esa-sceva/satcom-chunk-collection huggingface dataset:
# BGPcoin: Blockchain-Based Internet Number Resource Authority and BGP Security Solution
## 7 Implementation and Deployment
### Experiments on an Official Test Network  
To show the practicability of our scheme, we deploy the official Ethereum test network Ropsten that mimics the real production network. Due to the limited balance, we only conduct experiments on the smallest database. Our contract addresses of BGPcoin_base, BGPcoin_client and BGPcoin_checker in Ropsten are:  
* 0 xb23e182 afb61096e51d9c5d22 dcb996c745b0c9.
* 0 xa9 fcbaeb1a113e7b894 eb57 df8b25a674d635 aad.
* 0 xf5b285e3c7 aea8f67c100b8 ac036 fda022 dd52a5.  
**Results:** We estimated the cost of creating the BGPcoin contract and each type of BGPcoin transaction, including the approximate computational steps in Ethereum's gas and the approximate costs in U.S. dollars in Table 3. Note that in September 2018, 1 ether = $289.15 and 1 gas = \(1.8\times 10^{-8}\) ether ([[https://ethstats.net/](https://ethstats.net/)]([https://ethstats.net/](https://ethstats.net/)), [[https://coinmarketcap.com/](https://coinmarketcap.com/)]([https://coinmarketcap.com/](https://coinmarketcap.com/))), and compared with the paid service provided by the current Internet resource management, the cost of BGPcoin is relatively low. Moreover, the participating organization motivates the mining and validation of transactions by increasing the fees on demand.  
Figure 8: Performance of two expensive operations: IP register and IP allocate.  
**Scalability analysis:** According to the seven-day BGP profile from 28 August 2018 00:00-3 September 2018 23:59 [36], although the recent peak prefix update rate per second is 1818 at 19:36:30 Wednesday, 29 August 2018, we observe that the average prefix updates per second is only 5.60. Considering Ethereum has 7-15 transactions per second ([[https://en.wikipedia.org/wiki/Ethereum](https://en.wikipedia.org/wiki/Ethereum)]([https://en.wikipedia.org/wiki/Ethereum](https://en.wikipedia.org/wiki/Ethereum))) and, moreover, advanced consensuses ([[https://github.com/ethereum/EIPs/issues/225](https://github.com/ethereum/EIPs/issues/225)]([https://github.com/ethereum/EIPs/issues/225](https://github.com/ethereum/EIPs/issues/225))) are in progress to promote Ethereum's throughputs to thousands of transactions per second, it is viable to have BGPcoin have 5 tran/s of throughput for BGP advertisement on average. We believe BGPcoin poses a feasible and credible BGP security solution, and an extensive experiment for the global deployment of BGPcoin has been conducted to further measure the practical scalability.
## 3 Results
### The Analysis of the Selected Conditions for Hexanal Biosynthesis
#### 3.2.2 Effect of Linoleic Acid Concentration on Lipoxygenase Activity and Hexanal Production  
In the present studies on the production of hexanal, the impact of the substrate concentration (linoleic acid) on the level of its consumption by yeasts and the lipoxygenase activity demonstrated was analyzed (Figure 5). In the research, 25, 50, and 100 g/L additions of linoleic acid were used.  
Studies have shown that the higher the concentration of linoleic acid in the substrate, the lower the efficiency of its biotransformation. With the increasing concentration of the substrate in the medium, the activity of lipoxygenase increases in the cell extract and decreases in the supernatant. With the addition of 25 g/L linoleic acid, the LOX activity in the supernatant was 2260 \(\pm\) 122 U/mL and that in the cellular extract 1579 \(\pm\) 55 U/mL. When the addition of acid was 100 g/L, the LOX activity in the supernatant was about 2.5 times lower and amounted to 964 \(\pm\) 111 U/mL, while in the cell extract it increased to 2534 \(\pm\) 101 U/mL. Along with the increase in the concentration of linoleic acid in the medium, not only did the LOX activity in the cell extract increase, but so did the concentration of the synthesized hexanal (Table 4). When using 100 g/L of linoleic acid, the concentration of hexanal in the cell extract was 189.44 \(\pm\) 29.23 mg/L, nearly 5 times higher than in the culture where 2.5 g/L of linoleic acid was used. The results showed no correlation between the hexanal concentration in the supernatant and the amount of linoleic acid in the yeast culture medium.
