esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 7 Other Metal-Oxide Nanomaterials
### ZnO-Based
For instance, a study synthesized nitrogen-doped (N-doped) ZnO micro-objhedrons to evaluate their photocatalytic activity toward formaldehyde degradation (Figure 23B) [192]. The UV-visible adsorption spectra indicated that the introduction of nitrogen atoms as dopants in ZnO significantly improved the photocatalyst's adsorption properties in the visible light range. The N-doped ZnO photocatalyst exhibited a degradation rate of approximately 85.6% for formaldehyde, outperforming both N-doped P25 and pure ZnO. The high photocatalytic activity of the N-doped ZnO catalyst can be attributed to three main factors: the enhanced adsorption capacity under visible light, the increased charge carrier transfer rate on the ZnO surface, and the enhanced separation of charge carriers due to the formation of an intermediate band in the ZnO band gap at around 1.3 eV above the valence band, induced by the introduction of nitrogen atoms. When compared to most other photocatalytic materials, ZnO is more widely available, inexpensive, and easy to synthesize, making it an economically viable option for large-scale photocatalytic air purification systems. It can also be synthesized in various nanostructures (e.g., nanorods, nanowires, nanoparticles), which provide high surface areas and tunable properties for improved catalytic activity. Still, ZnO nanoparticles tend to aggregate, reducing their effective surface area and, consequently, their photocatalytic efficiency. Over the years, ZnO has proven effective in decomposing a wide range of air pollutants, including VOCs, carbon monoxide (CO), and particulate matter, due to its strong oxidative properties. Moreover, ZnO has also demonstrated antimicrobial properties, making it useful in air filtration systems for controlling airborne pathogens. ZnO primarily absorbs UV light, which constitutes only \(\sim\)5% of the solar spectrum, limiting its effectiveness under natural sunlight. Extending its activity to the visible light range remains a significant challenge. Moreover, ZnO is prone to photocorrosion under prolonged light irradiation, particularly in humid conditions, which can degrade its structure and reduce long-term stability.
## 2 Quadrotor-Load Model  
The quadcopter used has a cross structure, as shown in Figure 1. It consists of a frame with four motors that provide the forces that lift and move the vehicle. The suspended load is attached to the center of the frame.  
The quadcopter with a suspended load system can be considered as a multi-body dynamical system [20]. Two frames are typically used to describe the movements of a quadrotor vehicle. The earth frame, also known as the inertial frame, is the first frame. The second frame is a body-fixed frame, in which the origin OB represents the quadrotor's center of mass.  
The position of the quadrotor body frame with respect to the earth frame is given by \(x\), \(y\), and \(z\), which represent the longitudinal, lateral, and vertical motions, respectively. The orientation of the body frame with respect to the earth frame is given by the angles \(\phi\), \(\theta\), and \(\psi\), which represent the roll, pitch, and yaw, respectively [21].  
The suspended load can be described as a point mass that behaves as a spherical pendulum. Therefore, its motion can be described by two angles, \(\theta_{L}\) and \(\phi_{L}\), where \(\phi_{L}\) is the load oscillation angle in the xz plane, and \(\theta_{L}\) is the load oscillation angle out of the xz plane.
## 2 Results  
### Physicochemical Analysis of UrGES Protein  
The _U. rhynchophylla_ genome was assembled to a size of 627.72 Mb with a contig N50 of 1.80 Mb. Integrated genomic and transcriptomic analyses identified 46,909 genes [25], from which the coding sequence (CDS) of _UrGES_ was obtained. The genome-derived _UrGES_ sequence showed consistency with the full-length cloning results (Figure S1). Following validation, the _UrGES_ sequence was registered in GenBank with accession number BankIt2972777 seq1 PV822539. The CDS comprised 1761 bp, translating into 586 amino acids, corresponding to an estimated molecular mass of 67.5 kDa. The theoretical pI, instability index and grand average of hydrophobicity (GRAVY) were predicted to be 5.64, 51.14, and \(-\)0.266, respectively. Plant-mPloc predicted chloroplast localization for UrGES, with TargetP-2.0 identifying a 44-amino-acid chloroplast transit peptide at its N-terminus (Figure 1). Furthermore, transmembrane region analysis revealed no transmembrane domains in UrGES (Figure S2).  
