esa-sceva/satcom-chunk-collection huggingface dataset:
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
## 3 Preliminaries  
### Vanets  
VANETs are considered as an essential part of ITS to connect vehicles either to each other or to an infrastructure. In VANETs, a broad range of mobile communication technologies are integrated into roadside unit (RSU) and vehicles to relieve traffic congestion, improve safety, and enhance productivity. As shown in Figure 1, vehicles with on-board units (OBUs) adopt dedicated short-range communication (DSRC) to communicate with RSUs or other OBUs to obtain the required services [27]. RSUs provide safety-related services, efficiency-related services, entertainment-related services, and so forth for vehicles with surrounding vehicles. The base station can communicate with external networks and provide network communication services for VANETs. Consequently, two different types of communication are used to support the application and services in VANETs. (1) Vehicle-to-infrastructure (V2I) communication--V2I refers to the communication between the vehicle and RSU. When vehicles enter the signal coverage range of RSUs, vehicles are able to adopt DSRC to request RSUs to obtain the services. RSUs deployed and managed by motorway operators own external network communication capability through interconnection with surrounding base stations, and provide necessary services for surrounding vehicles; (2) vehicle-to-vehicle (V2V) communication--V2V refers to the communication between vehicles, which is completed by vehicles independently, without the participation of central entities. This type of communication is usually carried out without the RSU or in some specific scenarios.
# The UV Plasmonic Behavior of Rhodium Tetrahedrons--A Numerical Analysis
###### Abstract  
Rhodium (Rh) nanoparticles have attracted a lot of attention due to their strong and ambient-stable UV plasmonic response. Very recently, the synthesis of Rh tetrahedra with and without concave defect-rich surfaces serving in plasmon assisted photocatalytic energy conversion has been reported. In this work, we perform a systematic numerical study on plasmonic behavior and surface charge distribution in order to optimize the use of Rh tetrahedra in surface-enhanced spectroscopies and photocatalysis. We analyze the effect of the edges and corners reshaping, a deformation already reported to appear in Rh nanocubes which have been repeatedly re-used in photocatalytic processes. It is demonstrated that rounding the edges and corners weakens both the near-field enhancement and surface charge densities in these locations, which in turn are the more reactive regions due to the presence of uncoordinated sites. In addition, we study how the near-field and charge density is redistributed on the surface of the tetrahedra when concavities of different sizes and depths are introduced. Through this study, we show that, in order to simultaneously maximize the near-field enhancement and surface charge densities in the concavity and at external edges and corners, medium size deep concavities are needed.  
Keywords: rhodium; photocatalysis; UV plasmonics; tetrahedron; nanoparticles +
Footnote †: journal: Applied Physics Letters B
## 3 System Model
### Latency Performance  
We defined the potential slice traffic as \(f_{(u,v)}^{(s,k),(s,l)}\geq 0\), which is needed to be forwarded when virtual link \((k,l)\) on the network slice \(s\) is mapped into the substrate processing link \((u,v)\). Each processing link \((u,v)\) has an upper-bound capacity that corresponds to the maximum rate over that link for each time instance, defined as \(C_{(u,v)}^{p}\). We use \(\alpha_{(u,v)}^{(s,k),(s,l)}\) to denote the transport network latency, which can be interpreted as a cost function. In order to improve the network load balance, we consider the network characteristics to formulate the transport network latency. The latency formulation consists of two parts. The first part is the transmission time, related to the network capacity limitation of substrate DCs. We use the network capacity \(C_{(u,v)}^{p}\) and forwarded traffic \(f_{(u,v)}^{(s,k),(s,l)}\) to capture the number of time-slots required by a substrate link to forward traffic, satisfying the slice target rate. The second part is the propagation time, related to the actual location of mapped substrate DCs and assigned link bandwidth \(B_{ij}^{p}\). The transport network latency is defined as follows:  
\[\alpha_{(u,v)}^{(s,k),(s,l)}=\frac{f_{(u,v)}^{(s,k),(s,l)}}{C_{(u,v)}^{p}}+ \sum_{ij\in L^{p}}\theta\cdot\frac{l_{(r,d),ij}^{p}}{B_{ij}^{p}}, \tag{7}\]  
where \(\theta\) is a weighting factor, balancing the network characteristics. In this regard, the lower network capacity or the farther location of the mapped substrate node, the higher transport network latency of NSIs.  
