esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 A Proposed Robust Scheme Based on Hop-Count Analysis (MHA)
### Route Establishment in MHA  
The source node launches a route discovery procedure if there is a communication requirement. It first adds IP\({}_{\text{S}}\), IP\({}_{\text{D}}\) and RREQ ID in the RREQ packet, then sets the hop-count = 0 and attaches an expiration time to the RREQ packet. Later, the RREQ packet is broadcasted into the network and received by surrounding nodes. In Figure 4 (a), the nodes which receive the RREQ packet process it as follows:  
1) Check CF to confirm the broadcast type. When CF = 0, examine IP\({}_{\text{S}}\), IP\({}_{\text{D}}\) and RREQ ID with cached values; 1) If all values are the same, drop the packet; 2) If not, cache IP\({}_{\text{S}}\), IP\({}_{\text{D}}\), RREQ ID and CF; 2) Add its own IP address into the intermediate list; 3) Hop-count + 1; 4) Construct a route back to where the RREQ packet came from; 5) Rebroadcast the RREQ packet and switch to waiting mode. The nodes in this mode can only used for transferring control packets.  
Figure 4: Route establishment in MHA protocol. (a) Processing a RREQ packet. (b) Processing a RREP packet.  
With the flooding of RREQ packets, the destination node finally receives them. The destination node then generates the RREP packets with following steps:  
1. Copies IP\({}_{\rm S}\), IP\({}_{\rm D}\), RREQ ID and CF from the RREQ packet and writes in the corresponding field of the RREP packet, then sets the hop-count = 0;
2. Unicasts the RREP packet to the source node through each route.  
In Figure 4(b), when intermediate nodes receive the RREP packet, they process it as follows:  
1. Hop-count + 1;
2. Compare IP\({}_{\rm S}\), IP\({}_{\rm D}\), RREP ID and CF with cached values; 1. If they are all the same, i.e., there already exists a route to the destination node, compare the new hop-count in the RREP packet with cached value. If the new one is smaller, or  
Figure 4: Cont.  
the lifetime of the original route expired, replace the original route with the new one and renew the corresponding fields with values in the new RREP packet; otherwise, drop it; b) If any of them are different, cache \(\mathrm{IP_{S}}\), \(\mathrm{IP_{D}}\), RREP ID, CF and hop-count.  
The destination node then establishes a route back to where the RREP packet came from and sets the route as active thus it can be used for transferring data packets.
# A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature
## 4 Experiment
### Results and Analysis
#### 4.4.3 Analysis  
The experiment in Section 4.4.1 shows that the joint features based on the Time-Frequency domain and time-Delay-Doppler domain proposed in this article can effectively improve the recognition accuracy of ship targets. Under the same recognition model conditions, comparing the recognition results of Time-Frequency domain features and time-Delay-Doppler domain features, the recognition accuracy of the joint features can be improved by 5-7%, and the improvement effect is relatively significant. The recognition results indicate that a single Delay-Doppler domain feature cannot improve the recognition accuracy of the model, and the recognition effect of Delay-Doppler domain features is the worst.  
The multi-input target recognition model can significantly improve the accuracy of target recognition. The recognition results in Section 4.4.1 show that the recognition accuracy of a single input target recognition model can only reach up to 80%, and even if joint features are used to recognize targets, the recognition accuracy is only slightly higher than 80%. The recognition results in Section 4.4.2 show that the multi-input target recognition model can effectively improve the accuracy of ship target recognition. Compared to the recognition results of the VGG16, GoogLeNet, and ResNet models, the multi-input target recognition model has a simpler structure, the highest training efficiency, and a significant improvement in recognition accuracy. The recognition accuracy of the model has been improved to over 90%, with an improvement range of 15% to 20%.