# A High-Utility Differentially Private Mechanism for Space Information Networks
## 3 Proposed Mechanism
### System Structure  
The system structure diagram of Space Information Network is shown in Figure 3. The Space Information Network has three entities including the user, the master satellite, and satellites.  
The user is the person who uses the Space Information Network. The user can be operation and maintenance personnel who send management commands such as counting query to figure out how many satellites are active. The user can also be someone who uses the Space Information Network to communicate.  
Master satellite is the satellite that is directly connected to the user. In the Space Information Network, satellites keep moving and many of them are out of the user's reach. Thus, the user's messages or commands need a satellite to forward such that these messages or commands can be sent to satellites out of reach. In the context of this paper, the satellite which is responsible for interacting with the user is named the master satellite.  
Satellites are components of the Space Information Network. Satellites execute specific functions such as calculating queries sent by the user.  
Figure 2: Sample and aggregation mechanism.
## 3 Results
### Niche Metrics and Diet of Diplodus annularis
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **LP** & & **IP** & & **HP** \\ \hline
**Food Sources** & **Taxa** & **Contribution** & **Taxa** & **Contribution** & **Taxa** & **Contribution** \\ \hline TELEOSTS & & & & & \\ Actinopterygii & 4 & \(12.69\pm 0.49\) & 5 & \(10.84\pm 0.28\) & 3 & \(18.56\pm 0.78\) \\ CNIDARIANS & & & & & \\ Anthozoa & 2 & \(10.21\pm 1.31\) & 3 & \(5.15\pm 0.69\) & 1 & \(1.28\pm 4.75\) \\ Hydrozoa & - & - & - & - & - & - \\ BASAL RESQURCES & & & & & \\ Algae & 1 & \(2.01\pm 0.10\) & - & - & - & - \\ Detrius & 1 & \(1.56\pm 0.58\) & 2 & \(5.91\pm 0.29\) & 1 & \(3.02\pm 5.00\) \\ Phytoplankton & - & - & - & - & - & - \\ Aquatic plants & 2 & \(2.88\pm 0.91\) & 4 & \(8.07\pm 0.10\) & 1 & \(5.05\pm 0.28\) \\ MOLLUSC & & & & & \\ Bivalvia & - & - & 2 & \(6.24\pm 0.76\) & 2 & \(3.37\pm 0.44\) \\ Gastropoda & 6 & \(20.65\pm 1.06\) & 1 & \(1.08\pm 5.00\) & 2 & \(4.29\pm 1.23\) \\ ANELLIDS & & & & & \\ Clitellata (Oligochaeta) & - & - & - & - & 1 & \(2.73\pm 5.00\) \\ Polychaeta & 8 & \(9.71\pm 0.55\) & 3 & \(10.89\pm 0.69\) & 8 & \(34.76\pm 1.12\) \\ ECHINDEDRMS & & & & & \\ Eleutherozoa & 1 & \(0.99\pm 5.00\) & - & - & - & - \\ Euechniodea (Echinodea) & 1 & \(6.04\pm 5.00\) & - & - & - & - \\ Ophiuroidea & - & - & 1 & \(3.54\pm 0.75\) & - & - \\ ARTHROPODS & - & - & 1 & \(3.1\pm 1.06\) & 1 & \(0.87\pm 5.00\) \\ Instead & & & & & \\ Malacostraca & & & & & \\ Amphipoda & 3 & \(8.43\pm 1.13\) & 3 & \(9.86\pm 1.54\) & 2 & \(7.8\pm 1.10\) \\ Decapoda & 5 & \(22.03\pm 1.37\) & 5 & \(27.86\pm 1.69\) & 2 & \(14.67\pm 1.03\) \\ Isopoda & 3 & \(2.8\pm 0.31\) & 4 & \(4.85\pm 0.20\) & - & - \\ NEMERTEANS & & & & & \\ Nemertes & - & - & 1 & \(2.61\pm 0.71\) & 1 & 3.6 \(\pm\) 0.74 \\ BASAL RESQURCES & & \(6.45\pm 0.39\) & & \(13.98\pm 1.08\) & & \(8.07\pm 1.02\) \\ INVERTEBATES & & \(80.86\pm 2.7\) & & \(75.18\pm 2.46\) & & \(7.33\pm 3.61\) \\ FISH & & \(12.68\pm 0.49\) & & \(10.82\pm 0.28\) & & \(18.57\pm 0.78\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Diet composition of _Diplodus annularis_ in each lake. Proportional contribution (in %) of food sources to the diet of \(D\). _annularis_ obtained from ITU-based mixing models. LP: low anthropogenic pressure, IP: intermediate anthropogenic pressure, HP: high anthropogenic pressure. The contribution of each food source is reported as the mean (\(\pm\) s.e.). “Taxa” indicates the number of taxa belonging to the respective group in the diet of \(D\). _annularis_. The overall contribution of basal resources, invertebrates and fish is reported as the mean (%) \(\pm\) s.e.
