esa-sceva/satcom-chunk-collection huggingface dataset:
# Theoretical Study of SOF\({}_{2}\) Adsorption on Pd/Pt-Ni(111) Bimetallic Surfaces
## 2 Methods and Models  
All of the calculations in this paper were carried out based on DFT [23]. The electron exchange and correlation energy were calculated within the generalized gradient approximation (GGA) and the Perdew-Burke-Ernzerhof (PBE) function [24; 25]. Double numerical plus polarization (DNP) basis sets were used [26]. As spin polarization has major effects on the adsorption energies for a magnetic system, all the calculations were spin-polarized [27]. The convergence criterion for the total energy and maximum force were set at \(1\times 10^{-5}\) Ha and \(2\times 10^{-3}\) Ha/A, respectively. The self-consistent field (SCF) tolerance was \(1\times 10^{-6}\) Ha, and direct inversion of iterative subspace (DIIS) was made to accelerate convergence of SCF [28].  
The Ni(111) monometallic and Pd/Pt-Ni(111) bimetallic surfaces were modeled by periodic slabs with a supercell of a \(2\times 2\) unit cell. The vacuum area between the slabs is set to 20 A in order to avoid interaction between the adjacent superlattices. The Brillouin zone was sampled by using a \(5\times 5\times 1\) array of \(k\)-points in the Monkhorst-Pack grid [29]. As shown in Figure 1, four surfaces were explored in this work, including two surface structures named Pd-Ni(111) and Pt-Ni(111) surfaces, and two subsurface structures named Ni-Pd-Ni(111) and Ni-Pt-Ni(111) surfaces. Furthermore, both the Ni(111) monometallic and bimetallic Pd/Pt-Ni(111) surfaces provide four typical sites for the adsorption, including the t-top site, b-bridge site, fcc-face centered cubic site, and hcp-hexagonal close packed site.  
The adsorption energy (\(E_{\text{ads}}\)) of the molecule adsorption on Pd/Pt-Ni(111) was defined as Equation (1):  
\[E_{\text{ads}}=E_{\text{slab/gas}}-E_{\text{slab}}-E_{\text{gas}} \tag{1}\]  
\(E_{\text{slab/gas}}\) was the total energy of the adsorbed system, \(E_{\text{slab}}\) and \(E_{\text{gas}}\) were the energies of the surface and the free gas molecule, respectively. Thus, negative \(E_{\text{ads}}\) means the process of the adsorption was exothermic and that adsorption can happen spontaneously. The more energy released, the stronger the adsorption was.  
The electron density distribution was calculated by Mulliken population analysis. The charge transfer (\(Q_{\text{T}}\)) in the adsorption process was defined as Equation (2):  
\[Q_{\text{T}}=Q_{\text{a}}-Q_{\text{b}} \tag{2}\]  
\(Q_{\text{b}}\) and \(Q_{\text{a}}\) were the respective total charge of the gas molecules before and after adsorption. \(Q_{\text{T}}>0\) means electrons transfer from the gas molecules to the surface.
# Radiation-Balanced Lasers: History, Status, Potential
## 4 Development of Radiation-Balanced Lasers
### Athermal Bulk Lasers  
Since the first experimental observation in 1995, laser cooling of solids has been realized with Yb\({}^{3+}\), Er\({}^{3+}\), Tm\({}^{3+}\), and Ho\({}^{3+}\) ions doped in a wide variety of low phonon glasses and crystals. Yb\({}^{3+}\) ions are the most promising RE ions for laser cooling applications. They have only one excited manifold, so that Yb\({}^{3+}\) ions are free from excited state absorption, which can be a source of multi-phonon decay resulting in undesirable heat generation in the system.  
Experiments devoted to radiation-balanced lasers began at the Naval Research Laboratory (NRL) in 1999 [30]. New materials for laser cooling were investigated. In 2002, the first athermal bulk laser was experimentally demonstrated with an Yb\({}^{3+}\):KGd(WO\({}_{4}\))\({}_{2}\) crystal [31]. The laser design based on direct diode pumping of Yb\({}^{3+}\):KGd(WO\({}_{4}\))\({}_{2}\) crystals was developed further in [32].  
Successful laser systems are based on high-quality materials. YAG and fused silica are the best choice. They have the highest optical quality and lowest losses. In 2010, the Yb\({}^{3+}\):YAG laser rods with Yb\({}^{3+}\) concentrations of 1%, 2%, 3%, and 5% were investigated.  
