esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 4 Data Analysis
### Particular Cases  
ADCs of the _selection_ with outstanding efficiency under radiation are deeply analyzed below.  
AD7984: This COTS is a SAR ADC with the best FOM\({}_{W}\) of the _selection_ (122 fJ/level). It offers 16 ENOB sampling at 1.33 MS/s. No TID data were found in the literature, but its good electrical performance gives to this ADC a high FOM\({}_{SET}\) (\(9.6\times 10^{4}\) levels/mg). It also has a high SEL LET\({}_{th}\) (106.2 MeV\(\cdot\)cm\({}^{2}\)/mg).  
ADC128S102 QML-SP: This qualified SAR ADC is implemented with a 0.35 \(\upmu\)m CMOS process. Its 2.7 mW reduced power consumption together with its 11.7 ENOB at 1 MS/s give it a FOM\({}_{W}\) of 0.8 pJ/level. Its good FOM\({}_{W}\) combined with its performance under radiation give it the best FOM\({}_{SET}\) for SEU (2.7 \(\times\) 10\({}^{4}\) levels/mg). It also has a high FOM\({}_{TID}\) (1.2 \(\times\) 10\({}^{9}\) levels/mg for both LDR and HDR) and SEL LET\({}_{th}\) (121.8 MeV\(\cdot\)cm\({}^{2}\)/mg).  
Boeing-1: This pipelined ADC is implemented with a 32 nm CMOS SOI process. Its high 200 MS/s sampling rate together with its 7.4 ENOB and 39 mW power consumption give it a FOM\({}_{W}\) of 1.1 pJ/level. It performs exceptionally good under radiation (mainly due to its calibration features and the technology used for its implementation) which, combined with a good FOM\({}_{W}\), results in the best FOM\({}_{TID}\) and FOM\({}_{SET}\) within the _selection_ (\(8.8\times 10^{9}\) and \(3.37\times 10^{7}\) levels/mg, respectively).  
It also offers the best SEL LET\({}_{th}\) (170 MeV\(\cdot\)cm\({}^{2}\)/mg).
## 3 Object System by Using HS Theory
### HS Searching Procedure
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**Month**} \\ \cline{3-13}  & & **Jan** & **Feb** & **Mar** & **Apr** & **May** & **Jun** & **Jul** & **Aug** & **Sep** & **Oct** & **Nov** & **Dec** \\ \hline \multirow{3}{*}{Sanya (TZ)} & Ergodic & 47.2 & 37.7 & 21.8 & 3.6 & \(-11.7\) & \(-18.1\) & \(-15.2\) & 3.1 & 15.1 & 32.9 & 45.0 & 49.9 \\  & PSO & 47.45 & 37.17 & 22.23 & 3.74 & \(-11.88\) & \(-18.14\) & \(-14.67\) & 3.28 & 15.60 & 32.66 & 45.25 & 49.78 \\  & HS & 47.24 & 37.73 & 21.90 & 3.67 & \(-11.76\) & \(-18.12\) & \(-15.22\) & 3.15 & 15.11 & 32.95 & 45.08 & 49.86 \\ \hline \multirow{3}{*}{Shanghai (SZ)} & Ergodic & 59.0 & 50.1 & 34.9 & 16.6 & 5.3 & \(-7.6\) & 3.8 & 11.1 & 28.3 & 45.6 & 57.0 & 61.4 \\  & PSO & 58.19 & 49.17 & 33.89 & 15.22 & 5.49 & \(-8.32\) & 4.02 & 11.35 & 28.17 & 44.33 & 56.26 & 60.51 \\  & HS & 58.99 & 50.18 & 35.00 & 16.56 & 5.33 & \(-7.58\) & 3.81 & 11.07 & 28.26 & 45.66 & 56.96 & 61.40 \\ \hline \multirow{3}{*}{Zhengzhou (WT2)} & Ergodic & 61.9 & 53.2 & 38.2 & 19.8 & 8.0 & 5.5 & 6.4 & 14.2 & 31.6 & 48.8 & 59.9 & 64.3 \\  & PSO & 61.95 & 53.06 & 38.36 & 19.37 & 7.74 & 5.45 & 6.31 & 13.89 & 31.52 & 48.80 & 59.83 & 64.19 \\  & HS & 61.93 & 53.20 & 38.29 & 19.80 & 8.01 & 5.52 & 6.42 & 14.3 & 31.58 & 