esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Strong designated verifier signature can provide an efficient way to protect the identity privacy of the signer and the integrity of the data transmitted over the public channel. These characteristics make it very useful in outsourcing computing, electronic voting, electronic bidding, electronic auction and other fields. However, most strong designated verifier signature schemes are unable to identify the real signature generator when the signer and the designated verifier dispute a signature. In addition, the existing strong designated verifier signature schemes in the standard model rarely satisfy strong unforgeability, and thus cannot prevent the attacker from forging a valid signature on any previously signed message. Therefore, designing a strong designated verifier signature scheme without random oracles that satisfies strong unforgeability and undeniability is very attractive in both practice and theory. Motivated by these concerns, we design the first undeniable strong designated verifier signature scheme without random oracles, in which the arbiter can independently perform the judgment procedure to prove whether a controversial signature is generated by the signer or the designated verifier. Under standard assumptions, the scheme is proved to be strongly unforgeable in standard model. Furthermore, it not only achieves non-transferability and privacy of the signer's identity but also satisfies the undeniable property of traditional digital signature schemes. Performance analysis results show that the length of the signer's private key, the designated verifier's private key and signature length are 40 bits, 40 bits and 384 bits, respectively. Compared with he related schemes, the proposed scheme has higher performance in signature length, private key size and computational overhead. Finally, we show how to apply it to implement outsourcing computation in cloud computing.  
Keywords:strong designated verifier signature; undeniability; strong unforgeability; standard model +
Footnote †: journal: Applied Soft. 2019, 9, 2062; doi:10.3390/app9102062
## 2 Data and Methods
### Scalar Irradiance  
For studying physical processes in the ocean, e.g., the heating rate, downward or upward planar irradiance (i.e., energy flux through a horizontal plane) is essential. However, in biogeochemical studies, e.g., photosynthesis, photochemistry, UV effects on marine ecosystem, etc., the scalar irradiance (density of radiant energy) is more appropriate [46]. The OMI UV irradiance product provides the planar irradiance, \(E_{d}\), at the ocean surface. The spectral planar downward irradiance (energy flux through a horizontal plane) is defined as:  
\[E_{d}(\lambda)=\int_{2\pi}I(\lambda,\theta,\phi)cos\theta\,d\omega, \tag{3}\]  
where \(I\) is the radiance as a function of wavelength \(\lambda\), zenith angle \(\theta\) and azimuth angle \(\phi\); \(\omega\) is the solid angle, integration is performed over the upper hemisphere. The upward irradiance is defined similarly with integration over the lower hemisphere. The scalar irradiance (i.e., actinic flux in the atmospheric optics) is defined as:  
\[E_{o}(\lambda)=\int_{4\pi}I(\lambda,\theta,\phi)\,d\omega, \tag{4}\]  
where the integration is performed over the entire \(4\pi\) solid angle. The quantity \(E_{o}(\lambda)\lambda/(hc)\) gives a number of photons in a unit of volume. We propose to estimate scalar irradiance, \(E_{o}\), at the ocean surface by scaling the planar irradiance, \(E_{d}\), on a basis of Hydrolight computations: \(E_{o}=f(\lambda,\theta,\text{Chl},\Omega)E_{d}\), where the scaling factor, \(f\), depends on wavelength, \(\theta\), chlorophyll concentration (Chl) and total column ozone amount \(\Omega\), is considered in detail in Appendix B.
## 2 Operation Conditions  
In this study, cycle analysis was conducted to develop a high-efficiency hydrogen liquefaction system. Through the cycle analysis, the operating conditions of the component were determined. The target capacity of the hydrogen liquefaction system is condensing 0.5 tons of hydrogen per day. Figure 1a shows a schematic of a 0.5-TPD class hydrogen liquefaction plant. The helium working fluid absorbs the cold heat of the vaporized nitrogen through a heat exchanger (Hx1), and by flowing through a sequence of heat exchangers and expanders, the temperature is lowered so that hydrogen can be liquefied. Hydrogen in the gaseous state is liquefied through heat exchangers Hx1\(-\)Hx4 and stored in a tank.  
As shown in Figure 1a, the system consists of two expanders, one compressor, and four heat exchangers. The expanders are connected in series. In this study, design and optimization were conducted for one of the expanders.  
