esa-sceva/satcom-chunk-collection huggingface dataset:
# Nero: A Deterministic Leaderless Consensus Algorithm for DAG-Based Cryptocurrencies
## 1 Introduction
In a general DAG, each vertex can be connected to many vertices, and these connections are not made sequentially but concurrently instead. A blockchain can be seen as a DAG where only one edge can be connected to a vertex. In the context of cryptocurrencies, this allows for a greater throughput and less latency when validating and adding transactions to the ledger compared to a blockchain. However, the downside is that it is much more difficult to order those transactions and, consequently, for the nodes of the network to have a common sense of time. A common sense of time is useful, for example, for synchronous network upgrades, for pruning the ledger and bootstrapping. Another problem is scalability and decentralization, which are connected to each other. Many consensus algorithms are also used to assign a node the status of the leader. However, a leader-based approach has limitations, especially when nodes are geographically dispersed over a wide area network. Firstly, the latency for clients far from the leader is obviously increased. Furthermore, the leader can become a bottleneck or its network performance may degrade, leading to an overall decrease in system-wide performance decreases. Finally, if the leader fails, the whole system cannot serve new requests until an election of a new leader takes place, thereby affecting availability. Therefore, a ledger based on a DAG design is most suited for leaderless consensus algorithms, which can be probabilistic [21; 22] or deterministic [23]. In fact, Leaderless State Machine Replication (SMR) offers appealing properties with respect to leader-driven approaches. Protocols are faster in the best case, suffer from no downtime when the leader fails, and distribute the load among participants. In this paper, we present a new leaderless deterministic consensus algorithm and a mechanism that provides eventual order, making it suitable for DAG-based cryptocurrencies. Some theoretical properties of the algorithms are given, such as a complexity analysis and correctness proprieties. The final contribution is an open-source implementation in the Rust language. In Section 2, some preliminaries are described, in particular, multi-valued leaderless consensus properties are defined, and the overall system model is defined. In Section 3, details of the new Nero consensus algorithm are given. In Section 4, the correctness of the algorithm is discussed, and performance results from a proof of concept implementation in Rust are given. Finally, in the last section, a discussion and final conclusions are made.
###### Abstract
The novelty of this paper lies in the use of the multipath service identifier (MPSID) to bind the network addresses of the multipath service nodes and employ the multipath service nodes as relay nodes to construct multiple paths for data transmission. In addition, a cluster-based multipath service node selection (CMSNS) method has been adopted to guarantee the independence among multiple paths, which reduces the time complexity of multipath calculation. The goal of this paper is to make full use of the characteristics of ICN and the advantages of multipath transmission to improve the efficiency and reliability of data transmission. The main contributions of our work are as follows:  