Figure 2: Illustration of virtual link mapping.
# A Deep Learning Framework for Signal Detection and Modulation Classification
## 2 Communication Signal Description and Dataset Generation
### The Generation Processing of the Dataset  
Figure 3 presents the processing of our dataset construction and annotation. To make samples more diverse, we set sampling phase offset, frequency offset, phase offset, and amplitude attenuation in sample generation processing.  
Figure 3: The generation processing of the dataset. (**a**) dataset for signal detection; (**b**) dataset for modulation recognition.  
Figure 2: The eye diagram and vector diagram of different modulation signals in 15 dB (**a**) BPSK; (**b**) QPSK; (**c**) OQPSK; (**d**) 8 PSK; (**e**) 16 QAM; (**f**) 16 APSK; (**g**) 32 APSK; (**h**) 64 QAM.  
For signal detection, we need to determine the reconnaissance frequency range and set the signal number in the wideband at first. We set different frequency offset for each signal, and ensure that the signals do not overlap in the frequency domain. Then we perform the STFT on the wideband. Not only we record the modulation format of each signal, but also record the start-stop time, carrier frequency, and bandwidth. Then we convert them into the coordinates on time-frequency image, which are the label information for the network.  
For modulation recognition, traditional eye diagram and vector diagram are binary images, which do not consider the signal aggregation degree at a particular location. Hence, we consider the signal aggregation degree and enhance the traditional eye diagram and vector diagram. Figure 4 presents the enhancement processing of the dataset. In our initial research, since CNNs are insensitive to edge information, the signal amplitude is quantified between [-1.05, 105] by 128 after normalizing the amplitude. Furthermore, the parameter settings are obtained by experiments. For example, we choose 800 symbols and 4 symbols as a waveform group to generate the eye diagram and the vector diagram, and related experiments will also be described in detail in subsequent chapters. Moreover, we perform the following operations on the images to make the image details more prominent, where \(\mathbf{Im}_{0}\) is the original image, \(\mathbf{Im}_{1}\), \(\mathbf{Im}_{2}\), \(\mathbf{Im}_{3}\) are the channels of the enhanced image and \(\alpha,\beta\) are scaling factors.  
\[\begin{split}\mathbf{Im}_{1}=\text{units}\Big{(}\frac{\mathbf{Im} _{0}-\min(\mathbf{Im}_{0})}{\max(\mathbf{Im}_{0})-\min(\mathbf{Im}_{0})}\times 2 55\Big{)},\\ \mathbf{Im}_{2}=\text{unit8}(\alpha\times\log 2(\mathbf{Im}_{1}+1) ),\\ \mathbf{Im}_{3}=\text{unit8}(\exp(\mathbf{Im}_{1}/\beta))\end{split} \tag{21}\]
Nonetheless, from the financial institution perspective, the entity itself would be able to provide a robust and unbreakable security mechanism for its credit/debit card-based ATM systems by enabling our proposed solution without making major modifications to the existing infrastructure, as the location of the cardholder is considered when authenticating the payment. Further, as per the convenience of the user, this multi-factor authentication can be deactivated and later reactivated at any given time, enabling users to continue the previous way of performing transactions. Thus, motivated by the need to design a novel, robust geolocation-based multi-factor authentication method for securing ATM payment transactions, following the introduction, a brief literature review pertaining to the security of payment transactions is provided. Afterward, the importance of multi-factor authentication in improving the security of payment transactions is highlighted, and then the methodology is provided for designing our proposed geolocation-based multi-factor authentication solution. In this regard, the major contributions of the study are described in the following. * Design a novel geolocation-based multi-factor authentication method for improving the security of ATM payment transactions, where it also employs a novel design approach toward further controlling the ownership of transactions. * Discuss how the proposed approach does not require any major modifications to the existing banking infrastructure, which would be an ideal solution for securing ATM transactions around the world. * Highlight the implementation results, pros, and cons of the proposed approach, along with how the proposed approach can be further extended. * To validate our work and differentiate it from recent similar research in terms of security aspects, we provide a brief comparison of our work with the state of the art. The remainder of the paper is organized in the following order. Following the introduction, the Section 2 offers a brief literature review pertaining to the security of electronic payment transactions. Following the literature review, in the next section we highlight our proposed solution and the methodology adopted, and afterward, in the next section, the implementation of our proposed solution is highlighted. Thereafter, a discussion is offered in the next section, highlighting the results and the evaluation of our proposed solution. Next, the study will conclude with recommendations for improvement of the solution and with the conclusions derived through the research. Abbreviation part presents the acronyms found in this paper.