The scheme of the radiation-balanced laser based on the Yb\({}^{3+}\):YAG laser rod is presented in Figure 6. High power pumping with fibre lasers at 1030 nm produced a near diffraction limited laser at 1050 nm. The mean fluorescent wavelength for Yb\({}^{3+}\):YAG is 1010 nm. Lower thermal loading and better beam quality were reached with the 2% Yb\({}^{3+}\):YAG rod. The average power of up to 200 W of output with a beam quality of M\({}^{2}=1.2\) was demonstrated without significant thermal loading or thermo-optic distortions [33]. A typical thermal image of the Yb\({}^{3+}\):YAG rod inside the athermal laser is presented in Figure 7. Thermal loads were reduced to below 0.01% relative to the laser output power. As one can see in Figure 7, fluorescence for the laser rod heats the mirror on the left. The thermally insulated laser rod is almost not heated. Its temperature is very close to the ambient temperature.  
Figure 5: Dependence of the signal and pump powers from the length of the athermal fibre amplifier for three different input signal powers: (1) 25 W, (2) 30 W, and (3) 40 W [26].  
To improve laser performance, the laser rod was replaced with a slab composed of Yb\({}^{3+}\):YAG and sapphire (Figure 8) [34]. As one can see in Figure 8, a single 2 mm \(\times\) 110 mm 3% Yb\({}^{3+}\):YAG sample was fusion-bonded to a sapphire plate along its length. The sapphire plate provides a refractive index mismatch of 0.07, significantly reducing fluorescence trapping and providing guiding of the pump at the wavelength 1030 nm. The sapphire bond permitted to reduce the fluorescence lifetime from 2 ms to 1.25 ms. Using this laser design, 1 W of heat for every kilowatt of output laser power was realized.
# Ferroelectric Materials: A Novel Pathway for Efficient Solar Water Splitting
## 3 Solar Energy Conversion of Ferroelectric Materials  
### Ferroelectric Effect and Materials  
The basic concept of ferroelectricity was introduced by [PERSON] salt in 1920: if an external electric field is applied to a ferroelectric material, the dipoles in the crystalline or polycrystalline structure are induced to produce a ferroelectric phenomenon with spontaneous polarization and alignment with the external field. Even when the electric field is switched off, the material maintains a spontaneous polarization. This is called the ferroelectric phenomenon. Furthermore, the spontaneous polarization is reversed by the application of an electric field. Ferroelectrics exhibit a phase transition phenomenon.  
Namely, the polarization of the dipoles exhibits interactive electric dipole moments with and without an applied electric field. When the ferroelectric is at a higher temperature than the phase transition temperature, the spontaneous polarization is lost due to thermal fluctuations.  
When the spontaneous polarization is reversed, it causes the ferroelectrics to exhibit the property of piezoelectricity. Ferroelectric materials can be categorized into two types: the order-disorder type and the displacement type, based on the physical mechanism used to generate the ferroelectric characteristics.  
Ferroelectric materials, which are mostly used as condenser and as semiconductor elements, are a cubic system with a perovskite structure. When the crystal structure of the perovskite exceeds the Curie temperature \(T_{c}\) (\(\sim\)120 \({}^{\circ}\)C), similar to a ferroelectric crystal of the ABO\({}_{3}\) perovskite structure, it changes its form to a paraelectric phase in which the crystal structure is the cubic symmetry [26; 27].
# Temperature Sensing with Nd\({}^{3+}\) Doped YAS Laser Microresonators
## 4 Results and Discussion
### Displacement of the WGM  
The emission spectrum shown in Figure 3 was obtained for different laser powers. As a consequence, two different effects related to the laser heating of the microsphere were observed. The first one was the difference of the ratio of the intensities from the \({}^{4}\)F\({}_{5/2}\) and the \({}^{4}\)F\({}_{3/2}\) to \({}^{4}\)I\({}_{9/2}\) emissions due to the thermal redistribution of population. This made it possible to obtain the temperature of the microsphere using the thermal calibration obtained in the previous section, under the assumption that the WGM resonances do not affect the overall shape of the emission band.  
The second one was the redshift of the WGM. Thereby, a study of the displacement of the WGM as a function of temperature could be performed. The shift of the resonances could be explained as follows: due to laser heating, the volume of the sphere and the refraction index increased, and as a consequence, the wavelengths of the WGMs changed according to Equation (3). As expected, a linear red shift of the wavelength of the WGM was observed, as shown in Figure 5.  