## 4 Experimental Validation of the Geometrical Efficiency Calculation
### Metal-Based Cage Armour Systems
#### 4.1.2 Experimental Validation
As in this case, the mesh size is large enough to let the projectile pass unaltered for certain impact locations; the effective diameter \(C_{\mathit{eff}}\) must be determined. As a first approximation, \(C_{\mathit{eff}}\) was for this case, considered to be equal to the cone diameter of the shaped charge, being 75 mm and 84 mm for, respectively, the PG-7 and PG-7L warhead. The results for the different cases are shown in Table 4. It is clear that the model is able to reproduce the experimental trend for the two cases where a sufficiently large number of projectiles were shot to give a statistically valid sample size. The fit of the model could actually be improved by fitting the values for \(C_{\mathit{eff}}\), as the latter is an indirect measure of the intensity of the interaction between the cage armour and the incoming projectile, which will vary with the actually considered cage armour system and type of projectile. Although the differences between the theoretical and experimental probabilities are larger for the angled shots, the model is able to capture the relative performance difference for the two considered projectiles. As already mentioned, the experimental sample size is, however, considered to be too small to be representative of the actual performance of the system.
# Estimation of Fatigue Crack AE Emissivity Based on the Palmer-Heald Model
###### Abstract  
This article is devoted to materials testing by the acoustic emission (AE) method, which is the analysis of models and diagnostic parameters to assess the probability of detection of a defect in steel structures. The paper proposes to evaluate the emissivity of the material quantitatively by the number and dynamics of the accumulation of acoustic emission impulses. Experimental studies were carried out on pearlitic structural steels, including the loading of samples with fatigue cracks. It was established that the number of AE impulses emitted during loading of an object with a fatigue crack is a random variable corresponding to the normal distribution law. The results show that an estimate of the number of AE impulses emitted during the loading of samples with fatigue cracks can be obtained by distributing the multiplicative parameter \(D\) of the Palmer-Heald model by taking into account the maximum value of the applied load.  
acoustic emission; diagnostics; fatigue crack; structural steel; emissivity Article
# Usage of V2X Applications in Road Tunnels
## 3 Methodology
### Testing Scenarios
#### 3.2.2 Test of V2X Communication  
In the second case, communication between vehicles was tested. One vehicle was inside the tunnel and stopped to simulate the stationary vehicle. The vehicle was placed in the first section of the tunnel (500 m from the entrance, 600 m before the first exit), because this part was dedicated to testing during the closure. The stationary vehicle was continuously generating the V2X use case, so during the entire test, standardized C-ITS messages of type DENM (Decentralized Environmental Notification Message) [43] containing its position, event position, traces leading to the event, etc., were broadcasted to the surroundings, and the backend system as well, while the second vehicle arrived after a few minutes in the tunnel and along the way it was receiving messages from the surroundings and the backend. A basic scenario schema is shown in Figure 5.  