# A Review of SATis5: Perspectives on Commercial and Defense 5G SATCOM Integration
## 7 Discussion and Conclusions
When this happens, a similar study like this one is of interest to the commercial and defense SATCOM industry. Conceptualization, T.M.N. and K.D.P. ; methodology, T.M.N. ; formal analysis, T.M.N., K.D.P. and J.N. ; investigation, T.M.N., K.D.P., J.N., G.C., C.H.L. and S.B. ; writing--original draft preparation, T.M.N., K.D.P. and J.N. ; writing--review and editing, T.M.N., K.D.P., J.N., G.C., C.H.L. and S.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Acknowledgments:** The authors express their sincere appreciation to [PERSON] of IFT for her careful review of our paper. The first author would like to express his appreciation for continuous support from the Center for Computational and Applied Mathematics at CSUF. He also wants to express his deepest appreciation to his wife, [PERSON], for her unbounded patience and constant moral support during the process of writing this review. **Conflicts of Interest:** The authors declare no conflict of interest. This work was carried out using our personal time and resources. **Nomenclature**  
Abbreviation 3 GPP  The 3 rd Generation Partnership Project 4G  The fourth Generation wireless communication system 5G  The fifth Generation wireless communication system 5 GIC  The 5G Internet Connection 6G  The sixth Generation wireless communication system  ABR  Adaptive Bit Rate  AC-STE-iDrect  Avant Communications and ST Engineering iDirect  AMF  Access and Mobility Management Function  ARTES  Advanced Research in Telecommunications Systems  ASTRA  Name of a satellite  BATF  Backhauling and Tower Feed  BS  Base Station  C2  Command and Control  C4 ISR  Command, Control, Communication Computer, Intelligence, Surveillance, Reconnaissance  CCAM  Center for Computational and Applied Mathematics  CDN  Content Distribution Networks  CMAF-CT  Common Media Application Format-Chunked Transfer Encoding  COMSATCOM  Commercial Satellite  COTM  Communication on the Move  COTS  Commercial of the Shelf  CP  Control Plane  CPE  Customer Provided Equipment  CSP  Satellite Service Provider  CT  Core Network and Protocol  CU  Central Unit  DU  Distributed Unit  eMBB  Enhanced Mobile Broadband  E2E  End-to-End  ESA  European Space Agency  ESTEC  European Space Research and Technology Centre  ETSI  European Telecommunications Standards Institute  EU  European  GEO  Geosynchronous Earth Orbit  GHz  Gega Hertz  GNSS  Global Navigation Satellite System  GSN  Ground Station Network  GW  Gateway  HAP  High Altitude Platform  HARQ  Hybrid Automatic Repeat Request  HYMP  Hybrid Multiplay  IEI  Integrated Enterprise Infrastructure  ISL  Inter-Satellite Link  ISR  Intelligence, Surveillance Reconnaissance  JADC2  Joint All Domain Command and Control  LAP  Low Altitude Platform  LEO  Low Earth Orbit
## 2 Theory
### Akaike Information Criterion  
AIC selector of seismic signals applies the AIC theory to the autoregressive model (AR) to accurately pick up the first arrival position of the effective signal in the time window. For a signal at time \(t\), its mathematical expression of AR can be expressed as [36]:  
\[x_{t}=\sum_{m=1}^{M}a_{m}^{i}x[t-m]+e_{t}^{i} \tag{7}\]  
where \(a_{m}^{i}\) is the \(m\)-th autocorrelation coefficient, \(M\) is the selected window length, and \(e_{t}^{i}\) is the error predicted by the model, which follows the Gaussian distribution with an average value of zero. By dividing the entire time series into noise and signal segments, we derive the approximate likelihood function \(L\) of the error:  
