esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Order Tracking Based on the Proposed GAD Method
### Rotational Speed Estimation and Order Tracking  
In this section, the rotating phase of the gravity acceleration component is first calculated from its analytic signal based on Hilbert transformation. The analytic signal of the gravity acceleration component can be obtained as follows:  
\[\overline{S}_{gc\_d}(t)=S_{gc\_d}(t)+j\hat{S}_{gc\_d}(t)=A(t)\bullet e^{j\varphi (t)} \tag{18}\]  
\[\hat{S}_{gc\_d}(t)=H\Big{\{}S_{gc\_d}(t)\Big{\}}=S_{gc\_d}(t)\otimes\frac{1}{ \pi t}=\int_{-\infty}^{+\infty}\frac{S_{gc\_d}(\tau)}{t-\tau}d\tau \tag{19}\]  
where \(\hat{S}_{gc\_d}(t)\) represents the signal after Hilbert transformation, and \(H[t]\) means the Hilbert operator. \(\overline{S}_{gc\_d}(t)\) denotes the obtained analytic signal. \(A(t)\) and \(\varphi(t)\) refer to the instantaneous amplitude and phase, which can be determined as follows [30]:  
\[A(t)=\sqrt{S^{2}_{gc\_d}(t)+\hat{S}_{gc\_d}^{2}(t)} \tag{20}\]  
\[\varphi(t)=\arctan\frac{\hat{S}_{gc\_d}(t)}{\hat{S}_{gc\_d}(t)} \tag{21}\]  
Based on Equations (22) and (23), the amplitude and phase of the gravity acceleration component are obtained. It can be seen from the estimation principle of rotational speed based on the decoupling of gravity variation that there is a one-to-one correspondence relationship between \(\theta\) and \(t\). Hence, the rotational angle and speed can be estimated further from \(\varphi(t)\). Since the instantaneous phase angle of \(\varphi(t)\) is in the interval \([-\pi,\pi]\), the real-time rotation angle \(\varphi^{\prime}(t)\) of the rotating disk or shaft relative to the initial moment can be obtained by using the unwrap method; thus, the obtained rotation angle is calculated, as illustrated in Figure 3.  
Suppose the obtained phase angle is noted as \(\theta=[\theta_{1},\,\theta_{2},\,\ldots,\theta_{N}]^{T}\), the instantaneous rotational speed can be calculated as follows:  
\[\omega(t)=\frac{\Delta\theta}{\Delta t}=[\Delta\theta_{1}\bullet f_{s}\;\Delta \theta_{2}\bullet f_{s}\;\Delta\theta_{3}\bullet f_{s}\;\ldots\;\Delta\theta _{N-1}\bullet f_{s}]^{T} \tag{22}\]  
\[\Delta\theta=[\Delta\theta_{1}\;\Delta\theta_{2}\;\Delta\theta_{3}\;\ldots\; \Delta\theta_{n-1}]^{T}=[\theta_{2}-\theta_{1}\;\theta_{3}-\theta_{2}\;\theta _{4}-\theta_{3}\;\ldots\;\theta_{N}-\theta_{N-1}]^{T} \tag{23}\]  
\[\Delta t=\frac{1}{f_{s}} \tag{24}\]  
Therefore, the rotational speed can be obtained based on the above methodology. It can be inferred that the resolution of the instantaneous angle in the proposed method is  
Figure 3: A schematic plot of the unwrapped phase angle of the gravity acceleration component.  
equal to the sampling frequency minus 1, which is much lower compared to the traditional encoder method.
## 3 Results
### Backscatter C-Scan to Map Heterogeneity  
To obtain a qualitative measure of the spatial heterogeneity, the DED sample was scanned using a 2-axis scanner and the same setup as the backscatter measurement. Compared to the backscatter measurement, the entire sample was scanned at a spatial resolution of 500 \(\upmu\)m in both scan and index directions. A backscatter C-Scan was developed by focusing at the mid-plane of the sample, and isolating the time-domain signal at the focus using a 2 \(\upmu\)s gate. The time-domain signals were further converted into frequency domain, and a sum of the absolute magnitude in the frequency domain was carried out. This will result in a single value at each pixel, and repeating this process over the entire scan area will  
Figure 6: The raw A-Scan data used for calculating the FOM are shown. The time domain response of (**a**) reference front wall (FW) echo, (**b**) backscatter region along D1 and (**c**) backscatter region along D3. The corresponding spectral response is shown in for (**d**) FW, (**e**) frequency domain response of spatial RMS magnitude (\(\Gamma_{rms}\)) along D1 and (**f**) \(\Gamma_{rms}\) along D3. D3 is the build direction.  
