esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### XRD Analysis  
Figure 1 shows the XRD patterns of the hybrid materials based on polyvinylpyrrolidone (PVP) functionalized with Ag or Pd doped LaMnO\({}_{3}\) type perovskite structures. The structural parameters were refined by fitting the XRD patterns using the Rietveld refinement technique with High Score software was used to investigate the crystalline structure of the new hybrid materials obtained. Thus, the XRD pattern of pure PVP showed two broad characteristic peaks at 20 of 11.25\({}^{\circ}\) and 21.21\({}^{\circ}\), which is in good agreement with results reported by [PERSON] et al. [34] and [PERSON] et al. [35]. The obtained hybrid materials present well defined peaks, mainly corresponding to the perovskite phase, which leads to the conclusion that the polymer did not mask the perovskite material.
## 5 Results and Discussions
### Comparison with 2D Wind Tunnel Data
In summary, good agreements are achieved for the lift and pressure coefficients between the field measurements and 2D wind tunnel measurements, which verifies that the designation, implementation, and data reducing methods of the aerodynamic field measurement platform developed in this paper are reasonable and reliable. To evaluate the dispersion of instantaneous lift coefficients in Figure 15, the standard deviations were calculated by Equation (12), where \(n\) is the sample size of the lift coefficient dataset, \(Cl_{k}\) is the angle-averaged lift coefficient defined in Equation (11). Figure 20 shows standard deviations of lift coefficient at different angle-average angles of attack, which are about 0.2. This means that the natural inflow condition in the field will introduce about standard deviation of 0.2 for lift coefficient when aerodynamic loads are predicted by aerodynamic data from 2D wind tunnel tests. In order to investigate the source of the dispersion of instantaneous lift coefficients, aerodynamic data in time history were analyzed further:  
\[\sigma(Cl_{k})=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(Cl_{ki}-Cl_{k})^{2}} \tag{12}\]  
Figure 19: Comparison of pressure coefficients: (**a**) \(\alpha=6^{\circ}\), (**b**) \(\alpha=12^{\circ}\). Figure 20: Standard deviations of the lift coefficient.
## 4 Conclusions  
In this study, an ANN-based surrogate model was proposed for accelerated seismic risk assessment of a system-level bridge transportation network. To accurately evaluate the performance of the bridge network, TSTT through traffic analysis was considered as a performance index, and the MCS framework phase was adopted for performance evaluation according to each event. TSTT has high accuracy but high calculation cost, so it is difficult to adopt it easily as a performance index. However, this study proposed a methodology to predict TSTT without direct TSTT calculation by introducing an ANN-based surrogate model to reduce excessive time cost. In addition, a maximum failure event generation strategy that can efficiently reduce the number of system events was introduced to efficiently handle the entire event vector.  
To demonstrate the proposed methodology, an actual bridge transportation network was reconstructed with 48 bridge structures and five damage states (1440 nodes and 3490 links). For various earthquake scenarios, the performance of the entire network system was estimated stochastically with respect to the amount of total travel time. Training data were generated with an earthquake magnitude of 7.0, and TSTT values of various earthquake magnitudes at the various epicenter locations were evaluated to verify the robustness of the epicenter and earthquake magnitude. As a result of the numerical analysis, it was confirmed that the ANN-based surrogate model predicts TSTT very accurately for the earthquake magnitude and the location of the epicenter.  
Seismic risk assessments have been performed using the proposed methodology. However, the methodology proposed in this study is expected to be applied for seismic resilience and optimal decision making for civil infrastructure networks requiring numerous iterative analyses. Therefore, it will be possible to evaluate the performance of the network system efficiently prior to earthquakes and plan maintenance strategies immediately after earthquakes even in a high-dimensional bridge transportation network.  
