esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Analysis of Results
### Experimental Results and Analysis
#### 5.3.2 Results and Analysis of Comparative Experiments
In contrast, the acceleration ratio of RSA is only 0.45, which indicates that the traditional public key encryption scheme does not perform well in high-frequency data transmission. In summary, the AES+Pre scheme combines the high efficiency of AES and the optimization ability of preprocessing to achieve the best balance between performance and complexity in terms of transmission efficiency and adaptability. Despite the delay and throughput advantages of ECC in specific scenarios, its implementation complexity and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number/Time** & **AES** & **ECC** & **RSA** & **AES+Pre** \\ \hline
5000 & 1.2072525024414062 & 0.5344216823577881 & 2.6969704627990723 & 0.6211769580841064 \\
10,000 & 2.4104361531418652 & 1.108201026916504 & 5.4023583573979126 & 1.2400457859039307 \\
15,000 & 3.614360809326172 & 1.6645398139953613 & 8.0227794642168 & 1.86015939721525441 \\
20,000 & 4.825251579284668 & 2.2253086471557617 & 10.642373085021973 & 2.4794530868530273 \\
25,000 & 6.026888847351074 & 2.8103675842285156 & 13.329286575317383 & 3.0894854068756104 \\
30,000 & 7.2344605922698975 & 3.3680198192596436 & 16.033092498779297 & 3.7016258239746094 \\
35,000 & 8.444983959197998 & 3.9443726539611816 & 18.652731298797607 & 4.307711601257324 \\
40,000 & 9.649745225906372 & 4.517022371292114 & 21.318593978881836 & 4.919358730316162 \\
45,000 & 10.861185073852539 & 5.087323188781738 & 24.055016040802002 & 5.52838921546936 \\
50,000 & 12.068187713623047 & 5.655813455581665 & 26.671456336975098 & 6.142529487609863 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The time overhead of the experimental scheme was compared. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Programs/Indicators** & **Average Transmission Delay** & **Throughput** & **KBS/s** & **Acceleration Ratio** \\ \hline AES & 0.00193091 & 517.8905191 & 1 \\ ECC & 0.00090493 & 1105.057663 & 2.13376693 \\ RSA & 0.004267433 & 234.3329108 & 0.452475769 \\ AES+Pre & 0.000982805 & 1017.496133 & 1.964693493 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the corresponding index results of the experiment. resource requirements make it somewhat limited in practical deployment. Therefore, AES+Pre is more suitable as a widely applicable high-performance encryption transmission scheme, which can meet the needs of a variety of complex scenarios.
## Data
### OHS Image Stripe Noise Analysis  
The OHS has 32 bands, and the wavelength ranges from 400 to 1,000 nm. The first 16 bands and the latter 16 bands have different expressions in the image. The first 16 bands have a small change in grayscale, which is reflected in uniform brightness in the image, while the latter 16 bands have a large change in grayscale, which is reflected by an obvious difference in light and dark in the image. Therefore we should consider this feature when removing OHS stripe noises.  
Non-uniformity is widely present in CMOS sensors. When we use a uniform light source as the illumination source, the output signals from each pixel of the image sensor should be the same under perfectly ideal conditions [32]. However, due to the inherent structure of the CMOS sensors, the response between CMOS sensor units will be inconsistent, which is mainly due to the influence of the CMOS fabrication process or the doping concentration of the material, and appears as a column stripe on the image.  
We chose the original image of OHS for stripe analysis. Figure 1 shows images with \(5000\times 5000\) pixels of different bands.  
It can be observed that the stripe noise of OHS is vertically distributed along the track direction. Stripes are generally randomly appearing in multiple columns or single columns, and the stripes are so wide and dense that the real image information is lost in some bands. Also, it holds the characteristics of obviousness, non-periodicity, globality and the random distribution of light and dark stripes. Finally, there is no correlation between stripe noises of different bands, and the noise level varies greatly between different bands.  
