esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Optimisation of Tennis Players' Match Strategies from the Perspective of Momentum
## 4 Construction of a Momentum-Based Player Match Strategy Optimisation Model
### Prediction of Match Trends
#### 4.2.2 BP Heteroskedasticity Test  
The BP test [19] is employed to detect heteroscedasticity by analysing the relationship between the squared residuals and the explanatory variables. Initially, the original regression model is estimated to obtain the residual sequence \(\varepsilon_{i}\). Subsequently, the squared residuals \(\varepsilon_{i}^{2}\) are used as the dependent variable, and an auxiliary regression is performed with the explanatory variables \(x_{1},x_{2},\ldots,x_{k}\) in the model. The resulting auxiliary regression equation is shown in Equation (13).  
\[\varepsilon_{i}^{2}=\delta_{0}+\delta_{1}x_{i1}+\delta_{2}x_{i2}+\ldots+\delta _{k}x_{ik}+u_{i}, \tag{13}\]  
where \(u_{i}\) represents the residual term of the auxiliary regression. The R2 value of the auxiliary regression, denoted as \(R_{\text{new}}^{2}\), is calculated and used to construct the BP test statistic. This statistic is typically represented as \(nR_{\text{new}}^{2}\), where n is the sample size. Under the null hypothesis \(H_{0}:\delta_{1}=\delta_{2}=\ldots=\delta_{k}=0\) (i.e., no heteroscedasticity exists), this statistic follows a chi-square distribution, as shown in Equation (14).  
\[nR_{\text{new}}^{2}\quad\overset{d}{\rightarrow}\quad\chi^{2}(K-1), \tag{14}\]  
where \(\text{K}-1\) represents the number of explanatory variables in the model. The obtained \(p\)-value of 0.000, which is less than 0.05, indicates that the null hypothesis is rejected at the 95% confidence level, suggesting the presence of heteroscedasticity in the disturbance term and rendering the statistical measures invalid. Given the operational simplicity of the robust standard error method and its demonstrated effectiveness in reliable statistical inference [20], this study employs robust standard errors to address heteroscedasticity, thereby obtaining the corresponding results.
## 3 Results and Discussion
### Optimal UAE Conditions  
For numerical optimization of the UAE process, the Design-Expert software used searches for a combination of factor levels that simultaneously met the requirements placed on each dependent and independent variable. The optimization required that goals be defined for both types of variables, in order to combine all goals into a desirability function. To find a good set of conditions that met the desired goals, the three independent variables were set within the experimental range, while the response or dependent variable was set at maximum. In addition, equal \"importance\" of goals was given to the variables. The model-predicted UAE conditions that maximize each individual response to optimal values are presented in Table 3. The maximum extraction yield of 61% was obtained by processing the kiwi peel sample at 483 W for 34.4 min with 34% ethanol. In turn, 1.82 mg/g dw of total flavonoids can be reached by sonicating the powdered sample at a power of only 5 W for 25 min, using 69% ethanol. While B-type (epi)catechin dimer was better extracted with a mere 11 min processing with a high power of 393 W and 95% ethanol, epicatechin required 24 min sonication at 223 W, using 42% ethanol. The quercetin glycosides required a longer sonication time of 39-45 min at 191-258 W using 53-59% ethanol, yielding 0.312 mg/g dw of both compounds.  
Since for the industrial sector interested in bioactive plant extracts it is important to obtain a high amount of both extract and bioactive compounds through sustainable processes, global UAE conditions that simultaneously maximize all responses were also determined (Table 3 and Supplementary Materials Figure S2C). Based on this second optimization step, 15 min sonication at 94.4 W, using 68.4% ethanol as solvent, were found to be the optimal conditions to simultaneously maximize the response variables as much as possible (46 \(\pm\) 1% extraction yield and 1.49 \(\pm\) 0.03 mg/g dw of flavonoids).