One of the research questions within the experiment was also whether it was possible to deliver the C-ITS message over the ITS-G5 communication network outside the tunnel. If so, how far is it possible? It was also important to compare both approaches to communication and find differences. The next question was related to the validity of the message with respect to the GNSS supplement by calculating the time and position using the OBU algorithm.
## 4 Feature Extraction
### EEG Sleep Related Features
The Brain Symmetry Index (BSI): BSI, which calculates the brain symmetry, is one of the popular EEG-derived parameters used in the research field for the purposes of stroke prognostication [66]. It is defined as the mean of the absolute value of the difference in mean hemispheric power in the frequency range from 1 to 25 Hz [67]. \[BSI=\frac{1}{NM}\sum_{j=1}^{M}\left|\sum_{i=1}^{N}\frac{R_{ij}-L_{ij}}{R_{ij}+ Lij}\right|. \tag{6}\]  
where \(R_{ij}\) and \(L_{ij}\) are the power spectral density obtained using [PERSON]'s method of the right and the left hemisphere, respectively. While \(M\) and \(N\) are the total number of Fourier coefficients \(j=1,2,\dots,\)\(M\) and total number of electrode pairs \(i=1,2,\dots,\)\(N\)[64]. Specifically, \(BSI\) is also divided according to different sleep stages and sleep cycles, i.e., we obtain a set of features as \(BSI_{(s_{j},c\
u_{k_{i}},C_{x})}\). In addition to the existing researches concentrated on power spectral density, which has been revealed to be an efficient indicator, it is well accepted that the brain is a complex system and nonlinear measures should be taken into account for modeling and analysis [68]. Therefore, we also extracted three nonlinear features, including Sample Entropy, Detrended fluctuation analysis and Lempel-Ziv complexity. Sample Entropy (SampEn): SampEn is an effective metric to improve the approximate entropy method [69], and it characterizes the complexity and regularity of short-time series and has been widely used in bioinformatics [70]. Moreover, SampEn is not sensitive to the noise, making it appropriate for analyzing the EEG data [71]. Specifically, there are two important parameters when calculating SampEn, which are m (the embedding dimension) and r (the tolerance threshold). In this work, we set m = 2 and r = 0.2 * standard deviation, according to the empirical results. Detrended fluctuation analysis (DFA): DFA, which eliminates the trends in time series, is a method for analyzing variability of biomedical signals [72], and its result indicates the EEG fluctuation by scaling exponent [73]. In this work, we use DFA to reveal the long-term inner correlations of the EEG data by setting the window size as 16 to 512. Lempel-Ziv complexity (LZC): LZC is calculated based on the method introduced by [PERSON] and [PERSON] [74]. It characterizes the disorder of a time series by testing the emergence rate of a new model of the biological signals [75], especially the EEG data [76]. In this paper, EEG data are segmented into consecutive sequences of 5 seconds and a binarization process is conducted according to the mean-value of the EEG time series. The same as the power spectral density, we calculate all these nonlinear features according to different sleep cycles and sleep stages, including SampEn\({}_{(s_{j},c\
u_{k_{i}},C_{x})}\), SampEn\({}_{(F_{i},s_{j},c\
u_{k_{i}},C_{x})}\), DFA\({}_{(s_{j},c\
u_{k_{i}},C_{x})}\), LZC\({}_{(s_{j},c\
u_{k_{i}},C_{x})}\), and LZC\({}_{(F_{i},s_{j},c\
u_{k_{i}},C_{x})}\). Table 1 lists all the main notations we use in this paper.