Conceptualization, H.-Y.T. and Y.-J.L.; formal analysis, S.Y.; software, S.Y. and M.K.; methodology, S.Y., J.K., and Y.-J.L.; supervision, Y.-J.L.; validation, S.Y., J.K., and Y.-J.L.; writing--Original draft, S.Y.; writing--Review and editing, W.S., M.K., J.K., and Y.-J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (20 SCIP-B146946-03) from the Construction Technology Research Program funded by the Ministry of Land, Infrastructure and Transport of Korean government.  
This research was supported by a grant (20 SCIP-B146959-03) from Construction technology research program funded by Ministry of Land, Infrastructure and Transport of Korean government.  
The authors declare no conflict of interest.  
Figure 12: Relative importance of bridge number (**a**) 1 to 12, (**b**) 13 to 24, (**c**) 25 to 36, and (**d**) 37 to 48.
## 3 BBC Code Design and Optimization  
The analysis above shows that the main lobe noise caused by BPSK modulation exceeds the phase-tracking requirements for gravitational wave detection by two orders of magnitude, which will severely impact the phase tracking accuracy. According to its generation mechanism, the DC component produced by an unbalanced PRN code causes main lobe noise, thereby increasing internal interference within the system and affecting the phase extraction accuracy. Therefore, a design method for BBC sequences is proposed that ensures bit balance within any length of the PRN sequence during data encoding to eliminate the impact of the DC component.
## 4 Experimental Verification
### Result in High-Speed Tactile, Acceleration, and Gyro Sensor Network System  
We constructed a high-speed tactile, acceleration, and gyro sensor network system. Figure 9 shows the experimental result obtained by one tactile sensor, one gyro sensor, and two acceleration  
Figure 8: Experimental results with various sampling rates: **(a)** sampling time is 1 ms, **(b)** sampling time is 0.5 ms, and **(c)** sampling time is 0.2 ms.  
sensors. Figure 9 shows that the data-acquisition synchronization with the proposed method was achieved successfully in the developed sensor network system using the various sensors. Consequently, the proposed method can also be applied to the high-speed sensor network system using specific sensors based on the data-acquisition synchronization.
###### Abstract  
The transmission of signals in a hybrid satellite-terrestrial system (HSTS) in the presence of co-channel interference (CCI) is considered in this study. Specifically, we examine the problem of amplify-and-forward (AF)-based relaying in a hybrid satellite-terrestrial link, where the relay node is operating in the presence of a dominant co-channel interferer. It is assumed that direct connection between a source node (satellite) and a destination node (terrestrial receiver) is not available due to masking by obstacles in the surrounding. The destination node is only able to receive signals from the satellite with the help of a relay node located at the ground. In the proposed HSTS, the satellite-relay channel follows the shadowed Rice fading; and the channels of interferer-relay and relay-destination links experience generalized Nakagami-\(m\) fading. For the considered AF-based HSTS, we first develop the analytical expression for the moment generating function (MGF) of the overall output signal-to-interference-plus-noise ratio (SINR). Then, based on the derived exact MGF, we derive novel expressions for the average symbol error rate (SER) of the considered HSTS for the following digital modulation techniques: M-ary phase shift keying (M-PSK), M-ary quadrature amplitude modulation (M-QAM) and M-ary pulse amplitude modulation (M-PAM). To significantly reduce the computational complexity for utility in system-level simulations, simple analytical approximation for the exact SER in the high signal-to-noise ratio (SNR) regime is presented to provide key insights. Finally, numerical results and the corresponding analysis are presented to demonstrate the effectiveness of the developed performance evaluation framework and to view the impact of CCI on the considered HSTS under varying channel conditions and with different modulation schemes.  
hybrid satellite-terrestrial system (HSTS); amplify-and-forward (AF) relay; co-channel interference (CCI); land mobile satellite (LMS) channel; M-PSK; M-QAM; M-PAM +
Footnote †: journal: Sensors
# Ferrites and Nanocrystalline Alloys Applied to DC-DC Converters for Renewable Energies
## 2 MFT Design and Magnetic Materials
### Magnetic Materials  
Magnetic materials of cores are key in defining the performance of MFTs, its losses, and its nominal frequency of operation. Parameters and characteristics of magnetic materials such as permeability and flux density are two main elements to be considered by MFT designers. In the present work, two core materials suitable for the medium frequency range, nanocrystalline alloys, and ferrites, are used for designing and implementing MFTs. The main characteristics of nanocrystalline alloys and ferrites important for the design of MFTs are presented in Table 2.  