# Effect of Surfactant on Water Content of Phosphogypsum
## 3 Results and Discussion
### Effect of Surfactant on Morphology of Phosphogypsum  
The SEM of phosphogypsumis shown in Figure 4. The results indicate that morphology of the phosphogypsum was displayed as tabular and the particle size was about 20-25 \(\upmu\)m without surfactant. The phosphogypsum grew to 25-30 \(\upmu\)m with the addition of SDBS, while the phosphogypsum changed to vrigate with the addition of SR and PDMS, and the particle size grew to 30-35 \(\upmu\)m. The particle size changed little with the addition of two species of surfactant, remaining 20-30 \(\upmu\)m.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & **Contact Angle** & **Mean Deviation** \\ \hline No Surfactant & 80.12\({}^{\circ}\) & 1.2\({}^{\circ}\) \\ SDBS & 72.54\({}^{\circ}\) & 1.4\({}^{\circ}\) \\ SDS & 78.69\({}^{\circ}\) & 0.9\({}^{\circ}\) \\ SR & 79.53\({}^{\circ}\) & 0.8\({}^{\circ}\) \\ PDMS & 73.25\({}^{\circ}\) & 1.0\({}^{\circ}\) \\ SDBS+SDS & 71.89\({}^{\circ}\) & 1.1\({}^{\circ}\) \\ SDBS+SR & 70.65\({}^{\circ}\) & 0.9\({}^{\circ}\) \\ SDBS+PDMS & 72.68\({}^{\circ}\) & 1.0\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Of the species of surfactant on the contact angle of the leaching solution of phosphate rock.  
Figure 3: Of surfactants on water molecules.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & **Surface Tension** & **Mean Deviation** \\ \hline No Surfactant & 75.70 mN/m & 1.2 mN/m \\ SDBS & 60.99 mN/m & 1.4 mN/m \({}^{-1}\) \\ SDS & 64.75 mN/m & 1.3 mN/m \\ SR & 62.54 mN/m & 1.5 mN/m \\ PDMS & 64.36 mN/m & 1.3 mN/m \\ SDBS+SDS & 53.21 mN/m & 1.4 mN/m \\ SDBS+SR & 40.26 mN/m & 1.2 mN/m \\ SDBS+PDMS & 59.77 mN/m & 1.1 mN/m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Of the species of surfactant on the surface tension of the leaching solution of phosphate rock.  
SDS, or PDMS decreased the supersaturating of the phosphate rock, and avoided the deposition of fine grain on the surface of rock. The diffusion velocity of Ca\({}^{2+}\) and SO\({}_{4}\)\({}^{2-}\) was increased due to the change of surface tension, which was beneficial for the growth of phosphogypsum.  
The fractal dimension of the phosphogypsum was calculated according to Equation (1) and the results are displayed in Table 5. The correlation of contact angle with fractal dimension was calculated (see Equation (2)) and the R\({}^{2}\) was 0.9986 shown in Figure 5. Low fractal dimension indicated low contact area, which was not beneficial for adsorption of water at low fractal dimension. However, it was good for separation of the leachate and phosphogypsum, as well as increasing the filtrate rate and decreasing water content in the phosphogypsum.  
\[\mathrm{D}=\frac{\mathrm{InP-In\delta}}{\mathrm{InA}^{\frac{1}{2}}-\mathrm{In \delta}+\mathrm{Ina}} \tag{1}\]  
\[D=-5\times 10^{-6}\theta^{5}+0.0014\theta^{4}-0.166\theta^{3}+8.4517\theta^{2} -161.2\theta\qquad R^{2}=0.9986 \tag{2}\]
# New Trends in Separation Techniques of Lithium Isotopes: A Review of Chemical Separation Methods
## 1 Introduction
Challenges in Development of Separation Methods for Lithium Isotopes Using Chemical Exchange Methods in Two Liquid Phases  
The paradigm of this type of separation method is that the lithium compounds will move between the two immiscible stages if the solvation changes slightly. Research indicates that there are not many chemical exchange methods for isotope separation. Gas-liquid systems make up most of the systems that have been reported so far. Because there are no suitable gaseous lithium compounds for such isotope exchange [16], the liquid-liquid partition must be used for lithium isotope separation. In the single-stage liquid-liquid partition of Li isotopes, the separation coefficient is defined according to the Equation (1). \[\propto=\frac{\left(\frac{{}^{6}\mathrm{Li}}{{}^{7}\mathrm{Li}}\right)_{1}}{ \left(\frac{{}^{6}\mathrm{Li}}{{}^{7}\mathrm{Li}}\right)_{2}} \tag{1}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Main Advantages/Disadvantages**} \\ \cline{2-5}
**Method Type** & **Separation Factor** & **Eco Friendly** & **Complexity of Methodology** & **Costs** \\ \hline Chemical exchange methods in two liquid phases & High & Very little & Low & Very high \\ Electrochemical exchange methods & Low & Moderate & High & Moderate \\ Displacement chromatography methods & Low & Moderate & Relatively low & Moderate \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The main advantages/disadvantages of the main groups of extraction methods. where (\({}^{6}\)Li/\({}^{7}\)Li) represents the isotope ratio, and the subscripts 1 and 2 denote the two phases or two compartments used in the separation tests. An alternative form of separation coefficient was used in the Li isotope fractionation tests, which is usually called \({}^{7}\)Li fractionation (expressed as \(\delta\) \({}^{7}\)Li. The commonly expressed stable isotope ratio of a given compound is the deviation \(\delta\) [%] from an international standard [17], similar to carbon and other isotopes according to the Equation (2). \[\delta^{7}\mathrm{Li}=\left[\frac{\left(\frac{{}^{7}\mathrm{Li}}{{}^{6}\mathrm{ Li}}\right)_{\mathrm{sample}}}{\left(\frac{{}^{7}\mathrm{Li}}{{}^{6}\mathrm{Li}} \right)_{\mathrm{L-SVEC}}}-1\right]1000 \tag{2}\]  
where L-SVEC represents an international standard with a well-defined isotopic composition (\({}^{7}\)Li/\({}^{6}\)Li ratio = 12.33 \(\pm\) 0.03).