1. We present MPTS-ICN, which defines three kinds of logical entities, including ICN edge nodes, multipath service nodes and the name mapping and resolution system (NMRS). We store the mapping relationship between MPSID and multipath service node network addresses in NMRS. By requesting MPSID from NMRS, ICN edge nodes use multipath service nodes as relay nodes to construct multiple paths for data transmission. In addition, we propose a weighted cost data scheduling (WCDS) method to guarantee the performance of ICN multipath transmission. 2. The selection of multipath service nodes is the key to constructing multiple paths in ICN. We propose CMSNS, which takes link overlap and link state into account. To reduce the overhead of path link overlapping degree calculation, the independence between paths is guaranteed based on the independence between different autonomous systems. In addition, by measuring the path link state, the optimal multipath service node in the same autonomous system is selected to establish a sub-path. 3. We simulate the implementation of MPTS-ICN and other data transmission schemes based on the NS3 network simulator and compare differences in their performance in data flow completion time and throughput. In addition, we verify the impact of different node selection algorithms on our proposed multipath scheme. The rest of this paper is organized as follows: In Section 2, we review the multipath transmission schemes in IP networks and ICN networks and analyze their shortcomings. In Section 3, the architecture of MPTS-ICN is described and its advantages are analyzed. In Section 4, we study the selection algorithm of multipath service nodes. In Section 5, we simulate the performance verification of MPTS-ICN and the multipath service node selection algorithm, then analyze the results. In Section 6, we provide a summary and concluding remarks, and introduce future research work.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{_E. coli (1)_} & \multicolumn{3}{c}{_E. coli (2)_} & \multicolumn{3}{c}{_E. coli (3)_} & \multicolumn{3}{c}{_E. coli (4)_} \\ \hline Class of Inhibition & & & & & & & & & & & & & \\ Zone & A & B & C & A & B & C & A & B & C & A & B & C \\ \hline I (0–10 mm) & 10 & 10 & 10 & 11 & 11 & 11 & 10 & 12 & 11 & 25 & 26 & 34 \\ II (10.1–12 mm) & 4 & 13 & 15 & 7 & 11 & 12 & 3 & 8 & 13 & 10 & 16 & 9 \\ III (12.1–14 mm) & 17 & 11 & 13 & 17 & 19 & 16 & 18 & 13 & 14 & 11 & 5 & 6 \\ IV (14.1–18 mm) & 18 & 15 & 11 & 14 & 8 & 10 & 18 & 16 & 11 & 3 & 2 & 0 \\ \hline \multicolumn{11}{c}{_L. monocytogenes (1)_} & \multicolumn{3}{c}{_L. monocytogenes (2)_} & \multicolumn{3}{c}{_L. innocua_ (ATCC} & & & & & & \\ \multicolumn{11}{c}{33090 TM)} & & & & & & & & & & & \\ \hline I (0–10 mm) & 28 & 28 & 29 & 30 & 30 & 30 & 31 & 28 & 34 & & \\ II (10.1–12 mm) & 5 & 6 & 6 & 2 & 6 & 2 & 4 & 15 & 11 & & \\ III (12.1–14 mm) & 3 & 4 & 7 & 6 & 6 & 12 & 10 & 5 & 4 & & \\ IV (14.1–18 mm) & 13 & 11 & 7 & 11 & 7 & 5 & 4 & 1 & 0 & & \\ \hline \multicolumn{11}{c}{_Salmonella_ spp. (1)} & \multicolumn{3}{c}{_Salmonella_ spp. (2)} & & & & & & & & \\ \hline I (0–10 mm) & 9 & 10 & 10 & 8 & 9 & 10 & & & & & \\ II (10.1–12 mm) & 2 & 11 & 17 & 4 & 5 & 7 & & & & & \\ III (12.1–14 mm) & 10 & 11 & 11 & 8 & 16 & 13 & & & & \\ IV (14.1–18 mm) & 18 & 17 & 21 & 20 & 19 & 19 & & & & \\ \hline \multicolumn{11}{c}{_B. cereus (1)_} & \multicolumn{3}{c}{_B. cereus (2)_} & & & & & & & & \\ \hline I (0–10 mm) & 39 & 40 & 40 & 46 & 47 & 48 & & & & \\ II (10.1–12 mm) & 8 & 7 & 6 & 3 & 2 & 1 & & & & \\ III (12.1–14 mm) & 1 & 2 & 3 & 0 & 0 & 0 & & & & \\ IV (14.1–18 mm) & 1 & 0 & 0 & 0 & 0 & 0 & & & & \\ \hline \multicolumn{11}{c}{_Erwinia_ spp. (1)} & \multicolumn{3}{c}{_Erwinia_ spp. (2)} & & & & & \\ \hline I (0–10 mm) & 7 & 13 & 18 & 3 & 4 & 5 & 5 & 7 & 13 & \\ II (10.1–12 mm) & 12 & 19 & 17 & 8 & 20 & 22 & 4 & 21 & 23 & \\ III (12.1–14 mm) & 17 & 13 & 12 & 24 & 19 & 19 & 26 & 18 & 12 & \\ IV (14.1–18 mm) & 13 & 4 & 2 & 14 & 6 & 3 & 14 & 3 & 1 \\ \hline \end{tabular} A: cell-free supernatant (CFS) of LAB isolates without any treatment; B: cell-free supernatant (CFS) with pH neutralized to 6.0; C: cell-free supernatant (CFS) with pH neutralized to 6.0 and H\({}_{2}\)O\({}_{2}\) eliminated; I, II, III, IV: classes of inhibition zones (I: 0–10 mm, II: 10.1–12 mm, III: 12.1–14 mm and IV: 14.1–18 mm).  
