilizes reactive jamming rules to improve the frequency-detection capability during actual payload transmission. The simulation results show that this method not only improves communication performance with the increase in jamming signal power and stronger anti-jamming ability when combating high-speed reactive jamming, but also better utilizes reactive jamming to improve communication performance in complex jamming environments.  
Keywords:anti-jamming; anti-reactive jamming; jamming utilization; deep learning; spectrum waterfall +
Footnote †: journal: Electronics Letters
# Junction Selection Based on Optimal-Weighted Multiple Attributes for VANETs
## 2 System Model
If there is no node closest to the destination than the forwarding node itself, then the forwarding node will store the data packet till finding a neighbor or reaching the next junction [16].
# A Class of Algorithms for Continuous Wavelet Transform Based on the Circulant Matrix
## 4 Numerical Experiments
It is seen from Table 2 that the wavelet entropy for Algorithm 2 is the smallest of all the methods. Therefore, Algorithm 2 is the most suitable method for this temperature data. Figures 6 and 7 are calculated with different data preparation methods. To be specific, Figure 6 is computed with the frequency-domain method of CWT with data preparation method given in  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **N** & **CPU** & **Entropy** & \(Err_{1}\) & \(Err_{2}\) & \(Err_{3}\) & \(Err_{4}\) \\ \hline cwt & 1024 & 0.2468 & 8.7125 & \(3.3605\times 10^{-2}\) & \(3.5635\times 10^{-2}\) & \(-1.4622\times 10^{-2}\) & \(-4.0837\times 10^{-2}\) \\  & 2048 & 0.2560 & 9.3815 & \(3.3605\times 10^{-2}\) & \(3.5635\times 10^{-2}\) & \(-1.4622\times 10^{-2}\) & \(-4.0837\times 10^{-2}\) \\ \hline Zhao’s & 1024 & 0.0495 & 8.7068 & \(-1.0144\times 10^{-2}\) & \(3.6888\times 10^{-3}\) & \(-1.6761\times 10^{-2}\) & \(-2.8041\times 10^{-2}\) \\  & 2048 & 0.0562 & 9.3762 & \(-1.0144\times 10^{-2}\) & \(3.6888\times 10^{-3}\) & \(-1.6755\times 10^{-2}\) & \(-2.8041\times 10^{-2}\) \\ \hline Direct & 1024 & 0.4724 & 8.7097 & \(-2.0333\times 10^{-4}\) & \(-1.8504\times 10^{-15}\) & \(-1.9882\times 10^{-4}\) & \(4.9072\times 10^{-14}\) \\  & 2048 & 0.8697 & 9.3784 & \(-2.0333\times 10^{-4}\) & \(-7.0314\times 10^{-15}\) & \(-1.9767\times 10^{-4}\) & \(-9.6064\times 10^{-11}\) \\ \hline Overlap-Add & 1024 & 0.0291 & 8.7083 & \(-2.0333\times 10^{-4}\) & \(-1.8504\times 10^{-15}\) & \(-1.9882\times 10^{-4}\) & \(4.8517\times 10^{-14}\) \\  & 2048 & 0.0359 & 9.3777 & \(-2.0333\times 10^{-4}\) & \(-6.4763\times 10^{-15}\) & \(-1.9767\times 10^{-4}\) & \(-9.6065\times 10^{-11}\) \\ \hline Algorithm 2 & 1024 & 0.0204 & 8.7083 & \(-2.0333\times 10^{-4}\) & \(-1.8504\times 10^{-15}\) & \(-1.9882\times 10^{-4}\) & \(4.8517\times 10^{-14}\) \\  & 2048 & 0.0291 & 9.3777 & \(-2.0333\times 10^{-4}\) & \(-6.4763\times 10^{-15}\) & \(-1.9767\times 10^{-4}\) & \(-9.6065\times 10^{-11}\) \\ \hline Algorithm 3 & 1024 & 0.0155 & 8.7083 &  
Figure 5: This contour is computed by using _CWT_ function in wavelet toolbox of Matlab with the Morlet wavelet parameter \(\sigma^{2}=1,\eta=5\) for Data 2. Figure 6: This contour, computed with frequency-domain method of CWT with data preparation method given in previously published papers, namely, \(M=2^{q},q=\min_{n\in Z}\{2^{n}>N\}\), manifests edge effect.
