esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Discrete-Time Volterra Series Model for TIADC
### Discrete-Time Volterra Model for a Single-Chip ADC  
In general, ADCs have a low nonlinear error, and the higher-order nonlinear error has less impact on the system [12]; thus, this paper ignores the nonlinear effects of the third and higher orders when building the Volterra model. After a certain time, for electronic devices with a memory fading effect, such as ADC, the higher-order memory effect has less impact on the system [20]. Similarly, this paper assumes that the memory order of ADCs is three, which means that the current output state of ADCs is only affected by the current input state and the previous two input states, and the previous input states have no impact on the current output state. Based on this, the discrete-time Volterra series model of a single-chip ADC is established as follows:  
\[\begin{split} y(n)&=V[x(n)]\\ &=h_{0}+\sum_{k_{1}=0}^{2}h_{1}(k_{1})x(n-k_{1})\\ &+\sum_{k_{1}=0}^{2}\sum_{k_{2}=0}^{2}h_{2}(k_{1},k_{2})x(n-k_{1} )x(n-k_{2})\end{split} \tag{1}\]  
where \(k_{i}\) is the memory length and \(h_{i}\) is the coefficient kernel of each order, and the 0 th-order term of the Volterra series is a constant term that indicates the magnitude of the offset error of this ADC; the first-order term of the Volterra series indicates the magnitude of the gain error of the ADC and the effect of the past state on the current state, and the second-order term of the Volterra series indicates the memory and nonlinear effects of the ADC, i.e., the effect of memoryless nonlinearity versus memory nonlinearity on the system. The block diagram of the Volterra series model for a single ADC is shown in Figure 2, where \(H_{0}[x(n)]\) indicates the orders mentioned above.
# Laser Finishing of Ti6 Al4V Additive Manufactured Parts by Electron Beam Melting
## 2 Materials, Equipment, and Methods  
### Materials  
The laser treatment was performed on the external surface of titanium alloy samples. The samples were built by the EBM technique (Arcam A2X), using gas-atomized Ti6 Al4V ELI powders (particle size in the range of 45-100 \(\upmu\)m) [23]. The main system characteristics are reported in Table 1. The samples were printed with a layer thickness of 50 \(\upmu\)m. The chamber pressure before starting the process was \(5\times 10^{-4}\) mbar; then helium gas was introduced into the chamber to avoid the smoke effect [3], and the pressure reached \(2\times 10^{-3}\) mbar. During the process, a defocused beam of electrons pre-heated the laying powder (up to 750 \({}^{\circ}\)C), then a focused beam melted the powder according to a pattern achieved during the slicing phase.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Characteristics**} & **Units** & **Value** \\ \hline Maximum build size & mm\({}^{3}\) & \(200\times 200\times 380\) \\ Beam power & W & 3000 \\ Cathode type & – & Tungsten filament \\ Min. beam diameter & \(\upmu\)m & 250 \\ Max. EB translation speed & m/s & 8000 \\ Vacuum base pressure & mbar & \(5\times 10^{-4}\) \\ Build atmosphere (partial pressure of He) & mbar & \(2\times 10^{-3}\) \\ He consumption & l/h & 1 \\ CAD interface standard & – & STL \\ \hline \hline \end{tabular}
\end{table}
Table 1: Arcam A2X characteristics. Abbreviations: CAD: computer aided design; STL: standard triangulation language.
###### Abstract  
Neonatal porcine islets-like clusters (NPICCs) are a promising source for cell therapy of type 1 diabetes. Freshly isolated NPICCs are composed of progenitor cells and endocrine cells, which undergo a maturation process lasting several weeks until the normal beta cell function has developed. Here, we investigated the effects of short-chain fatty acids on the maturation of islet cells isolated from two to three day-old piglets. NPICCs were cultivated with acetate, butyrate and propionate (0-2000 uM) for one to eight days. Incubation with butyrate resulted in a significant upregulation of insulin gene expression and an increased beta cell number, whereas acetate or propionate had only marginal effects. Treatment with specific inhibitors of G-protein-coupled receptor GPR41 (\(\beta\)-hydroxybutyrate) and/or GPR43 (GPLG0974) did not abolish butyrate induced insulin expression. However, incubation of NPICCs with class I histone deacetylase inhibitors (HDACi) mocetinostat and MS275, but not selective class II HDACi (TMP269, MC1568) mimicked the butyrate effect on beta cell differentiation. Our study revealed that butyrate treatment has the capacity to increase the number of beta cells, which may be predominantly mediated through its HDAC inhibitory activity. Butyrate and specific class I HDAC inhibitors may represent beneficial supplements to promote differentiation of neonatal porcine islet cells towards beta cells for cell replacement therapies.  