From the measurements of the wavelength displacements corresponding to the maxima of three WGM resonances, plotted in Figure 5, an average displacement rate of 12.9 pm/K was obtained for the 890 nm band. Higher values of the shifts were obtained for the peaks in the 1300 nm band, averaging 17.5 pm/K.  
To compare this method with the FIR technique, the sensitivity of the wavelength displacement was calculated using Equation (6), and a maximum relative sensitivity of 15 \(\times\) 10\({}^{-6}\) K\({}^{-1}\) was obtained at the lowest temperature in the measured range.  
The WGM limit of resolution of the resonant wavelength is about 1% of its linewidth [21, 22]. The narrowest linewidth observed in the emission spectra of the microspheres was about 0.3 nm in the 1300 nm band. Using these values in Equation (8),  
Figure 5: Displacement of three WGM peaks as a function of temperature.  
the temperature resolution is in the order of 0.2 K. Therefore, the resolution of the WGM technique is one order of magnitude higher than that of the FIR technique.
# A MAP Overhead Aware Two-Dimensional OFDMA Burst Construction Algorithm
## 3 Problem Statement
Let a two-dimensional matrix, \(M\), represent adopted MCSs in different subchannels for each connection, and \(M(x,y)\) denote the MCSs used by \(C_{i}\) in the \(y\)-th subchannel. Let \(MCS_{i}^{j}\) and \(S_{i}^{j}\) denote the adopted MCSs and the number of occupied slots by \(B_{i}^{j}\) of \(C_{i}\), respectively. A burst is in a continuous area, and it can be represented as the starting slot to the ending slot; burst \(B_{i}^{n}~{}=~{}[(x_{1},y_{1}),(x_{2},y_{2})]\), where \((x_{1},y_{1})\) and \((x_{2},y_{2})\) represent the starting slot and the ending slot, respectively. The throughput \(Tp_{i}\) for connection \(C_{i}\) is computed by \(S_{i}^{1}\times MCS_{i}^{1}+\ldots+S_{i}^{n}\times MCS_{i}^{n}\) where \(S_{i}^{n}\times MCS_{i}^{n}\) is the bandwidth that \(B_{i}^{n}\) can support. Figure 5 shows when \(B_{1}^{2}\) is allocated in the poor subchannel condition, the throughput \(Tp_{1}\) of \(C_{1}\) is low. However, the throughput \(Tp_{1}\) of \(C_{1}\) is high since \(B_{1}^{1}\) is allocated in good subchannel condition. When \(Tp_{i}\) exceeds required bandwidth \(BWR_{i}\) connection \(C_{i}\) only requires \(BWR_{i}\) to transmit its data, the effective throughput is only \(BWR_{i}\). To well utilize the bandwidth resources, the internal fragmentation should be avoided. For any connection \(C_{i}\), the number of occupied slot \(S_{i}^{1}+S_{i}^{2}\ldots+S_{i}^{n}\) is sufficient for providing the requested bandwidth \(BWR_{i}\). The connection should be occupied \(S_{i}^{1}+S_{i}^{2}\ldots+S_{i}^{n}\) rather than \(BWA_{i}\) because  
Figure 4: The MAP is allocated in a column-wise order. Figure 5: Subchannel diversity. unused slots internal to a burst are wasted. Figure 6 shows connection \(C_{i}\) allocated 14 slots while it only uses 12 slots to transmit its data and the remaining two slots are wasted.
# Additively Manufactured Polymers for Electronic Components
## 3 Polymers Used for AM in Electronic Applications
### Extrinsically Conductive Polymers (ECPs)
#### 3.2.4 Filler-Polymer Interactions  
Understanding and engineering the polymer-filler interface is essential to enhance polymer-filler adhesion and improve conductivity in ECPs. Surface functionalisation of fillers can introduce strong covalent or intermolecular bonding, which can enhance phase affinity, reduce interfacial tension, improve dispersion, and lower percolation thresholds,while reinforcing mechanical cohesion. For example, CNTs and graphene can be functionalised with -COOH, -OH, amine groups, or polymer wrapping to improve interaction with the polymer host, enhance dispersion, and facilitate -r-r or hydrogen-bonding interactions [120]. MXenes also present in-built tunable surface terminations (-OH, -F, and -O), enabling strong hydrogen bonding and ion-dipole coupling with polar polymers. This improves dispersion, antioxidant surface passivation, and resistance to humidity-induced degradation [121].  