Figure 1b illustrates the temperature-entropy (T-s) diagram of the hydrogen liquefaction plant. Table 1 shows the components in each process in the T-s diagram. A heat exchanger (process 4-5 in Figure 1) is installed between Expander 1 and Expander 2. The expander that was studied in this research was Expander 1, which relates to processes 3-4 in the system.  
Figure 1: The 0.5-TPD class hydrogen liquefaction plant: (**a**) schematic of hydrogen liquefaction plant and (**b**) T–s diagram of hydrogen liquefaction plant.  
\begin{table}
\begin{tabular}{c c} \hline Process @ & Component \\ T–s Diagram & \\ \hline \hline \��❝�1–2�1 & Heat exchanger 1 \\ \hline \❝�2–3�1 & Heat exchanger 2 \\ \hline \❝3–4�1 & Expander 1 \\ \hline \❝4–5�1 & Heat exchanger 3 \\ \hline \❝5–6�1 & Expander 2 \\ \hline \�6–7�1 & Heat exchanger 4 \\ \hline \�7–8�1 & Heat exchanger 3 \\ \hline \�8–9�1 & Heat exchanger 2 \\ \hline \�9�1–10�1 & Heat exchanger 1 \\ \hline \�1–11�1 & Compressor with cooler \\ \hline \hline \end{tabular}
\end{table}
Table 1: Components involved in each process of the T–s diagram.  
Table 2 presents the operation conditions of the expanders. The considered expansion ratios of Expander 1 and Expander 2 were 1.6 and 3.69, respectively.
## 4 Discussion  
Traffic accidents, vehicle breakdowns, climate change, and other contingencies may occur whether there is urban or rural traffic. If they are not found and handled in time, it is bound to cause traffic jams and even traffic accidents. The function of the traffic flow parameter detection method is to monitor the information of these incidents in real-time and take effective control measures to solve them. Specifically, its functions were as follows:  
1. The information of abnormal changes of traffic conditions of expressway in real-time was collected; the information was processed scientifically in time; we released it by the variable message sign and roadside broadcast and reported the road conditions to drivers;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Times** & **Paper Method** & **Reference [5] Method** & **Reference [6] Method** \\ \hline
1 & 0.9868 & 0.9158 & 0.8578 \\
2 & 0.9877 & 0.9154 & 0.8658 \\
3 & 0.9888 & 0.9099 & 0.8647 \\
4 & 0.9865 & 0.9028 & 0.8824 \\
5 & 0.9868 & 0.8999 & 0.8457 \\
6 & 0.9876 & 0.9011 & 0.8459 \\ Mean value & 0.9874 & 0.9075 & 0.8604 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analysis results of the denoising performance of the three methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Experiments** & \begin{tabular}{c} **Experience Group** \\ \end{tabular} & \
\begin{tabular}{c} **Control Group** \\ \end{tabular} &
\begin{tabular}{c} _p Value_ \\ \end{tabular} \\ \hline
20 & 1.68 & 0.01 & 0.04 \\
40 & 2.15 & 0.47 & 0.01 \\
60 & 0.75 & 0.24 & 0.03 \\
80 & 6.07 & 0.17 & 0.04 \\
100 & 4.24 & 0.14 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results of traffic flow parameter detection results.  
2. We provided the road users with the best driving route and running speed in real-time so as to realize the dynamic balance of traffic flow on the road network through the variable speed limit sign, variable message sign, and ramp control equipment;
3. We sent emergency information and relevant instructions to the rescue departments, such as hospitals and public security, and organizations, such as the service area and maintenance work area;
4. The information monitoring of mechanical and electrical equipment included the display and control of equipment operation status, the detection and response between routes, the link and transmission delay detection, the configuration parameter tracking, and the network management data test.  
In this article, the symmetrical difference algorithm was applied to the process of traffic flow parameter detection. A method to automatically detect traffic flow parameters of a vehicle-mounted sensor system based on the symmetrical difference was then designed. This has achieved good results in practical application. In the detection of traffic flow parameters, the denoising method was as high as 0.9874, so it has considerable application value.