\end{table} 
Table 3: Distribution of bacteriocin-producing LAB and _Bifidobacterium_, with respect to their class of inhibition zone against various pathogens. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Isolated LAB and Bifidobacteriuma**} & \multirow{2}{*}{**Control \({}^{*}\)**} & \multicolumn{6}{c}{_Listeria innocua (ATCC 33090 TM)_} & \multirow{2}{*}{**Control**} & \multicolumn{6}{c}{_E. coli 015/F47 (ATCC 69373)_} \\ \cline{3-11
## 2 Materials and Methods
### Neutron Reflectometry Measurements  
NR measurements were performed using a neutron reflectometer (BL16 SOFIA; Japan) with a vertical scattering geometry installed at the MLF in J-PARC [20; 21]. The wavelength range of the incident neutron beam was tuned to approximately 2.2-8.8 A by a disk chopper. The covered \(Q_{z}\) range was selected as 0.006 or 0.008-0.2 A\({}^{-1}\) (the incident angles were 0.3\({}^{\circ}\)/0.8\({}^{\circ}\)/2.0\({}^{\circ}\) or 0.4\({}^{\circ}\)/0.9\({}^{\circ}\)/2.0\({}^{\circ}\)) for air-liquid reflection measurements. On the other hand, the covered \(Q_{z}\) range was selected as 0.016-0.055 A\({}^{-1}\) (the incident angle was 0.6\({}^{\circ}\)) for liquid-liquid reflection measurements, because large NR data changes were predicted in the area of \(Q_{z}\) = 0.01-0.08 A\({}^{-1}\). A 20 mm \(\times\) 20 mm beam footprint was maintained on the  
Figure 1: Schematic diagram of the new sample cell for measuring NR. Photos of a new type of cell with the PTFE frame are shown in Figure S1.  
sample interface using double-slit collimation. The TOF neutron data were collected by a two-dimensional scintillation detector with spatial resolution. The \(\Delta Q_{z}/Q_{Z}\) resolutions were 5% for the air-liquid reflection measurements and 7% for the liquid-liquid reflection measurements, respectively. The transmitted neutron beam intensity was measured 1 mm above the liquid/liquid interface and the NR data were normalized using the transmitted neutron intensity data. All the measurements were taken at ambient temperature. The data reduction, normalization and subtraction procedures were performed using a program installed in the BL16 SOFIA system. Motofit software [22] was used to fit the NR profiles with a least-squares approach to minimize the deviation of the fit. The thickness (\(t\), A), SLD (\(\rho\)) and Gaussian roughness (\(\sigma\)) values were evaluated using Motofit. On the other hand, the NR data of a limited \(Q_{z}\)-range would have caused ambiguity in the analysis results. Therefore, in order to obtain the thickness of the SDS layer, the thickness and SLD of the SDS layer were constrained to the values optimized manually by referring to previous research works on the SDS adsorption layer structure (these parameters are shown in Tables S1 and S4) [14; 15; 16; 23].
## 4 Characteristics of Different Time Distribution Systems  
In the U.S. power grid, where each interconnection spans vast geographical areas, achieving accurate timing presents significant challenges. Time errors among devices  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Time Source** & **GPS** & **LEO** & **Pulsar** & **WWVB** \\ \hline Accuracy & Within 100 ns & 100 ns\(\sim\)1 μs & \(\sim\)1 μs & 1\(\sim\)15 ms \\ Flexbility & Flexible & Flexible & Not flexible (telescope is needed) & Flexible \\ Cost & Low & Low & High & Low \\ Reliability and security & Vulnerable & More resilient & More secure in terms of attacks & Vulnerable \\ Application & Most popular & Used in other sectors not in power system & Limited in lab experiment for now & Popular \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of four different time sources.  
can potentially be misinterpreted as power system disturbances [23]. To ensure safe grid operation, a reliable and accurate time distribution system is essential. Building on our understanding of time-synchronized applications and the specific clock sources that support them, the following section will examine the characteristics of various time distribution systems, assessing their effectiveness in delivering precise and resilient time signals across the power grid infrastructure.