islet development; short chain fatty acids; butyrate; HDAC inhibitor; porcine islets; NPICCs; beta cell differentiation +
Footnote †: journal: Cells  
10 November 2021
# A Survey of Using Swarm Intelligence Algorithms in IoT
## 3 Applications of Swarm Intelligence Algorithms in WSN
### WSN Routing Protocols Based on SI  
Different from traditional wireless networks, the scale of WSN is often very large, and nodes are often randomly deployed in the network. The WSN still has limited capabilities in a node or a network. For example, each node has limited calculation and communication capabilities, and its energy is often restricted. Therefore, the efficient design of routing protocols needs to take into account the communication path between sensor nodes (source) and the command center (receiver) to save energy. Furthermore, protocols must be self-organizing and robust to failures and losses. The SI algorithms have promising characteristics--such as self-adaptability, robustness, and search ability--to solve complex non-linear problems, which are key attributes required by the WSN. Therefore, a growing number of SI-based routing algorithms have been developing in WSN routing. In this paper routing protocols according to distributed (D)/centralized (C), Besteffort (B)/QoS (Q), single path (S)/multipath (M), event-driven/query-based routing, routing fault tolerance, energy awareness, loop free.
## 2 Materials and Methods
### Evaluation of Antioxidant Activity  
#### 2.5.1 DPPH Assay  
The antioxidant activity was measured by electron paramagnetic resonance (EPR) spectroscopy using microwaves with a frequency of 9.3 GHz from an X-band (Mini Scope MS 200 EPR spectrometer, Magnettech GmbH, Berlin, Germany) and 2,2-diphenyl-1-picrylhydrazyl radical (DPPH). EPR measurements were performed with the following parameters: microwave power: 12 mW, central field: 330.48 mT, sweep range: 9.92 mT, sweep time: 20 s, modulation amplitude: 0.10 mT, and room temperature: 25 \({}^{\circ}\)C. Briefly, 0.5 mL of DPPH solution was added to 50 \(\mu\)L of _H. perforatum_ extract. A blank test was performed by replacing the extract with the solvent (ethanol). The samples were mixed and left in the dark at room temperature for 20 min. EPR spectra of the extract and blank were recorded as derivatives of absorption curves, and the absorption signal area was obtained by double integration. Three replicates were performed for each sample. Free radical-scavenging activity was expressed as the percentage of DPPH decrease based on the following formula:  
\[\text{DPPH scavenging activity (\%)}=[(\text{I}_{0}-\text{I}_{1})/\text{I}_{0}] \times 100,\]  
where  
I\({}_{0}\)--the signal intensity of the DPPH radical from the control;  
I\({}_{1}\)--the signal intensity of the DPPH radical from the sample.
# Performance-Based Reliability Estimates for Highway Bridges Considering Previous Inspection Data
## 4 Performance-Based Reliability Analysis Considering Site Environments
### Effect of Previous Inspection Data
In the case of a normal corrosive environment with normal traffic characteristics and service lifespan of 75 years, the reliability was 3.02 (\(P_{f}=1.3\times 10^{-3}\)) for the basic model, 2.74 (\(P_{f}=3.1\times 10^{-3}\)) for the standard model, 2.66 (\(P_{f}=3.9\times 10^{-3}\)) for the calibrated model (1-W/O), and 2.80 (\(P_{f}=2.6\times 10^{-3}\)) for the calibrated model (2-W/O). When compared with the basic model, the standard model showed an increase in the probability of failure by 2.4 times, the calibrated model (1-W/O) by 3.1 times, and the calibrated model (2-W/O) by 2.0 times. Based on the service lifespan of 75 years, the calibrated model (1-W/O) with normal traffic characteristics had a reliability of 3.04 (\(P_{f}=1.2\times 10^{-3}\)) in a mild corrosion environment, 2.66 (\(P_{f}=3.9\times 10^{-3}\)) in a normal corrosion environment, and 2.18 (\(P_{f}=1.5\times 10^{-2}\)) in a severe corrosion environment. Figure 12: Reliability degradation under normal traffic characteristics (without previous inspection). Figure 13: Reliability degradation under heavy traffic characteristics (without previous inspection). According to Figures 8-13, for a service lifespan of 75 years, the difference in reliability between the calibrated model (1-W) and calibrated model (1-W/O) is approximately 5-9%, while the difference in reliability between the calibrated model (2-W) and calibrated model (2-W/O) is approximately 3-8%. These results suggest that although previous inspection results have an impact on the reliability estimates of bridges, they are not very sensitive to the availability of previous inspection data as long as the calibrated performance degradation model includes current performance conditions. This also implies that the proposed calibrated method can provide a stable performance degradation model that does not rely too heavily on previous inspection data.
# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 4 Results
### Comparing Proposed Fitness Function with MPC-Based Methods  
It has been proposed that MPC-based MPPT algorithms are of high accuracy and robustness [30]. With this technique, the MPPT is tracked by correcting V and I values until \(\frac{dP}{dV}=0\). Exploiting the Lambert function and finding I(V), as in (21), the above equation can be reformed and is illustrated as  
\[\frac{dP}{dV}=\frac{d(V\cdot I)}{dV}=I(V)+V\cdot\frac{dI(V)}{dV}=0 \tag{25}\]  
Using the above (25), a fitness function to find the MPP was formed and is expressed as follows:  
\[f\big{(}\mathbf{V_{gen}}\big{)}=|I(V)+V\cdot\frac{dI(V)}{dV}|,\,f\,:\,[0,V_{ \infty}]\rightarrow\mathbb{R}\]  
The genetic algorithm was also tested with the above fitness function, and there were no substantial deviations between the results using the fitness function defined in (22). However, \(\frac{dI(V)}{dV}\) should be calculated, making the above fitness function much more complex than the proposed solution in this paper. Speaking of complexity, \(\frac{dI(V)}{dV}\) can be calculated using the Lambert function and (21), as follows:  
\[\frac{dI(V)}{dV} = -\frac{V_{t}}{R_{s}}\cdot W_{0}\Bigg{[}\frac{(I_{t}\cdot R_{s})} {(1+\frac{R_{s}}{R_{\Delta t}})\cdot V_{t}}\cdot e^{\frac{V+R_{s}(k+I_{ph})}{(1 +\frac{R_{s}}{R_{\Delta t}})\cdot V_{t}}}\Bigg{]} \tag{26}\] \[\qquad\qquad+\frac{(I_{ph}+I_{s}-\frac{V_{t}}{R_{\Delta t}})}{1+ \frac{R_{s}}{R_{\Delta t}}}\]  
To summarize, the solution proposed in this paper is not only less complex than other solutions to track the MPP, but the genetic algorithm also offers the flexibility to use another technique (e.g., MPC, as above) by modifying the fitness function.
## 3 Field Testing Results on Performance
### Gradeability  
In this testing, an 11-meter steel pipe is lifted by a crane to simulate the environment. For each incline angle, the testing is conducted five times. The testing results are depicted in Table 3, where the rate speed is 0.755 m/s.  
Figure 9 shows the velocities and currents of four motors during this testing. For better visualization, this paper only shows four motors. In Figure 9, there is almost no change when the incline angle increased from 0\({}^{\circ}\) to 37\({}^{\circ}\). Besides, the overall trend of currents is shown as a piecewise function curve with distinct segments.
# A Data Sharing Scheme for GDPR-Compliance Based on Consortium Blockchain
## 2 Background and Related Work
### Zero-Knowledge Proof  
Zero-Knowledge proof (ZK-proof) [11] is an agreement that makes the verifier believe that a certain assertion is correct when the prover does not provide any valuable information for the verifier. The scheme of this article adopts non-interactive proof. The zk-SNARK [12] is a kind of ZK-proof, which we used in the proposed solution. The zk-SNARK can provide the corresponding proof for the calculation that generates a specific output, the speed of verifying the proof is much faster than the speed of performing the corresponding calculation, the algorithm can enable the smart contract to confirm the legality of the transaction when the transaction information is ciphertext. The specific process will be introduced in Section 3.2.