# A 0.3 V, Rail-to-Rail, Ultralow-Power, Non-Tailed, Body-Driven, Sub-Threshold Amplifier
## 4 Amplifier Design and Simulation Results
### Results and Comparision
The proposed amplifier has not been optimized for large signal performance, and the negative slew rate is limited by the bias current set by \(M_{6}\) resulting in a limited value for the worst case large signal \(FOM_{4_{W\mathrm{C}}}\). If we consider the average slew rate for the computation of the large signal \(FOM_{4}\), the obtained values are comparable with state-of-the-art class-A amplifiers. Much better \(FOM_{4}\) is achieved only by [12,13] that present a class-AB behavior. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **This Work** & [13] & [18] & [22] & [12] & [23] & [14] & [10] & [9] & [8] \\ \hline Year & 2021 & 2020 & 2020 & 2020 & 2018 & 2018 & 2015 & 2014 & 2014 & 2007 \\ Technology \([\mu m]\) & 0.13 & 0.18 & 0.18 & 0.18 & 0.18 & 0.04 & 0.065 & 0.18 & 0.13 & 0.35 \\ \(V_{DD}\)\([V]\) & 0.3 & 0.3 & 0.3 & 0.5 & 0.3 & 0.6 & 0.35 & 0.5 & 0.25 & 0.6 \\ \(V_{DD}/V_{TH}\)\([V]\) & 0.86 & 0.6 & 0.6 & 1.19 & 0.6 & 1.27 & 1.16 & 1.29 & 1.16 & 1.02 \\ \(DC_{gain}\)\([dB]\) & 64.6 & 64.7 & 39 & 69.5 & 65.8 & 41.35 & 43 & 70 & 60 & 69.4 \\ \(C_{L}\)\([pF]\) & 50 & 30 & 10 & 15 & 20 & 5 & 3 & 30 & 15 & 15 \\ \(GBW\)\([kHz]\) & 3.58 & 2.96 & 0.9 & 36 & 2.78 & 8260 & 3600 & 18 & 1.88 & 11.3 \\ \(m_{\varphi}\)\([deg]\) & 53.76 & 52 & 90 & 65 & 61 & 86.6 & 56 & 55 & 52.5 & 65 \\ \(SR_{+}\)\([V/ms]\) & 1.7 & 1.9 & - & 9.7 & 6.44 & 844 & 5600 & 3 & 0.64 & 14.6 \\ \(SR_{-}\)\([V/ms]\) & 0.15 & 6.4 & - & 9.7 & 7.8 & 844 & 5600 & 3 & 0.77 & 14.6 \\ \(SR_{\text{arg}}\)\([V/ms]\) & 0.93 & 4.15 & - & 9.7 & 7.12 & 844 & 5600 & 3 & 0.71 & 14.6 \\ \(CMRR\)\([dB]\) & 61 & 110 & 30 & 90 & 72 & 52 & 46 & - & - & 75 \\ \(spot-noise\)\([\mu V/\sqrt{Hz}]\) & 2.69 & 1.6 & 0.81 & 0.91 & 1.85 & - & 0.93 & 0.31 & 3.3 & 0.29 \\ @freq & 100 & - & 1000 & 1000 & 36 & - & - & 1000 & 100 & 1000 \\ _Power \([nW]\)_ & 11.4 & 12.6 & 0.6 & 60 & 15.4 & 15,735 & 17,000 & 75 & 18 & 548 \\ _Mode_ & BD & BD & GD & BD & GD & BD & GD & BD & BD \\ \(FOM_{1}\)\([MHz\cdot pF/mA]\) & 4711 & 2114 & 4500 & 4500 & 1083 & 1575 & 223 & 3600 & 392 & 186 \\ \(FOM_{2}\)\([V\cdot pF/(\mu s\cdot mA)]\) & 1224 & 2964 & - & 1213 & 2790 & 161 & 346 & 680 & 146 & 240 \\ \(FOM_{3}\)\([MHz\cdot pF/mW]\) & 15,702 & 7047 & 15,000 & 9000 & 3610 & 2625 & 635 & 7200 & 1568 & 311 \\ \(FOM_{4}\)\([V\cdot pF/(\mu s\cdot mW)]\) & 4079 & 9880 & - & 2425 & 9247 & 268 & 988 & 1200 & 587 & 400 \\ \(FOM_{4_{W\mathrm{C}}}\)\([V\cdot pF/(\mu s\cdot mW)]\) & 658 & 4524 & - & 2425 & 8364 & 268 & 988 & 1200 & 583 & 400 \\ \(Area\)\([mm^{2}]\) & 0.0064 & 0.0085 & 0.00047 & 0.0034 & 0.0082 & - & 0.0050 & 0.057 & 0.083 & 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison with ultralow voltage (ULV) state of the art amplifiers.