## 2 Related Works
### [PERSON] et al.'s Method
#### 2.2.3 Extraction Process  
1. Divide the stego image into nonoverlapping blocks, each of which contains \(1\times 3\) pixels.
2. Reorder palette \(P\) to obtain a new palette \(P^{\prime}\) through the palette reordering process used in the embedding process.
3. According to the location map, consider an embedded block \(B\) with three pixels (\(B_{0},B_{1},B_{2}\)). Let \(I_{i}\) be the color index of \(B_{i}\) in \(P^{\prime}\), \(i=0,\ 1,\ 2\).
4. Calculate \(t_{0}\) and \(t_{1}\) using the following equations: \[t_{0}=I_{0}\bmod 2.\] (10) \[t_{1}=(I_{1}+I_{2})\bmod 4.\] (11)
5. Extract \(w\) according to Table 1, \(t_{0}\) and \(t_{1}\).
6. Go to Step 3 until all embedded blocks are processed.  
In [PERSON] et al. and [PERSON] et al.'s methods, for data extraction, the receiver requires the positions of embedded blocks. Thus, a location map with one bit for each block should be transmitted to the receiver; this will reduce the embedding capacity for secret data, and will be discussed in Section 4. To overcome this disadvantage, a novel BBM was proposed; it does not require a location map.
# Multiple-Mode Orthogonal Time Frequency Space with Index Modulation
## 4 Simulation Results and Discussion
Under the same spectral efficiency, the BER performance of the OTFS-DM-IM system was slightly better than that of the MM-OTFS-IM system when \(M\)-QAM modulation was used. This is because the noise produces strong interference on the transmitted signals at a low SNR region. Thus, the application of index modulation causes a high occurrence probability of index bits detection error during demodulation, resulting in the BER performance of OTFS-IM, OTFS-DM-IM, and MM-OTFS-IM systems being worse than that of the OTFS systems. Under the condition of a high SNR region, because all grid points in the MM-OTFS-IM system were activated, and a large number of index bits were transmitted implicitly through the combination of different modes of the constellation, the BER performance of the MM-OTFS-IM system was improved. Figure 5: BER performance comparison between the MM-OTFS-IM and the OTFS-IM systems under different SEs. Figure 8: BER performance comparison between the MM-OTFS-IM, OTFS, OTFS-IM, and the OTFS-DM-IM systems under SE with 5 bps/Hz. Figure 6: BER performance comparison between the MM-OTFS-IM, OTFS, the OTFS-IM, and the OTFS-DM-IM systems under SE with 3 bps/Hz. Figure 7: BER performance comparison between the MM-OTFS-IM, OTFS, OTFS-IM, and the OTFS-DM-IM systems under SE with 4 bps/Hz. Figure 9 shows the effect of different UE speeds on the BER performance of the proposed MM-OTFS-IM systems. In the figure, the SE of the MM-OTFS-IM system is 4 bps/Hz, and the speed of the UE is 120 km/h, 300 km/h and 500 km/h, respectively. As can be seen from the figure, under the condition of low SNR, the BER performance of the MM-OTFS-IM system was almost the same under different UE speeds, while the BER performance of the MM-OTFS-IM system decreased with the increase in the UE speeds at high SNRs. The reason is that the noise produced serious interference to the transmitted symbols for low SNRs, which resulted in a high error probability of index detection. At this time, the impact of noise on the system performance was far greater than that of the user equipment speed. For high SNRs, the influence of noise on the system performance was light, and the influence of the UE speed on the system performance gradually appeared. The increase in the UE speed led to the increase in the Doppler frequency offset, which aggravated the interference between transmitted symbols. That affected the index detection and symbol demodulation and reduced the BER performance of the system. Therefore, the increase in the UE speed led to the deterioration of the system BER performance.
