esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Validation of AOD Estimation Methods
#### 3.2.3 Statistical Analysis for the Entire Numerical Domain  
Previously, we verified AOD estimations using a single ground observation, focusing on the Seoul metropolitan area. To expand our analysis for the entire domain of the NIER operational model, we also evaluated AOD estimation for wider regions over both East Asia and the Korean Peninsula. The AOD from the 27 km grid was compared with the 15 sites of the AERONET station in East Asia, and the 9 km grid was compared with the six sites on the Korean Peninsula, as shown in Figure 1a. We selected these observational stations because other stationary data had missing values of longer than 30 days during our analysis period.  
Statistical analyses of AERONET and estimated AODs are summarized in Table 3 for all periods in terms of R, RMSE, and MB. The number of observed data points used in these analyses are also shown; there is seasonal variation in these numbers. Regardless of the estimation method, all AODs gave small values of MB, confirming good performance; this is in contrast to the single observational comparison shown in Figure 9, despite being from a 9 km grid. For D01, all the R values were larger than those from a single observational value. The R values were highest during summer and lowest during springtime for all estimations. For the M3 estimation, all R values were better than those of M1 and M2's estimation. This raises the same question as that raised in Section 3.2.1 from the single observational analysis, but understanding the discrepancy between large and local regions would be challenging. Analyzing RMSE with R, the M1 method with maximum RH = 93% was superior to the M2 during all seasons, even though the M2 method gave a good performance for the Seoul metropolitan area. The best skill score was still obtained using the M3 estimation, consistent with the single observational comparison. The highest R  
Figure 8: Time series of daily aerosol optical depth (AOD) from the Yonsei-YSU Aerosol Robotic Network (AERONET) station (black) and estimated AOD from the forecast model during December 2016-March 2017 (wintertime). Estimated AODs in (**a,b**) represent the 27 and 9 km resolution model results, respectively. Yellow, green, blue, and red lines indicate M1 with maxRH = 93%, M1 with maxRH = 96%, M2, and M3, respectively.  
and lowest RMSE were obtained by the M3 method for both D01 and D02. From the D02 analysis, the statistical results were similar to those of D01, except for the R values.  
Figure 9: Daily aerosol optical depth (AOD) from the Yonsei-YSU station and estimated AOD from the forecast model during December 2016–March 2017 (wintertime). The left and right panels represent the results from the 27 and 9 km resolution model results, respectively. Estimation methods are (**a,b**) M1 with max RH = 93%, (**c,d**) M1 with max RH = 96%, (**e,f**) M2, and (**g,h**) M3.
## 3 Results and Discussion
### Fundamental Thermodynamic Principles for Efficient Carbon Nanotube Dispersion
The plan for further research will include the following points: The ultrasonic dispersion process of multi-walled carbon nanotubes (MWCNTs) in hydrocarbon plasticizers is accompanied by the action of numerous forces that determine the nature of interactions within the system and influence the distribution of nanotubes in the solution. The main forces and mechanisms are as follows: hydrodynamic forces (shear forces), cavitation forces, van der Waals forces (attractive forces), electrostatic forces (play a minor role in nonpolar plasticizers), adhesion forces (influencing the wettability of MWCNTs), friction and viscous resistance forces, intermolecular interaction forces (\(\pi\)-\(\pi\) interactions), and thermodynamic forces (osmosis and diffusion). These forces and mechanisms will be considered in further thermodynamic calculations. Conducting additional research will allow a deeper understanding of the thermal and surface phenomena during the dispersion of carbon nanotubes in hydrocarbon plasticizers, as well as the mechanisms of their interaction with hydrocarbon plasticizers. The practical research will focus on the following directions: 1. scaling up and process optimization (investigating the feasibility of integrating MWCNT-modified asphalt binders into existing industrial production lines; analyzing the economic viability of incorporating MWCNTs, including cost assessments and energy requirements for processing); 2. long-term performance testing (conducting fatigue and durability tests on asphalt samples enhanced with MWCNTs under various climatic conditions to simulate real-world performance; evaluating resistance to cracking, deformation, and aging over extended periods); 3. addressing industrial limitations: identifying and mitigating potential barriers to the adoption of MWCNT-enhanced asphalt, such as compatibility with conventional equipment, and environmental impact; 4. environmental and sustainability considerations (assessing the environmental benefits of using recycled MWCNTs or nanomaterials derived from waste products; exploring the contribution of MWCNT-modified asphalt to the principles of a circular economy, such as increased longevity of roads and reduced material consumption).