## 3 Results
### Correlations between Microbial Community Structure and Volatile Compounds  
The relationship between the top 20 microbial genera in terms of relative abundance and the top 29 volatile compounds in terms of concentration was investigated to explore the causes of changes in the profile of flavor compounds in the third-round fermented grains (Figure 6). For bacterial genera, the relative abundance of _Lactobacillus_ was positively correlated with ethyl acetate, ethyl caprylate, ethyl phenylacetate, phenethyl acetate, ethyl tetradacanoate, ethyl caprate, ethyl laurate, acetic acid, 5-dodecenoic acid, and (5Z)-, while it was negatively correlated with furfural and 3-hydroxy-2-butanone (\(p\leq 0.001\)). The relative abundance of _Oceanobacillus_, _Pseudogracilibacillus_, _Virgibacillus_, _Scopulibacillus_, _Kroppenstedia_, _unclassified_f_Bacillaceae_, _Brachybacterium_, _Corynebacterium_, _Streptomyces_, _norank_f_Pseudonocardiaceae_, _Pediococcus_, _Saccharomyces_, and _Weissella_ was positively correlated with furfural and 3-hydroxy-2-butanone, while it was negatively correlated with ethyl caproate, phenethyl acetate, ethyl phenylacetate, 9-hexadecenoic acid, ethyl ester, and phenethyl alcohol (\(p\leq 0.001\)). For fungal genera, the relative abundance of _Byssochlamys_ was positively correlated with furfural and 3-hydroxy-2-butanone, while  
Figure 5: Correlation between fermentation parameters and microbial community. Redundancy analysis (RDA) of the dominant genera and the physicochemical indexes. (**A**) Bacterial; (**B**) fungal. Correlation heatmap between fermentation parameters and dominant genera. (**C**) Bacterial; (**D**) fungal. Asterisk shows significant correlations. *: \(p<0.05\), **: \(p<0.01\), ***: \(p<0.001\).  
it was negatively correlated with 5-dodecenoicacid, (5Z)-, ethyl caprylate, phenethyl acetate, ethyl phenylacetate, ethyl caprate, ethyl myristate, 9-octadecenoic acid, ethyl est, acetic acid glacial, and ethyl caproate. The relative abundance of _Issatchenkia_, _Picha_, and _Hypophichia_ was negatively correlated with furfural and 3-hydroxy-2-butanone but was positively correlated with ethyl caprylate, ethyl caproate, ethyl palmitate, 9-hexadecenoic acid, ethyl ester, ethyl phenylacetate, phenethyl acetate, acetic acid glacial, 9-Octadecenoic acid, ethyl est, ethyl caprylate, gamma-nonanolactone, and phenethyl alcohol. In addition, the relative abundance of _Wickerhamomyceses_, _Wallemia_, _Apiotrichum_, and _Rasamsonia_ was positively correlated with ethyl caprylate and ethyl phenylacetate (Figure 6).  
Figure 6: Relationship between the relative abundance of the key genera and concentrations of flavor compounds during third-round fermentation. (**A**) Correlation analysis between key bacterial genera and flavor compounds. (**B**) Correlation analysis between key fungal genera and flavor compounds. Red represents a positive correlation and blue represents a negative correlation. “**”, 0.01 \(<\)\(p\)\(<\) 0.05; “**”, 0.001 \(<\)\(p\)\(<\) 0.01; “**”, \(p\)\(<\) 0.001.
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 5 Proposed Solution
### Tools and Technologies
#### Framework Architecture  
In addition to the specific technologies, another important aspect is the significant architectural difference residing right at the framework level between the asynchronous and reactive approaches, specifically the Spring MVC and Spring WebFlux variants.  
Spring MVC is the conventional variant of the framework, based on an imperative programming model. It leverages blocking I/O and utilizes a thread for each request [27]. Although this model is simple to understand, implement, and debug, it can quickly become inefficient when facing a large number of concurrent requests, due to the blocking resources and the complexity associated with managing the lifecycle of threads.  
Spring WebFlux, on the other hand, embraces the reactive model, using non-blocking I/O, the execution is based on an event loop [28]. The pool of threads is reduced, and unlike the conventional model, there is no direct mapping between active threads and requests, which fundamentally enables this model to efficiently handle a large number of concurrent requests using a limited number of threads. By leveraging reactive data types and functional paradigm principles, the reactive variant makes it possible to manage data flows and backpressure optimally, while providing much greater scalability under conditions of intensive load.
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
## 5 Two Modes of Signature Proofs
### Mode 2: Computationally Powerful Signer (e.g., OpenAI)  
In scenarios where the signer has limited computational resources, such as a digital camera, VerITAS employs a lightweight signing process optimized for efficiency. The process involves the following steps:  
For signers with significant computational resources, such as cloud-based systems or AI platforms, VerITAS adopts a more powerful signing process that shifts the computational burden to the signer. The process involves the following steps:1. Polynomial Commitment: The image is represented as a polynomial, and a polynomial commitment is computed. This commitment serves as a compact representation of the image.
2. Signing the Commitment: The polynomial commitment is signed using a post-quantum signature scheme, such as CRYSTALS-Dilithium.
# An Efficient Library for Reliability Block Diagram Evaluation
## 5 Results and Discussion: Evaluation of librbd Performance
### Comparison with SHARPE  
SHARPE provides a Graphical User Interface for specifying performance, reliability and performability models through the usage of a specification language. It then provides analysis and solution methods for the previously generated models. The analysis methods are available as a Command Line Interface executable. This tool acquires the input model through the usage of a SHARPE specification language file and produces as output a textual log file.  
