esa-sceva/satcom-chunk-collection huggingface dataset:
# Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
## 4 Results and Analysis  
This section presents the results of computational experiments. Trade-offs are evaluated between the three contribution strategies presented in Section 3.1 and two baselines: \"full\" where all users contribute, and \"random\" where users have 50% probability of contributing at each round (50% chance of contribution does not imply 50% chance of success because each contribution is on average greater than 1.).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Simulation length & 5000 \\ Repetitions & 20 \\ Population size & (5,7,15,20,30,50) \\ \hline
**Scenario: Smart grid** \\ \hline Minimum value & 1 \\ Maximum value & (2,3,4,6,8,10) \\ \hline
**Scenario: Participatory sensing** \\ \hline Minimum value & 1 \\ Maximum value & (3,4,6,8,10) \\ Public Good Threshold & 0.8 \\ \hline
**Q-Learning** \\ \hline Alpha & 0.001 \\ Gamma & 0.0 \\ Dropout probability & 0.8 \\ Hidden layer size & 10 \\ \hline
**Aspiration Learning** \\ \hline \(\lambda\) & \(10^{-4}\) \\ \(c\) & 0.05 \\ \(h\) & 0.01 \\ \(\zeta\) & 0.05 \\ \(\epsilon\) & \(10^{-4}\) \\ \(\bar{\rho}\) & \(1.1\) \\ \(\underline{\rho}\) & \(-1.1\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of parameters in the simulation environment. Tuples represent the values of parameters that have been tested during the parameter sweep.  
All experiments are performed in a Python simulation framework and run on the computing cluster of ETH Zu. Results presented in this paper represent the average state of 20 simulations after 5000 timesteps, and error bars represent the confidence intervals at 95%. The choice of cost, value and public good values is described in Section 2.1.  
Full results are shown in Figure 3 and present the comparisons of the contribution strategies by the six measures discussed in Section 3.2. The plots show aggregated results across a range of population sizes, from 5 to 50 users. The decision to aggregate the visualization is motivated by the independence of the results on the size of the population, i.e., the number of sensors, caused by the choice of the public good threshold to be proportional to the size of the population. For completeness, Figures 13 and 14 in the Appendix illustrate the (lack of) variation of these results with respect to the population size and action-state space size. If the threshold would be constant, an increase in the number of sensors, i.e., potential contributors, would make the creation of the public good easier and hence affect all measures.
# Methods for Improving Image Quality for Contour and Textures Analysis Using New Wavelet Methods
## 1 Introduction
They are demonstrating that the methods have the capability to beat the performance degradation and produce more visually appealing results. [PERSON] et al. [39] use 3D block-matching and 3D filtering to identify in the entire image the similar blocks. They want to identify among the edge direction all the matching blocks and to perform the denoising shrinking of the 3D transform coefficients applied on the groups. This method improves the visual quality, numerical results and preserves the textures and edges. This research paper has as main objective the presentation of the results obtained in the case study with reference to the improvement of image quality by Wiener filtering in the field of wavelet and cellular neural networks for the preservation of contours and the improvement of image quality. In the present article, the authors present the results of research work on improving image quality through two powerful procedures for improving images obtained by soft truncation of wavelet coefficients correlated with nonlinear cellular networks. New filtersare proposed: one based on the use of the translation invariant wavelet transform, which mediates the results obtained by repeating this procedure for several sets of wavelet coefficients and method based on Wiener filtering in the wavelet domain. The Wiener filters in the wavelet domain were called multiple Wiener filter, mediated Wiener filter and iterated Wiener filter. The practical applicability of the proposed methodology is addressed generically to the medical act, respectively, the medical imaging essential especially for neurosurgery. It should be noted that CT is performed at the macroscopic level, while the algorithm proposed in this research work contributes to the analysis of medical imaging at the microscopic level so that tumors and areas of transition from diseased tissue to healthy tissue are highlighted much better. The subsequent sections have the structure organized as follows: Section 2 presents the steps followed as a research methodology, namely Section 2.1. Improving image quality by Wiener empirical filtering in the wavelet domain; Section 2.2. Wiener filter in the field transformed; Section 2.3. Empirical Wiener Wavelet Filter; Section 2.4. Multiple Wiener filter in wavelet domain; Section 2.5. Conjugated use of the invariant wavelet transform in translation and Wiener filtering in the wavelet domain to improve the image quality obtained by soft truncation of the wavelet coefficients; Section 2.6. Reconstruction of contours from images using nonlinear cellular network (NCN) algorithm. In Section 3, the results are highlighted and critically analyzed based on the proposed research methodology, namely algorithm testing and the combined use of the two methods covered in this article, the results being superior to those obtained by individual use of each method. Finally, the main conclusions of this research work are presented in Section 4.