As presented in Table 2, both the maximum flux density and the permeability are higher in nanocrystalline alloys than in ferrites, but the latter has lower cost per unit volume (USD/cm\({}^{3}\)). It should also be noted that ferrites have flux densities of 0.2-0.3 T at 20 kHz [18], while nanocrystalline alloys are 0.9 T at 5 kHz [16; 17]. Then, a question arises, is the performance, efficiency, cost, and size of a nanocrystalline alloy MFT better than a ferrite MFT at 20 kHz. Although nanocrystalline alloy cores tend to achieve higher power density, the low cost of ferrites gives these materials a chance to a lower-cost MFT prototype.  
Figure 2: Design process.
## Materials and Methods
### Headspace SPME-GC-Ms  
Frozen onion powder (0.5 g) was mixed with 2 mL of 90% methanol in a 20 mL headspace vial. The sample was swirled and left to stand in an ice slurry for 10 min to inactivate the enzyme activity. Saturated NaCl solution (8 mL) and fenchyl alcohol (internal standard (IS), 100 mL of 10 ppm) were then added to the headspace vial and sealed with a PTFE-coated silicon septum screw cap. Samples were then loaded on to the auto sampler (PAL RSI 85, CTC Analytics, Zwingen, Switzerland) for analysis.  
Volatile compounds were extracted using a divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS; 50/30 \(\upmu\)m) SPME fiber (Supelco, Sigma-Aldrich) for 30 min at 40 \({}^{\circ}\)C. Volatile compounds were analyzed using an Agilent 6890N gas chromatograph equipped with a VL MSD 5975B mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). Separations were achieved on a Zebron ZB-WAX capillary column (60 m \(\times\) 0.32 mm ID, 0.50 \(\upmu\)m d.f., Phenomenex, Torrance, CA, USA) using helium as the carrier gas at 1.0 mL/min constant flow. The sample desorption was performed for 5 min at 240 \({}^{\circ}\)C in the GC injection port (split/splitless inlet) operated in splitless mode for 2 min. The oven temperature was held at 40 \({}^{\circ}\)C for 3 min, increased at 5 \({}^{\circ}\)C/min to 120 \({}^{\circ}\)C, and then increased at 10 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C with a hold time of 5 min (total run time 38 min).  
The MS was operated at a source temperature of 230 \({}^{\circ}\)C, interface temperature of 150 \({}^{\circ}\)C, using electron impact (EI) mode with electron energy at 70 eV. All the analyses were carried out in a full scan mode, scanning in the range of 30-250 amu at a scan rate 1.56 u/s (6.1 scans/s). MSD ChemStation software (version D.03.00.611, Agilent Technologies, Santa Clara, CA, USA) was used for peak analysis of the volatile compounds. Compounds were tentatively identified by comparing the mass spectra to the National Institute of Standards and Technology (NIST 2014) database. The extracted ion count of the detected compounds was manually integrated, using specific mass ions. Linear retention indices were calculated to support the identification of the compounds. Samples were analyzed in a randomized order, blocked by biological replicates.
## 3 Proposed Scheduling Algorithm  
We assumed in this paper that the convergecast tree for each node to transmit its sensor date to the sink is given and each node has a fixed number of packets to transmit in each slot frame. The proposed scheduling algorithm is a centralized algorithm and after the convergecast tree is constructed, the scheduling server receives the following information for scheduling from each node k.  