Based on the analysis of the image stripe noise, we draw the following conclusions. Firstly, stripe noise is very serious in some bands, and the destriping ability of some existing destriping methods is directly related to the degree of image detail loss. When destriping this serious noise, larger regularization parameters will be adopted, resulting in a great loss of details. So we need to consider first to suppress the image noise to a certain extent without losing the image information. Moreover, the stripe noise degree between different bands varies, so the existing method cannot remove all band stripe noise at the same time. Based on the above conclusions, we propose the algorithm in Sections 3 and 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Size** \\ \hline Spatial resolution & 10 m \\ Swath width & 150 \(\times\) 500 km \\ Quality & 67 kg \\ Signal to noise ratio & \textgreater{}30 \\ Inclination & 97.4\({}^{\circ}\) \\ Regression cycle & 5 days \\ Bands & 256 (32 bands can be selected arbitrarily) \\ Spectral resolution & 2.5 nm \\ Wavelength & 400 nm–1000 nm \\ On-orbit life & \textgreater{}5 years \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Main Parameters of Orbita hyperspectral satellite (OHS) Image.  
Figure 1: Raw image of OHS. (**a**) band 8; (**b**) band 16; (**c**) band 20; (**d**) band 31.
## 2 Materials and Methods
### EEG Analysis
#### 2.4.1 Functional Connectivity  
We measured wPLI to explore the weighted connectivity between the two channels. The wPLI is a major indicator of functional connectivity, representing the phase lag interaction detected in the two signals [23]. It is also widely used because it is resistant to volume conduction and noise artifacts [37]. Also, while the weighted symbolic mutual information index, another functional connectivity index, can detect purely nonlinear interaction dynamics, the wPLI used here is more sensitive to mixed linear and nonlinear interdependence [38].  
\[wPLI=\frac{|E\{\mathcal{J}\{X\}\}|}{E\{|\mathcal{J}\{X\}|\}}=\frac{|E\{| \mathcal{J}\{X\}|sgn(\mathcal{J}\{X\})\}|}{E\{|\mathcal{J}\{X\}|\}} \tag{1}\]  
where \(\mathcal{J}\{X\}\) indicates the imaginary component of the cross-spectrum \(X=Z_{i}Z_{j}^{*}\) between two channels \(i\), and \(j\), \(Z_{i}\) and \(Z_{j}^{*}\) represent the complex-valued Fourier transform of the signal of channel \(i\) and the complex conjugate of \(Z_{j}\), respectively. In addition, \(E\{\bullet\}\) represents the expected-value operator [39]. This value ranges from 0 to 1, with 0 referring to no synchronization and 1 referring to synchronization [36].  
For intrahemispheric connectivity, we calculated the wPLI by dividing it within the sensorimotor region (left M1 and right M1) and between the M1, and the wPLI in mu and beta bands were averaged in each region.
## 2 Results and Discussion  
### Products and Functional Analysis of LLA66 in S. cerevisiae  
To determine the function of LLA66, it was expressed in a _rer_2\(\Delta\) mutant yeast lacking the _cis_-prenyltransferase activity. Comparing that to the normal _rer_2\(\Delta\) yeast, _rer_2\(\Delta\) yeast expressing LLA66 synthesized [13] and [1] extra products composed of 8 and 9 isoprene units, respectively (Figure 2a). The _rer_2\(\Delta\) yeast showed temperature-sensitive growth. Transformation with pYES2-LLA66, similar to the empty vector, could not restore growth of the _rer_2\(\Delta\) mutant (Figure 2b). In contrast, the human homologue of yeast [1], DHDDS restored the growth of the _rer_2\(\Delta\) mutant (Figure 2b). The above observations suggested that LLA66 was not capable of supporting glycoprotein biosynthesis because it synthesized shorter-chain products.  
Figure 1: The reactions catalyzed by cis-prenyltransferases.  
Figure 2: Analysis of LLA66 products and their ability in rescuing the growth of _rer2\(\Delta\)_ mutant yeast cells. (**a**) Standards (top) and polyisoprenoids isolated from the _rer2\(\Delta\)_ mutant yeast transformed with the pYES2 vector (middle) or the pYES2-LLA66 plasmid (bottom). The extra peaks at the elution time of 15 and 16.6 min produced by LLA66 were P8 and P9, as compared to the standards. (**b**) The colonies of _S. cerevisiae_ wild type (WT), _rer2\(\Delta\)_ mutant, and the _rer2\(\Delta\)_ mutant transformed with the vector pYES2 (empty), pYES2-DHDDS, and pYES2-LLA66, respectively, were examined. Five-fold serially diluted yeast cultures starting from OD\({}_{600}\) of 0.4 were plated on solid YP-2% galactose medium and incubated at 22 \({}^{\circ}\)C (left) or 37 \({}^{\circ}\)C (right) for five days.