## 2 Materials and Methods
### Experimental Protocol  
At the time of admission (T0) and before the administration of the first dose of anti SARS-CoV-2 vaccine, the enrolled patients underwent two venous blood samples: One (plasma) for the evaluation of SARS-CoV-2 and the second (blood) for the therapeutic drug monitoring (TDM) of immunosuppressive drugs. Each patient sample was analyzed separately. The plasma sample was processed using SARS-CoV-2 IgG II Quant Assay Kit (Abbot Diagnostic, Irving, TX, USA) through the Architect I1000 instrument (Abbot Diagnostic, Irving, TX, USA). This assay is an automated two-step chemiluminescent microparticle immunoassay in which patient samples are incubated with the SARS-CoV-2 antigen coated paramagnetic microparticles, followed by the anti-human IgG acridinium labelled conjugate to generate a chemiluminescent reaction. The index (sample/control) was calculated by comparing the relative light units in the sample to the calibrator relative light units. Samples were interpreted as positive or negative according to the manufacturer's instructions, with a cut-off index value of 1.0. The assay sensitivity was >99%. The samples were stored at 4-8 \({}^{\circ}\)C for up to one week, then frozen at \(-\)20 \({}^{\circ}\)C and thawed only once.  
Blood samples, collected to evaluate immunosuppressiveant blood concentrations, were processed using liquid mass spectrometry (Shimadzu, Milan, Italy LCMS 8060/NX/50/45/40).  
Follow-up was performed at several times: T1, II dose of vaccine: 3 weeks after T0; T2: 2 months after T1; T3: 3 months after T2; T4: 6 months after T1, in this time, the patients underwent the III dose of the vaccine; T5: 3 weeks after T4, end of the study. During the follow-ups, two blood samples (serum and blood) were taken from each patient for pharmacological studies. Moreover, during these times, each patient was evaluated for the development of adverse drug reactions using the Naranjo questionnaire, in agreement with our previous studies [8; 9].
## 7 Fusion Imaging  
The evaluation of cardiac function in real-time with increased spatial resolution has become critically important during transcatheter interventions and is gaining ground day by day as a mainstay in cardiac care with regard to treatments for structural heart disease. These procedures, while minimally invasive for patients, often have added degrees of patient-related and procedure-related complexity. Fluoroscopy has long been the cornerstone of interventional procedures due to the excellent device visualization and due to the fact that it provides real-time feedback. On the contrary, fluoroscopy offers poor characterization of non-radiopaque structures and provides only 2-D projections of important 3-D cardiac anatomy, lacking essential spatial resolution. Fusion imaging combines data from different imaging modalities, fluoroscopy, computed tomography, and echocardiography, in order to produce a fused image, video, or live streaming (Figure 14). With these methods, we have access to high-resolution detailed anatomic information from CT imaging combined with functional, real-time motion and flux information from echocardiography. The fusion of the pre-operative CT and the peri-procedural 4D-TE provides us with an excellent visualization of both images in the same visual perspective on the echo screen in the catheterization laboratory [38; 39; 40; 41; 42].
## 2 Experimental Design
### Experiment 3: ACPT in a Noisy and Reverberant Environment  
1. Experimental environment  
The test was conducted in a classroom with a length of 10 m, a width of 7.8 m, and a height of 4 m, which is a small-sized classroom. The experimental classroom is shown in Figure 4.  
Figure 3: Reverberation room environment.  
Setting of acoustic scenes  
Referring to the results of experiments 1 and 2, three RTs (0.34 s, 0.76 s, and 1.80 s) and three SNRs (\(-\)5 dB, 5 dB, and 15 dB) representing low, medium, and high signal-to-noise ratios in order, were selected for a total of 3 \(\times\) 3 = 9 acoustic transmission conditions.  
The experimental corpus was played using a directional sound source like the human voice. The sound source was placed in the middle of the classroom podium, and its radiation center was 1.5 m above the ground, and a speaker was placed at each of the four corners to play the noise. The subjects sat in the second row near the middle of the classroom. The experimental equipment also required a multichannel sound card and a 16-channel digital mixer.  
3. Subjects  
Twenty-eight Chinese college students participated in the test, aged 21.9 \(\pm\) 0.8, without cognitive impairment and normal hearing. All subjects received listening training.  