Figure 1: Amino acid sequence alignment of UrGES with geraniol synthases (GESs) in other plants. The highly conserved DDoxD and NSE/DTE motifs are highlighted within red frames. Chloroplast transfer peptide of UrGES is highlighted within the dark blue frame. The following protein sequences were used for multiple sequence alignment: CaGES1 (XP_027063605.1) and CaGES2 (XP_027063694.1) from _Coffea arabica_; GJGES (QCT83300.1) from _Gardenia jasminoides_; CeGES1 (XP_027169926.1) and CeGES2 (XP_027169969.1) from _Coffea eugenitioides_; CoTPS4 (A0A7C5 KLV3.1) from _Cananga adorata_; SmGES (QKS73430.1) from _Swertia mussoiti_; CrGES (AFD64744.1) from _Catharanthus roseus_; GeGES (AJT35508.1) from _Gentiana crassa_; and RgGES (QJD13722.1) from _Relmannia glutinosa_.
## 2 Materials and Methods
### Instrumental Texture Measurement  
Texture analysis of venison samples was conducted using both texture profile analysis (TPA) and the Warner-Bratzler (WB) shear test. For TPA, samples were prepared as 12 mm diameter by 12 mm height cylinders and measured using an SMS TA.XT Plus Texture Analyzer (Stable Micro Systems Ltd., Godalming, UK) with Texture Exponent 32 software 4.0 version. A cylindrical probe with a 35 mm diameter applied compression at speeds of 3.0 mm/s (pre-test phase), 1.0 mm/s (test phase), and 3.0 mm/s (post-test phase). The probe compressed 70% of the sample height, retracted, paused for 2 s, and performed a second compression. Resistance (N) was recorded every 0.01 s and plotted as a force-time curve.  
For the WB shear test, wild red deer meat samples were removed from vacuum packaging and sliced into pieces with dimensions of 10 mm thickness, 10 mm width, and 100 mm length. Each sample was placed under the V-blade, and shear force measurementswere recorded. The blade moved downward at a speed of 1 mm/s to cut through the sample. The peak shear force (N) was recorded, providing a measure of the meat's tenderness. Each sample was evaluated 10 times to ensure consistency and reliability of the data.  
Analysis of Sarcoplasmic and Myofibrillar Proteins by Sodium Dodecyl-Sulfate Polyacrylamide Gel Electrophoresis  
Sarcoplasmic and myofibrillar proteins were extracted following a modified method based on [PERSON] et al. [16] and [PERSON] [17], as described in our previous study [14].
## 4 Numerical Results Analysis
### Acoustic Field Results Analysis  
#### 4.4.1 Fluid-Borne Noise  
The flow data file of the last 10 impeller rotation cycles (including the pressure, velocity, and fluid density values) is used as the initial sound source file for the hydrodynamic noise calculation at \(Q_{r}\). The coupling effect of the pump shell is considered in the simulation. Distribution map of sound pressure level and the spectrum curve of field point located at the pump outlet are shown in Figure 18. It can be seen from the figure that the sound pressure level is steadily decreasing from volute inlet to volute outlet. The regions with the highest sound pressure level are distributed at the impeller outlet, especially the volute tongue area. That might be because of the rotor-stator interaction between the rotating impeller and stationary volute. Flow-path space of the spiral volute start from the first cross-section is increased and eventually becomes big enough for the passing fluid, which results in weakening of sound source intensity. The frequency spectrum of predicted noise is dominated by discrete frequencies. The sound pressure level in the low-frequency area is relatively high overall; \(f_{0}\) and 6\(f_{0}\) (675 Hz) are the main frequencies, which are consistent with the aforementioned noise test and flow field simulation results. There are relatively large peaks between 3000-6000 Hz, which might be due to the coupling of the pump shell and the vibro-acoustic interaction.  