# Chemical Constituents, Neuroprotective and Antioxidant Potential of _Klasea centauroides_ Leaves
## 2 Materials and Methods  
### Plant Material and Chemicals  
Leaves of _Klasea centauroides_ were collected in the Republic of Buryatia (Ivolginsky District, 51\({}^{\circ}\)45\({}^{\prime}\)55.2\({}^{\prime\prime}\) N, 107\({}^{\circ}\)09\({}^{\prime}\)34.3\({}^{\prime\prime}\) E), dried in a ventilated oven (50 \({}^{\circ}\)C), and storedin a Plant Repository of the IGEB (at 4 \({}^{\circ}\)C). Before analysis leaves were ground using A11 basic analytical mill (IKA\({}^{\text{@}}\)-Werke Gmbh & Co. KG, Staufen, Germany) and sieved on an ERL-M1 sieving machine (Zernotekhnika, Moscow, Russia; particle size 0.5 mm). The commercial reference standards used: arbutin (\(\geq\)98%; No. A4256), 3-O-caffeoylquinic acid (\(\geq\)95%; No. C3878), 4-O-caffeoylquinic acid (\(\geq\)98%; No. 65969), 5-O-caffeoylquinic acid (\(\geq\)98%; No. 94419), 3-A-di-O-caffeoylquinic acid (\(\geq\)90%; No. SMB00224), 3,5-di-O-caffeoylquinic acid, 4,5-di-O-caffeoylquinic acid (\(\geq\)85%; No. SMB00221) (Sigma-Aldrich, St. Louis, MO, USA); methyl arbutin (\(\geq\)98%; Scientific Laboratory Supplies Ltd., Wilford, UK, No. G5041); 20-hydroxycedysone (\(\geq\)95%; Selleck Chemicals, Houston, TX, USA, No. S2417), ecdysone (\(\geq\)99%; AdooQ\({}^{\text{@}}\) Bioscience, Irvine, CA, USA), turkesterone (\(\geq\)98%; MedKoo Biosciences, Inc., Morrisville, NC, USA, No. 462668), polypodine B (\(\geq\)98%; BioCrick BioTech, Sichuan, China, No. BCN0474), apigenin 7-O-glucuronwide (\(\geq\)98%; No. CFN98500), luteolin 7-O-glucuronwide (\(\geq\)98%; No. CFN98512) (ChemFaces, Wuhan, China), chrysoeriol 7-O-glucuronwide (\(\geq\)99%; MedChemExpress, Monmouth Junction, NJ, USA, No. HY-N2376), diosmetin 7-O-glucuronwide (\(\geq\)95%; Apollo Scientific Ltd., Bredbury, UK, No. BICL5032). 20-Hydroxycedysone 3-O-glucose, integristerone A, 2-deoxy-20-hydroxycedysone, 20-hydroxycedysone 3-O-acetate, 2-deoxycedysone were isolated and NMR-characterized in our laboratory [8; 9; 19]. Acetonitrile for HPLC (\(\geq\)99.9%; No. 34851); formic acid (\(\geq\)95%; No. F0507); (-)-scopolamine hydrochloride (\(\geq\)99.9%; No. 37022); formalin (10%; No. 65346-M); cresyl violet acetate (\(\geq\)70%; No. C5042); D-mannitol (\(\geq\)98%; No. M4125); sucrose (\(\geq\)99.5%; No. S7903); 3-(N-morpholino)propanesulfonic acid (MOPS) (\(\geq\)99.5%; No. M5162); ethylene glycol-bis(2-aminoethylether)-N,N,N,N-tetraectic acid (EGTA) (\(\geq\)97%; No. E0396) were purchased from Sigma-Aldrich (St. Louis, MO, USA).