## 7 Conclusions  
In order for IoT platforms to comprise a reliable and efficient technological infrastructure for nowadays as well as future CPS applications, novel hardware, real-time approaches are required in order to enhance the platforms' capabilities while minimizing resource wastage. This paper addresses a notorious challenge of wireless interfaces' excessive utilization considering highly demanding application scenarios such as bio-signal monitoring. To tackle respective shortcomings of nowadays platforms, the two main pillars of this paper are (a) the design and implementation of an efficient, real-time compression algorithm, and (b) the design and implementation of a highly efficient and resource conservative hardware accelerator for the proposed algorithm. The idea behind this approach is to offer on-the-fly hardware compression to any IoT platform, thus overloading the main processing unit, which is usually of limited processing capabilities. Consequently, this paper offers a holistic solution to the problem of wirelessly transmitting excessive volumes of data, tackling all aspects from the design and software algorithmic performance, to the design of the actual hardware accelerator and finally the overall system performance. With respect to the latter, taking into consideration the power consumption of a real prominent IoT platform and respective delay transmission capabilities, the proposed hardware compression accelerator can yield an approximate 70% system wide delay and energy consumption reduction. Furthermore, the proposed module comprises a highly feasible and practical solution since it captures just 21% of the Spartan3 FPGA's configurable logic block slices and the maximum performance gains are achieved at just 1/3 of the maximum frequency clock tested. Such characteristics advocate the integration of the proposed module to nowadays WSN platforms.  
This study is part of the collaborative project armor which is partially funded by the European commission under the seventh framework programme (FP7/2007-2013) with grant agreement: 287720 and through the horizon 2020 programme (H2020/PHC-19-2014) under the research and innovation action \"radio-robots in assisted living environments: unobtrusive, efficient, reliable and modular solutions for independent ageing\".  
[PERSON] conceived and designed the experiments concerning the proposed compression algorithm. [PERSON] conceived and implemented the hardware implementation of the compression algorithm's module. [PERSON] conceived, designed and carried out the integrated experiments, Both [PERSON] and [PERSON] contributed on data analysis. Both [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Multivariate Analyses on Semi-Quantitative Data
In this case, we obtained a variance of about 87% for the first three PCs, lower than the one calculated both for the complete dataset and for Data Set I. Score plot and loading plot for Data Set II are reported in Figures 8 and 9, respectively; larger dots in the scatter plot represent the group centroids, while small dots represent the samples. For PC1 and PC2 alone, the explained variance is about 78%, but the different groups are already evident and more compact that those obtained for Data Set I even if they explain a lower variance. Similarly to what happened considering Data Set I, the dendrogram obtained for Data Set II (not reported) misclassified some samples, and it is not as useful a reference as the diagram obtained considering all the detected elements. More in detail, using the elements selected for Data Set II--i.e., those considered in the quoted papers for bivariate graph--to distinguish subgroups led to the clear discrimination of only Pantelleria samples, while the other groups are more confused. Figure 6: Score plot obtained considering Data Set I. The variance accounted by the first two PC is 76%. Colors are the same as in Figure 1; samples’ names are not reported for sake of clarity. Figure 7: Loading plot obtained considering Data Set I. The variance accounted by the first two PC is 76%. Indeed, the more elements we consider, the better result we achieve through HC analysis. It is worth stressing once again that lower-Z elements, such as Si, were not considered for MV data elaboration even if detected so as to not introduce statistical noise, as the quantification of their peak areas was affected by a large error due to the low detector efficiency for low energies.