48.85 & 59.88 & 64.28 \\ \hline \multirow{3}{*}{Harbin (MTZ)} & Ergodic & 71.5 & 63.5 & 49.1 & 30.8 & 16.5 & 12.6 & 14.0 & 24.7 & 42.6 & 59.3 & 69.7 & 73.7 \\  & PSO & 71.59 & 63.46 & 49.83 & 30.67 & 16.15 & 12.82 & 13.96 & 23.67 & 42.69 & 59.14 & 69.41 & 73.58 \\  & HS & 71.52 & 63.65 & 49.23 & 30.49 & 16.56 & 12.62 & 13.96 & 24.73 & 42.58 & 59.29 & 69.47 & 73.68 \\ \hline \multirow{3}{*}{Mohe (CTZ)} & Ergodic & 77.9 & 70.5 & 56.8 & 38.7 & 23.0 & 16.6 & 19.2 & 32.7 & 50.3 & 66.5 & 76.3 & 80.0 \\  & PSO & 77.69 & 70.33 & 56.90 & 38.47 & 22.12 & 16.57 & 19.20 & 31.20 & 50.60 & 66.30 & 76.48 & 80.04 \\  & HS & 77.90 & 70.51 & 56.86 & 38.57 & 23.06 & 16.59 & 19.16 & 32.76 & 50.33 & 66.47 & 76.34 & 80.00 \\ \hline \multirow{3}{*}{Lhasa (TPZ)} & Ergodic & 57.5 & 48.6 & 33.3 & 14.9 & 3.8 & \(-8.9\) & \(-5.7\) & 9.5 & 26.5 & 44.0 & 55.4 & 59.9 \\  & PSO & 57.56 & 48.73 & 33.14 & 14.97 & 3.58 & \(-8.62\) & \(-5.64\) & 8.99 & 26.48 & 44.18 & 55.31 & 59.98 \\ \cline{1-1}  & HS & 57.46 & 48.54 & 33.28 & 14.92 & 3.88 & \(-8.91\) & \(-5.69\) & 9.55 & 26.53 & 43.99 & 55.44 & 59.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimum tilt angles of each station obtained by ergodic, particle swarm optimization (PSO), and harmony search (HS) methods. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multicolumn{2}{c}{**Ergodic Method**} & \multicolumn{2}{c}{**PSO Method**} & \multicolumn{2}{c}{**HS Method**} & \multirow{2}{*}{\(\beta_{\text{opt}}=\Phi\)} & \multirow{2}{*}{\(\beta_{\text{opt}}=\Phi+15^{\circ}\)} & \multirow{2}{*}{\(\beta_{\text{opt}}=\Phi-15^{\circ}\)} \\ \cline{2-2}
# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 4 Experiments
### Computational Complexity and Cost  
To validate the complexity and computational costs of MT-IRN, we compared its number of parameters and computation time with other methods, as shown in Table 9. It can be observed that MT-IRN has 35.30 M parameters, which is higher than that of other methods. This is primarily because we employed a pre-trained ResNet-50, which enhances the model's feature extraction capability but also introduces more parameters. The average processing time of MT-IRN is 0.31 s, which is competitive compared to other methods and lower than the GAN-based Hall-IQA. In summary, MT-IRN demonstrates competitive performance in terms of complexity and computation time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**LIVE**} & \multicolumn{2}{c}{**CSIQ**} & \multicolumn{2}{c}{**TID2013**} \\  & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline Distorted Images & 27.499 & 0.715 & 27.433 & 0.770 & 26.864 & 0.773 \\ Single-Task & & & & & & \\ Pseudo-Reference & 28.707 & 0.732 & 29.238 & 0.788 & 27.992 & 0.816 \\ Multitask & & & & & & \\ Pseudo-Reference & **29.085** & **0.751** & **29.607** & **0.800** & **29.009** & **0.827** \\ Images & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average PSNR and SSIM of pseudo-reference images and distorted images. The top results are shown in bold font.
