esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 1 Introduction
### Organization  
The remainder of this paper is organized as follows. In Section 2, we present the preliminaries, and in Section 3, we review [PERSON] et al.'s scheme [16]. In Sections 4 and 5, we assess the security flaws of [PERSON] et al.'s scheme [16] and present a secure and efficient authentication scheme for GLOMONET to overcome the security flaws of [PERSON] et al.'s scheme [16]. In Section 6, we demonstrate the security of our scheme using informal security analysis and formal security analysis, including Burrows-Abadi-Needham (BAN) logic and the real-or-random (ROR) model. In Section 7,  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Properties** & **Description** \\ \hline Three-factor security & This should remain secure even if any two of the three factors are compromised. \\ \hline Resisting known attacks & This requires that the authentication protocol for GLOMONET is secure from various known attacks, including privileged insider, replay, session key disclosure, MITM, and masquerade attacks. \\ \hline Resisting stolen mobile device attack & If an unauthorized person obtains the lost/stolen mobile device, it is impossible for him to impersonate a valid user with a counterfeit login request by using the information extracted from the mobile device. \\ \hline Forward and backward secrecy & This requires that the attacker is not able to obtain the previous session keys or future ones by using the compromised session key. \\ \hline Secure mutual authentication and key agreement & This is an essential requirement in the GLOMONET scenario, and requires the communication parties to be able to authenticate each other and generate a shared session key to provide confidentiality of messages in public channels. \\ \hline User friendliness & The mobile user should freely select his/her own identity and password. In addition, the mobile user should be allowed to update the password without the assistance of the home agent. \\ \hline Anonymity and untraceability & A malicious attacker is incapable of revealing and tracking the real identity of the legitimate user, and this is an important privacy-preserving requirement for users. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Security requirements for authentication and key agreement protocols.  
we report a formal security validation utilizing the Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool. In Section 8, we compare the performance properties of our protocol to existing protocols. We present our conclusions in the final Section 9.
## 4 Bi-Objective Optimization Allocation Problem Model and Algorithm
### Industrial Robot-Monitoring Workflow Assignment Problem (IRMWAP) Formulation  
Robot-monitoring workflow (RMW), define \(G_{\text{R}}=(\text{T},\text{ E})\) to denote the graph structure of the RMW, the vertex set \(\text{T}\) denotes the set of tasks in the workflow, and the edge set \(\text{E}\) denotes the set of before-and-after relationships between tasks. For vertex \(\text{t}_{\text{i}}\in\text{ T}\) can be represented by the triple \((\text{id}_{\text{i}}\), instruction\({}_{\text{i}}\), memory\({}_{\text{j}})\), and for edge \(\text{e}_{\text{ij}}\in\text{E}\) can be represented by the triple \((\text{id}_{\text{ij}}\), pattern\({}_{\text{ij}}\), data\({}_{\text{ij}}\) ). RMW is the abstract data structure of workflow of Figure 2.  
Cloud edge network (CEN), define \(G_{\text{N}}=(\text{V},\text{ F})\) to represent the graph structure of the CEN, the vertex set \(\text{V}\) denotes the set of nodes with computing power in the network, and the edge set \(\text{F}\) denotes the set of network links between nodes. For the vertex \(\text{v}_{\text{x}}\in\text{V}\) can be represented by the seven-tuple \((\text{id}_{\text{x}}\), latency\({}_{\text{x}}\), comSpeed\({}_{\text{x}}\), comCost\({}_{\text{x}}\), comCap\({}_{\text{x}}\), bandwidth\({}_{\text{x}}^{\text{up}}\), bandwidth\({}_{\text{x}}^{\text{down}}\)), and for the edge \(\text{f}_{\text{xy}}\in\text{F}\) can be represented by the four-tuple \((\text{id}_{\text{xy}},\text{ commCap}_{\text{xy}},\text{ commCost}_{\text{xy}},\text{ l}_{\text{xy}})\). CEN is the abstract data structure of architecture of Figure 1.  