## 2 Materials and Methods
### FT-IR Spectral Fingerprints of Samples
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Individual** & **S1 *** & **S2 *** & **S3 *** & **S4 *** & **S5 *** & **S6 *** & **S7 *** \\
**Carotenes** & **mg/mL** & **mg/mL** & **mg/mL** & **mg/mL** & **mg/mL** & **mg/mL** & **mg/mL** \\ \hline Zexanathin & 30.57 (2.10) & 4.26 (0.06) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 9.38 (0.14) \\ Cantaxanthin & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 \\ Trans-beta-apo-8\({}^{-}\) & - & 39.20 (1.20) & 0 (0) & 5.62 (0.08) & 0 (0) & 0 (0) & 0 (0) & 7.25 (0.11) \\ Astaxanthin & 49.70 (2.21) & 38.64 (0.58) & 77.49 (1.16) & 0 (0) & 0 (0) & 0.93 (0.01) & 23.33 (0.35) \\ \(\alpha\)-carotene & 128.80 (3.32) & 93.79 (1.41) & 81.19 (1.22) & 0 (0) & 0 (0) & 2198.98 (32.98) & 398.18 (5.97) \\ \(\beta\)-carotene & 156.86 (3.10) & 0 (0) & 1035.42 (15.53) & 701.14 (10.51) & 0 (0) & 324.11 (4.86) & (143.78) \\ Lutein & 0 (0) & 254.90 (3.82) & 527.84 (7.92) & 0 (0) & 2769.00 (41.54) & 0 (0) & 509.70 (7.64) \\ Lycopene & 0 (0) & 0 (0) & 490.79 (7.36) & 1192.62 (17.89) & 1418.16 (21.27) & 752.42 (11.28) & 321.62 (4.82) \\ Total & 405.15 (4.10) & 391.60 (5.87) & 2218.38 (33.27) & 1893.76 (28.41) & 4187.17 (62.80) & 3276.45 (49.14) & 10.855.35 \\ \hline \end{tabular}
\end{table} 
Table 7: Determination of carotenoids using HPLC-DAD (three replicates). The spectral peaks of the FT-IR control samples showed OH stretching of alcohols at 3600-3100 cm\({}^{-1}\), C-H stretching of aldehydes at 2830-2695 cm\({}^{-1}\), C-H curve of alkanes at 1470-1350 cm\({}^{-1}\), C-N stretching of aliphatic amines at 1250-1020 cm\({}^{-1}\), and C-O stretching of esters at 1300-1000 cm\({}^{-1}\). The FT-IR spectral peaks showed N-H stretching of amides at 3700-3500 cm\({}^{-1}\), C=C-H asymmetric stretching of aromatics at 3100-3000 cm\({}^{-1}\), C-N stretching of nitriles at 2300-2200 cm\({}^{-1}\), C=O stretching of unsaturated \(\alpha\), \(\beta\) aldehydes and ketones at 1710-1665 cm\({}^{-1}\), asymmetric stretching of nitror compounds at 1550-1475 cm\({}^{-1}\), C-N stretching of aromatic amines at 1335-1250 cm\({}^{-1}\), and CH out-of-plane deformation of aromatics at 900-675 cm\({}^{-1}\). The control and tomato paste samples presented asymmetric stretching of alkanes at 3000-2850 cm\({}^{-1}\); the tomato paste showed spectral peaks of N-O symmetric stretching of nitror compounds at 1360-1290; and the control, the oven-dried tomato, and the tomato paste showed spectral peaks of C=C stretching of alkynes at 2260-2100 cm\({}^{-1}\) and C=C stretching of alkenes at 1680-1640 cm\({}^{-1}\). The FT-IR spectrum of the control and processed samples showed slight variations in the peak positions. There was a total contrast in the peak position of oven-dried tomatoes compared to fresh tomatoes, which could be attributed to the presence of impurities [30].