# Lithium Metal: The Key to Green Transportation
## 4 Discussion
### Lithium and Electric Vehicles Market  
Recently, lithium supply has become an expanding concern owing to e-mobility obtaining a choice of internal combustion engine e-vehicles. Due to their remarkable energy storage capacity [15; 40; 93], Li-ion batteries have been critical contributors to e-mobility in recent decades. For example, driving 60 km in an e-vehicle requires only 1.4 to 3.0 kg of lithium [44]. Its consumption has also seen a significant increase due to the high demand for rechargeable lithium-ion batteries that are extensively used in electric vehicles, portable electronic devices, and other electric tools [41].  
By considering the sources and market for lithium, EV batteries consume 65% of global lithium production. While other products require lithium metal, such as pharmaceutical tools, enamel, glass, ceramics, and lubricating grease [40; 44], the global lithium demand was intended to rise to 77,200 tons [44] in 2019. An estimation shows that the consumer demand will continue to rise up to 188,000 tons by 2027 [44] and about 78% of the global supply is only from Australia and Chile, which makes it a critical metal [41; 44; 94]. Taking an example of China, the price of lithium carbonate reached approximately 170,000 rmb/ per ton at the end of 2017, then it underwent a relatively slow decline to 41,000 rmb at the end of 2020 due to the COVID-19 pandemic that partially suspended industrial activities. Surprisingly, the price resurges from 42,000 rmb in January 2021 to 497,000 rmb per ton in March 2022. The increase in price surged by over 265% due to the unprecedented demand for lithium batteries for electronics and EVs [4]. The global electric vehicle market increased sales in 2021 and is expected to double sales and delivery in China during 2022, with an estimation of over 5 million sales and 9.5 million units worldwide [95]. This should come with a secure lithium long-term supply channel to avoid increasing mineral scarcity and environmental concerns. Since 2010, when EV ideology influenced the vehicle market, the global total of electric vehicles sold account for more than 17.6 million in 2021. Statistics for 2021 show that among global sold passenger vehicles, 8.3% are electric vehicles [69; 96].  
According to canalysis [68], the global electric vehicle market estimates that 6.5 million electric vehicles were sold worldwide in 2021. It increased by 109% compared to 2020 sales. Those electric vehicles include fully electric and plug-in hybrid passenger cars. However, the global market itself did not grow much, only growing 4% due to the consequences of COVID-19 travel restrictions and chip shortages. Currently, China is the global leading country in consuming EVs, with 50% of all global sold EVs (Figure 12) and is also the largest manufacturer of LIB [71]. Europe comes second with 35%, and together with China make up 85% of total EV global sales, which accounts for 15% of all new car sales.
## 3 Photon and Phonon Tunneling through a Vacuum Gap  
### Nanoscale Radiative Heat Transfer  
Consider heat transfer due to thermal radiation between parallel plates separated from each other by a distance \(d\). In the case of a large distance \(d\gg\lambda_{T}\), the interfacial heat flux due to radiative heat transfer in the far-field is limited by [PERSON]'s law of black-body radiation \(\left(T_{1}^{4}-T_{2}^{4}\right)\left(\pi^{2}k_{B}^{4}/60c^{2}\hbar^{3}\right)\), where \(T_{1}\) and \(T_{2}\) are the temperatures of the bodies [36]. Thus, the interfacial thermal conductance associated with radiative heat transfer in the far-field does not exceed the value determined by the black-body radiation limit represented by Equation (5) [36; 37; 38; 39; 40]:  
\[G_{bb}=\frac{\pi^{2}k_{B}^{4}T^{3}}{15c^{2}\hbar^{3}}, \tag{5}\]  
where \(G_{bb}\) is about 6 W/m\({}^{2}\)K at \(T=300\) K. However, heat transfer due to thermal radiation can be increased by many orders of magnitude when the distance \(d\) becomes smaller than the characteristic wavelength \(\lambda_{T}=c\hbar/k_{B}T\). In this case, evanescent electromagnetic waves existing near interfaces can be transmitted between closely spaced bodies. This phenomenon, known as radiation (photon) tunneling, significantly affects the heat transfer between two bodies separated by a nanoscale gap.
# Conversion between Logic and Algebraic Expressions of Boolean Control Networks
## 3 Conversion of Expressions of Boolean Functions
### From Algebraic Expression to Logic Expression  
After studying a logical dynamic system in the algebraic state space, it is necessary to convert the algebraic expression back to the logic expression in order to design digital circuits to implement the desired control functions (see, e.g., [3,5]). For a Boolean function, it is claimed in [16] that 'converting an algebraic form back to logical form is not an easy job', and a'mechanical procedure' is provided for converting the algebraic expression back to the logic expression as below.  