Figure 17: Mode shapes of pumps structure with amplitude unit mm: (**a**) 1 st mode; (**b**) 4 th mode.  
Figure 18: Fluid-borne noise characteristics at \(Q_{r}\): (**a**) the \(L_{\text{p}}\) contour at 6\(f_{0}\) with unit dB; (**b**) spectrum curve.
## 3 Results
### Application of the Method
#### 3.1.3 Accuracy Analysis and Training of BPNN  
In our application, the structure of the BPNN contains the input layer with six input feature vectors, one hidden layer with 25 processing neurons, and the output layer with one neuron. Before training the BPNN, we assessed the accuracy of the extracted sample sets to validate the availability of our method. Table 6 shows the average value of the 10 results of 10-fold cross-validation with error matrix, omission error (OE), commission error (CE), overall accuracy (OA), and Kappa coefficient (K) value [9; 24; 31; 67]. The 10-fold cross validation method shows that the extracted sample sets achieve high values of overall accuracy and Kappa coefficient as well as low values of omission error and commission error. The average values of overall accuracy and Kappa coefficient are 98.68% and 97.46% respectively, which indicates that the predicted outputs were close to the target outputs. Thus, the training sample sets are applied to the BPNN for burned area detection.  
Figure 8: Separability index M of each spectral index on the comparisons between burned area and unburned types: (**a**) M results of Hot Pot Fire; (**b**) M results of Range 12 Fire; (**c**) M results of Rey Fire.  
The sample sets generated from the optimal spectral indices (MIRBI, NBR, CSI, bsVI, NSEv1, and NSTv1) of the five land cover types were used to train the network. The training setting was set with a stopping criterion on a mean squared error value of 0.001. The network was trained until it reached the local minimum. Afterward, the trained network was applied to other wildfire cases to validate the applicability of the new burned area mapping method (see Sections 3.2.2 and 3.2.3 for the details).
## 2 Materials and Methods
### Nontargeted Metabolic Profiling  
To determine significantly changed metabolites (SCMs) between the control treatment and salt stress treatment, three biological replicates of leaves were analyzed. The leaf samples were delivered to Novogene Biotech Co., Ltd. (Beijing, China) to analyze the nontargeted metabolome. The \(D\). _officiale_ leaf samples (100 mg) were homogenized and were extracted with pre-chilled 80% methanol solution containing 0.1% (\(p/v\)) formic acid. After being incubated on ice for 5 min, the extracted samples were centrifuged at 15,000\(\times\)\(g\), 4 \({}^{\circ}\)C for 10 min. Then, the supernatant of each leaf sample was diluted to final concentration containing 53% methanol by liquid chromatography-mass spectrometry (LC-MS) grade water and was injected into the LC-MS/MS system analysis. LC-MS/MS analyses were performed using an ExionLCTM AD system coupled with a QTRAP(r) 6500+ mass spectrometer (both from SCIEX, Framingham, MA, USA). Equal volumes of each experimental sample were combined for quality control (QC) samples. Blank samples were 53% methanol aqueous solution containing 0.1% formic acid instead of experimental samples and were pretreated in the same way as the experimental samples. The high-performance liquid chromatography (HPLC) and MS conditions, as well as metabolite identification and quantification, were described as per by [PERSON] et al. [24].  