## 2 Materials and Methods
### Performance Evaluation Index  
The OAM mode detection performance of the network is evaluated by two indicators: detection accuracy and BER. The detection accuracy is defined as the ratio of the number of correct OAM mode detection samples to the total number of vortex light intensity distributions on the test set, determined using Equation (7):  
\[\text{Accuracy}=\frac{\sum\limits_{m=1}^{M}f(m)}{M} \tag{7}\]  
where \(M\) represents the total number of light intensity distribution maps of vortex light and \(m\) represents the OAM mode. \(f(m)\) is 1 when the identification is correct and 0 when it is wrong.  
BER and the symbol error rate (SER) are commonly used to evaluate the probability of transmission errors in communication systems. The SER is defined as the probability of a symbol transmission error, i.e., the ratio of the number of erroneous symbols at the receiving end to the total number of transmitted symbols:  
\[\text{SER}=\sum\limits_{i=1}^{M}\left[p_{i}(1-p(s_{i}|s_{i}))\right] \tag{8}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Software** & **Edition** \\ \hline Windows & Windows10 (21H2) \\ \hline Python & 3.7 \\ \hline Pytorch & 1.7.1 \\ \hline Torchvision & 0.8.2 \\ \hline CUDA & 11.0.2 \\ \hline CuDNN & 11.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Software used in experiments.  
Figure 4: Connection between ResNet18 and the refinement network.  
where \(M\) represents the number of symbol types, \(p_{i}\) denotes the probability of transmitting each symbol with the value \(1/M\), and \(p(s_{i}|s_{i})\) represents the correct conditional probability density detected by the receiver in a certain OAM mode.  
\[p(s_{i}|s_{i})=\frac{1}{\sqrt{\pi}}\int_{-\infty}^{+\infty}\exp(h_{T}-\eta_{s_{ i},s_{i}}GL_{path}\sqrt{\gamma_{\beta}})\prod_{k=1,k\
eq i}^{M}[1-0.5 erfc(h_{T}- \eta_{s_{i},s_{i}}GL_{path}\sqrt{\gamma_{\beta}})]dh_{T} \tag{9}\]  
where \(s_{i}\) represents the OAM mode and \(ht_{T}\), \(G\), \(L_{path}\) are constants, representing the power detection threshold, the average gain of the receiver, and the path loss, respectively. The value of \(p(s_{i}|s_{i})\) is theoretically mainly determined by signal-to-noise ratio \(\gamma_{\beta}\) at the receiving end and the helical spectral distribution.  
The relationship of BER with the SER is as follows:  
\[\text{BER}=\text{SER}/(\log_{2}M) \tag{10}\]  
In our simulation experiments, the eight OAM superposition modes are one-to-one, corresponding to the octal symbols. At the receiving end, by identifying the light intensity distribution, the code word sequence is obtained by inversion. This is then compared with the theoretical code word sequence, while the ratio of the number of wrong code words to the total number of code words is the BER.
## 1 Introduction
(2008) compared the spatial footprint of \(b\) values before and after the Wenchuan earthquake in the Longmenshan fault zone, and the results showed that the \(b\) values tend to change from lower in the southern region to higher in the northeastern region. The temporal change in \(b\) values before the Wenchuan \(M_{\rm S}\) 8.0 earthquake showed a decreasing trend [28; 29; 30], and the Lushan \(M_{\rm S}\) 7.0 earthquake showed similar temporal change trends in \(b\) values [31]. These studies focused on the variation trend of \(b\) values before and after the two large events along the Longmenshan fault. However, the fundamental effect of the Wenchuan and Lushan earthquakes on the size distribution of earthquakes along the Longmenshan fault has not been quantified, which limits our understanding of how the apparent stress changes in the region affect the size distribution of earthquakes. In this study, we evaluated the spatiotemporal evolution of the \(b\) values along the Longmenshan fault in the past nearly 20 years. Moreover, we estimated the levels of the \(b\) value for the stable state before and after the Wenchuan and Lushan earthquakes and quantified the effects of the two large earthquakes on the size distribution of subsequent events at different times. In addition, the spatial evolution process of the deep seismogenic environment in the Wenchuan and Lushan aftershock zones in two and three dimensions was illustrated via spatial scanning and data fitting, which can be used to analyze the aftershock activity of the two large earthquakes.