# A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment
## 3 Problem Description for the Proposed Methodology
### Assumptions  
The current study considers the following assumptions similar to the work presented by [PERSON] and [PERSON] (2018) [15].  
1. The workflow application is assumed to be executed in a single cloud data center, so that one possible source of execution delay, storage cost, and data transmission cost between data centers is eliminated.
2. An on-demand pricing model is considered, where any partial utilization of the leased VM is charged as a full time period.
3. The communication time for the tasks executed on the same VM is assumed to be zero.
4. The scheduling of tasks is considered to be non-preemptive, which means that a task cannot be interrupted while being executed until it has completed its execution.
5. Each task can be assigned to a single VM, and a VM can process several tasks.
6. Multi-tenant scenarios are not considered, i.e., each VM can only run one task at a time.
7. The processing capacity of a VM is provided either from the IaaS provider or can be calculated based on the work reported by [PERSON] et al. (2010) [33]. The estimation times are scaled by the processing capacity of VM instances, i.e., 1 s of each task in a workflow runs for 1 s on a VM instance with one Elastic Compute Unit (ECU). Note that an ECU is the central processing unit (CPU) performance unit defined by Amazon. The processing capacity of an ECU (based on [EMAIL_ADDRESS] GHz performing 4 flops per Hz) was estimated at 4.4 GFLOPS (Giga Floating Point Operations Per Second) [33].
8. When a VM is leased, a boot time of 97 seconds for proper initialization is considered based on the measurements reported by [PERSON] and [PERSON] (2011) [34] for the Amazon EC2 cloud.
9. We adopted a performance degradation of 24% in the Amazon EC2 cloud, similar to the work presented in [6; 35; 36] based on results achieved by [PERSON] et al. (2010) [37] and [PERSON] (2015) [38].
## 3 Evaluation
### Analysis of the Results  
We can observe that the hybrid implementation of the clustering algorithm is able to lead to better results for the three types of scenarios because of the dynamic _skip_ value (see Table 3). Thus, in comparison with EUDFC, which employs a constant keep value of 2, DCAGBS with a _skip_ value of 13 (5% of 250 nodes), considerably increases the value of rounds for FND in Scenario 1 and 3. For Scenario 2, due to the long distance to the BS, the value of rounds to the FND are quite similar because the set of CH in DCAGBS are kept constant for a longer time. Then, the number of nodes alive at HND for DCAGBS clearly outperforms EUDFC and EEDCF due to the saved energy in the first period. In the phase from the FND to the HND, which uses the second _skip_ value of 7 (2.5% of 250 nodes), DCAGBS keeps saving energy that will eventually allow it to obtain the best LND for the three scenarios. From the third message, when half of the nodes are dead, all nodes set the _skip_ value to 2. This allows DCAGBS a better balance of the remaining energy in the network while it keeps saving energy. In addition, the input variables in the fuzzy logic system configured for DCAGBS seem to be more appropriate to model the evolution of the network when we compare the results with CHEF.