# Distributed Edge Computing to Assist Ultra-Low-Latency VANET Applications
## 3 System Structure
RSUs are centralized remote management units that are responsible for forwarding and exchanging service control packages. The second layer represents the heterogeneous edge cloud servers deployed at the edge of the RAN. Two main types of MEC servers are considered for the proposed system; micro-cloud edge servers and mini-cloud edge servers. Each RSU is connected to a micro-cloud edge server, as well as the cellular base stations. The micro-cloud edge server is powered with limited computing and energy capabilities. Each group of micro-cloud servers are connected with a mini-cloud edge server, with higher computing and energy resources. The third layer represents the SDN network deployed at the core network. This layer contains two main sub-layers; data plane sub-layer and control plane layer. The data plane layer contains distributed OpenFlow switches that support the appropriate version of the OpenFlow protocol. The control plane contains distributed SDN controllers that control and manage the whole network. The SDN network is managed via application programming interfaces (APIs). The core network provides an interface to the higher layer that represents the application server. Figure 2: Layer system of the proposed multilevel VANET system. The proposed system provides two main network interfaces at the first layer. The first interface is the car-car and the second is the car-infrastructure network. This can be done over the appropriate IEEE 802.11 communication interface, as previously introduced. The distributed edge servers in the second layers are connected over a high speed fiber wireless (FiWi) network [37]. Where, each RSU station is connected to a MEC server to provide the computing capabilities at the edge of the access network. This in turn, offloads the data and reduces the traffic passed to the core network, which reduces network congestion and packet loss.
_Applied_  
_sciences_  
_Article_  
**Numerical Investigation on Aerodynamic**  
**Performance of SCO\({}_{2}\) and Air Radial-Inflow Turbines**  
**with Different Solidity Structures**  
**[PERSON] 1, [PERSON] 2, [PERSON] 1,*(r) and [PERSON] 2**  
1 MOE Key Laboratory of Thermo-Fluid Science and Engineering, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China; [EMAIL_ADDRESS]  
2 Shaanxi Engineering Laboratory of Turbomachinery and Power Equipment, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China; [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (Y.X.)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-29-8266-6559  
Received: 24 February 2020; Accepted: 9 March 2020; Published: 19 March 2020  
**Featured Application: This work can provide the design reference of rotor solidity for SCO\({}_{2}\) and air radial-inflow turbines as well as a new splitter structure to improve the performance of low solidity case.**  
**Abstract:** Supercritical carbon dioxide (SCO\({}_{2}\)) is of great use in miniature power systems. It obtains the characteristics of high density and low viscosity, which makes it possible to build a compact structure for turbomachinery. For a turbine design, an important issue is to figure out the appropriate solidity of the rotor. The objective of this research is to present the aerodynamic performance and provide the design reference for SCO\({}_{2}\) and air radial-inflow turbines considering different solidity structures. For the low solidity case of SCO\({}_{2}\) turbine, new splitter structures are proposed to improve its performance. The automatic design and simulation process are established by batch modes in MATLAB. The numerical investigation is based on a 3D viscous compressible N-S equation and the actual fluid property of SCO\({}_{2}\) and air. The distributions of flow parameters are first presented. Rotor blade load and aerodynamic force are then thoroughly analyzed and the aerodynamic performances of all cases are obtained. The SCO\({}_{2}\) turbine has larger power capacity and higher efficiency while the performance of the air turbine is less affected by rotor solidity. For both SCO\({}_{2}\) and air, small solidity can cause the unsatisfactory flow condition at the inlet and the shroud section of the rotor, while large solidity results in the aerodynamic loss at the trailing edge of rotor blade and the hub of rotor outlet. A suction side offset splitter can greatly improve the performance of the low solidity SCO\({}_{2}\) turbine.  