4. Procedure  
Subjects were required to perform a pre-experiment before the formal test. Each subject was required to complete tests under 9 different acoustic conditions, with an interval of 2 weeks between the different experimental conditions in order to prevent the effects of practice and subject fatigue on the reliability of the experimental results.
## 2 The Model for GNSS/INS/LEO/5G Integrated Navigation
### The Structure of Federated Filtering Algorithm
#### 2.1.4 5G/INS Sub-Filter  
The 5G/INS sub-filter adopts a tight coupling structure similar to the GNSS/INS sub-filter. The difference is that the measured value of the 5G/INS sub-filter is the 5G pseudo-range measurement value and pseudo-range rate obtained by adding Gaussian noise to the theoretical distance between the receiver and four 5G base stations. The innovation matrix of the 5G/INS sub-filter is calculated using the measurements derived from INS mechanical arrangement of pseudo-range and pseudo-range rate measurements generated by 5G signals.
## 4 Case Study
### Case 1 (Sto = 18, 19, 20)
The minimum values of load A survivability are 67%, 39%, and 24%, which appear the first time in each event period because the load decreases over time. In the emergency MG operation with preparation, load A survivability is greater than the set value of \(\alpha\)%, which is equal to 95%. The load A survivability is 95%, which excludes \(t=23\) and \(t=24\). The load A survivability values are 97%, 98%, and 99% at \(t=23\) of \(STO_{19}\) and \(t=23\), \(t=24\) of \(STO_{20}\). These load A survivability values are determined by reflecting the ECLS of load A.  
Figure 6: Pre-event MG operation without preparation and with preparation in case 1. The emergency-mode operation costs contain the ECLS and the other generation costs. These depend on the available power, such as the generation output capability and the stored energy in the ESS secured at pre-event times. The emergency mode operation costs without and with preparation are USD 156,555, USD 116,769, and USD 86,401, and USD 145,392, USD 90,315, and USD 47,795, respectively. The maximum values for the emergency mode operation costs are shown at \(STO_{18}\) because the total load of the event periods is the highest in \(STO_{18}\), \(STO_{19}\), and \(STO_{20}\). The maximum difference in the emergency mode operation costs is shown at \(STO_{20}\) because the SoC difference is the highest at \(t=19\) in the normal mode. The simulation results in Case 1 show that the proposed method employs proactive actions to ensure the survivability of the critical loads and lower the total operation cost, which includes the ECLS. When comparing normal-mode MG operating cost, an additional cost of USD 574 is incurred during normal mode to operate in preparation mode. However, in the case of MG operation without preparation, there are cases where the critical load survivability under \(\alpha\%\) and operation costs for all \(STO_{l}\) are higher than that of MG operation with preparation. Table 3 shows the total MG operation costs in Case 1 according to \(STO_{l}\).
## 5 Comparison of Different Decomposition Methods
### Minimal Decomposition of Two-Qubit Gates  
From the theoretical lower bounds, we know that at least 2.25 CNOT gates are needed for a two-qubit gate. This rounds up to three CNOTs, and a circuit that achieves that number is shown in Figure 6[15].  
To obtain the values for the gates of this circuit, first angles \(\alpha\), \(\beta\) and \(\delta\) are found as in the ZYZ decomposition (Section 5.3). These are used to make circuit \(v\) so that the following holds:  
\[(a\oplus b)v(c\oplus d)=U(4) \tag{8}\]  
The circuit \(v\) is shown in Figure 7.  
Figure 5: Minimal universal quantum circuit for a one-qubit gate [15].  
Figure 6: Minimal universal quantum circuit for a two-qubit gate using 18 elementary gates [15].  
Then, to get the one-qubit gates, first matrix \(A\in SO(4)\) can be found so that \(AUU^{T}A^{\dagger}\) is diagonal (\(SO(n)\) is the special orthogonal group, which means that the inverse of a matrix \(Q\) is equal to its transpose: \(Q^{-1}=Q^{T}\) and \(det(Q)=1\)). Through more diagonalization, \(B\in SO(4)\) can be found so \(AUU^{T}A^{T}=Bvv^{T}B^{T}\) and matrix \(C\) as \(C=v^{\dagger}B^{T}AU\in SO(4)\). This leads to \(A^{T}BvC=U\), and because \(A\), \(B\) and \(C\) are in the special orthogonal group, they can be implemented by two unitary gates. After combining \(A^{T}\) and \(B\), the four gates can be found as [15]  
\[A^{T}B =a\oplus b \tag{9}\] \[C =c\oplus d \tag{10}\]  
which gives the circuit in Figure 6. The four one-qubit gates can be implemented by three rotation gates each, through ZYZ decomposition, so that the total rotation count is \(4\times 3+3\) and the total CNOT count is just the ones for the circuit \(v\), and thus three. This matches the theoretical lower bounds for an arbitrary two-qubit gate.