## 5 Discussion
### Analysis of Head Loss  
The greater the roughness and tortuosity of the rough fracture, the more complicated the flow path. The increase in seepage resistance led to a significant increase in head loss. The head loss, as a function of seepage flow, can fully show the influence of fracture roughness and tortuosity on fluid seepage behavior. According to the Darcy-Weisbach equation and Darcy's law, Equation (15) can be substituted into the Darcy-Weisbach equation to obtain the total head loss of the fluid in the seepage process based on the modified frictional resistance coefficient model:  
\[\Delta P=f_{0}\cdot\frac{L}{2e}\cdot\frac{\rho v^{2}}{2}=\frac{2\tau-1}{\tau- 1}\cdot f\cdot\frac{L}{2e}\cdot\frac{\rho v^{2}}{2} \tag{16}\]  
where \(f\) is the frictional resistance coefficient. \(\tau\) is the fracture tortuosity. \(L[L]\) is the fracture length. \(e[L]\) is the fracture aperture. \(\Delta P\big{[}ML^{-1}T^{-2}\big{]}\) is the head loss of the fluid. \(\rho\big{[}ML^{-3}\big{]}\) is the fluid density. \(\
u\big{[}LT^{-1}\big{]}\) is the average velocity of the fluid.  
Equation (16) shows that the main factors that cause the total head loss of a fluid in rough fractures are as follows: \(2\tau-1/\tau-1\) represents the local head loss caused by the tortuousness of the seepage path, \(f\) represents the frictional head loss caused by the roughness of the fracture, \(L/2e\) represents the ratio of the length of the fracture channel to the aperture of the fracture, and \(\rho v^{2}/2\) represents the kinetic energy of the fluid.  
Equation (16) was used to calculate the total head loss of the fluid in the rough fracture based on the modified frictional resistance coefficient model. Using Equation (16), the total head loss caused by fracture roughness and tortuosity was clarified, and the reason for the decrease in the fluid's flow rate in the process of fluid seepage was explained more accurately. The fluid seepage behavior in the rough fracture can thus be analyzed more comprehensively.  
The model of Equation (16) enables us to fully understand the reasons for the decrease in the fluid flow rate and the increase in head loss from the perspective of roughness and tortuosity, which provides a reference for the numerical simulation of nonlinear seepage of compressed rock under different flow conditions.
# Quality and Defect Inspection of Green Coffee Beans Using a Computer Vision System
## 2 Background  
### Green Coffee Bean Defects  
Many factors can affect the quality of green coffee beans. Figure 1 presents a classification of the green coffee beans used in this paper to identify defects and quality. Figure 1a shows the sour defect type, where beans can be recognized by yellow or brown and after cutting these beans tend to have a sour smell and flavor. Figure 1b shows the very long berry defect type and refers to green coffee beans that are very long or not round. Figure 1c displays the black defect type, which are more than 25% black or dark brown, being very prejudicial for coffee taste [29]. Figure 1d shows the broken defect type, which are beans that are cut or bruised during a previous processing stage. Figure 1e shows the small defect type, characteristic of immature and pea-berry coffee beans that are considered of low quality. Finally, Figure 1f shows the normal or high-quality coffee bean, with no significant defects to consider. A further 13 defect types have been reported in the literature to classify coffee beans [24; 28]; however, they are not used in this research to evaluate quality.  
Figure 1: Coffee bean types: (**a**) sour defect type, (**b**) very long-berry defect type, (**c**) black defect type, (**d**) broken defect type, (**e**) small defect type, and (**f**) high-quality coffee bean.