Figure 7: FOM as a function of frequency of the DED sample measured along (**a**) D1, (**b**) D2 and (**c**) D3 directions. D3 is the build direction.  
result in what is referred to as a backscatter C-Scan as shown in Figure 8. Since the main objective is to only capture the intra-sample heterogeneity, the amplitudes were normalized and plotted in dB scale as shown in the figure. It is interesting to note that the heterogeneity in D1 as shown in Figure 8a only shows a \(-5\) dB variation in spatial amplitude, but D2 and D3 as shown in Figure 8b,c show up to \(-10\) dB in spatial variability.
## 2 Materials and Methods
### Model Description
#### 2.2.5 Power and Energy Efficiencies  
The power (\(P_{ext}\)) extracted by the external load of a resistor was the product of the external voltage and current:  
\[P_{ext}=\it{U_{ext}I} \tag{22}\]  
The power extracted by the electrical circuit of MFC (\(\rm{P_{cell}}\)), or termed as an MFC cell, was the product of the electromotive force and current:  
\[\rm{P_{cell}}=\it{E_{emf}I} \tag{23}\]  
The power lost through the polarization of the activation overpotentials of anode and cathode, the contact resistance, and the medium resistance, was the power emitted in a form of heat:  
\[\rm{P_{heat}}=(U_{c}+U_{m}+\eta_{act,\ A}-\eta_{act,C})I \tag{24}\]The rate of the substrate (acetate) loss through MFC cell (r\({}_{\rm e}\)) could be calculated as follows:  
\[{\rm r_{e}=MP_{cell}/(V\Delta H_{c})} \tag{25}\]  
where \(\Delta H_{c}\) was the heat of combustion of acetic acid, and M was the relative molecular weight of acetic acid.  
Besides the energy loss by heat generation in MFC, the chemical energy of substrate was also lost by providing energy for the growth of the suspended and attached bacteria. The corresponding powers for the suspended (P\({}_{\rm u}\)) and attached (P\({}_{\rm w}\)) bacteria could be calculated as follows:  
\[{\rm P_{u}=\gamma^{-1}\Delta H_{c}u\mu_{u}V/M} \tag{26}\]  
\[{\rm P_{w}=\gamma^{-1}\Delta H_{c}\delta w\mu_{w}V/M} \tag{27}\]  
Although some studies [13] suggested that the energy efficiency was further reduced by the more positive potential (close to 0 V, NHE) of redox intermediates than that of the substrate, this energy loss was not considered because our experiment achieved anode potentials close to that of the substrate. Therefore, the MFC cell energy efficiency (\(\eta_{cell}\)) and overall energy efficiency (\(\eta_{overall}\)) were defined as follows:  
\[\eta_{cell}=P_{ext}/P_{cell} \tag{28}\]  
\[\eta_{overall}=P_{ext}/(P_{cell}+P_{u}+P_{w}) \tag{29}\]  
Please note that in batch mode, the overall energy efficiency was calculated differently:  
\[\eta_{overall}=\int_{0}^{t}P_{ext}dt/(\Delta H_{c}\Delta SV/M) \tag{30}\]