**Keywords:** radial-inflow turbine; supercritical carbon dioxide; air; rotor solidity; aerodynamic performance
# A Low-Complexity Security Scheme for Drone Communication Based on PUF and LDPC
## 3 Security Architecture
### Error Correction Schemes
#### 3.4.1 Cyclic Matrix Selection  
The response generated by identity PUF can be represented as \(I\hat{D}=ID\oplus\varepsilon\) (see Section 3.2), while the registered ID after scrambling by the GS can be represented as \(ID_{\delta}=ID\oplus\delta\). Therefore, the response can be represented as \(I\hat{D}=ID_{\delta}\oplus\delta\oplus\varepsilon\). When the GS uses \(ID_{\delta}\) to derive random noises \(n\) and \(m\), the disturbance is passed on to the parity information of \(ID_{\delta}\), causing the parity information obtained by the microcontroller to actually be \(\hat{C}_{\delta}=C_{\delta}\oplus\delta\oplus\varepsilon\). Therefore, for the microcontroller, the error positions in \(ID_{\delta}\) are symmetric with the error positions in \(C_{\delta}\).  
Therefore, the coding matrix used in this authentication scheme requires as few symbol bits on the main diagonal as possible. The reason for this is as follows: by examining Figure 2, it can be seen that the matrix is diagonal-like, as shown in Figure 3. If we represent the base matrix \(A\) as \(I_{32}\) and divide the parity bits and correction subunits into eight similar parts, when an error occurs in \(M_{j}\), errors will occur symmetrically in \(C_{j}\) as well. According to decoding theory, if errors occur in \(M_{j}\), the related syndrome \(S_{K_{j}}\) will also change. In the current matrix, where \(j\in K_{j}\), this means that if an error occurs in \(M_{j}\), both the coding error and the symmetric error will affect the same \(S_{K_{j}}\) This undoubtedly greatly increases the decoding difficulty. The encoding matrix used in this study is the unit of circular right Shift 2 in Figure 2, and the column index algorithm in Table 5 needs to shift the corresponding loop right by 64 bits.
# A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes
## 2 Trust Metric Model of Unmanned Aerial Vehicle Cluster Nodes Based on Hierarchical Blockchain
### Blockchain Data Sharing Mechanism under Resource Constraints
#### 2.3.1 Integrated Blockchain and UAV System  
Figure 3 illustrates the overall framework of reputation management for UAVs using blockchain. This framework is based on the clustering structure of UAVs, divided into two layers: the first blockchain for the PBFT consensus within clusters and the second blockchain for the PoW consensus between clusters. Let \(M=\{1,\ldots,m,\ldots,M\}\) represent the set of clusters, and \(U_{m}=\{1,\ldots,i,\ldots,U_{m}\}\) represent the set of UAV nodes within cluster m. The first blockchain within clusters adopts the PBFT consensus mechanism. Nodes collect and record interaction data with neighboring nodes, including packet loss rate, packet forwarding rate, trusted interaction degree, and probe packet reception rate. They evaluate the comprehensive trustworthiness of surrounding nodes and send the evaluation data to the consensus leader node. Consensus is reached among nodes within the cluster, and block generation (we call it PBFT-block) is completed. The cluster head node updates the reputation values of nodes within the cluster based on the data in the first blockchain. For the second blockchain between clusters, considering the control requirements for the reputation update cycle and the distance factor between cluster head nodes, the PoW consensus mechanism is adopted to record the collected interaction data within the cluster on the blockchain. In addition to packaging PBFT blocks, the secondblockchain block (we call it PoW-block) also records real-time reputation data of nodes within the cluster to achieve the goal of reputation data sharing among all network nodes.  
In this framework, we have made improvements to the structure of two types of blocks, namely PBFT-block and PoW-block, to facilitate the integration of a two-tier blockchain. As shown in Figure 4, the structures of the PBFT-block and PoW-block are similar to regular blocks. The header contains the hash, the hash of the previous block, and the timestamp. The body includes the body hash value, the digitally signed signature encrypted with the private key, and the public key, among other key fields. However, for ease of lookup, we have added metadata as a marker in the PBFT-block header, and in the body section of the PoW-block, we have included a list containing multiple PBFT-block metadata. The reputation value data in the PBFT block is hashed to form the trunk unit, and the hash values are successively taken upwards to construct a Merkle tree, with the Merkle root serving as a sub-block in the block header.