# Model-Based Virtual Components in Event-Based Controls: Linking the FMI and IEC 61499
###### Abstract  
Various development and validation methods for cyber-physical systems such as Controller-Hardware-in-the-Loop (C-HIL) testing strongly benefit from a seamless integration of (hardware) prototypes and simulation models. It has been often demonstrated that linking discrete event-based control systems and hybrid plant models can advance the quality of control implementations. Nevertheless, high manual coupling efforts and sometimes spurious simulation artifacts such as glitches and deviations are observed frequently. This work specifically addresses these two issues by presenting a generic, standard-based infrastructure referred to as virtual component, which enables the efficient coupling of simulation models and automation systems. A novel soft real-time coupling algorithm featuring event-accurate synchronization by extrapolating future model states is outlined. Based on considered standards for model exchange (FMI) and controls (IEC 61499), important properties such as real-time capabilities are derived and experimentally validated. Evaluation demonstrates that virtual components support engineers in efficiently creating C-HIL setups and that the novel algorithm can feature accurate synchronization when conventional approaches fail.  
Functional Mock-up Interface; IEC standards; real-time systems; synchronization; automation; delays Article
# Comparative Study of Blockchain Hashing Algorithms with a Proposal for HashLEA
## 3 Background
### Hash Functions
#### 3.3.8 Equihash  
Equihash is a memory-oriented PoW mining algorithm, resistant to ASIC mining, hence promoting decentralization. It is a class of asymmetric memory-hard PoW algorithms based on the general birthday problem in cryptography. Equihash is applied in several cryptocurrencies to support a more decentralized and accessible mining ecosystem. Equihash possesses several distinguishing features, such as inherently being a memory-hard algorithm: it requires that real RAMs participate in the mining process with equality and makes the engineering of ASIC miners much harder to manufacture. As noted above, this feature ultimately grants participation in mining for an average PC with a big enough RAM, therefore more inclusivity. Furthermore, Equihash is designed to be able to divide the mining workload into parts, enabling average users to distribute the workload between many systems, which enhances its scalability and decentralization. Equihash is resistant to ASIC mining, ensuring that mining remains open to simple miners and not highly centralized within large-scale mining operations. In addition, its security is grounded on the generalized birthday problem, adding more cryptographic robustness to it. These features make Equihash a good fit for cryptocurrencies like Zcash, where it enables efficient verification and scalability, particularly for users with limited device capabilities [63, 69].
# Stability of Single Electron Devices: Charge Offset Drift
## 2 Experimental Observations of Charge Offset Drift
Thus, it is clear that charge offset drift prohibits integration of SEDs in a way analogous to the way threshold voltage differences prohibit integration of conventional transistor devices [9]. It is worth mentioning that the \(1/f\) noise that typically occurs at \(f>1\) Hz in these devices does not limit the ability to parallelize devices such as for a current standard; only the large \(\Delta Q_{0}\) at \(f<1\) Hz precludes integration [31]. Charge offset drift also deleteriously affects work at the single device level. As a practical matter, keeping a single SED tuned in gate voltage space to exhibit the desired behavior becomes challenging over the experimental run time. For instance, a technique used in quantum computation known as spin-dependent tunneling is often used to read out the spin state of a single electron after having performed some manipulation [32; 33; 34]. The technique is illustrated in Figure 2. An applied magnetic field is used to split the spin states of the single electron charging level. To perform the readout one adjusts the voltage on the plunger gate to move the spin split chemical potentials of the dot so they straddle the Fermi level of a charge reservoir. If the spin on the dot is in the spin-up state, then the electron tunnels to the reservoir, while if it is in the spin down state, it does not tunnel. Thus, one infers the spin state from the charge dynamics. The readout level of the dot is carefully tuned to obtain the best fidelity in the readout process. In this case, charge offset drift will manifest itself in a lower readout fidelity since the fluctuations driving the charge offset drift will result in fluctuations of the readout level. To combat a moderate level of drift, automatic feedback has been implemented at the cost of increasing the complexity of the measurement [35]. Figure 2: (**a**) schematic of single shot readout of a single phosphorus atom with an SED. Zeeman-split spin up electrons tunnel to the single electron transistor (SET) giving a current pulse while spin down electrons do not tunnel since that process is energetically uphill and do not give a current pulse on the SET. Reproduced with permission from reference [33], Copyright 2010, Nature Publishing Group; (**b**) pulse sequence which loads an electron from the SET island (acting as a reservoir), reads the electron state, and then empties the donor to reinitialize. Current traces for different spin states. Reproduced with permission from reference [34], Copyright 2015, American Physical Society. Having seen how large values of \(\Delta Q_{0}\) can affect device operation and integration, we now move on in the next sections to catalog what has been measured and the empirical correlations between high and low values of \(\Delta Q_{0}\).