# Multiple-Diffraction Subtractive Double Monochromator with High Resolution and Low Stray Light
## 3 Methods
### Stray-Light Analysis
Denoting the optical efficiency of the signal light by \(P_{0}\) (0 dBm, as described above), the optical efficiency of the first-order stray light by \(P_{S1}\), and the stray-light coefficient of the FM by \(\varepsilon_{1}\) (\(10^{-5}\), as described above), we obtain:  
\[P_{S1}=P_{0}+10\log_{10}(\varepsilon_{1})=-50\text{dBm} \tag{15}\]  
Figure 6: Stray light at different slits: (**a**) \(x-\delta\lambda\) graph at S\({}_{2}\); (**b**) optical power distribution at S\({}_{2}\); (**c**) \(x-\delta\lambda\) graph at S\({}_{3}\); (**d**) optical power distribution at S\({}_{3}\). 2a, 2b, and 2c are widths of S\({}_{1}\), S\({}_{2}\), and S\({}_{3}\), respectively. The JKMN region represents all the rays that can pass through S\({}_{3}\). The difference in the areas between JKMN and EFGH indicates the second-order stray light in the SM. The optical efficiency of the second-order stray light is denoted by P\({}_{\text{S2}}\), and the stray-light coefficient of the SM is denoted by \(\varepsilon_{2}\), which is based on the optical symmetry between the FM and the SM:  
\[ \begin{split}&\varepsilon_{2}=\varepsilon_{1}=10^{-5}\\ & P_{\text{S2}}=P_{\text{S1}}+10\log_{10}(\varepsilon_{2})=-100 \text{dBm}\end{split}  \tag{16}\]  
Figure 6d shows the ideal and actual optical power distributions at S\({}_{3}\). Compared with that shown in Figure 6b, the MSDM mount significantly suppressed the stray light, and the OSNR consequently improved to 70 dB.
# Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network
## 2 SuDoRM-RF++
### Encoder  
The encoder \(\mathcal{E}\) architecture is a one-dimensional convolutional network with \(C_{\mathcal{E}}\) channels, kernel size \(K_{\mathcal{E}}\), and stride \(K_{\mathcal{E}}/2\). To ensure non-negativity, the output of the convolutional network is passed via a rectified linear unit (ReLU) layer. Hence, the encoded representation of the input mixture is expressed as:  
\[\mathbf{v_{x}}=\mathcal{E}(\mathbf{x})=\mathrm{ReLU}(\mathrm{Conv1D}(\mathbf{x }))\in\mathbb{R}^{C_{\mathcal{E}}\times L}. \tag{1}\]
# Possibility of Bridge Inspection through Drive-By Vehicles
## 6 Summary and Conclusions  
Based on the series of 2D and 3D virtual VBI simulations conducted in this study, the following conclusions are derived:  
* FFT of vehicle accelerations during drive-by inspection can capture bridge frequencies accurately. In the 2D VBI model, the captured frequencies can reflect the damage with a decrease in their magnitudes. In the 3D VBI model, the FFT method can capture most frequencies of multiple span bridges with high accuracy, even with damages included.
* STFT method works well in identifying damages through drive-by vehicle responses in the VBI models. Energy change in STFT results of drive-by vehicle responses can identify the damage location and the damage level in high accuracy.
* Vibration caused by roughness affects the initial entering conditions of vehicles in the VBI models. The offset distances of the vehicle before entering the bridge are found to ensure the accurate bridge and vehicle responses through VBI simulations, which requires a 10 m offset distance for speed less than 20 m/s, a 15 m offset distance for speed between 20-25 m/s, and more than 20 m offset distance is needed for speed over 30 m/s for a class B road profile.
* Roughness increases the difficulty in identifying bridge damages through drive-by vehicle responses. However, the STFT method still has the capability of detecting damage levels and damage location in bridges.  
[PERSON] designed the concept and directed the numerical simulations. [PERSON] carried out the numerical simulations and drafted the paper. All authors have read and agreed to the published version of the manuscript.  
This work is supported by NSF ND EPSCoR Faculty Seed Grant.  
Figure 30: Average PSD energy over the damaged area (7.5 m to 7.6 m).  
Data Availability StatementOur data are available from the Dryad Digital Repository: [[https://doi.org/10.5061/dryad.44j02](https://doi.org/10.5061/dryad.44j02) pc3]([https://doi.org/10.5061/dryad.44j02](https://doi.org/10.5061/dryad.44j02) pc3). [[https://datadryad.org/stash/share/7](https://datadryad.org/stash/share/7) rxb4 exZWJC15 Tphc3 vYEDIq9 usDG7 hUGpeCer7 fysw]([https://datadryad.org/stash/share/7](https://datadryad.org/stash/share/7) rxb4 exZWJC15 Tphc3 vYEDIq9 usDG7 hUGpeCer7 fysw).