## 4 Numerical Simulation and Discussion
### The Definition of Model Parameters
#### 4.2.2 Boundary Conditions  
Prior to the numerical calculation, boundary conditions of the reservoir area should be defined according to the pre-project hydrogeological explorations in Section 2. For the mechanical boundary conditions, normal constraints are assigned to the truncated boundaries of the simulated area. A 400 m-head is provided to the vertical boundaries around the model based on linear interpolation of the measured seepage pressure. An impermeable boundary is also assigned to the bottom of the model. The hydraulic head in the river valley is set at 370 m before impounding to simulate a stationary state. After impoundment, the hydraulic head of the upstream valley in the reservoir is set to be the dynamic reservoir level (\(h_{rl}\)) based on the daily water level measurement which has been shown in Figures 5 and 7. The downstream valley remains unchanged. A heat-insulated boundary condition is assigned to the vertical boundaries. A constant temperature of 45 \({}^{\circ}\)C is set for the bottom of the model, according to a former study on the geothermal field [24,25]. The top surface of the model is set to the mean annual temperature, except the river valley is set to the water temperature (14.5 \({}^{\circ}\)C) according to the former study [28,29]. The schematic boundary conditions are shown in Figure 12.
## Framework
### The Application Layer  
In the application layer, a cloud services platform is constructed to provide cloud services and publish logistics tasks through cloud clients. The cloud client includes thin clients, mobile apps, and web browsers, etc. Thus, these cloud services can be well integrated with embedded devices and mobile devices, such as smartphones and tablets. In this layer, a TCPN model is developed to depict and control the behaviors of key equipment by adjusting the schedule of token sequences. A rule base and a knowledge base are set up based on real-time data and historical data. The rules and knowledge are learned from historical data and are tuned by real-time data.  
In order to implement production-logistics collaboration, a self-adaptive collaborative strategy is designed as follows. Firstly, smart machines actively publish logistics tasks on the cloud services platform at the beginning of machine processing. Then, smart AGVs actively request the logistics tasks once published and provide logistics services. The nearest AGV with enough space will be selected as a feasible option. The selected AGV will accept or reject the logistics task according to the priority of the task. Real-time routing optimization is implemented during the empty trip and the transport.  
For the data communication between key equipment and servers, a variety of connection technologies and protocols for wireless communication including fourth-generation (4G) connectivity and Wi-Fi access can be used to realize the proposed framework. 4G Long-Term Evolution (LTE) standard provides data transfer rates of 100 Mb/s in the downlink and 50 Mb/s in the uplink, which is enough to satisfy the demand of speed and time [20; 21]. Thus, the proposed framework is technically feasible.
