esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Vertical Ground Motion on the Design of Common R/C Frames
###### Abstract  
In this article, the response of reinforced concrete frames concurrently subjected to both horizontal and vertical seismic motions is assessed. The article is not limited to the variation in response quantities but aims to identify which specific design parameters are affected and how, as well as which specific code provisions could be violated due to the omission of vertical oscillations during the design process. Furthermore, the consequences that a design against vertical ground motion would cause in both technical and economic terms were investigated. For this purpose, six eight-storey 2D frames were designed, neglecting the vertical seismic component in compliance with code provisions. Subsequently, the seismic response of the frames to the concurrent action of horizontal and vertical ground motion was evaluated by applying both modal response spectrum and inelastic dynamic analyses. It was found out that several code violations occurred, mainly due to the fluctuation of the columns' normalized axial forces and the amplification of up to two times or more of the beam bending moments. Thereafter, the frames were redesigned without neglecting the vertical seismic component, and the changes in the members' cross-sectional dimensions and reinforcement were determined. Finally, it was estimated that the economic impact of redesigning did not exceed 4% of the initial total construction cost of the frames.  
seismic design; planar frames; vertical ground motion; response spectrum analysis; time history analysis; construction cost
# Fast Ferroelectric Liquid Crystal Based Optical Switch: Simulation and Experiments
## 2 Experiments
### Cell Thickness Optimization  
A wedge FLC cell (Figure 2a) was made of glass plates coated with RN1175 polymer and rubbed in an antiparallel direction to achieve a well-aligned planar configuration. The plates were glued with spacers of 1 \(\upmu\)m along one edge and 30 \(\upmu\)m along the opposite edge. The orientation of LC molecules imposed by the glass substrates was parallel to the boundaries and to the wedge edge.  
The wedge cell was placed on a hot stage under crossed polarized states and the light transmission through the cell was measured using an Avaspec spectrometer, which provides accuracy of wavelength better than 10%. The empty cell thickness (Figure 2b) was measured from the interference fringes reflected from both glass substrates, and is calculated as  
\[d=\lambda_{k}\lambda_{k+n}n/2(\lambda_{k+n}-\lambda_{k}) \tag{1}\]  
where \(\lambda_{k}\) and \(\lambda_{k+n}\) are the wavelengths of two minima or maxima and \(n\) is the number of fringes. In the vicinity of each fringe, the cell is the same as a system of flat cells and the thickness varies slowly from one edge to the other edge.
## 3 Application in Green Concrete  
In this study, the 1 st to 11 th groups of the 66 concrete compressive strength mix proportions and tests results from XU, G.Q. [37] were adopted. Table 1 is the mix proportions of the green concrete and Table 2 is the tested results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Cement(/kg/m\({}^{3}\))**} & \multirow{2}{*}{**Limestone**} & **Fly** & \multirow{2}{*}{**Sand(/kg/m\({}^{3}\))**} & \multirow{2}{*}{**Gravel(/kg/m\({}^{3}\))**} & \multirow{2}{*}{**Water(/kg/m\({}^{3}\))**} \\  & & **Flag(/kg/m\({}^{3}\))** & & & & & **Reducer(/kg/m\({}^{3}\))** \\ \hline
1 & 252 & 54.0 & 54.0 & 828 & 1053 & 158.4 & 9.00 \\
2 & 252 & 64.8 & 43.2 & 828 & 1053 & 158.4 & 9.00 \\
3 & 252 & 75.6 & 32.4 & 828 & 1053 & 158.4 & 9.00 \\
4 & 252 & 86.4 & 21.6 & 828 & 1053 & 158.4 & 9.00 \\
5 & 252 & 97.2 & 10.8 & 828 & 1053 & 158.4 & 9.00 \\
6 & 252 & 108.0 & 0 & 828 & 1053 & 158.4 & 9.00 \\
7 & 216 & 72.0 & 72.0 & 828 & 1053 & 158.4 & 9.00 \\
8 & 216 & 86.4 & 57.6 & 828 & 1053 & 158.4 & 9.00 \\
9 & 216 & 100.8 & 43.2 & 828 & 1053 & 158.4 & 9.00 \\
10 & 216 & 115.2 & 28.8 & 828 & 1053 & 158.4 & 9.00 \\
11 & 216 & 129.6 & 14.4 & 828 & 1053 & 158.4 & 9.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mix properties of green concrete.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Compressive Strength/MPa**} \\ \cline{2-4}  & **3 d** & **7 d** & **28 d** \\ \hline
1 & 17.6 & 23.4 & 34.0 \\
2 & 17.0 & 22.6 & 33.3 \\
3 & 17.0 & 25.0 & 34.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tested compressive strength of green concrete.  