* \(T_{k}\): the number of packets to be transmitted by the node in each slot frame;
* \(P_{k}\): parent node of node \(k\); and
* \(R_{k}\): PRR value of link \(<\)_k_, \(P_{k}\)\(>\).
# A Synchronous Spin-Exchange Optically Pumped NMR-Gyroscope
## 1 Introduction
### Bloch Equation
We optically pump along \(\hat{x}\) such that \(\mathbf{R}=R(t)\hat{x}\), and the solution to Equation (2) is  
\[S_{+}=\frac{R(t)}{\Gamma^{\prime}}+i\frac{R(t)\Omega_{z}^{S}}{ \Gamma^{\prime 2}}=\frac{R(t)}{\Gamma^{\prime}}e^{i\varepsilon_{z}}, \tag{3a}\] \[S_{z}=-\frac{R(t)}{\Gamma^{\prime 2}}(\Omega_{y}^{S}-\frac{ \Omega_{z}^{S}}{\Gamma^{\prime}}\Omega_{x}^{S})=\frac{-R(t)}{\Gamma^{\prime 2 }}\text{Im}[\gamma^{S}b_{K}^{S}K_{+}e^{-i\varepsilon_{z}}], \tag{3b}\]  
where  
\[\varepsilon_{z}=\tan^{-1}(\frac{S_{y}}{S_{x}})\equiv\tan^{-1}(\frac{B_{z0}}{B _{w}})<<1 \tag{4}\]  
is the magnetometer phase shift. \(B_{w}=\Gamma^{\prime}/\gamma^{S}\) is the magnetic width of the magnetometer. A stray \(B_{z}\) effectively rotates the quantization axis of the Rb magnetometer thus causing a phase shift (\(\varepsilon_{z}\)) in the measurement of a rotating \(B_{\perp}\). The Xe NMR can be driven by modulating the transverse Rb polarization near each isotope's resonance frequency while the bias pulsing frequency is kept fixed. Such an excitation scheme is desirable as it effectively AC couples \(S_{z}\). Hence, we Fourier expand the Rb polarization as \(S_{+}(t)=e^{i\varepsilon_{z}}\sum_{p,q}s_{pq}e^{i(p\omega_{1}+q\omega_{2})t}\) where \(s_{pq}\) is the Fourier coefficient of \(S_{+}\) at \(p\omega_{1}+q\omega_{2}\). With the substitution \(K_{+}=K_{\perp}e^{\pm i(\omega_{d}t+\delta)}\), where \(\delta\) is the phase shift of the Xe relative to the phase of \(S_{+}\) and \(\omega_{d}\) is the drive frequency, we find the real part of Equation (1a) to be  
\[\frac{dK_{\perp}}{dt}=-\Gamma_{2}K_{\perp}+\Gamma_{S}^{K}\sum_{p,q}s_{pq}\cos[ (\omega_{d}+p\omega_{1}+q\omega_{2})t+\delta\pm\varepsilon_{z}]. \tag{5}\]  
The steady state solution of this equation is \(K_{\perp}=\Gamma_{S}^{K}s_{pq}/\Gamma_{2}\) when \(\omega_{d}\) is chosen to satisfy the resonance condition \(\omega_{d}=-p_{0}\omega_{1}-q_{0}\omega_{2}\) for some \(p_{0}\) and \(q_{0}\) and when \(\delta,\varepsilon_{z}<<1\). Similarly, the imaginary part of Equation (1a) (once again for \(\delta,\varepsilon_{z}<<1\)) is  
\[\frac{d\delta}{dt}=-\Delta-\Gamma_{2}(\delta\mp\varepsilon_{z}), \tag{6}\]  
where \(\Delta=\omega_{d}-\gamma B_{z}\pm\omega^{R}\). The sign in front of \(\varepsilon_{z}\) is isotope dependent because the Xe isotopes precess in opposite directions. We can solve this equation in the Fourier domain to find  
\[\tilde{\delta}=-\frac{\tilde{\Delta}\mp\Gamma_{2}\tilde{\varepsilon}_{z}}{ \Gamma_{2}+i\omega}, \tag{7}\]  
where the notation \(\tilde{f}=f(\omega)\) is used. By monitoring \(S_{z}\), we can obtain measurements of \(\delta\) for each Xe species. These measurements of \(\delta\) are used to extract the Xe resonance frequencies. We perform comagnetometry using these Xe resonance frequencies to remove magnetic field correlations and arrive at a measurement of inertial rotation. In Section 2 we detail the implementation of our Xe excitation scheme, and in Section 3 we demonstrate the performance of our system.