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 3 Materials and Methods: A Novel Proposed EEG Analog Front-End Circuit System
### EEG Sub-Circuit Modeling the ESD Protection and the Instrumentation Amplifier  
After the output of the first band-pass filter, the design integrates electrostatic discharge (ESD) protection utilizing the circuit proposed in [55]. This sub-circuit guarantees the safety of the EEG circuit and of its user, by creating a path to the positive and negative supplies for transient electrical currents that may arise from contact between objects with disparate electrical charges, known as the ESD protection circuit.  
Consequently, the signal is passed to the instrumentation amplifier, which has, as input, both the channel one electrode and the reference electrode. The amplifier has a gain of 199 V/V and reaches 99.9999% of its maximum value in approximately 15 usec of settling time. The AD8422 is chosen as an instrumentation amplifier because of its CMR performance. It has a specific CMRR of 110 dB, much over the minimum requirement of 90 dB [29]. The resistance of the gain resistor RG is set to 100 \(\Omega\), ensuring a theoretical gain of G = 199 [56].  
The equation describing the instrumentation amplifier gain above is as follows:  
\[\mathrm{G}=1+\frac{19.8\ \mathrm{k\Omega}}{\mathrm{R_{G}}} \tag{2}\]  
Figure 12 depicts the EEG sub-circuit modeling the ESD protection and the instrumentation amplifier.  
Figure 11: EEG sub-circuit modeling the first band-pass filter.
## 5 Performance Evaluation of FVCOM
### Real Experiment: FVCOM-NWP  
The grid for the real experiment of FVCOM-NWP was constructed with about eight million grid points (= 8,369,391) between 110\({}^{\circ}\) to 150\({}^{\circ}\) longitude and a 20\({}^{\circ}\) to 55\({}^{\circ}\) latitude centered on Korea. The depth for the ultra-high resolution grid was interpolated with combined data of KorBathy30s [16] and ETOPO1 [17]. KorBathy30s topography was used for the coastal areas of Korea, and ETOPO1 topography was used for the other broad areas. The grid was constructed to thoroughly represent complex coastlines and islands while considering the advantages of the unstructured grid model (Figure 17). The vertical grid was divided into 10 layers and optimized for a tide simulation.  
The time interval for the model integration was 1 s. Similarly to the other experiments, the computational performance was evaluated without analyzing the accuracy of the simulation results due to the short simulation time. Figure 18 shows the SSH field for the simulation results in the KNL system (using 256 cores). The high tide was prominently simulated near the west coast of Korea.  
Figure 16: Performances of FVCOM-Benchmark by the number of cores for the ideal experiment: (SKL+CLS)/(2*KNL) = 44%.  
Figure 17: Ultra-high resolution grid for FVCOM-NWP.  