# Research on Short Video Hotspot Classification Based on LDA Feature Fusion and Improved BiLSTM
## 3 Models and Improvements
### Self-Attention  
The BiLSTM can output context-related semantic information but cannot highlight the importance of this information in the text. The attention mechanism is widely used in image processing, machine translation, sentiment analysis [32], etc. because it can extract important features of objects in sparse features. Adding an attention mechanism after the BiLSTM model can effectively reflect the importance of semantic information in text and enhance the feature expression in semantic information. The Attention function is essentially a mapping function consisting of multiple Query and Key-Value. The calculation process is mainly divided into the following three steps:  
1. Calculate the similarity between the query and each key to obtain the corresponding weights, as shown in Formula (15):  
\[f\left(Q,K\right)=QK^{T} \tag{15}\]  
2. Normalize the weights with the softmax function, as shown in Formula (16):  
\[a_{i}=\text{softmax}\left(f(Q,K)\right) \tag{16}\]  
3. The weights and the corresponding key value are weighted and summed to obtain the final Attention value, as shown in Formula (17):  
\[\text{Attention}(Q,K,V)=\sum a_{i}V \tag{17}\]  
In this paper, the model is optimized using Self-Attention, which is an improvement of the attention mechanism that reduces the reliance of the network on external information and is better at capturing the relevance of the data or features. As shown in Formula (18):  
\[\text{Attention}=\text{softmax}\bigg{(}\frac{X\cdot X^{T}}{\sqrt{d_{k}}} \bigg{)}X \tag{18}\]  
In Formula (18), \(X\in R^{n}\), \(X\) is the n-dimensional output vector of output unit, \(\sqrt{d_{k}}\) is the regulatory factor, and this paper refers to the dimensionality of the input word vector. Its function is to avoid a situation where the softmax value is exactly 0 or 1 due to too large inner product of \(X\cdot X^{T}\). Input the topic feature fusion vector into the BiLSTM-Self-Attention model, use the hidden layer state vector H obtained by the BiLSTM as the query, and use the hidden layer state vector hi output in each time step of the BiLSTM as the Key-Value. For example, Formulas (15-17) calculates it to obtain the weighted feature vector, which is finally input to the softmax layer for classification. The structure diagram of the BiLSTM-Self-Attention model is shown in Figure 3.
## 1 Introduction
Adaptive optimal weighted data fusion (AOW) has provided an adaptive way to fuse the data with high efficiency and accuracy, which is beneficial for the storage, transmission and processing of natural signals, without the restriction of the fixed characteristics of normal data fusion that the algorithm cannot vary with the changing signal [35; 36; 37]. The AOW not only fuses the sensor data efficiently and accurately with relative little complexity and calculation, but also brings the benefits of simple adaptive fusion and compression in a WSN, which meets the limited resource constraint of WSNs [38; 39; 40]. Based on the above discussion, this study aims to evaluate the temperature and relative humidity, and table grapes' quality property in cold chain logistics to improve the transparency and traceability of cold chain logistics and to ensure the quality and safety of the table grapes by adopting the WSN as the fundamental network infrastructure and the AOW for the adaptive data fusion. The cold chain process, data fusion performance and the firmness quality of table grapes are evaluated to realize more precise and accurate monitoring, tracing and control for the table grapes' cold chain logistics.
# Recent Advances on Surface Modification of Halloysite Nanotubes for Multifunctional Applications
## 2 Surface Modification of HNTs and the Relevant Properties  
Surface modification of HNTs means that the HNTs maintain the original properties and meanwhile still bring about new properties such as hydrophilicity, biocompatibility, antistatic properties, and dyeing performance. At present, many methods of surface modification of HNTs are reported including surfactant modification, coupling agent modification, intercalation modification, surface coating modification, free radical modification, and etc. The HNTs can be selectively modified according to the different demands.