The identified metabolites were annotated using the KEGG database ([[http://www.genome.jp/kegg/](http://www.genome.jp/kegg/)]([http://www.genome.jp/kegg/](http://www.genome.jp/kegg/)), accessed on 1 March 2021), the HMDB (human metabolome database, [[http://www.hmdb.ca/](http://www.hmdb.ca/)]([http://www.hmdb.ca/](http://www.hmdb.ca/)), accessed on 1 March 2021), and the Lipidmaps database ([[http://www.lipidmaps.org/](http://www.lipidmaps.org/)]([http://www.lipidmaps.org/](http://www.lipidmaps.org/)), accessed on 1 March 2021). The metabolites with the variable importance in the projection (VIP) \(>\) 1, \(p\) value \(<\) 0.05, and the absolute value of log\({}_{2}\)FC \(\geq\) 1.3 were considered to be differential metabolites.
## 3 System Model and Architecture Design
### Data Throughput  
The average uplink sum rate per cell (data throughput) can be computed as follows:  
\[DT=K\cdot\left(1-\frac{\mu K}{U}\right)B\text{log}_{2}\Big{(}1+\beta_{D_{g}}( r)\Big{)} \tag{4}\]  
where \(DT\) is the data throughput and SINR is the signal-to-interference and noise ratio, \(K\) means the multiplexed users, \((1-\mu K/U)\) symbolizes the data fraction per frame, and \(B\text{log}_{2}\Big{(}1+\beta_{D_{g}}(r)\Big{)}\) is the data rate per user [29].
# Research on a Service Load Prediction Method Based on VMD-GLRT
## 2 Materials and Methods
### Signal Decomposition Method and Process  
Before the data are input into the GRU, considering that different services show different business characteristics under the load, and the model feature extraction will also affect the model's performance, the model's data characteristics should show certain regularity, highlighting its business-aware characteristics. Therefore, in this paper, the input data are decomposed by variational mode decomposition. In this paper, it is assumed that any set of load sequences is composed of multiple variables \(v_{k}(t)\), and \(\omega_{k}\) is the center band frequency of each component, where \(k\) is the component sequence number of mode decomposition. For each set of input service load data,  
* Firstly, \(v_{k}(t)\) is transformed by Hilbert transform to obtain the analysis signal and its center band is modulated: \[L_{k}(t)\ =\left[\left(\delta(t)+\frac{j}{\pi t}\right)\times v_{t}(t) \right]e^{-j\omega_{k}t}\] (3)  
Figure 1: Overall structure diagram of the model.  
* The squared norm \(L^{2}\) of the modulation center band is calculated and the bandwidth of each IMF is estimated as follows: \[\left\{\begin{array}{c}\min\limits_{\{v_{k}\},\ \{\omega_{k}\}}\left\{\sum\limits_{k}\| \hat{\rho}_{t}L_{k}(t)\|^{2}\right\}\\ s.t.\quad\sum\limits_{k}v_{k}=s\end{array}\right.\]
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 2 Moments from 3D Shapes
### Moments of a Shape  
The moments \(\mu_{i}\) of a shape are the projections of the function \(f\) over a set of basis functions \(\mathbf{\Psi}=\{\psi_{i}\}\):  
\[\mu_{i}=\langle f,\psi_{i}\rangle=\int_{D}f(\mathbf{x})\overline{\psi_{i}(\mathbf{x}) }\mathrm{d}\mathbf{x},\]  
where \(\overline{\psi}\) is the complex conjugate of \(\psi\). The properties of a particular moment based representation are therefore determined by the set of functions \(\mathbf{\Psi}\). There are two properties of this set that are desirable, namely:_Orthonormality._ The set of functions \(\boldsymbol{\Psi}\) is orthonormal if \[\langle\psi_{i},\psi_{j}\rangle=\delta_{ij},\] for all \(\psi_{i}\) and \(\psi_{j}\) in \(\boldsymbol{\Psi}\) (\(\delta\) is the Kronecker symbol, i.e., \(\delta_{ij}=1\) if \(i=j\), and 0 otherwise). 2. _Completeness._ The set of functions \(\boldsymbol{\Psi}\) is complete if for all functions \(f\in L^{2}(D)\): \[f=\sum_{i=0}^{\infty}\mu_{i}\psi_{i}.\]  