Figure 1: Overview of the model and methodology. SQP: quadratic programming; CCSDOT: concrete compressive strength development over time.
###### Abstract  
This study proposes a fast correlation-based filter with particle-swarm optimization method. In FCBF-PSO, the weights of the features selected by the fast correlation-based filter are optimized and combined with backpropagation neural network as a classifier to identify the faults of induction motors. Three significant parts were applied to support the FCBF-PSO. First, Hilbert-Huang transforms were used to analyze the current signals of motor normal, bearing damage, broken rotor bars and short circuits in stator windings. Second, Relieff, symmetrical uncertainty and FCBF three feature-selection methods were applied to select the important features after the feature was captured. Moreover, the accuracy comparison was performed. Third, particle-swarm optimization (PSO) was combined to optimize the selected feature weights which were used to obtain the best solution. The results showed excellent performance of the FCBF-PSO for the induction motor fault classification such as had fewer feature numbers and better identification ability. In addition, the analyzed of the induction motor fault in this study was applied with the different operating environments, namely, SNR = 40 dB, SNR = 30 dB and SNR = 20 dB. The FCBF-PSO proposed by this research could also get the higher accuracy than typical feature-selection methods of Relieff, SU and FCBF.  
fast correlation-based filter (FCBF); back propagation neural network (BPNN); Hilbert-Huang transform (HHT); motor failure; Relieff; symmetrical uncertainty (SU); feature selection; particle-swarm optimization (PSO)
## 4 Results and Discussion
### Environmental Availability  
For most soil samples from the plot treated with MWS, the concentrations of PHE released after reaction with deionized water were generally less than 1 \(\upmu\)g kg\({}^{-1}\) with a few exceptions (Table 4), resulting in trivial fractions of less than 0.10%, calculated as the percentage of the total PHE concentration that was removed by the water extraction. In environmental terms, the fraction of PHE that may be available for plant uptake and easily leachable from the soil was assessed to be negligible. Zinc, Ni, and Cu were the PHE that showed the highest water-extractable concentrations, with their maximum values being measured in the leachates of the samples T50 (154 \(\upmu\)g kg\({}^{-1}\)), T40 (41.38 \(\upmu\)g kg\({}^{-1}\)), and Tl0 (31.4 \(\upmu\)g kg\({}^{-1}\)), respectively. Remarkable differences were observed when comparing the PHE concentrations extracted with water from both groups of soil samples under study. The contents of Cu, Zn, Ni, Cd, Cr, and Pb in the extracts from the upper sampling layers (up to 20 cm depth) of the amended plot were significantly lower than those from the control soil sample (\(p<0.01\)). No meaningful changes were detected in the concentrations of As and Sb released from the surface soil, although they were higher in the 10-20 cm depth interval of the amended soil compared to control. A significant decrease (\(p<0.01\)) was also observed in the water-extractable amounts of all PHE at the amended soil depth of 20-50 cm, with the exception of Pb (at 20-30 cm depth) and Sb (at 40-50 cm depth). At deeper depths (50-70 cm), the
# An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform
## 4 Simulations and Comparative Analysis
At the same time, in order to verify the superiority of the proposed method in selecting the center position of the resonant frequency band, the spectral kurtosis method is used for the demodulation analysis of the test signal, and after three Kurtogram layers, the signal is decomposed into several sub-signals of the same bandwidth, as shown in Figure 7a. The highest Kurtosis band is shown in the dotted circle, with a center frequency of 4250 Hz and a bandwidth of 1500 Hz. Its envelope spectrum is displayed in Figure 7b. It can be observed that the fault feature information cannot be extracted in the envelope spectrum because of the large error in selecting the center position. Figure 5: Hilbert envelope spectrum of the optimal merging mode. Figure 6: Analysis results by the EMD method of (**a**) the waveforms first five IMFs and (**b**) the Hilbert envelope spectra. In the actual industrial environment, noise interference is unavoidable. The parameterless scale-space histogram method is sensitive to noise, which leads to the narrow boundary division of the spectrum. It is difficult to demodulate the fault information in corresponding modes. Therefore, the reconstructed mode can be effectively used for fault feature recognition by selecting the center of the resonance band and reasonably combining the adjacent modes. The result analysis in the analog signal shows that the AEWT has a better performance than the EMD and FK methods. To further verify the effectiveness of this method, it is applied to the task of actual mechanical fault identification.