\[L(x,k,M,a_{j}^{i},\sigma_{i}^{2})=\prod_{i=1}^{2}\left((2\pi\sigma_{i}^{2})^{n /2}\cdot\exp{(-\frac{1}{2\sigma_{i}^{2}}\sum_{m=1}^{M}a_{m}^{i}x[j-m])}\right) \tag{8}\]  
By finding the maximum likelihood estimate of the variance \(\sigma_{i}^{2}\), the maximum value of the function can be solved:  
\[\log(L)=-\frac{1}{2}(K-M)\log(\sigma_{1\max}^{2})--\frac{1}{2}(N-K+M)\log( \sigma_{2\max}^{2})+C \tag{9}\]  
where (\(K\)-\(M\)) is the window length of noise, and \(N\) is the total time window length, \(\sigma_{1}^{2}\) and \(\sigma_{2}^{2}\) indicate the variance of noise and signal, respectively, and \(C\) is a constant. Then maximum likelihood estimation of the AIC value can be calculated:  
\[AIC(K)=(K-M)\log(\sigma_{1\max}^{2})+(n-K+M)\log(\sigma_{2\max}^{2})-2C \tag{10}\]  
In order to obtain the best AIC value, it is necessary to determine the appropriate position to minimize the error variance of the noise and the signal. Due to too many parameters and complex calculations, [PERSON] [37] improved the calculation method of the AIC function and got the following equation:  
\[AIC(k_{w})=k_{w}\log(\mathrm{var}(x(1,k_{w})))+(n_{w}-k_{w})\log(\mathrm{var} (x(k_{w}+1,n_{w}))) \tag{11}\]  
where \(k_{w}\), sliding along the time axis, represents the position of the window boundary. The position where the minimum value of AIC appears is the boundary between two adjacent time windows with different characteristics. There will be some errors in positioning a certain point of the microseism if only considering the entire waveform. Therefore, it is necessary to improve the AIC algorithm to better adapt to the characteristics of MS signals.
## 2 Materials and Methods  
### Slab Preparation by 3D Printing  
A reprap project series clone model of a 3D printer (CTC i3, Zhuhai Electronic Ltd, Zhuhai City, Guangdong, China) operated by an Arduino(r) controller, was used to prepare the slabs. The printer operated in Fused Deposition Modeling (FDM) mode, had a single nozzle (diameter of 0.4 mm) and used a 1.75 mm polymer filament. The filament was a short carbon fiber reinforced PET-G polymer composite by 20 wt% (NEEMA3D(tm) Carbon: plus) of NEEMA3D(tm), Petroupolis, Greece. The filament had a Young's modulus of E = 3800 MPa and a yield strength of 52.5 MPa according to the manufacturer's specifications.  
Rectangular slabs of \(60\times 12\times 3\) mm\({}^{3}\) (hereafter referred to as type-A slabs) were prepared by means of the FDM printer, which was paused once the desired layer height was reached, so as to allow for attaching the 2826 MB Metglas(r) magnetoelastic (20 \(\upmu\)m) strip of \(25\times 5\) mm\({}^{2}\) (kindly donated by Prof. [PERSON], University of Patras). The magnetoelastic strips were attached to the desired depth by means of an epoxy adhesive. Subsequently, the magnetoelastic strip was covered by successive material layers until obtaining the final slab thickness (Figure 1). In this way, one set of type-A slabs with \"hidden\" built-in magnetoelastic strip sensors, hereafter referred to as M-sensors, was prepared. Again, another series of type-A slabs without magnetoelastic strips (type-A blanc) was also prepared for testing purposes.  
Figure 1: Smart slab preparation showing the strip of magnetoelastic 2826 Metglas® ribbon material and two views of the carbon fiber PET-G layers (**a**) during the process of ribbon (**b**) integration.  