The comparison experiments were performed as described in Section 4.2 and the obtained results are presented in Table 15. We observe that the execution times of librbd are significantly lower than the ones obtained by using SHARPE.
# Nonlinear Dynamics of an Internally Resonant Base-Isolated Beam under Turbulent Wind Flow
## 5 Conclusions  
The aeroelastic behavior of a tall building modeled as an Euler-Bernoulli beam is considered here. Base isolation with a nonlinear viscous-elastic device is assumed, for vibration mitigation purpose. The steady component of the wind triggers galloping, whereas the turbulent component, with harmonic evolution, is in principal parametric resonance with the first mode of the beam. Internal resonance in ratio 1:3 between the first and second beam mode, reached after suitable calibration of the base device, is considered as well.  
After the application of the Multiple Scale Method in direct approach, a bifurcation analysis is carried out to evaluate the effects of the nonlinear viscous term of the device, the internal resonance and the structural nonlinear geometric and inertial terms.  
The main conclusions can be summarized as follows:  
* The base device, through its linear viscous component, pushes forward the bifurcation point, as expected;
* The nonlinear viscous term of the device reduces the amplitude of the post-critical evolution, as compared to the use of a purely linear device;
* The galloping induced by the mean wind on the first mode represents the leading phenomenon, being the second mode marginally involved in the motion due to its smaller amplitudes;
* The structural nonlinearities provide weak modification to the response, confirming the almost linear behavior of the cantilever, even when internal resonance occurs.  
S.D.N.: Conceptualization, Methodology, Investigation, Validation, Writing--original draft; D.Z.: Conceptualization, Methodology, Investigation, Validation, Writing--original draft; A.L.: Conceptualization, Methodology, Supervision, Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Bifurcation diagram for \(\sigma_{e}=0\), \(\sigma_{i}=-0.06\): (**a**) amplitude \(a_{1}\); (**b**) amplitude \(a_{2}\) (solid line: stable; dashed line: unstable).
## 3 Flexible Demand Resources (FDRs) and Demands Response Program (DRPs)
### Time-Ahead Dynamic Pricing (TADP) Demand Response Program
#### 3.3.3 Gradient Boosted Regression Trees (GBRT) Model for Time-Ahead Forecast of Generation  
In this work, the forecasting tasks are treated as regression problems and machine learning regression algorithms on scikit-learn package in Python are adopted to build the models using the Gradient boosted regression trees (GBRT) algorithm. The GBRT algorithm has a superior advantage of not requiring complex data pre-processing of dimension transformations or reduction and does not suffer any loss of input variable interpretation [53]. The significant feature of the accurate implementation of the GBRT algorithm is the parameter \(\alpha_{gbr}\) called the learning rates. The learning rate is a scaling parameter that determines the individual contribution of each decision tree to the final ensemble model. The accuracy of the model is continuously improved by fitting the residual decision iteratively until the desired model is obtained for the best learning rate. Algorithm 1 illustrates the GBRT pseudo code algorithm.  
```
Start:
1. Precondition: Input the training data set \(M=(m_{i},o_{i});i=1..n\) and a differentiate loss function \(L_{f}(o_{i},\delta)\)
2. Initialization: Initialize the model with a constant value: \(F_{0}(m)=argmin\sum_{N}^{i=1}L_{f}(o_{i},\delta)\)
3. Estimation:for\(i=1..k\); grow k trees 1. Calculate the Pseudo residuals; \(r_{ik}=-\left[\frac{\partial L_{f}(o_{i},\delta)}{\partial\tilde{r}(m_{i})} \right]_{F(m)=F(m_{i})}i=1..n\) 2. Fit a residual value regression decision tree \(I(m)\)  and establish the terminal leaves for\(J=1...j_{K}\); determine the output of each leaves  that minimizes; \(\tilde{\sigma}_{jk}=argmin\sum_{m_{i}\in R_{ij}}L_{f}(o_{i},F_{k-1}(m_{i})+ \tilde{\sigma})\)
4. Update: \(F_{k}(m)=F_{k-1}(m)+\alpha_{gbr}\sum_{j=1}^{j_{k}}\tilde{\sigma}_{jk}I(m\epsilon R _{jm})\) End: For
```  
**Algorithm 1**Gradient boosted regression trees (GBRT) pseudo code algorithm.  
In order ascertain the accuracy of forecasting algorithms, three performance evaluation metrics are used: Mean Absolute Error (MAE), Root mean squared error (RMSE) and Coefficient of Determination (\(r^{2}\)).