## 3 Results and Discussion
### Effects of Axial Excitation Amplitude on Axial Vibration of the Feed System  
It is widely known that the axial vibration of the worktable is the key factor for the machining accuracy and stability of the feed system. For axial excitation amplitudes \(F_{a}\) = 1000 N, 2000 N and 3000 N, the amplitude-frequency curves of the axial vibration of worktable with the excitation amplitude variation are shown in Figure 9. The amplitude-frequency curves appear as softening type nonlinearity around the excitation frequency \(\omega\) = 500 rad/s. With the further increase of the excitation frequency, the vibration amplitude increases obviously, and the hardening type nonlinearity and jump phenomenon appear after the resonance frequency. In addition, the main resonance frequency moves to the high frequency region and the region width of main resonance increases with the increase of excitation amplitude.  
The 3-D frequency spectrum of the worktable vibration along the \(z\) direction for \(F_{a}\)= 3000 N is shown in Figure 10, and it can be observed that the different frequency components (\(3f\), \(5f\) et al.) appear in the low excitation frequency region. With the excitation frequency increasing, the amplitudes of frequency components \(f\) and \(3f\) increase and the jump phenomenon appears near the excitation frequency \(\omega\) = 1580 rad/s. To further analyze the nonlinear characteristics of the feed system, the time histories, spectrums, phase diagrams and Poincare sections are investigated for \(F_{a}\) = 1000 N, 2000 N and 3000 N, as shown in Figures 11-13. The vibration waves present periodicity; the frequency component \(3f\) appears at \(\omega\) = 500 rad/s and 1500 rad/s and disappears with the excitation frequency increasing. In addition, the phase diagrams and Poincare sections at \(\omega\) = 500 rad/s, 1500 rad/s and 2000 rad/s are a closed circle and single point, and these illustrate that the vibration responses of the feed system are periodic-1 motions.  
Figure 8: Acceleration comparison along \(z\)-axis between theoretical and experimental results.  
Figure 9: Amplitude-frequency curves of the feed system for different excitation amplitudes.  
Figure 11: Vibration responses at \(\omega=500\) rad/s: (**a**) Time history; (**b**) Spectrum; (**c**) Phase diagram and Poincaré section.  
Figure 12: Vibration responses at \(\omega=1500\) rad/s: (**a**) Time history; (**b**) Spectrum; (**c**) Phase diagram and Poincaré section.  
Figure 10: 3-D frequency spectrum of the feed system.
## 3 Shield Tunneling Anomaly Detection and Scenario Identification
### Anomaly Detection Model Based on Dynamic Threshold
#### 3.2.2 Dynamic Threshold Setting  
The setting of the threshold also impacts the results of anomaly detection, and it is essential to avoid setting an inappropriate threshold that may cause false negatives or false positives. In anomaly detection, thresholds are typically set as fixed values [34]. However, during the shield tunneling process, differences in data characteristics under different scenarios and local normal fluctuations in the time-series data can interfere with the threshold setting process. The method of using a constant threshold is simple and intuitive, but its lower flexibility leads to poorer performance in anomaly detection. Therefore, the model dynamically adjusts the threshold based on the reconstruction error values of the training data to ensure the reasonability of threshold setting. Assuming the reconstruction error of the training data with a length of \(N\) is denoted as \(error=[er_{1},er_{2},\ldots,er_{N}]\), \(er_{i}\) is the reconstruction error of the \(i\)-th sample in the training set, which is calculated as follows:  
\[er_{i}=(x_{i}-\hat{x}_{i})^{2} \tag{3}\]  
Considering that the fluctuating data and outliers in the training set may cause significant disturbances, using the maximum value of the reconstruction errors in the training set as the threshold directly is not precise enough. Therefore, the threshold \(\varepsilon\) is dynamically adjusted based on the mean and standard deviation of the reconstruction errors of the training set.  