# Prediction of Post-Intubation Tachycardia Using Machine-Learning Models
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Feature Set** & **ANN** & **LR** & **NB** & **RF** & **SVM** & **XGB** \\ \hline All feature & & & & & & \\ Accuracy & 59.9 & 72.0 & 51.9 & 69.1 & 57.6 & 67.1 \\ Precision(0/1) & 59.4/60.7 & 67.7/71.4 & 51.7/52.5 & 65.0/72.2 & 57.7/55.7 & 66.7/66.2 \\ Recall(0/1) & 61.2/63.8 & 72.2/72.3 & 51.8/51.4 & 72.7/66.9 & 57.0/58.4 & 66.2/72.1 \\ F1 score(0/1) & 58.6/56.9 & 68.4/68.9 & 50.2/52.4 & 67.9/67.1 & 57.7/55.7 & 66.6/64.2 \\ AUC & 0.65 & 0.80 & 0.56 & 0.72 & 0.62 & 0.71 \\ \hline RFE-based feature & & & & & & \\ Accuracy & 69.6 & 71.8 & 70.3 & **74.2** & 71.8 & 71.5 \\ Precision(0/1) & 69.3/70.1 & 71.0/70.1 & 68.2/70.7 & **73.0/72.8** & 72.3/72.7 & 71.2/70.4 \\ Recall(0/1) & 67.7/75.8 & 69.3/75.9 & 73.4/71.8 & **71.8/78.3** & 73.1/75.7 & 68.2/77.0 \\ F1 score(0/1) & 69.2/67.8 & 69.7/69.1 & 68.5/66.6 & **72.2/72.2** & 69.6/68.8 & 68.9/69.6 \\ AUC & 0.79 & 0.79 & 0.79 & **0.79** & 0.81 & 0.78 \\ \hline GI-based feature & & & & & & \\ Accuracy & 70.1 & 71.0 & 69.3 & 71.1 & 68.8 & **73.5** \\ Precision(0/1) & 68.7/68.7 & 66.5/69.1 & 65.7/70.3 & 69.2/70.6 & 65.6/66.8 & **70.9/74.9** \\ Recall(0/1) & 68.4/72.3 & 66.0/74.4 & 71.6/70.2 & 72.4/72.9 & 65.1/72.8 & **76.5/73.6** \\ F1 score(0/1) & 66.7/67.0 & 68.4/69.1 & 68.2/66.2 & 68.3/67.7 & 67.2/66.4 & **70.9/70.1** \\ AUC & 0.76 & 0.78 & 0.76 & 0.77 & 0.74 & **0.79** \\ \hline UST-based feature & & & & & & \\ Accuracy & 71.6 & 72.1 & 66.4 & 73.2 & 66.1 & 70.8 \\ Precision(0/1) & 68.7/73.0 & 69.2/70.3 & 60.2/81.1 & 70.9/72.7 & 63.6/68.5 & 69.7/70.9 \\ Recall(0/1) & 72.9/72.3 & 70.3/73.2 & 87.7/45.0 & 74.1/74.1 & 71.3/63.9 & 73.4/69.5 \\ F1 score(0/1) & 68.7/68.8 & 68.2/70.4 & 71.3/60.1 & 70.3/69.5 & 66.4/61.0 & 70.5/67.2 \\ AUC & 0.80 & 0.79 & 0.78 & 0.79 & 0.74 & 0.79 \\ \hline \hline \multicolumn{2}{l}{\(P\)-based feature} & & & & & \\ Accuracy & 74.7 & 77.0 & **71.3** & 68.8 & 75.0 & 68.8 \\ Precision(0/1) & 72.3/73.8 & 74.3/75.3 & **66.4/75.2** & 64.6/67.5 & 72.3/75.5 & 67.4/68.5 \\ Recall(0/1) & 75.8/76.7 & 75.9/79.1 & **78.7/66.3** & 70.9/64.0 & 77.0/76.7 & 72.7/67.1 \\ F1 score(0/1) & 72.5/72.1 & 73.8/74.6 & **71.9/66.3** & 67.7/66.5 & 72.7/72.7 & 67.7/65.3 \\ AUC & 0.83 & 0.84 & **0.80** & 0.76 & 0.84 & 0.74 \\ \hline \hline \multicolumn{2}{l}{Hand-crafted feature} & & & & \\ Accuracy & **79.8** & **80.5** & 70.3 & 69.8 & **80.5** & 68.1 \\ Precision(0/1) & **78.0/79.2** & **79.8/79.9** & 73.2/68.2 & 66.1/70.7 & **79.8/80.0** & 67.7/65.2 \\ Recall(0/1) & **75.0/85.1** & **76.5/85.1** & 66.9/77.7 & 75.3/64.1 & **77.7/84.7** & 66.2/70.7 \\ F1 score(0/1) & **78.6/79.4** & **78.4/79.9** & 66.8/68.6 & 68.6/64.9 & **79.0/79.6** & 70.1/66.1 \\ AUC & 0.84 & **0.85** & 0.81 & 0.74 & **0.86** & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of machine learning models with different feature sets, where each precision, recall, and F1 score is a pair of two results for non-tachycardia(0) and tachycardia(1).