The total optimization objective of latency includes communication latency \(\text{f}_{\text{xiyj}}^{\text{t}}\) and computation latency \(\text{c}_{\text{xi}}^{\text{t}}\). Communication latency includes network distance delay and bandwidth transmission delay. Computation latency is the time required to execute computation instructions, and computation resources need to communicate with the monitor when the task is started or finished. \[\text{T}=\sum\text{f}_{\text{xiyj}}^{\text{t}}\text{*d}_{\text{xi}}\text{*d}_{ \text{yj}}+\sum\text{c}_{\text{xi}}^{\text{t}}\text{*d}_{\text{xi}}\]  
\[\text{f}_{\text{xiyj}}^{\text{t}}=\text{l}_{\text{xy}}+\text{data}_{\text{ij}} *\left(\frac{1}{\text{bandwidth}_{\text{y}}^{\text{up}}}+\frac{1}{\text{ bandwidth}_{\text{y}}^{\text{down}}}\right) \tag{1}\]  
\[\text{c}_{\text{xi}}^{\text{t}}=\left\{ \begin{array}{ll}\text{latency}_{ \text{x}}&\text{task i is the start or end,}\\ \text{instruction}_{\text{i}}&\text{others.}\end{array} \right.\]
###### Abstract  
This study presents the development of insoles using 3D scanning and additive manufacturing; additionally, the feasibility of implementing cellular structures in their design was evaluated. Using finite element models, the displacements and Von Mises stresses in the insoles were obtained considering the exerted pressures of a person during walking. The insoles developed in this work presented a decrease of 91.48% in deformation while the maximum Von Mises stress increased by 32.62%, compared with what other authors reported. The Von Mises stresses and displacements in the inside were calculated when two cellular topologies, triply periodic minimal surfaces (TPMS) and body-centered cubic (BCC), and different relative densities (10.33%, 14.67%, and 20.19%) were implemented. When compared to solid insoles, the Von Mises stresses and displacements for the models with cellular structures increased. The maximum Von Mises stresses and displacements resulted for the insoles with a relative density of 10.33%; for the inside with the BCC cellular structure, the displacement was 2.06 mm, and the Von Mises stress was 22.17 MPa, while for the TPMS structure, these were 2.7 mm and 23.84 MPa, respectively. The designs were additively manufactured, and the printing defects were visually characterized.  
3D scanning; additive manufacturing; cellular structures; finite element method; insole 4.0/. +
Footnote †: journal: Applied Sciences
# Animal Movement Prediction Based on Predictive Recurrent Neural Network
## 4 Experiments
### Movement Prediction
Figure 13a-c shows the input sequence, ground truth, and predicted sequence of case 9, respectively. The input and ground truth sequences show the migration movement of long-billed curlew from 16 November 2017 to 3 May 2018. In Figure 13a,b, we can see the starting migration movement at T = 14, pre-breeding migratory and migration for breeding at T = 15 to 18, and migration ending at T = 19 to 24. Table 15 shows daily comparison of RMSE, SSIM, and centroid distance in case 9. In this experiment, the input sequence showed a static movement and the ground truth sequence showed a dynamic movement pattern from T = 15 to 18. However, our model did not predict such dynamic movement precisely. Figure 14a-c shows the input, ground truth, and predicted sequences of case 10, respectively. The input and ground truth sequences are the migration movements of long-billed curlews from 19 May 2018 to 14 May 2019. We used the 15-day geo records to make a one-movement density image and as a sequence to predict the migration pattern for one year. As shown in Figure 14a,b, we observe \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Prediction day T**} \\ \hline  & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** & **21** & **22** & **23** & **24** \\ \hline RMSE & **4.54** & 7.98 & 15.64 & 15.11 & 16.36 & 16.27 & 15.36 & 15.49 & 14.37 & 8.39 & 7.67 & 7.06 \\ SSIM & **0.97** & 0.96 & 0.93 & 0.94 & 0.92 & 0.93 & 0.93 & 0.93 & 0.94 & 0.95 & 0.95 & 0.96 \\ Cent-Dist in pixels & **3.6** & 7.0 & 13.7 & 16.4 & 16.1 & 15.9 & 15.7 & 14.1 & 10.4 & 5.7 & 6.2 & 5.4 \\ Cent-Dist in km & **87.7** & 170.2 & 329.6 & 394.5 & 388.5 & 383.7 & 377.9 & 338.9 & 250.0 & 139.2 & 150.8 & 130.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Daily comparison of RMSE, SSIM, and centroid distance of case 9. Figure 12: Movement prediction result-(**a**) input sequence; (**b**) ground truth; (**c**) predicted sequence. Figure 13: Movement prediction result-(**a**) input sequence; (**b**) ground truth; (**c**) predicted sequence. that non-breeding season migration was shown at T = 1 to 3 and T = 21 to 24, and breeding season movement was shown at T = 4 to 20. Table 16 shows the daily comparison of RMSE, SSIM, and centroid distance of case 10. The result shows that there are relatively low distance errors at T = 13 to 20, but as migration starts at T = 21 to 33, we can see relatively high distance errors.