\[\varepsilon=\upmu(error)+b\sigma(error) \tag{4}\]  
\(b\) is an ordered set of positive values, and the value is chosen to maximize Formula (5):  
\[\frac{\frac{\upmu(error)}{\upmu(error)}+\frac{\upalpha(error)}{\upsigma( error)}}{\left|error_{a}\right|}\] (5
# Active Tendon Control of Stay Cable by a Giant Magnetotrstive Actuator Considering Time-Delay
## 2 The State Equation of a Small Sag Cable with Axial Force  
The mechanical model of a small sag cable with axial control force is shown in Figure 1. In Figure 1, the left end of the cable is fixed and the right end of the cable is assumed to be a rolling bearing. \(H^{\mathrm{s}}\) is the static axial tension, \(U(t)\) is the control force provided by the GMA to the cable along the axial direction. In addition \(\sigma^{(s)}\) is the static axial stress. \(L\) is the length of the cable and \(\rho\) is the mass per volume of the cable. The separation of variables method is employed to obtain the in-plane vertical displacement of the cable as the following form,  
\[w(x,t)=\sum_{n}\phi_{i}(x)q_{i}(t) \tag{1}\]  
where \(\phi_{i}(x)\) is the \(i\)-th mode function and is assumed with a form of \(\phi_{i}(x)=\sin(i\pi x/L)\), \(q_{i}\) is the \(i\)-th modal displacement. Employing the Lagrange formulation and ignoring the second and higher order nonlinear terms, the governing equations of the transverse motion of the small-sag cable can be obtained as follows [21],  
\[+\left[ \begin{array}{cccc}\alpha_{1}&0&\cdots&0\\ 0&\alpha_{2}&\cdots&0\\ \vdots&\vdots&\cdots&\cdots\\ 0&0&\cdots&\alpha_{n}\end{array} \right]U(t)\left( \begin{array}{c}q_{1}\\ q_{2}\\ \vdots\\ q_{n}\end{array} \right)-\left[ \begin{array}{c}\beta_{1}\\ \beta_{2}\\ \vdots\\ \beta_{n}\end{array} \right]\ddot{U}(t)=\left[ \begin{array}{c}F_{1}(t)/m_{1 }\\ F_{2}(t)/m_{2}\\ \vdots\\ F_{n}(t)/m_{n}\end{array} \right] \tag{2}\]  
where \(\omega_{n}\) is the \(n\)-th undamped natural circular frequency of the cable, \(\beta_{n}\) and \(\alpha_{n}\) are the elements of coefficient matrixes, respectively. \(m_{i}\) is the modal mass of the \(i\)-th mode. \(F_{i}\) is the \(i\)-th generalized modal force acting on the small-sag cable. The aforementioned parameters can be expressed as follows,  
\[{\omega_{n}}^{2}=\frac{\sigma^{(s)}}{\rho}\left[1+\frac{2{\lambda}^{2}{\left[1 +{\left(-1\right)}^{n+1}\right]}^{2}}{n^{4}\pi^{4}}\right]{\left(\frac{n\pi}{ l}\right)}^{2} \tag{3}\]  
\[\beta_{n}=\frac{2{\left[1+{\left(-1\right)}^{n+1}\right]}L^{2}}{{\left(n\pi \right)}^{3}}\frac{mg\cos\theta}{{\left(\sigma^{(s)}A\right)}^{2}} \tag{4}\]  
\[\alpha_{n}=\left[\frac{1}{\rho A}{\left(\frac{n\pi}{L}\right)}^{2}\right] \tag{5}\]  
\[m_{i}=\frac{1}{2}\rho AL \tag{6}\]  
\[F_{i}=\int_{0}^{L}Y\phi_{i}dx \tag{7}\]  
where \(Y\) is the external excitation in \(y\) direction. \(\lambda^{2}\) is dimensionless parameter of geometric and elastic effects and expressed as follows:  
\[\lambda^{2}=\left[E/\sigma^{(s)}\right]\left[\rho SL\cos\theta/\sigma^{(s)} \right]^{2} \tag{8}\]  
where \(E\) is the modulus of elasticity of the stay cable and \(\theta\) is the inclination angle of the stay cable. Due to vibrations of the cable being multi-modal vibrations and low-order  
Figure 1: Mechanical model of cable vibration control system. modes being the primary vibrations, only the first three modes are considered in this paper.