# On the Performance of Interleavers for Quantum Turbo Codes
## 2 Classical Interleavers for Quantum Turbo Codes
### System Model
The classical syndromes \(R_{2}\) and \(\mathrm{S}_{1}^{x}\), obtained in the inverse decoders \(\mathcal{V}_{2}^{\dagger}\) and \(\mathcal{V}_{1}^{\dagger}\), respectively, are then provided to the iterative syndrome decoder consisting of two serially concatenated Soft-In Soft-out (SISO) decoders, as shown in Figure 1. Based on \(R_{2}\) and \(\mathrm{S}_{1}^{x}\), as well as the channel information \(\mathrm{P}_{ch}(P_{2})\), both SISO decoders engage in degenerate iterative decoding [3, 6] to estimate the most likely error coset \(\mathcal{L}_{1}\) that has corrupted the information quantum state. Based on such an estimation, a recovery operation \(\mathcal{R}\) is applied to the corrupted state \(\mathcal{L}_{1}\ket{\psi_{1}}\), yielding the recovered output \(\ket{\bar{\psi}_{1}}\). As already mentioned, the aim of this paper is to show that interleaver design is beneficial to reduce the error floors of QTCs. To that end, the inner and outer QCCs used to construct the concatenated error correcting system presented in Figure 1 are the EXIT-optimized codes introduced in Reference [7]. The parameters of the inner and outer codes are presented in Table 1. Such configuration, utilizing a random interleaver for the serial concatenation of the constituent QCCs, was studied extensively in Reference [7]. Therefore, it is an excellent benchmark for the interleaver constructions proposed in this paper. Figure 1: Schematic of the Quantum Turbo Code (QTC). Note that the \(c\) pre-shared EPR pairs \(\ket{\Phi}^{+}\) are needed for the inner encoder to be both recursive and non-catastrophic [6]. \(\mathrm{P}_{i}^{a}(. )\) and \(\mathrm{P}_{i}^{e}(. )\) denote the a-priori and extrinsic probabilities related to each of the SISO decoders used for turbo decoding. From the configuration parameters presented in Table 1, it can be seen that the QTCs used in this paper will be rate 1/9 turbo codes with an entanglement consumption rate of 6/9. The noise limit \(p^{*}\) can be found from the entanglement-assisted hashing bound, which for such parameters is \(p^{*}=0.3779\)[6].
## 5 Conclusions  
For coherent optical PAM4 systems, we propose the PA-DFE and MA-DFE, which are designed to compensate for CD and nonlinear impairments. Moreover, the proposed equalizer is robust to carrier phase noise, thus eliminating the need for additional carrier phase recovery operations. We built an 80 Gb/s coherent optical PAM4 system operating at a single wavelength in the C-band for demonstration and set the appropriate equalizer parameters based on the experiments. The experimental results show that the MA-DFE and PA-DFE effectively compensated for CD and nonlinear impairments, achieving a transmission over 25 km of SSMF with a sensitivity of \(-\)23 dBm, and the link budget reached 27 dB. Additionally, we explored the performance of the equalizers under different operating states of the SOA at the transmitter. The results show that in mitigating signal distortion caused by SOA gain saturation, the PA-DFE exhibits superior equalization performance compared to the MA-DFE and ultimately achieves a link budget of 29 dB.  
Conceptualization, Z.L.; methodology, Z.X.; software, S.L.; validation, A.T.; formal analysis, B.C.; investigation, Z.X.; resources, Z.L.; data curation, Z.X.; writing--original draft preparation, Z.X.; writing--review and editing, Z.L.; visualization, S.L.; supervision, A.T.; project administration, B.C.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by part by National Key Research and Development Program of China (2021 YFB2900800).  
Not applicable.  
Data Availability Statement: Dataset available on request from the authors.  
The authors declare no conflicts of interest.
# Computer Vision Techniques for Growth Prediction:
## 3 Results (Extraction & Analysis)
### Rq2: Which Technologies Are Used for Growth Prediction Using Image Processing?
#### 3.4.2 Generative Adversarial Network (GAN)  
GAN is a method for learning a generator \(G^{*}\) by optimizing discriminator \(D\) to maximize and generator \(G\) to minimize to satisfy the following Equation (2) [33].  
\[\min_{G}\max_{D}V_{GAN}(G,D)=E_{x\sim p_{data}}[log(D(x))]+E_{z\sim p_{z}}[1- log(D(G(z)))], \tag{2}\]  
where \(p_{data}\) and \(p_{z}\) are the actual and random noise data distributions. First, this training maximizes \(D\)-loss, which means that the \(D\) in the first term should be optimized to output 1 for the correct data distribution, and the \(D\) in the second term should output 0 for the distribution of fake data. Next, training minimizes \(G\)-loss. \(G\)-loss trains to correct the fake data \(G(z)\) generated by taking the noise \(z\) from the distribution \(p_{z}\) as input, i.e., optimizes the output to 1. This is also highly compatible with biological growth prediction technology, as it enables growth prediction by correctly learning the next state in time series data.  