**Lemma 5** ([16]).: _If Boolean function (1) has an algebraic expression as (3), then_  
\[f(x_{1},x_{2},\ldots,x_{n})=[x_{1}\wedge f_{1}(x_{2},\ldots,x_{n})]\vee[-x_{1} \wedge f_{2}(x_{2},\ldots,x_{n})], \tag{9}\]  
_where \(M_{f}=(M_{f_{1}}\ |\ M_{f_{2}})\), i.e., the structure matrices of \(f_{1}\) and \(f_{2}\) are the first and last half of \(M_{f}\), respectively._  
It can be seen that Equation (9) is implicit, which converts the algebraic expression of a Boolean function into an irregular form of the logic expression. In fact, the minterm canonical form of a Boolean function can directly be obtained from the structure matrix.  
**Theorem 2**.: _If the structure matrix of Boolean function (1) is shown in (8), then the minterm canonical form of \(f\) is_  
\[f(x_{1},x_{2},\ldots,x_{n})=\sum_{i\in\mathcal{S}}m_{i}, \tag{10}\]  
_where \(\mathcal{S}=\{i\in\{0,1,\ldots,2^{n}-1\}\ |\ c_{i}=1\}\)._  
Proof.: Suppose that the truth table of \(f\) is \((s_{0}\ s_{1}\ \ldots\ s_{2^{n}-1})^{\intercal}\). By Lemma 4,  
\[\mathcal{S}=\{i\,|\ c_{i}=1\}=\{i\,|\ s_{i}=1\}=\{i\,|\ m_{i}\ \text{is a minterm of}\ f\}.\]  
Then, the minterm canonical form of \(f\) is (10).  
In contrast to the implicit Equation (9) given in Lemma 5, Equation (10) presented in Theorem 2 is explicit, by which the algebraic expression is directly converted into a canonical form of the logic expression. Let us compute the minterm canonical form of the Boolean function whose structure matrix is given in ([16], Example 6) using Theorem 2.  
Consider the Boolean function \(f\) with structure matrix  
\[M_{f}=\delta_{2}[1,2,2,1,2,1,2,1,1,1,2,2,2,1,1,2].\]  
In (8), \(c_{1}=c_{2}=c_{6}=c_{7}=c_{8}=c_{10}=c_{12}=c_{15}=1\). By (10), the minterm canonical form of \(f\) is  
\[f(x_{1},x_{2},x_{3},x_{4})=\sum m(1,2,6,7,8,10,12,15).\]  
Furthermore, the minimization form of \(f\) is  
\[f(x_{1},x_{2},x_{3},x_{4})=x_{1}\xi_{3}\xi_{4}+x_{2}x_{3}x_{4}+\xi_{1}x_{3}\xi _{4}+\xi_{2}x_{3}\xi_{4}+\xi_{1}\xi_{2}\xi_{3}x_{4}.\]  
Comparing the above example with ([16], Example 6), the explicit equation makes the conversion straightforward. Moreover, given the truth table of a Boolean function, it is possible to write the function as various propositional formulas with irregular forms, while the minterm canonical form is unique up to the order of minterms.