###### Abstract  
Background: Perimplantitis is a disease linked to oral virulent bacteria such as _P. gingivalis_ that grow in dental implants surrounding tissues and between implants and abutments. Antimicrobial gels previously described to fill these sites lose their effectiveness and resorb over time. Objective: Characterization of biophysical and antimicrobial properties of an original hydrogel, Alginate-Catechol (Alg-Cat), combined to D-Cateslytin (D-CTL). Methods: Gelation kinetics, frequency and strain sweep measurements were performed by rheology. Antibacterial activity of the gels was tested against _P. gingivalis_, and the MIC was determined. Peptides released from the gels were purified by HPLC and characterized by MALDI-TOF mass spectrometry. The behavior of bacteria in contact with the gel was observed using optical and electronic microscopy (SEM and TEM). Results: Gelation was fast and was achieved in 2 min with a storage modulus between 25 and 30 Pa. The gels were stable under strain and showed an adhesive potential reinforced with aging at 18 h (5.4 kPa) under a slow retraction speed (4 \(\cdot\) m\({}^{-2}\) at 10 \(\upmu\)m/s) with a mixed rupture profile (adhesive/cohesive). The MIC of D-CTL inside the Alg-Cat gel against _P. gingivalis_ was equal to 470 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) after 24 h. Peptides recovered in the supernatant and inside the gel were fragmented, most of them conserving the \(\alpha\)-helix active site. No bacteria were visualized at the surface and inside the gel after 24 h. This gel is promising for clinical application for the prevention of perimplantitis.  
hydrogel; AMP; Alginate-Catechol; perimplantitis; _P. gingivalis_; rheology; gelation kinetic; D-cateslytin; chromogranin A; drug release
## 5 Performance Evaluation
### Performance Analysis
#### 5.3.1 Impact of Numerology (\(\mu\))  
Recall that, in the sub 6 GHz frequency band, NR V2X supports three different numerologies. One numerology is different from another by sub-carrier spacing, sub-channel availability, and resource selection window length. Figure 4 shows the impact of different numerologies in terms of CDF statistics of average PIR, PRR, and throughput under default simulation settings (Table 2). Recall that NR V2X supports three different numerologies: (1) \(\mu=0\) (SCS = 15 KHz); (2) \(\mu=1\) (SCS = 30 KHz); and (3) \(\mu=2\) (SCS = 60 KHz). Increasing numerology has a counterproductive effect in terms of the availability of sub-channels and selection window length. Increasing numerology (\(\mu\) is higher) reduces the number of available sub-channels (analogous to reducing resource blocks) but also reduces the selection window size. Having a higher number of sub-channels yields less contention among the transmitting vehicles. On the other hand, a smaller selection window size reduces the probability of overlapping window selection among the transmission vehicles. As a result, from Figure 4, we see that there is an almost identical performance between \(\mu=0\) and \(\mu=1\). However, for \(\mu=2\), the selection window length reduces to 8 ms (in comparison to 32 ms for \(\mu=0\)), which supersedes the negative impact of having a lower number of sub-channel availability. Hence, \(\mu=2\) has a better performance in terms of achieving a lower PIR and higher PRR and throughput.
###### Abstract
The micro-focusing frontier macromolecular crystallography (FMX) beamline at the National Synchrotron Light Source II (NSLS-II) implements three feedback loops for beam position correction [3]. Some automatic tasks have been considered at the X-ray absorption spectrum (XAS) beamline of the Karlsruhe Institute of Technology (KIT) synchrotron [14]. The macromolecular crystallography beamline (MX2) at the Australian Synchrotron uses a neodymium-doped yttrium aluminum garnet (YAG) crystal and a CCD camera to record the beam and move the focusing mirrors as feedback [15]. Although these feedback systems are widely used, few implementation details, such as the controlled devices, algorithms and feedback results, have been provided. In this paper, we present a fully automatic online feedback system for X-ray flux optimization before and during experiments in the SSRF BL08U1A beamline. The beamline provides photon beams ranging from 200 eV to 2000 eV, with energy resolutions of 16,000 at 244 eV and 12,700 at 401 eV when the Slit 2 size is set to 50 \(\upmu\)m (horizontal) and 20 \(\upmu\)m (vertical), respectively. The photon flux downstream to the exit slit is about 5 \(\times\) 10\({}^{11}\) phs/s at 244 eV and E/\(\Delta\)E = 6440, with a Slit 2 size of 50\(\upmu\)m (horizontal) and 50 \(\upmu\)m (vertical). The decrease in photon flux might be attributed to the photon energy adjustment, the EPU gap variation, and the mirror deformation that originates from the heterogeneity of the heating load distribution. In XAS experiments and long-time imaging experiments, beamline operators have to adjust the mirror positions and the exit slit size manually and frequently in order to obtain a higher X-ray flux, which favors improvements in the experimental quality. Based on this feedback system, the position of the elliptical cylindrical mirror can be adjusted automatically in the horizontal direction by simultaneously calculating the feedback signal of the blade-edge signals on the exit slit, thus contributing to maintaining the stability of photon flux. The hardware composition, system implementation, feedback logic, function and software design, system optimization and commission, as well as the online experimental results supported by the system, will be described in detail. The results of the experiments on the XAS conducted at the BL08U1A beamline of the SSRF show that the system meets the demand of the stable and automatic light flux adjustment.