# Dual-Channel Mapping-Gas Column Concentration Inversion Method Based on Multispectral Imaging
## 2 Method
### Dual-Channel Mapping-Gas Column Concentration Inversion Algorithm Based on Multispectral Imaging
According to Equations (2) and (6), the absorbance of the gas \(A\) can be calculated. In order to deduce the column concentration of the gas through the absorbance \(A\) of the gas, it is necessary to confirm the conversion relationship between gas column concentration and absorbance. Within the target spectral channel range, by measuring the absorbance at different gas column concentrations, the relationship curve between the absorbance and column concentration of target gas can be obtained; the absorbance-column concentration curve of gas is expressed as a quadratic function as  
\[ln\frac{1}{\tau_{gas}}=a\big{(}c_{gas}L\big{)}^{2}+b(c_{gas}L)+c \tag{7}\]  
where \(a\), \(b\) and \(c\) are the three parameters obtained after fitting, and \(c_{gas}\) is the column concentration of the gas. According to Equation (7), the corresponding column concentration can be obtained by inverting the absorbance within the wavelength \(\lambda_{1}\sim\lambda_{2}\). By combining Equations (2) and (7), the conversion formula of gas column concentration and absorbance can be obtained. When the temperature of the gas and the column concentration of the gas are low, the radiation amount of the gas can be ignored. Finally, the relationship between the gas column concentration and absorbance can be expressed as  
\[A=ln\frac{1}{\tau_{gas}}=ln\frac{DN(T_{B})}{DN_{s}}=a\big{(}c_{gas}L\big{)}^{ 2}+b\big{(}c_{gas}L\big{)}+c \tag{8}\]
## 2 Methods
### Numerical Conditions
#### 2.1.3 Computational Setup  
The FLUENT software (ANSYS 16.0, Canonsburg, PA, USA) was used for the simulation. The finite volume method was used to discretize the equations and the finite centered difference scheme was used to discretize the space. Because for LES computing, the SIMPLE (Semi-Implicit Method for Pressure Linked Equations) algorithm saves more computing resources than the PISO (Pressure Implicit with Splitting of Operators) algorithm, the pressure-velocity coupling was based on the SIMPLE algorithm in this paper. The time step was set as \(\Delta t=3\times 10^{-5}\) s. After statistical stability in the flow field was achieved, time-averaged statistics were performed. In particular, the six cycles after the flow field was stabilized were time-averaged with the fluid sweeping across the lower wall of the flat plate as a cycle. Figure 3 illustrates the structural parameters of VGs, where \(l\) is the VG length, \(H\) is the VG height, \(S\) is the distance between VGs, and \(\lambda\) is the spacing of the VGs (see Figure 3 for the difference between \(S\) and \(\lambda\)). The present study focused on the effect of the VG spacing on flow separation control. Table 1 lists the geometric parameters of the VGs, while the inflow velocity was set as \(U=82\) m/s, and the Reynolds number based on the VG height was about \(3\times 10^{4}\).  
Figure 1: Computational domain mesh and detail of the mesh around the vortex generators (VGs).  
Figure 3: VG geometric model.  
Figure 2: Computational domain dimensions and applied boundary conditions.  
The pressure loss coefficient at the inlet and outlet of the calculation domain was defined as \(C_{Ap}\), which can be calculated according to Equation (5), where \(P_{inlet}\) is the total pressure at the inlet and \(P_{outlet}\) the total pressure at the outlet. The pressure difference coefficient reflects the magnitude of pressure difference loss in the computational domain. For flow control, the smaller the pressure loss difference, the smaller the corresponding energy loss.  
\[C_{Ap}=\frac{(P_{inlet}-P_{inlet})}{P_{inlet}} \tag{5}\]  
In order to verify the mesh independence, three meshes with different densities were developed with 1,320,000 (coarse), 1,980,000 (medium), and 2,640,000 (fine) elements. The Richardson extrapolation method was used to verify the convergence of the mesh, and the results are presented in Table 2, where RE is the value obtained using a Richardson extrapolation, \(p\) is the order of accuracy, and \(R\) is the ratio of errors. The convergence conditions are 0 \(<R<\) 1 (monotonic convergence), \(R<\) 0 (oscillation convergence), and 1 \(<R\) (divergence). As it can be seen, the \(R=\) 0.22 result indicates monotonic convergence. Therefore, in this paper, the finest mesh was used for the numerical simulations.