NWP experiments were performed via the unique configuration of the grid (8,369,391 nodes, 10 layers) (refer to Table 3). We used 128, 256, and 512 cores for comparing the performances of the parallel systems according to the experimental results. In addition, a 1024 ultra-high resolution experiment was performed on the KNL system for testing performance limits.  
The results of each experiment are summarized in Figure 19. The KNL system performed at an overall rate of approximately 34% compared to the performances SKL and CLS. This is slightly slower than the benchmark experiment of FVCOM (44%) due to the loads of I/O accessing. The calculation amounts were different between cores because of the different I/O loads. The performances of FVCOM in the CLS and SKL systems were almost the same, unlike those of WRF and ROMS (see the green and blue lines in Figure 20). This is due to the evenly distributed I/O loads per computed node characterizing the unstructured grid model. This dynamic continuously increased the parallel performance of the KNL system.  
Figure 19: Performances of FVCOM-NWP with respect to the number of cores for the real experiment: (SKL+CLS)/(2*KNL) = 34%.  
Figure 18: Tide simulation results using an ultra-high resolution grid of FVCOM-NWP.
# LBFT: An Asynchronous Committee-Based Blockchain Storage Strategy on Zero Trust Model
## 5 Performance Analysis
### Computation Efficiency  
The computation efficiency of coding is greatly improved in LBFT through partitioning an original block into smaller pieces before encoding, compared to other coding-applied blockchain systems [3; 46]. In this section, we present the computational complexity in encoding and decoding in LBFT.  
For a (\(c\), \(n\))-RS schema, the computational complexity in encoding and decoding are:  
\[ERS(c)=O(c\cdot n), \tag{1}\]  
and  
\[DRS(c)=O(c^{3}). \tag{2}\]  
Assume that the average size of an original block is \(T\); thus, the computational complexity of encoding a block in LBFT can be deduced from Equation (1) as follows:  
\[O((\frac{T}{c})^{2}\cdot ERS(c))=O((\frac{T}{c})^{2}\cdot c\cdot n)=O(n\cdot \frac{T^{2}}{c}).\]  
In recovery of a block, the \(k\) committees compute a recovered value several times in parallel; that is, the decoding process will be performed \(k\) times concurrently. Thus, the computational complexity of a single decoding round can be reckoned from Equation (2) as follows:  
\[O(k\cdot(\frac{T}{c})^{2}\cdot DRS(c))=O(k\cdot(\frac{T}{c})^{2}\cdot c^{3})= O(k\cdot c\cdot T^{2}). \tag{3}\]  
As mentioned in Section 4, however, there exists a probability that the decoding fails since more than half the committees are dominated by malicious nodes. Considering this, the expected decoding rounds \(E\) might be calculated as:  
\[E=\sum_{i=1}^{N}i\cdot\mathcal{P}\cdot(1-\mathcal{P})^{i-1}, \tag{4}\]  
where \(\mathcal{P}\) is the probability that a block is successfully recovered, elaborated in Section 4. Combining Equations (3) and (4), we can compute the expected computational complexity of decoding in LBFT as:  
\[E\cdot O(k\cdot c\cdot T^{2}).\]  
In comparison, BFT-Store incurs higher computational complexity for two reasons: (1) it encodes every \(f+1\) blocks into \(n\) chunks without any data partitioning, which brings about the large computing scale of encoding and decoding; (2) it has to recover \(f+1\) blocks even if the client merely requests for one, preserving redundant computational work. Deduced from Equation (1) and (2), the computational complexity of encoding and decoding respectively reaches \(O(n^{2}\cdot T^{2})\) and \(O(n^{3}\cdot T^{2})\). Obviously, the \(O(n^{2})\) term is removed from the computational complexity in LBFT, where \(c\) is much less than \(n\) (e.g., in a system with \(n=10\),000 nodes, the number of members \(c\) in each committee may be 100) and \(k\) is a small number (e.g., 10).