# Model Parallelism Optimization for CNN FPGA Accelerator
## 4 Optimizations
### Overall Architecture  
Based on the above optimization strategies, an overall architecture could be designed. In the process of inference, interactions between the kernels of each layer were transmitted through channels, data were read by aggregation, and kernels were vectorized. In this design, the data transfer process overlapped with the parallel execution of the kernels on the FPGA, which avoided the impact on the FPGA computational performance. The overall architecture of design is shown in Figure 4. The overall architecture was composed of one CPU, two FPGAs, and DDR3 memory. The CPU played the role of synchronizing the data in the middle of the multi-FPGA devices during CNN inference.  
Figure 4: Model parallel computing architecture.  
The proposed FPGA internal design is shown in Figure 5, including the design of each module and the data flow control. MemRD was used to obtain the image data and weights from the host side. MemWR was used to write the computation results from the kernel side to the host side. Block represents the computation unit corresponding to the CNN. Each Block computation ended with residual learning, which needed to be written back to the host side via MemWR for synchronization. In addition, the on-chip memory was implemented using M20K memory blocks, each with a 20K bits capacity. Only when the memory depth was greater than the maximum depth of one M20K block was another M20K block added.
# UAV-Cooperative Penetration Dynamic-Tracking Interceptor Method Based on DDPG
## 5 Conclusions  
Based on the deep reinforcement-learning DDPG algorithm, this paper studies the UAV-cooperative penetration. Based on the mission scenario model, the UAV's action space, state space, termination conditions, and reward functions are designed. Through the training of the UAV, the coordinated penetration of multiple aircraft was realized. The main conclusions of this paper are as follows:  
The collaborative method based on the DDPG algorithm designed in this paper can achieve coordinated penetration between UAVs. After UAVs are trained, they can coordinate to evade interceptors without being intercepted by them. Compared with traditional algorithms, the UAV's penetration performance is stronger, the applicable environment is more complex, and it has great application prospects.  
The reinforcement learning collaboration method in this paper realizes the time collaboration between UAVs on the premise of exchanging state information between multiple aircrafts, and the movement of UAVs finally reaches the target area at the same time from different initial locations, achieving time coordination. However, this paper doesn't consider factors such as communication delays or failures between UAVs, which causes the UAV to receive the wrong information. This problem may be solved by predicting UAVs' states and information fusion. Further research can be carried out in the follow-up work.  
This paper only considers the movement of the engagement plane. In the follow-up work, the movement can be expanded to three dimensions, and multi-UAV-cooperative penetration in a 3D environment will put forward higher requirements on the algorithm.  
Conceptualization, J.S. and Y.L. ([PERSON]); methodology, Y.L. ([PERSON]) and K.Z.; software, Y.L. ([PERSON]); validation, Y.L. ([PERSON]), K.Z. and Y.L. ([PERSON]); formal analysis, J.S.; investigation, Y.L. ([PERSON]); resources, K.Z. and Y.L. ([PERSON]); data curation, J.S.; writing--original draft preparation, Y.L. ([PERSON]); writing--review and editing, Y.L. ([PERSON]);  
Figure 11: The distance curve between the UAV and the target.  
visualization, Y.L. ([PERSON]); supervision, J.S.; project administration, J.S.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.