# Prediction System for Overhead Cranes Based on Digital Twin Technology
## 4 Life Prediction for the Bridge Crane Based on Tent-SSA-BP
### Life Prediction and Analysis  
Through recording and analyzing production and operation parameters of overhead cranes, the parameters affecting the operating life of the equipment were integrated, and the remaining service life of the equipment was predicted using scientific analysis methods. With sufficient equipment production and operation data, the existing data could be used for mathematical modeling to achieve the fuzzy operation of the remaining service life of the equipment; when equipment production and operation data are sufficient, the algorithm can be used to estimate the remaining service life of equipment.  
Fatigue failure of the main girder of the bridge crane is the main failure mode. Thus, life prediction for bridge cranes mainly aims at the fatigue-life prediction for the main girder. The main factors affecting the fatigue life of mechanical equipment include the working environment and conditions, the stress state of the parts, and the material properties of the parts. Meanwhile, the cumulative fatigue damage theory was relatively complete. Due to the differences in the work tasks of bridge cranes, the load of the main girder was a random parameter, and the historical data of random load changes became the fatigue-life data of the main girder of the bridge crane, which was the premise of fatigue-life prediction and could reflect the change of load over time. Tent-SSA-BP was established through data acquisition and processing. Specifically, preprocessing was conducted on the stress-time curve of the main girder, and then a statistical analysis and average stress correction were performed.
## 7 Numerical and Results
### Average End-to-End Hop Count \(\eta\)  
Figure 6a,b display the average end-to-end hop count \(\eta\) versus \(\beta\) graphs. First, FN shows significantly smaller \(\eta\) compared to NN. When \(\beta=0.01\), both the theory and simulation curves are close to the blockage-free graphs \(\eta^{*}\). As \(\beta\) increases, \(\eta\) of FN increases due to less likelihood to find far-distance nodes. On the other hand, the NN shows the opposite trend because of the lower effective density of the LoS nodes in \(\mathcal{A}\) (less candidates), which results in less chance to find a LoS node with a short hop distance. Hence, the gap between the two routing strategies become smaller, as \(\beta\) increases.  
As expected, \(\eta\) with \(L=250\) m is about twice as many as that with \(125\) m. Moreover, comparing the theoretical and simulation results, the gap between the two is larger for \(L=125\) m compared to \(L=250\) m. This is expected because the error between the analysis and simulation becomes smaller, when the network size grows, as discussed in Section 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Routing**} & \multicolumn{2}{c}{\(L=125\) **m**} & \multicolumn{2}{c}{\(L=250\) **m**} \\ \cline{2-5}  & \(\mathbf{\Phi=40^{\circ}}\) & \(\mathbf{\Phi=60^{\circ}}\) & \(\mathbf{\Phi=40^{\circ}}\) & \(\mathbf{\Phi=60^{\circ}}\) \\ \hline FN & \(2.657\) & \(4.183\) & \(5.315\) & \(8.367\) \\ \hline NN & \(10.42\) & \(13.10\) & \(20.83\) & \(26.19\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Blockage-free end-to-end (EE) hop count \(\eta\)
# A Review on Carbon Source and Sink in Arabic Land Ecosystems
## 3 Main Research Methods of Carbon Sources and Sinks in Arable Land Ecosystems
### Direct Observation of Carbon Sources and Sinks among Arable Land Ecosystems
#### 3.1.2 Eddy Covariance Method  
The eddy covariance (EC) method refers to the vertical flux of a substance, which is the covariance between the concentration of the substance and its velocity. On the landscape scale, EC is the most widely used technology, and can comprehensively measure the trace gas flux in a large area [69]. This method provides a good opportunity to measure the NEE of the arable land system. If the NEE of the arable land ecosystem is greater than the amount of carbon released from the system in the form of agricultural practices and food consumption, there is potential for carbon sequestration [70]. EC is a direct measurement of CO2 exchange characterized by advantages such as the absence of interference in the environment and continuous observation of sample sites [71]. In the Midwest of the United States, [PERSON] et al. [70] assessed the net biological community productivity (NBP) of the corn/soybean no-tillage ecosystem by measuring the 6-year carbon flux and the carbon release related to agricultural practices. The results showed that the region was a large carbon sink. For the corn/soybean ecosystem, large-scale no-tillage systems could offset about 2% of the carbon emissions of the United States every year. [PERSON] et al. [72] studied the net carbon budget of winter-wheat-summer-maize continuous cropping systems using EC, crop growth and soil respiration data on the North China Plain. The results showed that the winter wheat system was a carbon sink of 90 g C m\({}^{-2}\), while the summer maize