# Visualization of Distributed Plasticity in Concrete Piles Using OpenSeesPy
## 1 Introduction and Background  
### _Motivation: Limitations of Lumped Hinges in Practice_  
The seismic design of reinforced concrete (RC) wahr structures commonly adopts simplified nonlinear modeling approaches, with lumped plastic hinges at pile-deck connections and pile bases. These idealizations, accepted by standards such as ASCE 61-14, ASCE 41-17, Eurocode 8, and ACI 318 [1, 2, 3, 4] and design guidelines for pile-supported wharves as POLB, POLA and OCDI [5, 6, 7], aim to concentrate inelastic behavior within discrete regions for computational efficiency [8]. However, they inherently assume zero-length plasticity zones and neglect critical mechanisms affecting actual seismic performance--particularly the spread of inelasticity and variation in axial load.  
Recent studies have underscored the limitations of lumped hinge models to represent damage progression under bidirectional or cyclic loading, underestimating displacements corresponding to the ultimate loads [9]. Also, yield and ultimate deformations are not accurately estimated, in addition to the dependency of plastic hinge locations [10; 11]. Furthermore, hinge lengths in these models are typically prescribed through empirical formulas, disregarding axial load fluctuations, the damage progression [12; 13], and its diminution due to the confining effect of surrounding soil [14]. Such simplifications may mislocate critical plastic regions and underpredict damage extent, posing risks in the performance of harbor facilities.
# Bacterial Diversity and Dominant Spoilage Microorganisms in Fresh-Cut Broccoli
###### Abstract  
Different cutting types not only affect the quality and bioactive substances of brocoli but may produce wounding stress that influences the quantity and diversity of microorganisms and finally leads to safety concerns. In this study, three different fresh-cutting types (floret (F), quarterly cut floret (QF) and shredded floret (FS)) and four storage stages (0, 1, 2 and 3 day(s)) were designed to detect the bacterial diversity in broccoli. Results showed that the quantity and growth of microorganisms in fresh-cut broccoli were positively correlated with the intensity of fresh-cut damage, and the 97 strains isolated using 16S rRNA gene sequences were mainly classified into four phyla: Proteobacteria, Actinobacteria, Bacteroidetes and Firmicutes. The predominant phylum was Proteobacteria, dominated by _Pseudomonas_ spp. and _Acinetobacter_ spp. Four representative strains of different species were selected to analyze their spoilage potential on fresh-cut broccoli. _Pseudomonas_ spp. had great impacts on the corruption of broccoli, which affected fresh-cut broccoli by metabolizing to produce nitrite and consuming nutrition. Moreover, _Comomonas_ spp. and _Acinetobacter_ spp. also affected the quality of broccoli, but received little attention previously. This study provides a reference for precise bacteriostasis and preservation of fresh-cut vegetables such as broccoli.  