# Conjugation of Urokinase to Water-Soluble Magnetic Nanoparticles for Enhanced Thromolysis
## 3 Results and Discussions of Thrombysis Experiment In Vitro
### Thrombysis Experiment In Vitro
This shows that the thrombolysis rate of nano-drugs is at least twice that of pure urokinase. This may be due to the accumulation of the prepared drug-loaded magnetic NPs  
Figure 8: Comparison of thrombolysis in physiological saline, pure PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs solution, pure urokinase solution, and urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs solution under static magnetic field generated by a NdFeB magnet for 90 min. at the thrombus under the guidance of the external magnetic field, resulting in an increase in drug concentration on the surface of the thrombus. When AC magnetic field is applied, the amount of thrombolysis further increases to 21 \(\pm\) 0.4 mg and 26 \(\pm\) 0.5 mg, corresponding to 0.7 mg/mL and 1 mg/mL urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs, respectively. Accordingly, the thrombolysis efficiency is increased by approximately four times that of pure urokinase. In comparison with the results using only static magnetic field, the further increase in thrombolysis can be attributed to the rapid release of urokinase from nanoparticles on the thrombus region triggered by alternating magnetic field. As a result, the contact area between urokinase molecules and thrombus becomes larger, which helps dissolve more thrombus in the same time range.
# Phoneme Recognition in Korean Singing Voices Using Self-Supervised English Speech Representations
## 4 Experiments
### Data Preparation and Performance Evaluation
#### 4.1.2 Preprocessing of the NIKLSEOUL and CSD Corpora  
The model's target is a phoneme sequence, not a word sequence or grapheme sequence. However, in the case of the NIKLSEOUL corpus, annotations are written in graphemes, rather than phonemes that reflect real pronunciation. We, therefore, employed a grapheme-to-phoneme (G2P) tool [36] called g2 pK to convert annotations into phoneme sequences that were consistent with the Korean standard pronunciation. g2 pK strictly annotates phonemes based on the Revised Romanization (RR) of the Korean system [37]. In contrast, the annotations in the CSD corpus are in phonemes, but are not exactly consistent with the RR system style notation. The CSD corpus treats diphthongs (except ui/i/and oe/oe/) as a combination of two vowel phonemes, instead of a single independent vowel in the g2 pK. We, therefore, manually unified the annotations into the CSD-style notation, and standardized the sampling rate to 16 KHz. We have 32 phoneme classes following the CSD-style notation, where we added a space symbol to distinguish syllables. We superadded a blank symbol for CTC loss so that we had 34 classes for phoneme recognition. The pitch range of the CSD corpus was F3\(-\)F5, which was 26 classes. We also added a symbol for silence or unknown pitches, so we had 27 classes for melodic supervision.
# Two-Dimensional Interference Estimator with Parallel Structure for Holographic Data Storage Channel
## 4 Proposed Model
### HDS Channel  
In this channel environment, the blurring effect and misalignment are modeled as a point spread function (PSF), and the noise is modeled as an additive white Gaussian noise (AWGN) [3; 17; 18].  
Figure 2: Proposed model.  
The continuous PSF is presented as follows:  
\[h(x,y)=\frac{1}{\sigma_{b}^{2}}\text{sinc}^{2}\bigg{(}\frac{x-m_{x}}{\sigma_{b}}, \frac{y-m_{y}}{\sigma_{b}}\bigg{)}, \tag{37}\]  
where \(\text{sinc}(x,y)=(\sin(\pi x)/\pi x)(\sin(\pi y)/\pi y)\); \(\sigma_{b}\) is the blur grade; and \(m_{x}\) and \(m_{y}\) are the misalignments in the horizontal and vertical directions, respectively. The discrete PSF \(h[j,k]\) can be calculated as follows:  
\[h[j,k]=\int\limits_{j-\alpha/2\,k-\alpha/2}^{j+\alpha/2\,k+\alpha/2}h(x,y)dxdy, \tag{38}\]  
where \(\alpha\) denotes the linear fill factor. We investigate the influence of \(\alpha\) in Section 5. We consider that the range of the discrete PSF, \(h[j,k]\), is \(5\times 5\) array pixels. The output of the HDS channel can be obtained as  
\[y[j,k]=a[j,k]\otimes h[j,k]+w[j,k], \tag{39}\]  
where \(\otimes\) is the 2D convolution operator and \(w[j,k]\) is the AWGN with zero mean and variance \(\sigma_{w}^{2}\). Finally, we define (SNR) as follows:  
\[\text{SNR}=10\text{log}_{10}\bigg{(}\frac{1}{\sigma_{w}^{2}}\bigg{)} \tag{40}\]
## 3 Ambient Vibration Analysis  
### Research on Vibration under Power Generation and Pumping Conditions  
The above research indicates that the ground vibration significantly changed under pumping conditions compared with shutdown conditions. Based on this, there may be discrepancies in the ground vibration between the power generation and pumping conditions. Therefore, the spectrum maps and root-mean of vibration diagrams of unit 4# and units 1# and 3# under power generation and pumping conditions were selected for investigation. The running powers of unit 4# and units 1# and 3# are shown in Table 4. The analysis results are shown in Figures 5-8.  