system was a carbon source of 167 g C m\({}^{-2}\); therefore, the double-cropping system in this area was a carbon source of 77 g C m\({}^{-2}\) on an annual basis, which was equivalent to the annual average loss rate of topsoil SOC stocks from 2003-2008 on comparison of the measured SOC data from 1998-2008. Although there are potential uncertainties in EC technology, including systematic errors from sensitivity to high-frequency turbulence, random errors from inadequate sample size associated with averaging period, and vertical and horizontal advection issues [73], the continuous operation of the flux network enables scientists to quantify the factors leading to interannual changes in annual and seasonal fluxes and to detect flux trends related to current environmental changes at the regional and global scales [60], providing an opportunity to understand the relationship between ecosystems and the atmosphere.
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 5 Conclusions  
This paper takes an important step by presenting a lightweight, efficient and easy to deploy collaborative DDoS mitigation scheme leveraging SDN. Using the proposed scheme, a SDN controller in any AS can directly communicate with the controllers in the adjacent network via secure C-to-C protocol and inform them about an ongoing attack. This helps in efficient propagation of attack definitions all the way from the victim to the attack sources. We also introduced three different deployment approaches i.e., linear, central and mesh in our testbed and tested the overall efficiency.  
Experiments with our prototype implementation show that the effect of mitigation is instantaneously transferred from destination to source. It took around 2.14 s to mitigate the attack in an eight hop linear deployment. Furthermore, it only requires somewhere between 290 to 330 ms to process and forward attack definitions between adjacent networks. The processing of attack definition payload (verification of signature and insertion of flow table entry) is also lightweight even on low end machines with a processing time of around 13 ms for a payload with 10,000 IP addresses. The performance benchmark of central deployment approach show reasonable CPU (35%) and memory (25%) utilization on average commodity hardware. The results also show that it only took 21 s to disseminate attack definition to 5000 AS. Of course, with high-end expensive servers the dissemination time can be significantly reduced.  
Figure 16: Avg. memory usage (%).  
Figure 15: Avg. CPU usage (%).  
**Author Contributions:** [PERSON] proposed the research problem and solution architecture for the Collaborative DDoS mitigation using SDN paradigm. He also drafted the manuscript. [PERSON] helped in prototype implementation and evaluations of the proposed solution.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 3 Verification Methods for Quantum Money in Quantum Wallets  
Building on the definitions and foundational concepts of quantum money and quantum wallets introduced in Section 2, a critical aspect for practical implementation is the ability to verify quantum money securely and reliably. Unlike classical money, verifying quantum money is inherently challenging due to quantum mechanical principles such as the no-cloning theorem and state collapse upon measurement. These constraints require innovative verification methods that preserve the integrity of the quantum states while confirming authenticity. In this section, we explore several theoretical approaches to verification -- including blind verification protocols, classical tokens paired with quantum states, non-destructive quantum verification, entanglement-based verification, and zero-knowledge proofs -- each designed to comply with quantum mechanics and support secure verification in future quantum financial systems.
###### Abstract
The coulombic efficiency and energy recovery are calculated from the COD removed, meaning that the amount of COD removed would affect the MFC performance. The hydraulic retention time (HRT) is an important factor affecting the CLR and COD removed, which can be controlled by the flow rate or reactor volume. In most cases, HRT is controlled by the flow rate. Nevertheless, it is difficult to control the HRT of each MFC in a hydraulic series-connected stack system through the flow rate. This is because an MFC with the same volume is typically used for the stack system, and the stack system has been operated at the same flow rate. The HRT of each MFC in the stack system consisting of MFCs of different volumes can be controlled. On the other hand, there are no reports the performance of MFC stack consisting of MFCs with different volumes. Therefore, this study investigated the performance of an MFC stack system comprised of three MFCs with different volumes and operated according to the hydraulic connection and electric connection methods. The performance was evaluated in terms of the voltage production, power density, energy recovery, and COD removal efficiency. These results will provide a unique option for the practical MFC applications to wastewater treatment.