The orthonormality property is important as it guarantees the mutual independence of the computed moments. The completeness property implies that we are able to reconstruct approximations of the original shape from its moments. Directly associated with these two properties is [PERSON]'s theorem. This theorem is an important property for example in Fourier analysis that states that the sum (or integral) of the square of a function is equal to the sum (or integral) of the square of its transform. It is true in fact for all complete orthonormal basis. Indeed,  
\[\begin{split}\int_{D}|f(\boldsymbol{x})|^{2}\mathrm{d}x& =\langle f,f\rangle=\langle\sum_{i=0}^{\infty}\mu_{i}\psi_{i}, \sum_{j=0}^{\infty}\mu_{j}\psi_{j}\rangle\\ &=\sum_{i=0}^{\infty}\sum_{j=0}^{\infty}\mu_{i}\overline{\mu_{j} }\langle\psi_{i},\psi_{j}\rangle=\sum_{l=0}^{\infty}\sum_{j=0}^{\infty}\mu_{ i}\overline{\mu_{j}}\delta_{ij}\\ &=\sum_{i=0}^{\infty}|\mu_{i}|^{2}.\end{split} \tag{2}\]
# Correlation of Gravity and Magnetic Field Changes Preceding Strong Earthquakes in Yunnan Province
## 2 Data Processing and Accuracy
### Data of Gravity and Geomagnetic Total Intensity
#### 2.2.2 Geomagnetic Data Processing and Accuracy  
The instrument used to measure the total strength of geomagnetism was the GSM-19T PPM. According to the principle of nuclear magnetic resonance, the instrument reflects the total strength of the magnetic field through the frequency of the signal. It has good stability and is not affected by temperature, humidity, etc., and the observation accuracy is high, the operation is simple, the instrument is easy to carry, and it has the characteristics of digitization of data collection and automation of precise measurement.  
To acquire the total magnetic field measurements, a GSM-19T (GEM Corporation, Canada) PPM with a sensitivity of 0.15 nT @ 1 Hz, a resolution of 0.01 nT and an absolute accuracy of \(\pm\)0.2 nT were employed.  
The daily variation in the nearest station substitution method and the long-term variation correction of the natural orthogonal quantity model were used to obtain the annual variation of the lithospheric magnetic field. Diurnal variation is mainly used to eliminate regular diurnal variation and other exogenous fields in observed data, and long-term variation is aimed at obtaining the annual variation in the lithospheric magnetic field [16].  
The annual variation in the lithospheric magnetic field is calculated by Formula (1):  
\[\Delta F_{Little\_T_{2}-T_{1}}=(F_{Int\_T_{2}}-F_{Int\_T_{1}})-(F_{Main\_T_{2}}-F _{Main\_T_{1}}) \tag{1}\]  
In the calculation formula, \(\Delta F_{Little\_T_{2}-T_{1}}\) is the difference between the lithospheric magnetic field from \(T_{2}\) to \(T_{1}\) of a certain geomagnetic element, \(F_{Int\_T_{1}}\) is the endogenous magnetic field of the geomagnetic element (the result of daily variation), and \(F_{Main\_T_{1}}\) is the main magnetic field (the long-term regular change).
## 4 Optimization of the Energy Management Strategy
### Simulation Calculation and Analysis of Optimization Strategy  
According to the simulation method in Section 3.2, the genetic algorithm optimization simulation of the energy distribution strategy of tram is carried out. In the simulation, the number of individuals for each generation of population is 50, and a total of 100 generations have been evolved. The simulation results are as follows:  
\[P\_{disch\_bt}=91.57\text{ kW};P\_{disch\_uc}=153.74\text{ kW};P\_{disch\_fc}=101.21\text{ kW}\]  
Figure 13 shows the evolutionary process diagram of the genetic algorithm simulation. It can be seen that at the 36 th generation, the fitness level reached the maximum, which corresponds to the minimum fuel consumption of the tram. From the 36 th generation to the 100 th generation, the optimal  
Figure 11: Structure of the tramcar energy management strategy.  
solution of the genetic algorithm remains unchanged, that is, the algorithm converges. In the end, the entire journey fuel consumption is equivalent to 2.8354 kg.  