# Deep Neural Network Equalization for Optical Short Reach Communication
## 2 Principles of Nonlinear Equalizer  
The presented BtB setup focuses on component nonlinearities, while the compensation of fiber nonlinearities has been shown before [7]. For this paper, DNN structures have been implemented from scratch with Matlab, not using any machine learning (ML) toolbox. Being trained on the same data as (MP-)VNLEs, DNNs learn to capture nonlinear effects of O/E components and fiber, as well as their memory characteristics.
#### Article
###### Abstract  
Precision and ultra-precision machining technology rely mainly on the machine tools' accuracy. To improve it, the measurement, calculation, prediction and control of geometric errors are critical. The traditional measurement methods have lower precision because of ignoring small angle errors. To obtain complete geometric errors of multi-axis machine tools, this paper proposes a new method of coupling and decoupling measurement. Specifically, we used a laser interferometer and dial indicators to measure 36 items of complete geometric errors of multi-axis machine tools. A homogeneous transformation matrix (HTM) was applied to model the error transfer route. The transfer law of complete errors for each machining point was explored and derived. Furthermore, we selected and calculated integrated errors of 36 machining points. Finally, we proved the correctness of the method by comparing the measurement result of a ball bar test and coupling and decoupling measurement of geometric errors. We found that items of small geometric angle errors have a greater impact on machining accuracy than those of geometric displacement errors. Complete geometric errors measured via the coupling and decoupling measurement method can evaluate integrated errors more precisely and comprehensively.  
Keywords:complete geometric errors; coupling and decoupling; homogeneous transformation matrix +
Footnote †: journal: Applied Sciences
# Development of a Spatial Heterodyne Terahertz Raman Spectrometer with Echelle Gratings
## 2 Principles
### Calibration Theory  
During actual experimental operation, system errors and certain external error factors mean that the actual spectral resolution and the spectral detection range of the system differ from the theory. Therefore, it is necessary to use a calibration light source to calibrate the system. Unlike the traditional grating spatial heterodyne terahertz Raman spectrometer, the echelle grating has multi-level diffraction characteristics, and each level has its own shining angle; therefore, when calibrating the system, the different levels must be calibrated separately. In this work, we selected a standard mercury lamp as the light source. Based on the known characteristic wavelength and the number of corresponding fringes, the Littrow wavelength of the instrument can be obtained as:  
\[\lambda_{L,j}=\frac{f_{2}-f_{1}}{(f_{2}/\lambda_{1})-(f_{1}/\lambda_{2})},j=m,m-1,m-2\cdots,j\geq 1 \tag{11}\]  
where \(\lambda_{L,j}\) are the corresponding Littrow wavelengths of the different levels, \(\lambda_{1}\) and \(\lambda_{2}\) are the characteristic wavelengths of the known calibration light source, and \(f_{1}\) and \(f_{2}\) are the corresponding fringe numbers of these two known wavelengths. Accordingly, the Littrow angle can be determined using:  
\[\theta_{L}=arcsin\bigg{(}\frac{j\lambda_{L,j}}{2d}\bigg{)},j=m,m-1,m-2\cdots,j\geq 1 \tag{12}\]  
In this case, the system resolution can be expressed as:  
\[\delta\sigma=\frac{1/\lambda_{1}-1/\lambda_{2}}{f_{1}-f_{2}} \tag{13}\]  
The spectral detection range of the system is expressed as:  
\[\Delta\sigma_{M}=M\cdot\Delta\sigma=\frac{MN}{8 Wsin\theta_{L}} \tag{14}\]  
The formula used to calculate the signal-to-noise ratio (SNR) of the instrument system is as follows:  
\[SNR=\sqrt{\frac{\eta A\Omega}{2N}l\delta\sigma T} \tag{15}\]  
Among these parameters, \(\eta\) is the optical efficiency, \(A\) is the effective system area, \(\Omega\) is the field of view of the system, and \(l\) is the photon intensity per unit area per unit wavelength per unit angle, and is proportional to the laser power. \(T\) is the total integration time.