# Structure-Aware Trail Bundling for Large DTI Datasets
## 3 Method  
To describe our joint rendering-and-bundling method for 3D DTI tracts, we first introduce some notations. Let \(V:\mathbb{R}^{3}\to\mathbb{R}^{6}\) be a 3D volume scan that encodes, per voxel, the symmetric 3 by 3 DTI diffusion tensor. For every point \(\mathbf{x}\in V\), we can compute the three eigenvectors \(\mathbf{e}_{i}\) and eigenvalues \(\lambda_{i}\), \(1\leq i\leq 3\), of \(V\)'s diffusion matrix, which give the directions, respectively strengths, of the largest, medium, and smallest diffusions recorded by the MRI data [46]. Our visualization method then consists of three steps: seeding and tracing DTI trails (Section 3.1); bundling trails to follow anatomical structures (Section 3.2); and structure-emphasizing rendering (Section 3.3).
## 4 Test Results and Mechanism Analysis
### Effect of Nano-SiO\({}_{2}\) on the Hydrophobicity of Soil Samples  
Figure 10 shows the contact angle of samples with different superhydrophobic nano-SiO\({}_{2}\) contents. With increased nano-SiO\({}_{2}\) content, the contact angle of samples increased from 15\({}^{\circ}\) to 67\({}^{\circ}\). After adding superhydrophobic nano-SiO\({}_{2}\), the hydrophilicity of expansive soil was reduced. The microstructure of superhydrophobic nano-SiO\({}_{2}\) was spherical and it had a flocculent and reticulate quasi-granular structure. Using scanning electron microscopy (SEM), the German scientist [PERSON] [32] observed the upper surface of the lotus leaf and found that its superhydrophobicity is due to the micron mastoid structure and waxy shape on the surface (Figure 11). We speculated that nano-SiO\({}_{2}\) formed a mastoid structure on the soil surface, similar to the surface of lotus leaves. Consequently, the modified soil surface had molecular-level hydrophobicity and became water repellent. To some extent, it can explain the gradual increase in the contact angle of soil samples with increasing nano-SiO\({}_{2}\) content. The hydrophobic surface model of the modified soil is shown in Figure 12.  
Figure 8: FTIR spectra of samples.  
Figure 9: The schematic diagram of the reaction between soil and superhydrophobic nano-SiO\({}_{2}\).
## 2 Model and Test Setup
### Single-Stage and Two-Stage AIC Method for Onset Time Determination  
The Akaike information criterion (AIC) was implemented for automatic onset time determination. The AIC function based on the autoregression (AR) model divides a signal into two vectors at the time \(k\), and compares the signal variance of prediction errors before and after the time \(k\) in a predetermined time window [15; 19; 30]. The proper time window size is required for precise onset time determination [21]. The equation of AIC function is expressed as follows:  
\[\text{AIC}(k)=k\log(\text{var}(x[1,k]))+(S-k-1)\cdot\log(\text{var}(x[k+1,S])), \tag{1}\]  
where \(S\) is the last sample of the time series, \(k\) increases from 1 to \(S\) through all samples of \(x\), var denotes the variance function, and var(\(x[1,k]\)) indicates that the variance of \(x\) from the first \(x_{1}\) to k-th \(x_{k}\). The \(k\) value with the minimum AIC value is determined as the onset time of the AE signal \(t_{min}\)[15; 31].  
In this study, the single-stage AIC method adopted the time window that started from the beginning of the original signal and ended at the maximum amplitude of the signal. In addition, the two-stage AIC method was applied to improve the accuracy of AE source localization. The two-stage AIC method consisted of two rounds of the AIC picking [32; 33]. The single-stage AIC method was carried out as the first round of AIC picker, as shown in Figure 4a. Thereafter, the second round of AIC picker was applied with a narrower time window with a constant size (Figure 4a). The arrival time difference was less than \(\sim\)200 us at the farthest accelerometer from the source in this particular case with the used material and the PRF geometry. Accordingly, the window size was fixed to be 200 us (Figure 4a). The onset time picked in the first round of AIC (\(t_{min}\)) was used as the reference point at each signal. For a fixed window size, the time interval from the beginning of the window to \(t_{min}\) is \(\Delta t_{FB}\), and the rest of the window is \(\Delta t_{FA}\), such that the total window size is \(\Delta t_{FA}+\Delta t_{FB}\). The window lengths before and after the onset time picked in the first round of AIC \(t_{min}\), \(\Delta t_{FB}\), and \(\Delta t_{FA}\) were determined as 8 us and 192 us, respectively, in all the cases. \(\Delta t_{FA}\) was assigned to be 8 us to increase the effectiveness of the second-round AIC picker. The rest of the window was determined to be \(\Delta t_{FB}\).  