## 2 Results and Discussion
### Elasticity and Adhesive Behavior of Collagen Fiber
Data from the force curves are used to calculate transverse elasticity and adhesion force; **(C)** Transverse elasticity coefficient and Young’s modulus (\(K_{S}\), and \(E_{S}\)) of native and antibody treated collagen fibers at overlap and gap zones in drying process; **(D)** Adhesion force (\(F_{A}\)) between AFM tips and collagen fiber at overlap and gap zones with time of antibody incubation prior to data collection. Note the significant increase in adhesive force for the overlap zone of the sample treated with gold-conjugated antibody. Collectively, these data indicate that the probes introduced minimal changes to the collagen fibril structure itself, other than to attach to it. This is further confirmed by the isomorphous nature of the X-ray diffraction data. That is, any significant change to the structure of the collagen fibrils that effected their ability to diffract X-rays would be apparent by either the degradation of diffraction quality or changes in the unit cell parameters leading to changes in the spacing between diffraction peaks. An isomorphous derivatization of the sample means little or no change in the position of the diffraction peaks with differences observed in diffraction intensity. In this experiment, the treatment of tendons with the antibody probe changes the intensity distribution in the diffraction pattern that originates from the unit cell (D-period) of collagen (See Figures 1-4) [2; 3; 14; 19; 20]. Detailed statistical analyses (one-way ANOVA and multiple comparisons between and within sample groups) are reported in Supplementary Information. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Incubation Time**} & \multirow{2}{*}{_n_} & **Adhesion Force** & **Percentage Error** & **Percentage Change** \\  & & **(nN)** & **(\%)** & **from Native (\%)** \\ \hline Native & & & & \\ (0 min) & 40 & 4.8 \(\pm\) 0.6 & 12.5 & - \\ \hline
20 min & 40 & 5.0 \(\pm\) 0.7 & 14.0 & 4.2 \\ \hline
1 h & 40 & 5.3 \(\pm\) 0.4 & 7.5 & 10.4 \\ \hline
2 h & 40 & 5.6 \(\pm\) 0.2 & 3.6 & 16.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Adhesion force measurements form the gap region after antibody treatment. Figure 2: (**A**) Native collagen fibrils (amplitude/height image) for comparison with (B); (**B**) Gold-conjugated antibody (Ab) labeled collagen fibrils from region 1 of (**C**), shown at the same scale as native (unlabeled fibrils) of (A); (**C**) Area scan of Ab labeled type I collagen fibrils, AFM tapping mode, amplitude height image. 1–3 marks the positions of enlarged inserts of (**B**,**D**,**E**). (**D**) Amplitude/height (log scale). The color scale is based on height signal, and thus Ab is revealed as pink/red at the highest relative height of the scan of this area of (**C**); (**E**) Phase/height (log scale). Inverse grayscale color map, Ab appears as dark/black patches. Scale bar is \(\sim\)667 nm for each part (A–E).
# An Eco-Driving Controller Based on Intelligent Connected Vehicles for Sustainable Transportation
## 2 The Cooperative Car-Following System Based on Eco-Driving  
The cooperative car-following system consisted of three layers including perception, decision, and control, where the information transmission between layers depends on various communications, as shown in Figure 1. In this system, the perception layer can obtain the information of the current vehicular operation state, traffic signal timing, and environmental information through the integrated sensors, including multiline LiDAR, V2X communication module, high-resolution camera, and millimeter-wave radar. Based on the information of the perception layer, the decision layer can compute the desired vehicular spacing, the desired velocity, the desired acceleration, and the desired front steering angle according to the current traffic state. Finally, the control layer will convert the received information into the corresponding control commands to operate the actuators, such as throttle, braking, and steering, through the vehicular controller area network bus (CAN-Bus).  
Figure 1: The framework of the cooperative car-following system based on eco-driving.  
In the succeeding sections, we first describe the hardware that composed the three layers of the cooperative car-following system, then the communication networks between three layers are described, followed by which the car-following model based on eco-driving for the decision layer is established.