In the original GAN, the random noise affects image generation, but GP-GAN [33] performs initialization by conditioning on tumor boundaries and tissue Feature Maps (FMs). GP-GAN builds generators based on the MRI image, and the corresponding feature maps \(FMs_{i}\) at a specific time point \(t_{i}\). At time points \(t_{i}\) and \(t_{i+1}\), GP-GAN is formulated as follows (However, we have corrected the part of the equation described as \(E_{x_{i}\sim x_{i}}\) in the paper [33; 37] to \(E_{x_{i}\sim p_{x_{i}}}\)):  
\[\begin{split}\min_{G_{i}}\max_{D_{i}}V_{GP-GAN}(G_{i},D_{i})=& E_{x_{i}\sim p_{x_{i}},x_{i+1}\sim p_{x_{i+1}}}[log(D(x_{i},x_{i+1}))]\\ &+E_{x_{i}\sim p_{x_{i}}}[1-log(D(G(x_{i})))].\end{split} \tag{3}\]  
Equation (3) does not differ significantly in structure from the original GAN formulation, except that the first term is calculated as the joint probability of \(t_{i}\) and \(t_{i+1}\). Conditional GAN (cGAN) [47] is also a conditioning method by output images. The Dice-loss function is added to Equation (3) [33]; in the paper [37], the Dice-loss and l1-loss functions are added to Equation (3) for optimization.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Name** & **Number of Studies** **(All = 29)** & **Conditions Included** \\ \hline Long Short-Term Memory (LSTM) & 6 [31; 36; 42] & ST-Conv LSTM (1) [30] \\  & 6 [31; 36; 42] & BiLSTM (1) [77] \\  & & STCN (spatio-temporal corrective network) (1) [32] \\ Generative Adversarial Network & 4 & Conditional GAN (1) [47] \\  & & BeyondMSE (GAN) (1) [30] \\  & & GP-GAN (2) [33; 37] \\ U-net & 4 [36; 37] & 3D-U-net (1) [77] \\ Spatial Transformer Network (STN) & 3 [35; 36; 59] & ResNet34 (1) [39] \\ \hline \hline \end{tabular}
\end{table}
Table 8: Distribution of different types of machine learning models that predict growth by images.
## 3 Results  
To have comparison terms at different percentage loads of the IM, currents, speed, powers, PF, and efficiency have been calculated at loads of 25%, 50%, 75%, and 100% of the rated value. We indicate the obtained data in Table 2.  
The results obtained are concentrated in Table 3; we emphasize that the values in the table correspond to the motor operation in the stabilized regime under the given conditions. Based on them, a series of graphical representations were made, the interpretation of which results in the IM behavior in harmonics. The most important remark is that the presence of a specific harmonic in the voltage wave leads to the appearance of a single harmonic of current, of the same order with the harmonic of \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Motor Load, \%**} \\ \cline{2-5}
**Parameter** & **25\%** & **50\%** & **75\%** & **100\%** \\ \hline Stator Current—I\({}_{\text{RMS}}\), A & 4.44 & 5.29 & 6.5 & 7.94 \\ Rotor Current—I\({}_{\text{RMS}}\), A & 1.94 & 1.74 & 3.1 & 9.38 \\ Rotor Speed—N, rpm & 1484 & 1468 & 1452 & 1435 \\ Electromagnetic Torque—Te, Nm & 7.13 & 13.81 & 20.47 & 27.15 \\ Active Power—P, kW & 1.2 & 2.28 & 3.39 & 4.53 \\ Reactive Power—Q, kVAr & 2.83 & 2.86 & 2.96 & 3.12 \\ Power Factor—PF & 0.39 & 0.62 & 0.75 & 0.82 \\ Efficiency—\(\eta\), \% & 86.11 & 89.75 & 89.68 & 88.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The characteristic sizes of the IM in the symmetrical and sinusoidal regime, for different loads. voltage, both in the stator and rotor currents. For some physical sizes, analytical identifications were searched for the highlighted dependencies, in order to provide a calculation basis for those interested.