# Design and Optimization of Lattice Structures: A Review
## 3 Design and Optimization of Uniform Lattice Structures
### Unit Cell Design Based on Geometric Wireframe  
Lattice structures are a kind of porous material formed by repeated arrangement of unit cells. The properties of lattice structures are directly related to the geometric size, shape, structure, and spatial arrangement of unit cells. Most of academic work on the properties of lattice structures are based on the design of unit cell structures, the change of unit cells arrangement, and the optimization. With the application of various modeling software, it is easy to design different unit cell structures. Designers can freely use CAD software to design the geometric structure of unit cells, and then analyze the performance of the unit cells by finite element or experimental methods, and finally form a uniform lattice structure according to a certain arrangement of unit cells. Many literatures have studied the design of unit cells [154; 155; 156; 157; 158; 159; 160; 161; 162; 163; 164; 165], the structures of unit cells in literature are shown in Appendix A.  
For the design of unit cell shape, [PERSON] [157] proposed a 3D texture mapping method, which enabled designers to select the unit cell structure that satisfied the design requirements from cell structure library (as shown in Figure 12), and then the internal structure was generated and defined in the Extensible Markup Language (XML) file combined with the selected unit cell structure. System can automatically convert the unit cell structure file into a CAD model. Finally, all the unit cells formed a lattice structure. The basic idea of 3D texture mapping is shown in Figure 13. For example, the designer  
\begin{table}
\begin{tabular}{c c c c} \hline
**AM Process** & **Material** & **Lattice Structures** & **Reference** \\ \hline FDM & PLA filaments & & [150] \\ \hline EBM & Ti-6 Al-4V & & [152] \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._knows in advance the stress state of solid cube structure on the left of plus sign, and then selects the lattice structure on the right side of the plus sign to map into the cube, forming a lattice structure by Boolean on the right side of the equal sign that can be made by additive manufacturing. The 3D texture mapping method proposed by [PERSON] is based on mapping a unit cell structure into a design space to generate a customized lattice structure, which makes the design of lattice structure easy.
# Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection
## 3 Materials and Methods
### Implementation of Meter Pointer Coordinate Mapping by Template Matching
#### 3.2.3 Matching of Inspection Image and Template Image  
(1) Key Point Matching and Alignment between Inspection Image and Registration Image  
The inspection image is indexed and referenced against the relevant registration image. The Scale-Invariant Feature Transform (SIFT) algorithm is then employed to identify key point correspondences between the inspection and registration images, as shown in Figure 7. Using these key points, a perspective transformation matrix \(M_{4}\) and its inverse \(M_{3}\) are computed, enabling the mapping of the pointer table area from the inspection image to the corresponding location in the registration image, and vice versa. This process ensures precise spatial alignment between the inspection and registration images, facilitating reliable detection and analysis of the pointer meters.  
Figure 6: Registration image with marked scale.  
(2) Coordinate transformation of pointer  
The pointer coordinates undergo two perspective transformations using transformation matrices \(M_{4}\) and \(M_{2}\) to convert the coordinates from the current image space to the target image space. This process mitigates the impact of factors such as angle deviation, rotation, and scaling that may occur during imaging, ensuring the precise depiction of the pointer's position. Initially, the pointer endpoints are transferred from the inspection image to the registration image, establishing an associative link. Subsequently, the mapped points are transferred from the registration image to the template image, enhancing the accuracy of matching and consolidating the information into the standardized template. This consolidation facilitates subsequent data processing and analysis. Ultimately, the pointer's position is accurately determined on the template image, enabling the retrieval of the indicated scale value.  
The transformed pointer line on the mask image denotes the pointer's position and direction. By analyzing the relationship between the restricted pointer area and the pointer's center point, the potential pointer is precisely identified to accurately select the actual pointer, as illustrated in Figure 8.