Figure 5: The spectrum maps of unit 4# under power generation and pumping conditions. (**a**) P1—upper reservoir; (**b**) P4—upper inclined shaft; (**c**) P8—horizontal flat tunnel; (**d**) P9—lower flat tunnel.  
Figure 6: The root–mean of vibration of unit 4# under power generation and pumping conditions.  
than in power generation conditions. Furthermore, the vibrations originated from the plant and the units and propagated outward, therefore these two locations with greater vibrations were close to the upper reservoir and the lower flat tunnel. The root-mean of vibration in the inner sections of the diversion pipeline gradually diminished as the distance increased. The maximum values of the root-mean of vibration were 0.13 cm/s\({}^{2}\) and 0.22 cm/s\({}^{2}\) in the lower flat tunnel, and the minimum values of the root-mean of vibration were 0.02 cm/s\({}^{2}\) and 0.03 cm/s\({}^{2}\) in the horizontal flat tunnel. There was no discrepancy compared with the background noise, so it was disregarded.  
As shown in Figure 7, the amplitude of the upper reservoir was much higher than that of the other measuring points because the measuring point of the upper reservoir was near both the reservoir and the factory buildings, and the testing process may have been influenced by an inevitable external interference. Compared with the other measuring points, the law of amplitude was the same as when unit 4# was running. Within 100 Hz, the energy was primarily concentrated at 60 Hz and 70 Hz. From 100 to 200 Hz, the energy was primarily concentrated at 120 Hz and 160 Hz.  
Based on Figure 8, the maximum values of the root-mean of vibration were 0.1 cm/s\({}^{2}\) and 0.2 cm/s\({}^{2}\) in the lower flat tunnel. The minimum values of the root mean of vibration were 0.02 cm/s\({}^{2}\) and 0.03 cm/s\({}^{2}\) in the horizontal flat tunnel. The other flaws were the same as unit 4# was running, so the specific description will not be elaborated on in this context.
## 5 Evaluation of Wireless Power Harvesting Metrics
### Efficiency of Power Conversion  
The difference between the input RF power and the output DC power at the load is used to calculate the efficiency of a rectifier circuit. This is how it is mathematically demonstrated:  
\[\eta_{PCE}=\frac{P_{load}}{P_{retrieed}} \tag{4}\]  
where \(P_{load}\) represents the power supplied to the load, while \(P_{retrieed}\) is the power harvested by the antenna. Note that the transmission loss of RF in space is not included in the expression. The circuit's conversion efficiency strongly influences the rate at which energy can be captured by a rectifier circuit. The more efficient the circuit is, the better it is. Therefore, a near-100% efficiency is required, which means that all the energy that reaches the rectifier is converted to direct current energy. Impedance matching circuits must be carefully optimized, and the load resistance at the system's output must be increased [65]. The following factors determine the efficiency of an RF-EH system:  
* The efficiency and gain of the receiving antenna are the primary considerations in its design.
* The impedance matching ensures the maximum power transfer.
* The rectifier circuit's power efficiency.
# BTDNet: A Multi-Modal Approach for Brain Tumor Radiogenomic Classification
## 3 Materials and Method
### Dataset  
The RSNA-MICCAI consortium developed a dataset [17] that comprises multi-parametric magnetic resonance imaging (mpMRI) scans (i.e., 3D volumes) from various institutions, annotated for the prediction of a specific genetic characteristic of glioblastoma, namely the MGMT promoter methylation status. Each mpMRI scan consists of four modalities: fluid-attenuated inversion recovery (FLAIR), T1-weighted pre-contrast (T1w), T1-weighted post-contrast (T1 wCE), and T2-weighted (T2). Each of these modalities offers a specific imaging perspective. For instance, the FLAIR modality provides post-cerebrospinal fluid suppression imagery, where fluidic signals such as water are muted to emphasize other components. Figure 2 shows some slices of each modality from the same mpMRI scan. Each modality contains a varying number of slices. Figure 3 illustrates the total number of slices of each modality for all mpMRI scans. In total, the dataset comprises 585 labeled samples, each with dimensions of \(512\times 512\).  
Figure 2: Example of a whole mpMRI scan, where one can see some slices from the FLAIR modality (**top row**); T1w modality (**second row**); T1 wCE modality (**third row**); and T2 modality (**bottom row**).