Two sets of larger slabs of \(150\times 25\times 3\) mm\({}^{3}\) were also prepared for the testing and may be seen in Figure 2. As seen in Figure 2, the magnetoelastic strip was placed inside the specimens during the 3D printing procedure, at heights of h = 1.15 mm for specimen's type A and h = 1.75 mm for specimen Types B and C, with respect to the specimen's total thickness, measuring from the bottom of the slabs.  
The first set involved M-sensor slabs (hereafter referred to as type-B slabs) and the series contained artificially damaged M-sensor slabs with one or two transverse notches (hereafter referred to as type-C1 or -C2 slabs, respectively), made with a diamond saw and featuring a depth of 1.5 mm.  
Not that preparing two different slab sizes (type-A and type-B/-C1/-C2) was necessary because of technical limitations imposed by the testing procedures, as explained in the next subsection. Details on the objectives to be achieved and the testing protocol are given in the following subsection.
## 5 Results
### Validation  
After the hardware components of the design have been carefully verified by simulations on a functional level, the PCIe-based Xilinx VCU1525 card was used for testing and performance evaluation. The bitstreams have been generated using Vivado 2019.1 with all configurations for synthesis and implementation set to default. The Xilinx board was used in a bare-metal manner with a Xilinx XDMA as an x16 Gen3 PCIe endpoint. The workstation (Intel Core i7 CPU 870 @ 2.93 GHz with 12 GByte DDR-3 RAM) communicates via the Xilinx XDMA Linux kernel module with the accelerator device. The test application supplies the proposed architecture, configured for Hamming distance calculation, with randomly generated signatures, configures the HLS-based management system, reads back the results, and compares them with a golden master computed by the CPU. The data sets used contain one million static and ten million dynamic signatures, resulting in a total of \(10^{13}\) comparisons. Since the number of processing elements on the FPGA is limited, the static signatures must be processed in chunks, resulting in several calculation cycles. In each cycle, static signatures are  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Component**} & **Initialization** & **Latency** & **Max. Throughput** \\  & **Interval** & **(Clock Cycles)** & **(Bit/second)** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} Systolic Array/Processing Element \\ \end{tabular} } & 1 & 1 \\ \cline{2-3} Hamming distance & XOR & 1 & 1 \\ \cline{2-3} calculation & PopCnt & 1 & \(\lceil\log_{2}(\lceil SIG\_LEN/6\rceil)\rceil\) \\ \hline \multicolumn{2}{c}{Threshold Checker} & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance characteristics of the individual components of a DCU.  
loaded into the available processing elements and in each run, the dynamic signatures are streamed completely from the DDR, by the HLS-core. To cover many relevant test cases and especially trigger pipeline stalls caused by FIFOs running almost full in the collector stages, different thresholds ranging from 0 (only exact matches pass) and 512 (all results pass) have been tested.
###### Abstract  
Rolling dynamic compaction (RDC) has been found to be useful for compaction soils and is now widely used globally. Because RDC is not often used in soft soils with poor engineering properties, field monitoring was used to study the soft clay embankment responses under RDC conditions in this study. Analysis of the monitoring data revealed that the response of the soil occurred mainly in the first 20 passes. Field monitoring revealed a strong correlation between settlement, horizontal displacement, and pore water pressure. The depth of impact of RDC on the soft soil embankment was between 3 and 3.5 m. Although settlement prediction is an important issue for construction, there is a lack of prediction methods for RDC-induced soil settlement. In this study, we used three different machine learning algorithms: random forest regression (RFR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) to predict the total settlement and uneven settlement induced by RDC on the soft soil embankment. The three prediction models were compared, and the predictive accuracy of these models was assessed. This study analyzes and summarizes the effect of RDC application on a soft clay embankment and explores the machine learning method used for settlement prediction based on monitoring data, which provides some methods and ideas for research on the application of RDC on soft soil foundations.  
rolling dynamic compaction; impact roller; ground improvement; field monitoring; settlement prediction +
Footnote †: journal: Applied sciences