# Enabling Horizontal Collaboration in Logistics Through Secure Multi-Party Computation
## 4 Toward an MPC-Based Solution for the CFDP
### Implementation and Evaluation
#### 4.4.1 Software Framework  
We have selected the MPyC platform for our purposes; MPyC is a Python library that allows for fast prototyping and proof-of-concept implementation, hence being very suitable for our needs. MPyC implements the protocol described in [18; 19], both of which were proven secure with a simulation argument in the stand-alone model (and also in the universal-composability framework of [PERSON] [42] for [18], under the assumption that the message delivery between honest parties cannot be influenced by the adversary [43]). These secret sharing-based protocols are secure against a passive adversary controlling a minority of the parties, where the security guarantees hold in an information-theoreticsense, i.e., even against computationally unbounded adversaries. In particular any coalition of less than half of the MPC parties, who exchange information with each other but further follow the instructions of our MPC solution, gains no information on the participants' inputs other than what can be deduced from the output values of the MPC solution. Within the MPyC, we have made use of the \"secure integer\" construct, with a bit-length of 10, which is sufficient to express the relatively small numbers (order volumes, node and haulier IDs, etc.) of our solution; MPyC then makes use of a suitable finite field \"under the hood\" to securely compute operations on these values. We remark that there are standard mechanisms, based on zero-knowledge proofs, to make this protocol actively secure, meaning that not even a coalition of parties that deviate from the instructions of the protocol can break its security [18]. These mechanisms, however, are not implemented in MPyC and would therefore require the usage of a different software framework. Given the purpose of this article, we believe security against passive adversaries to be sufficient.  
While MPyC is generally not deemed suitable for production code, it does simulate several MPC parties as separate processes and is therefore a suitable tool to assess the feasibility of a solution. We have fully implemented the solution described earlier and made the code available.
## 4 Analysis of the Influence of Blasting Vibrations on Slope Stability
### Evaluation of the Influence of Blasting Vibrations on Slope Stability  
After eliminating the obvious data noise, the least square method is used to analyze Equation (2), and the coefficient \(K\) and attenuation coefficient \(\alpha\), which are related to the terrain and geological conditions between the blasting point and the measuring point, are fitted. The fitting results are shown in Figure 9. The site coefficient \(K\) = 328.02 and the attenuation index \(\alpha\) = 1.809 are calculated. The formula for the vibration velocity attenuation of blasting vibration particles in the monitoring area of the Yinshan Mine is:  
\[V=328.02\ \times\ (\frac{\sqrt{Q}}{R})^{1.809} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Measuring Point**} & **Peak Vibration** & **Peak Vibration** & **Peak Vibration** & **Superposition** \\  & **Velocity in the X Direction (cm/s)** & **Velocity in the Y Direction (cm/s)** & **Velocity (cm/s)** & **Vibration** \\ \hline A1 & 0.8315 & 0.0255 & 0.4773 & 0.2453 \\ A2 & 2.9327 & 6.9657 & 7.8745 & 8.8547 \\ A3 & 0.4719 & 0.6274 & 0.2629 & 0.6832 \\ A4 & 0.0731 & 0.4694 & 0.0057 & 0.4706 \\ B2 & 0.0315 & 0.3421 & 0.2483 & 0.4014 \\ B3 & 0.4348 & 0.4670 & 0.2861 & 0.5179 \\ B4 & 0.1762 & 0.1338 & 0.0974 & 0.2008 \\ B5 & 0.0052 & 0.1298 & 0.0014 & 0.1298 \\ C1 & 0.1260 & 0.0075 & 0.6600 & 0.6605 \\ C2 & 1.0147 & 0.9307 & 1.1884 & 1.4475 \\ C3 & 0.9227 & 1.5859 & 1.4036 & 2.0280 \\ C4 & 0.0916 & 0.9070 & 0.9894 & 1.0875 \\ C5 & 0.3434 & 0.9278 & 1.0621 & 1.2176 \\ \hline \end{tabular}
\end{table}
Table 3: Blasting vibration monitoring data.  
Three blasting vibration tests were carried out on the blasting vibration measuring points. The following conclusions are drawn from the analysis. According to China's \"Technical Code for Non-coal Open-pit Slope Engineering\" (GB51016-2014) and _Technical Code for Safety Monitoring of Slope of Metallic and Non-metallic Open-Pit Mines_ (AQ2063-2018), the particle vibration velocity of the slope should be less than 24 cm/s. According to the monitoring results, the maximum one-stage charge of the three blastings is 410 kg, and the maximum blasting vibration is measured to be 7.8745 cm/s in the vertical direction (vector combined velocity 8.8547 cm/s), which meets the requirements of the safety specifications.