## 3 Results
### Effects of Different Configuration Modes on the Dry Matter Distribution Characteristics of Cotton
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{2020} & \multicolumn{3}{c}{2021} \\ \cline{3-10}
**Growth-** & **Position** & **Vegetative Organ** & **Generative Organ** & **Weight per** & **Vegetative Organ** & **Generative Organ** & **Weight per** & **Piant (g)** \\ \cline{2-10}
**Period** & **Field** & **Allocation** & **Scale** & **Allocation** & **Scale** & **Plant (g)** & **Allocation** & **Scale** & **Allocation** & **Scale** \\  & **Configuration** & **(g)** & **(\%)** & **(g)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{4}{*}{Seedling period} & CM1 & 0.99 b & 100.00 & — & — & 0.99 & 1.90 b & 100.00 & — & — & 1.90 b \\  & CM2 & 0.87 b & 100.00 & — & — & 0.87 & 2.37 b & 100.00 & — & — & 2.37 b \\  & CM3 & 0.81 b & 100.00 & — & — & 0.81 & 1.79 b & 100.00 & — & — & 1.79 b \\  & CK & 1.65 a & 100.00 & — & — & 1.65 & 3.42 a & 100.00 & — & — & 3.42 a \\ \hline \multirow{4}{*}{Budding period} & CM1 & 6.38 b & 97.36 & 0.16 b & 2.64 & 6.56 b & 19.11 a & 90.37 & 2.26 b & 9.63 & 21.23 ab \\  & CM2 & 8.46 a & 96.79 & 0.28 ab & 3.21 & 8.74 a & 16.14 a & 91.84 & 1.69 b & 9.62 & 17.58 b \\  & CM3 & 4.36 c & 96.88 & 0.14 b & 3.11 & 4.50 c & 16.92 a & 92.99 & 1.42 b & 7.80 b \\  & CK & 8.77 a & 96.26 & 0.35 a & 3.81 & 9.11 a & 18.96 a & 85.4 & 3.59 a & 16.20 & 22.16 a \\ \hline \multirow{4}{*}{Flowering and ball development} & CM1 & 32.61 ab & 80.85 & 7.73 ab & 19.15 & 40.33 ab & 62.63 b & 71.40 & 25.09 a & 28.60 & 87.72 ab \\  & CM2 & 38.19 ab & 75.78 & 12.07 a & 24.22 & 50.26 a & 63.19 ab & 66.18 b & 32.29 a & 33.82 & 95.48 ab \\  & CM3 & 29.36 b & 83.55 & 0.14 b & 14.65 & 34.40 b & 51.39 b & 67.95 & 24.24 a & 32.05 & 75.63 b \\  & CK & 41.48 a & 82.12 & 9.03 ab & 17.88 & 50.51 a & 71.30 a & 72.76 & 26.69 a & 27.24 & 97.99 a \\ \hline \multirow{4}{*}{Maturation period} & CM1 & 49.54 c & 47.81 & 54.09 a & 52.19 & 103.63 b & 78.17 b & 41.65 & 109.54 a & 58.35 & 187.71 ab \\  & CM2 & 64.28 ab & 46.09 & 75.20 a & 53.91 & 139.49 ab & 89.58 b & 49.06 & 29.99 ab & 50.94 & 182.58 ab \\  & CM3 & 52.27 b & 49.19 & 53.98 a & 50.81 & 106.25 b & 84.91 b & 54.43 & 71.08 b & 45.57 & 155.99 b \\  & CK & 66.78 a & 65.68 & 79.41 a & 54.32 & 146.19 a & 140.44 a & 60.32 & 92.37 ab & 39.68 & 232.81 a \\ \hline \hline \end{tabular} Note: Different lowercase letters indicate that the difference between different treatments of the same index is significant (\(p<0.05\)).  
\end{table} 
Table 2: Dry matter accumulation and distribution per plant at different growth periods of cotton.
# A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data
## 5 Discussion
### Limitations of the TSVD-Based Method  
Two problems are worth discussing. Firstly, TSVD plays an important role in the proposed solution. Since the ordinary truncation method is not suitable for this issue, a more adaptive truncation method is proposed in this paper. From Equation (23), it can be seen that sufficient polarizations are needed to estimate \(\sigma_{0}^{2}\), i.e., \(2m>n\)[23; 26]. In this paper, 10 polarizations are selected for the forest height inversion. Therefore, \(2m-n=7\) confirms the accuracy of the estimation of \(\sigma_{0}^{2}\)[23]. Secondly, due to the scattering mechanism, the inverted forest heights always follow a spatial trend, which can be seen in the results of both the three-stage method and TSVD. As a consequence, it is apparent that the far-range areas result in the overestimation of the forest height in Figure 6,b, and especially in Figure 6.
# Three-Level Hybrid Envelope Tracking Supply Modulator with High-Bandwidth Wide-Output-Swing
## 4 Simulation Results  
In this paper, the envelope tracking modulator is designed with 180 nm process. Figure 13 shows the layout of the ET supply modulator. The operation conditions of the modulator are that the supply voltage is 5 V, the equivalent load of PA is 6 ohms, and an on-chip integrated capacitor of 10 nF. In addition, a 1.1 uH off-chip inductor is used for simulation.  