## 2 The Principle of ISAIL Time-Frequency Imaging  
ISAIL imaging is the mapping of target echoes in the distance-Doppler plane. The model of the ISAIL m-D effect is described as Figure 1[16]. Assuming the echo density function of the target is \(\rho_{(xy)}\), the radar echo signal can be expressed as:  
\[S(t)=\exp[-j4\pi f_{0}R(t)/c]\int_{-\infty}^{+\infty}\int_{-\infty}^{+\infty} \rho_{(x,y)}\exp[-j2\pi(xf_{x}-yf_{y})]dxdy \tag{1}\]  
where \(f_{x}=2f_{0}\cos\theta(t)/c,f_{y}=2f_{0}\sin\theta(t)/c\).  
Figure 1: ISAIL geometric model.  
The distance \(R(t)\) and rotation angle \(\theta(t)\) of the instantaneous target can be determined by the initial position \(R_{0}\) and \(\theta_{0}\),the initial velocity \(V_{0}\) and initial angular velocity \(\omega_{0}\), the initial acceleration \(a_{0}\) and initial angular acceleration \(\alpha_{0}\) and other high-order terms.  
\[R(t) = R_{0}+v_{0}t+a_{0}t^{2}/2 \tag{2}\] \[\theta(t) = \theta_{0}+\omega_{0}t+\alpha_{0}t^{2}/2 \tag{3}\]  
The purpose of ISAIL imaging is to find the scattering density distribution function from the radar echo. It can be seen from Equation (1) that the Doppler frequency can be decomposed into \(f_{trans}\) and \(f_{rot}\) :  
\[f_{D}=f_{trans}+f_{rot}=\frac{1}{2}\frac{d\phi(t)}{dt}=\frac{2f_{0}}{c}v_{r}(t) +\frac{2f_{0}}{c}\Omega(t)\times r_{c}(t) \tag{4}\]  
where \(f_{trans}\) is generated by the target motion and it is determined by the radial velocity \(v_{r}(t)\) of the target. \(f_{rot}\) is generated by the rotation of the target and it is determined by the rotational speed \(\Omega(t)\) of the target and the position \(r_{c}(t)\) of the scattering point from the center of rotation. By motion compensation, \(f_{trans}\) can be compensated for \(f_{rot}\) to determine the position of the scattering point from the center of rotation. The traditional imaging method considers \(f_{rot}\) to be constant, which can be established when the target is flying at low speed.  
However, when the target is used for high-speed maneuvering, the Doppler spectrum is time-varying. If the conventional Fourier transform is used for spectrum analysis, the imaging will be blurred. In order to obtain high-resolution instantaneous Doppler spectra at various times, time-frequency analysis is required.  
Figure 2 is the process of ISAIL time-frequency imaging. It is assumed that the accumulated pulse number is M and the number of distance units is N during the observation period. The time-frequency analysis is performed on the data of each distance unit. After the time sampling, the distance-Doppler ISAIL images of the M frame \(N\times M\) can be obtained.
# Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN
## 2 Related Work
### Data Center Based on Traditional Network  
In the traditional data center model, the traffic is mainly generated between the server and the client, with a low proportion of east-west traffic among the data centers [10]. With the extensive use of the Internet and the integration of the mobile network, Internet of Vehicle [11], cloud computing, big data, and other new generations of network technology and development have come into being to deal with massive-scale data and large-scale distributed data centers, bringing about the accelerated growth of east-west traffic exchanged among servers, for example, the Google File System (GFS) [12], Hadoop Distributed File System (HDFS) [13], and Google framework MapReduce [14]. However, when the traditionally shortest path routing protocols in current data centers are used, congestion will frequently occur in the shortest path link, and it may further reduce the quality of network services due to the long delay and low throughput.  
Traffic scheduling and congestion control are important technologies to maintain network capacity and improve network efficiency. Traditional networks have some congenital defects. The main disadvantages can be summarized as follows: First, there is no global coordinative optimization. Each node independently implements the traffic control strategy, which can only achieve the local optimum. Moreover, there is no dynamic and self-adaptive adjustment. The predefined strategies in routers cannot meet the frequently changing demands of business flows. In addition, traditional networks find it difficult to achieve the effective and accurate control of every network device. The configurations of network devices are numerous and diverse where the commands are so complicated that it is very difficult to find the network errors caused by configurations. Consequently, it is of great urgency to figure out how to effectively manage and dominate network traffic, which has pushed network architects to take advantage of SDN to address these problems in data centers.