Figure 8: Storage and loss moduli as a function of frequency for GO inks and representative images of the corresponding liquid crystal (LC) phases at different concentrations: (**a**) 0.05 mg mL\({}^{-1}\); (**b**) 0.25 mg mL\({}^{-1}\); (**c**) 0.5 mg mL\({}^{-1}\), (**d**) 0.75 mg mL\({}^{-1}\), (**e**) 2.5 mg mL\({}^{-1}\), (**f**) 3.5 mg mL\({}^{-1}\), (**g**) 4.5 mg mL\({}^{-1}\), and (**h**) up to 13.3 mg mL\({}^{-1}\). Adapted and reprinted with permission from ref. [113].  
In situ polymerisation is also a strategy successfully applied to polyaniline/CNT composites wherein the polymer matrix forms around the filler, leading to covalent or supramolecular anchoring, and improving electrical pathways and mechanical fatigue resistance [122].
## 4 Experimental Results
### 2-DE Images Classification
#### 4.3.1 Comparison with Conventional Methods  
Table 3 lists the best classification accuracies (ACCs) with respect to changing \(H\) (=1, 2, 3, 4), including two baselines, PCA (using 188 features) + linear SVM (L-SVM) and kernel SVM (K-SVM) using a Gaussian kernel. In this study, due to the limited source domain data [2], a compact model with \(D_{1}=188\) by PCA using \(\mathbf{x}^{s}\) and \(\mathbf{x}^{t}\) (a cumulative contribution of 188 features is over 99.5%), with \(D_{1}\)= \(D_{2}=D_{3}=D_{4}\), was used. The best Gaussian kernel parameter was selected from \(\{1.0\times 10^{-4}\), \(1.0\times 10^{-3}\), and \(1.0\times 10^{-2}\), \(0.1\}\). The table also lists the positive predictive value (PPV), the negative predictive value (NPV), the Matthews correlation coefficient (MCC), and the F1-score (F1) as references. The MCC is used for evaluating the performance considering the imbalance of \(N^{t}(1)\) and \(N^{t}(2)\), while F1 is the harmonic value computed by PPV and sensitivity.  
Figure 4: Examples of 2-DE images. X- and Y-axes represent molecular weights and isoelectric points, respectively, and black regions represent protein spots. The position of the same protein is approximately the same for each patient because each axis represents an absolute physical quantity. **Left**: sepsis; **middle**: nonsepsis; **right**: source domain 2-DE images.  
As shown in this table, our method outperformed other methods. The results also confirmed that \(d\), in our method, became about five times larger than \(d\) in the conventional ATDL (0.19 \(\rightarrow\) 0.97). These results suggest that our method is effective for performing sepsis classification.
# Design of Optimized Energy Management Strategy for All-Wheel-Drive Electric Vehicles
## 5 Discussion and Conclusions  
This paper proposed energy management for all-wheel-drive electric vehicles based on the partly-known driving cycle. To overcome the shortcoming in real-time of the algorithm, the control strategy was divided into three parts. The parameters that affected the vehicle power demand and speed were extracted, and the torque distribution coefficient was determined according differ driving cycles, which is developed based on dynamic programming coordination strategy (DPCS) and rule-based control strategy (RBCS), respectively. In the end, the extracted parameters as shown in Figure 7 will be used in the on-line control strategy to extend the cycling mileage, and coordinating the power output of the battery and exerts the advantage of the motor comprehensively. The specific work could be summarized as the following aspects.  
1. In this paper, based on rule-based and dynamic programming coordination, a control strategy of the offline parameter extraction method is proposed. When the vehicle speed is higher than 25 km/h, the two rear motors get involved, with the front motors working more reasonably, along with increasing the vehicle demand power.
2. The dynamic programming coordination strategy is proposed, and the method obtains the optimal torque split ratio through a partly-known driving cycle. The benefit of this strategy is using electric power to the greatest extent and taking management of the motor, which will work and put the motor in an efficient range.
3. To verify the dynamic programming coordination strategy, the simulation was conducted based on MATLAB/Simulink. According to simulation results, the dynamic programming coordination strategy plays a significant role in all-wheel-drive performance, compared to a rule-based control strategy.  