The transient simulation results of the ET modulator are shown in Figure 14. The input signal comes from 5G NR 100 MHz envelope signal with maximum PAPR 7.5 dB (after the PAPR reduction process). Figure 14a shows the simulation waveform. From top to bottom, it shows the input envelope waveform, output waveform, and 3L-SWR switching node voltage. The total output current, SWR output current, and LA output current are shown in Figure 14b. Current transient simulation results show that the total output current is equal to the sum of the I\({}_{\text{LA}}\) and the I\({}_{\text{SWR}}\). When the I\({}_{\text{SWR}}\) is greater than the I\({}_{\text{Load}}\), the LA sinks the excess current. On the contrary, when the I\({}_{\text{SWR}}\) is less than the I\({}_{\text{Load}}\), the LA supplies the remaining current. This ensures that most of the I\({}_{\text{Load}}\) is provided by the SWR, further improving the overall efficiency of the system.  
Figure 12: Positive feedback loop of hysteretic comparator.  
Figure 13: Layout of the ET supply modulator.  
Figure 15 shows the curve of the overall efficiency and output power of the ET modulator with the change of load. When the load resistance is 10 ohms, the efficiency reaches the maximum close to 85%, and the output power is 0.92 W.  
Table 3 summarizes the performance of the envelope amplifier designed in this work and compares it with some previous work. The bandwidth of the envelope signal in this paper is 100 MHz; under this condition, the modulator has higher efficiency and output power. The output voltage range of the modulator is 0.3 V-4.6 V. The higher output voltage can provide a large power supply voltage to PA, which is suitable for a broad application. Thanks to the hysteretic comparator, the flying capacitor does not require an additional calibration circuit, which reduces the difficulty of circuit design.  
Figure 14: Transient simulation result of ET modulator for 100 MHz input signal: (**a**) input and output waveforms, 3L operation; (**b**) LA output current \(\rm I_{LA}\), SWR output current \(\rm I_{SWR}\), total output current \(\rm I_{Load}\).
# Generating Block-Structured Parallel Process Models by Demonstration
### Challenges  
Discovering models based on recorded scenarios of process execution has led us to process mining techniques and the \(\alpha\)-algorithm [2; 3; 22]. However, the \(\alpha\)-algorithm is only applicable to complete event logs [3] and did not allow us to achieve our goal. Our research in that area has resulted in a modification of the process mining technique as well as of the \(\alpha\)-algorithm itself by introducing the indirect relation [23] between activities and also in defining new types of event logs and the conditions that they fulfill. The results of that part of our research in the form of a modified PM technique, the \(\alpha^{\,\mid\,\mid}\)-algorithm, and causally complete and weakly complete event logs are presented in our previous papers [6; 7].  
In [6], we briefly introduced our modified PM technique and the \(\alpha^{\,\mid\,\mid}\) -algorithm, and we defined two types of logs, the so-called causally complete and weakly complete logs. Other than that, the results of the application of the modified PM technique and the \(\alpha^{\,\mid\,\mid}\)-algorithm on these types of logs, as incomplete event logs, has been shown in the example of the parallel business process.  
The paper [7] is largely devoted to causally complete logs. A more detailed presentation of our modified PM technique and algorithm is given in that paper, with the formal support of definitions, theorems, and proofs. Moreover, the comparison of results obtained by applying our algorithm with the results of other process mining algorithms on causally complete logs was done. Finally, that paper brings an extensive experimental analysis whose results are presented. In this analysis, values of the minimal sizes of complete logs and causally complete logs are compared for 100 real examples of parallel business processes. Furthermore, plug-ins are also presented in the existing ProM framework [24] designed for the needs of the experimental analysis.  
For the realization of the idea of interactive business process model generation, a modified PM technique and the \(\alpha^{\,\mid\,\mid}\)-algorithm applied on weakly complete event logs [6] were used. In that paper, the attention was focused on a specific class of parallel processes--block-structured parallel processes [7], which are suitable for the use of a modified PM technique and the \(\alpha^{\,\mid\,\mid}\)-algorithm, as it will be shown later in this paper.  
This paper is dedicated to incrementally generating models of block-structured parallel business process by direct manipulation through the created graphical user interface. Other than the detailed presentation of the procedure of generating models, the description of the manner of the graphical user interface functioning as an \"intelligent\", user interface is also given, which is essential for the realization of our idea.