The \(k\) value with the minimum AIC value in the second round defines the onset time of an AE event, as shown in Figure 4b. This two-stage AIC method was applied for the cases with inaccurate AE source localization with the single-stage AIC method.
###### Abstract  
Building-integrated photovoltaic/thermal (BIPV/T) systems can produce both electrical and thermal energy through the use of photovoltaic/thermal modules integrated with building envelope. Exterior shading is a common way to improve summer indoor thermal environment of the buildings in low latitudes. This study presents a BIPV/T solar water heating system for exterior shading of residences. In order to evaluate and optimize the system performances, a model was developed to simulate the thermal and electrical production of such system. The simulations for an example system in Guangzhou, a city in South China, were performed to investigate the influences of tank installation height and panel tilt angle on system performances. According to simulation results, the suggested tank installation height is 0.6\(\sim\)0.8 m. The shading coefficient ranges from 0.797 to 0.828 when the tilt angle varies from 14\({}^{\circ}\) to 38\({}^{\circ}\). The reduction of panel tilt angle causes a certain improvement of shading performance. The annual auxiliary heat reaches the minimum when the panel tilt angle equals 28\({}^{\circ}\) and the annual electric energy output changes little when the panel tilt angle ranges from 20\({}^{\circ}\) to 28\({}^{\circ}\). Comprehensively considering thermal, electrical, and shading performances, the suggested panel tilt angle is 20\({}^{\circ}\)-28\({}^{\circ}\). The average thermal and electrical efficiencies are respectively 38.25% and 11.95% when the panel tilt angle ranges from 20\({}^{\circ}\) to 28\({}^{\circ}\). The presented system is a promising way to provide hot water, electricity, and exterior shading for residences.  
BIPV/T; exterior shading; performance evaluation and optimization; thermosyphon photovoltaic/thermal water heater +
Footnote †: journal: Applied Sciences  
Article
## 4 Results  
### Laboratory Setup  
The results were obtained through simulation and an HIL test system. The HIL is shown in Figure 10, and consists of FPGA Zynq ZC706 and some interface boards (DACs  
Figure 9: Block diagram of the novel integral compensator MPPT method.  
and board adapters). The system was inspired by a controlled HIL system (C-HIL), with the controller and the emulated system (PV, boost converter, and DC microgrid) implemented in FPGA ZC706.  
Emulation of the PV system, boost converter, and microgrid was conducted using Simscape electrical components, followed by conversion into a linear state-space model. This conversion produced four typical state-space matrixes and a Simulink file with implementation of the state space. This file could then be coded into VHDL using an HDL coder in MATLAB. Then, the VHDL code was entered into Vivado from Xilinx (AMD) [45], compiled, and transferred to the FPGA.  
As the PV panel in Simscape was not available for the HDL coder, it was replaced by the PV model reviewed in Section 2.1 and presented in Figure 1. This implementation is shown in Figure 11.  
The VHDL code for the MPPT algorithms was produced using the HDL coder library in MATLAB. This tool requires the usage of single-precision floating point values, and is unable to use double-precision values. To fulfill this requirement, the blocks used in this implementation were configured to work with single-precision values. The use of single instead of double precision reduced resource utilization in the FPGA, but also reduced the precision of the mathematical operations.  
After code conversion, the produced VHDL code was added to Vivado in block design mode and connected to a PWM modulator or hysteretic blocks.  
Figure 11: HIL implementation diagram.  
Figure 10: MPPT HIL test setup. 1—Oscilloscope; 2—DAC7558 EVM; 3—expansion board XM105; 4—FPGA Xilinx ZC706; 5—computer; 6—router.  
For a clearer data visualization, some additional elements were added to Vivado to show the PV power that was instantaneously produced and the maximum available power. These signals were sent to the DAC board to enable visualization using an oscilloscope.  
Some additional functions were also added to change the parameters of irradiance and temperature in the HIL model using the FPGA AXI interface. In addition, a webserver was implemented in the Zynq processor to simplify the user's interaction with the system.  
The MPPT algorithms were designed in MATLAB Simulink to speed up the implementation process. Moreover, this process enabled simulation of the controller before its implementation.