broccoli; bacterial diversity; cutting type; microbiology; food safety
## 3 Results and Discussions
### SEM-EDS Analysis  
Figures 7-9 demonstrate the fracture micromorphology and EDS spot analysis of the hydraulic lime K3 after curing for 3, 7, and 28 days. At 3 days, flaky Ca(OH)\({}_{2}\) was still present, with some CaCO\({}_{3}\) and C-S-H forming. After curing for 7 days, Ca(OH)\({}_{2}\) reacted with H\({}_{2}\)O and CO\({}_{2}\) to form CaCO\({}_{3}\), while the hydration reactions produced certain quantities of C-S-H. When cured for 28 days, more CaCO\({}_{3}\) heavy crystals were produced, and larger quantities of C-S-H formed inside the hydraulic lime. Thus, the mesh structures that formed between the particles became interwoven. The EDS pattern of the marked area is shown in Figures 7a, 8a and 9a and the corresponding data are shown in Figures 7b, 8b and 9b and Table 3, respectively. From the analysis of the first spot, there is 60.54% O and 34.06% Ca, with very small amounts of C and Si, and the composition of the spot is Ca(OH)\({}_{2}\). The second spot contains 26.59% O, 9.06% Si, and 57.99% Ca, which, combined with the microstructure, shows that the spot is C-S-H. The third spot contains 16.55% C, 43.99% O, and 28.51% Ca, which is known to be CaCO\({}_{3}\) when combined with  
Figure 5: Hydraulic lime mortar blocks shrinkage rates.
## 3 Theory and Methods
### GaMD Simulations  
To improve conformational samplings of the WT and mutated HRAS, three independent GaMD simulations were run on each system. Firstly, each system was subjected to a 30,000-step steepest minimization followed by a 5000-step conjugate gradient minimization to remove bad atom-atom contacts. Subsequently, the temperature was gradually raised from 0 to 310 K for 1 ns using a gentle heating procedure under the NVT condition, wherein heavy atoms were constrained with a weak harmonic constant of 2 kcal\(\cdot\)mol\({}^{-1}\).A\({}^{2}\). A temperature of 310 K was adopted to reflect the temperature of the human body. Then, a 2 ns equilibrium process in 310 K was conducted to optimize six systems at the NPT ensemble, and a restriction similar to the heating process was also executed. At last, three conventional molecular dynamics (cMD) simulations were run for 50 ns on six systems at a constant temperature of 310 K and pressure of 1 bar using periodic boundary conditions and the particle mesh Ewald (PME) method [89; 90]. Additionally, the initial atomic velocities for each system were assigned with the Maxwell distribution. The last structures from the above three independent cMD simulations were treated as the starting structures for three independent GaMD simulations.  
GaMD simulations can reduce the free energy barriers of the systems through harmonic boost potential [43], which efficiently enhances conformational samplings of the WT and mutated HRAS. The duration of each independent GaMD simulation was 1.2 us. For each system, three independent GaMD trajectories were connected into a single joined GaMD trajectory (SJGT) to facilitate the post-processing analyses. The PyReweighting program [91] proposed by [PERSON] et al. was employed to reweight the data extracted from the post-processing analyses and detect the original free energy of six HRAS-related systems. The SHAKE algorithm constrained all bonds between hydrogen atoms and heavy ones during the cMD and GaMD simulations [92]. The Langevin thermostat with a collision frequency of 2.0 ps\({}^{-1}\) was adopted to control the temperature [93]. The PME method with an appropriate cutoff value of 10 A was used to estimate non-bond interactions. The module pmemd.cuda, inlayed in Amber 20, was applied to all simulations [94; 95].
## 3 Background
### General CubeSat Specifications
#### 3.1.5 GEVS and QB50 Testing Standard Considerations  
The QB50 project is a network of 50 CubeSats with 2 or 3U dimensions with the purpose of long duration exploration of Earth's lower thermosphere (200-380 km) for re-entry research and in-orbit demonstration of technologies and miniaturized sensors. The QB50 project database provides a detailed methodology and range of acceptance criteria for vibrational testing, while the GEVS documentation provides analysis of all other required testing methods [25].  
Figure 5: P-POD Exterior [1].