Figure 12: Optimization of the energy management strategy using a genetic algorithm.  
Figure 14 is the FC output power changing with running time. We can see that the train can satisfy the running power requirement.  
Figure 15 is the power output characteristics of each energy storage system after optimization. We can see that the FC is not shut down in the whole working condition, and the maximum output power is 101.21 kW. The output power of the SC fluctuates between \(-125\) kw and 250 kW. This is because the maximum charge current of the SC is three sevenths of the maximum discharge current. As for the battery, the utilization ratio is improved efficiently by exporting and absorbing more energy, which is the main reason for using the optimized energy management strategy to reduce the fuel consumption.  
Figure 14: The FC output power changing with train running time.  
Figure 13: The evolutionary process.  
Figure 15: The output power characteristics of each module after optimization. (**a**) Train running time–FC output power; (**b**) Train running time–SC output power; (**c**) Train running time–Battery output power.  
Figure 16 shows the tram running time-power status curve. It can be seen that the SOC of the super capacitor changes from 18% to 100%, and the SOC of the power battery drops significantly. This further proves that the optimized power distribution strategy has improved the utilization rate of power batteries. It should be noted that this figure only shows the status information of the battery and the super capacitor during the running of the tram, and does not record the process of charging the battery and capacitor to the initial state after the tram arrives at the station. This charging process has been considered in the optimization process.
## 3 Simulation Results
### Case III  
This experiment studied the stability of the system when the order of the SPM filter was insufficient. The order of the actual secondary path was set to 64 and the order of the SPM filter was set to 32. The other conditions were the same as those in Case 1. The simulation results are shown in Figure 5.  
Figure 4: The white Gaussian input: (**a**) the relative modeling error \(\Delta w(n)(dB)\); (**b**) the relative modeling error \(\Delta s(n)(\mathrm{dB})\); (**c**) the time-varying step-size upper bound parameter \(\mu_{\mathrm{max}}(n)\). The first-order AR process input: (**d**) the relative modeling error \(\Delta w(n)(dB)\); (**e**) the relative modeling error \(\Delta s(n)(\mathrm{dB})\); (**f**) the time-varying step-size upper bound parameter \(\mu_{\mathrm{max}}(n)\).  
It can be seen from Figure 5a,c that, when the secondary path order is insufficient, the ANC filter converges, but the relative error in the steady state is larger. The convergence speed is the same as that in Case I. Figure 5b shows that, compared with the normal order, the secondary path undergoes a sudden change when the order is insufficient, and the upper bound is more diminutive. This is because the error caused by the secondary path fitting is more significant. However, the ANC system can still converge in a stable manner.
# A Scalable Deep Network for Graph Clustering
## 3 The Proposed Method
### Quasi-GNN Module  
Although the auto-encoder can learn the embeddings from the data themselves, such as \(\mathbf{E}^{(1)},\mathbf{E}^{(2)}\), and \(\mathbf{E}^{(3)}\), it ignores the relationship between nodes. Therefore, the traditional deep clustering method needs to utilize the GCN module to capture the relationship between nodes as the supplements. The GCN module can solve this issue, but it is difficult to expand the model depth and the range of the neighborhood together, which limits the learning ability and architecture of the models. Therefore, we propose a Quasi-GNN module, which decouples the encoding process from the propagation process and it can not only capture the relationship between nodes, but also expand the range of the neighborhood and the depth of the model together, reducing the computational cost and improving the scalability.