# Fracture Toughness Determination on an SCB Specim by Meshless Methods
## 2 Overview on Meshless Methods
In relation to the nodal connectivity in NNRPIM, it is made by a set of nodes in the neighborhood of an interest point \(\mathbf{x}_{i}\in\mathbf{X}\), the NNRPIM influence-cell notion contributes to forming the node connectivity. It contributes to naturally controlling the influence domain associated with \(\mathbf{x}_{i}\). Meanwhile, only the determination of the 2D influence-cell is given, although this concept is applicable to \(n\)-dimensional space. In general, there are two types of influence-cells: the first-degree influence-cell and the second-degree influence-cell. The former one is composed of the first natural neighbors of \(\mathbf{x}_{i}\) and the latter one includes the natural neighbors of the nodes belonging to the first-degree influence-cell of node \(\mathbf{x}_{i}\) in addition to the first natural neighbors of \(\mathbf{x}_{i}\). This concept is well described in detail in [23]. The second-degree influence-cell enforces higher nodal connectivity when compared with the first-degree influence-cell. The literature [23] states that regardless of the studied phenomenon, higher degree influence-cells permit to achieve more accurate solutions. The standard FEM, RPIM and NNRPIM formulations are comprehensively demonstrated in [25] leading to a linear equations system exhibited as \(\mathbf{Ku}=\mathbf{f}\). In which, \(\mathbf{K}\) stands as the stiffness matrix, \(\mathbf{f}\) is the force vector and \(\mathbf{u}\) is the displacement field. In accordance with [PERSON]'s law, it is feasible to correlate a relationship between the strain and stress fields as,  
\[\mathbf{\sigma}=\mathbf{C}\mathbf{\varepsilon}\Leftrightarrow\left\{ \begin{array}{c} \sigma_{xx}\\ \sigma_{yy}\\ \tau_{xy}\end{array} \right\}=\frac{E}{(1+\
u)(1-\
u)}\left[ \begin{array}{ ccc}1&\
u&0\\ \
u&1&0\\ 0&0&\frac{1-\
u}{2}\end{array} \right]\left\{ \begin{array}{c}\varepsilon_{xx }\\ \varepsilon_{yy}\\ \tau_{xy}\end{array} \right\}. \tag{2}\]  
In this study, all integration cells are quadrilateral comprising of around 9 nodes and \(n_{Q}\times n_{Q}\) integration points inside, respecting the Gauss-Legendre quadrature scheme. The literature [23] suggests that this integration scheme aggrandizes the efficiency if \(n_{Q}=3\).
# An Efficient Library for Reliability Block Diagram Evaluation
## 2 Reliability Block Diagrams
### Basic Blocks  
An RBD is built by drawing success paths between blocks composing the system. In order to correctly model an RBD, the following basic blocks are defined:  
* **Singleton**. This block is the simplest one and it is composed by a single component. The block state is equal to _success_ if and only if the component is in _success_ state. An example is a stand-alone Power Supply.
* **Series**. This block is composed by \(N\) components. The block state is equal to _success_ if and only if all the components are in _success_ state. An example is a 2-out-of-2 computing system (2 oo2).
* **Parallel**. This block is composed by \(N\) components. The block state is equal to _success_ if and only if at least one component is in _success_ state, or, in other terms, the block state is equal to _failure_ if and only if all the components are in the _failure_ state. An example is a redundant Power Supply system with current sharing.
* **K-out-of-N (KooN)**. This block is composed by \(N\) components. The block state is equal to _success_ if and only if at least \(K\) components out of \(N\) are in _success_ state. An example is a 2-out-of-3 computing system (2 oo3).
* **Bridge**. This block is composed by 5 components arranged as shown in Figure 3. The block state is equal to _success_ if at least one of the four following conditions is satisfied: 1. Components \(A\) and \(B\) are correctly operating. 2. Components \(C\) and \(D\) are correctly operating. 3. Components \(A\), \(E\) and \(D\) are correctly operating. 4. Components \(C\), \(E\) and \(B\) are correctly operating. An example is a network infrastructure.