For this paper, all authors have participated in corresponding researches. [PERSON] conceived and designed the proposed prototype; [PERSON], [PERSON] conceived and performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper. [PERSON] and [PERSON], revised the English writing of this paper. All authors advised on the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 11: Rear motor efficiency under different control strategies.
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 5 Design of the DNN Accelerator and Acceleration Framework Based on FPGA
### Accelerator Design for Specific Application Scenarios  
In the practical application of neural networks, people often customize FPGA accelerators with required functions according to specific application scenarios. Since accelerators customized according to specific application scenarios are relatively easy to design and can effectively solve the corresponding problems, this method is commonly used to accelerate neural networks in specific application scenarios, especially in speech recognition, image processing, natural language processing, and other fields.
# A Novel Multi-Objective Five-Elements Cycle Optimization Algorithm
## 5 Simulation Experiment and Result Analysis  
In this section, we first compare the parameters of the proposed MOFECO algorithm, including number of cycles \(q\) and number of elements \(L\) in each cycle, update condition \(F_{ij\_r}\), the local-global probability \(P_{s}\) and the mutation methods. Secondly, we analyze the performance of MOFECO by empirically comparing it with three classic MOEAs, namely, NSGA-II [5], MOPSO [12], PESA-II [11] and the two latest algorithms KnEA [2] and NSLS [15]. The experiments were performed on 15 test problems, which were taken from four widely used test benchmarks suites the Zitzler et al's Test suite (ZDT) [37], the Deb et al's Test suite (DTLZ) [38], the Walking Fish Group (WFG) [39] and the Many objective Function (MaF) [40]. Among them, the ZDT series are bi-objective functions and the DTLZ series are tri-objective functions and we set the MaF and WFG series as four-objective functions. In terms of experimental configuration, all the implementations are done on a 2.39 GHz Intel(R) Xeon(R) CPU with 32 GB RAM under Microsoft Window 10. MOFECO has been implemented in MATLAB R2016a and for the NSGA-II, MOPSO, PESA-II, KnEA and NSLS, the programs are derived from the platEMO provided by the literature [41].
## 3 Results and Discussion
### Structural Characterization of the Composites
The XRD patterns in Figure 2i show that with the introduction and increasing content of the filler, the (100) interplanar spacing of PTFE increases, accompanied by an increase in the full width at half maximum (FWHM), indicating that the introduction of the filler relaxes the molecular structure of PTFE and reduces its crystallinity. It suggests that FGi restricts the crystallization of PTFE [32], which is consistent with the SEM analysis. Figure 2j demonstrates the curves of melting point (\(T_{m}\)) and crystallinity (\(X_{c}\)) with FGi contents, in which the \(X_{c}\) of PTFE is calculated according to the following equation:  
\[X_{c}=\frac{\Delta H_{m}}{\Delta H_{f}(1-x)}\times 100\% \tag{3}\]  
where \(x\) is the mass fraction of FGi in the composite, \(\Delta H_{m}\) is the melting enthalpy from DSC curves in Figure S3, and \(\Delta H_{f}\) is the fusion heat of completely crystalline PTFE (69 J\(\cdot\)g\({}^{-1}\)) [6]. As shown in Figure 2j, the \(T_{m}\) and crystallinity of the composites decrease with the increase in FGi content, and reach a minimum \(T_{m}\) of 326.1 \({}^{\circ}\)C and crystallinity of 52% at PTFE-0.75. Figure 2: Cross-sectional SEM images of FGi/PTFE composites with FGi loading of 0 wt% (**a**), 0.25 wt% (**b**), 0.5 wt% (**c**), and 0.75 wt% (**d**), 2D WAXS patterns of FGi/PTFE composites with FGi loading of 0 wt% (**e**), 0.25 wt% (**f**), 0.5 wt% (**g**), and 0.75 wt% (**h**), XRD patterns (**i**), \(T_{m}\) and crystallinity derived from DSC curves (**j**) of FGi/PTFE composites. The decrease in \(T_{m}\) and crystallinity can be attributed to the steric hindrance effect of the FGi, which restricts the crystallization of PTFE. In summary, the structural characterization demonstrates that FGi can induce the orientation of PTFE molecular chains. The oriented structure enhances the propagation efficiency of phonons in the in-plane direction, contributing to the increase in TC along this direction, and thus achieving anisotropic thermal properties [33; 34]. Meanwhile, due to steric hindrance effect of FGi, the crystallinity of the composites decreases. This induced effect may be driven by the interfacial interaction between FGi and PTFE during fabrication processes [35; 36].