## 2 L/F-Transferase and Its Biological Functions  
In _Escherichia coli_, L/F-transferase is the sole aminoacyl-tRNA-protein transferase [26; 27]. Specifically, L/F-transferase catalyzes the transfer of an amino acid from an aminoacyl-tRNA to the N-terminus of a protein (Figure 1) having an N-terminal basic residue (Arg/Lys). This enzyme catalyzes a substrate-assisted peptide bond formation reaction mechanism that is analogous to that proposed for the ribosome [28].  
Currently, there is only a minimal understanding on the physiological function of N-end rule in prokaryotes, which has been more elusive since only a couple of substrates have been verified to date. Roles in putrescine homeostasis, proline catabolism, peptide transport, growth phase-dependent proteolysis and stationary phase exit have been proposed [27; 29; 30; 31].  
Figure 1: L/F-transferase reaction. A schematic diagram depicting the transfer of an amino acid from an aminoacyl-tRNA substrate to the N-termini of a polypeptide substrate by L/F-transferase.  
To date, only two _in vivo_ protein substrates of L/F transferase have been identified and validated, and they are putrescine aminotransferase (PATase) and DNA protection during starvation protein (Dps) [30]. PATase catalyzes the aminotransferase reaction to convert putrescine to 2-oxoglutarate to generate L-glutamate and 4-aminobutanal, where putrescine is a polyamine and polyamines have been shown to also be involved in protein biosynthesis, oxidative stress and biofilm formation [32; 33; 34]. The degradation of PATase via the N-end rule may be a mechanism to ensure putrescine homeostasis. Dps plays a role in protecting DNA during starvation and oxidative stress by forming a complex with DNA or by chelating iron from toxic by-products of the Fenton reaction [35]. In bacteria, Dps levels are low during exponential growth but increase upon starvation and oxidative stress [36]. Dps levels have been demonstrated to be dependent on the growth phase by two separate mechanisms, via a ClpXP-dependent or an L/F-transferase-dependent targeted proteolysis [30; 31; 36; 37; 38].  
Recently, a more thorough study identified over 100 putative _E. coli_ N-end rule substrates, many of which belong to large protein complexes suggesting roles in remodelling or regulation of protein complexes [39]. Contrary to earlier understanding, there seems to be no correlation between L/F-transferase-dependent proteolysis and different growth phases since the authors observed that certain N-end rule substrates are enriched during exponential growth while others are enriched during the stationary phase. These observations and identified putative substrates of _E. coli_ L/F-transferase and N-end rule remain to be further tested and validated. If more evidence is provided to relate L/F-transferase's role in regulating these putative substrates, it has been suggested that the _E. coli_ N-end rule may play a more central role in biological processes than previously recognized including but not limited to cell division, DNA replication, transcription, translation, metabolism, as well as protein quality control [39].
## 2 Theoretical Background
### The Forward Model
#### 2.2.1 Convolutional Model  
Using Equation (6) to set up the convolution model in the discrete domain and the reflectivity formulation from [25], we arrive at:  
\[\mathbf{d}=\mathbf{S}\mathbf{A}\mathbf{D}\mathbf{z}+\mathbf{e}_{d}, \tag{4}\]  
where \(\mathbf{d}\) is a vector that comprises concatenated seismic traces (acquired at different angles) with dimensions \(n_{\theta}n_{d}\times 1\), \(\mathbf{z}\) is the logarithmic vector of elastic properties with dimensions \(3n\times 1\), \(\mathbf{S}\) is the convolution matrix of size \(n_{\theta}n_{d}\times n_{\theta}n_{d}\) incorporating the discretized wavelet for each angle, \(\mathbf{A}\) is a matrix of size \(n_{\theta}n_{d}\times n_{\theta}n_{d}\) containing coefficients \(a_{\alpha}(t,\theta)\), \(a_{\beta}(t,\theta)\), and \(a_{\rho}(t,\theta)\), \(\mathbf{D}\) is a first-order differential operator of dimension \(n_{\theta}\times n_{d}\times 3n\)[30], and \(\mathbf{e}_{d}\) represents the seismic noise.