esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### LC Separation-LTQ/Orbitrap MS Determination
According to the global desirability profile (Figures S3 and S4); the final optimum values for the studied parameters were as follows: injection volume 20 \(\upmu\)L, spray voltage 3.5 V, percent content in NH\({}_{3}\) 0.1% and oven temperature 20 \({}^{\circ}\)C. Mass accuracy of measured ions (vs. theoretical) was <2 ppm in all cases (Table S2). Further confirmation of the analytes residues was achieved through their main product ion under Collision Induced Dissociation (CID 45%) fragmentation process (Data-Dependent mode). The fragment ions produced were m/z 227.2006 \(\rightarrow\) 212.0845 for BPA, m/z 205.1598 \(\rightarrow\) 133.0662 for OP and m/z 219.1754 \(\rightarrow\) 106.0431 for NP isomers, in accordance with existing literature (Table S2) [13; 27]. Using the optimized conditions, seven-point calibration curves, in the range of 0.05-200 ng mL\({}^{-1}\), were constructed using the peak areas corresponding to each analyte. The calibration graphs were constructed from three replicate measurements per point of the peak area. In all cases, experimental points fit a linear model with relative standard deviation (RSDs) lower than 4%. Excellent linearity in detector response was observed for all target compounds (>0.9994) while the instrument LOQs ranged from 0.05 ng mL\({}^{-1}\) for BPA to 2.5 ng mL\({}^{-1}\) for 4-NP.
# Arsenic (V) Removal by an Adsorbent Material Derived from Acid Mine Drainage Sludge
## 3 Results and Discussion
### Practical Applicability of Adsorbent
#### 3.5.2 Lab-Scale Column Study  
A continuous adsorption experiment was conducted to evaluate the MIRESORB\({}^{\rm TM}\) and GFH adsorbents for practical applications as a water filtration material for arsenic removal. The environmental AMD sample collected from the gold mine was used as a feed solution, which contained As at 270-350 \(\mu\)g/L, Zn 250-1000 \(\mu\)g/L, Cd 40-260 \(\mu\)g/L, and  
Figure 5: (**a**) Influence of pH on the arsenic adsorption capacity of MIRESORB\({}^{\rm TM}\) and GFH materials. The initial As(V) concentration was 30 mg/L, the adsorbent dose was 0.5 g/L, the contact time was 48 h. (**b**) Point-of-zero charge determination for the MIRESORB\({}^{\rm TM}\) and GFH materials.  
had a pH in the range of 7.3-7.7. The flow rate was set to 1.2 mL/min, which resulted in an empty bed contact time of 11.8 min.  
The columns operated up to 5840 bed volume (BV) without apparent breakthrough for both adsorbent materials (Figure 6). The As concentration in the effluent was generally less than 10 \(\mu\)g/L, which met the WHO drinking water standard. The As concentration in the effluent exceeded 10 \(\mu\)g/L in a few samples; however, the As concentration was still lower than 50 \(\mu\)g/L, which was the same as the regulated concentration specified by Korea for water in the environment. The maximum As concentration in the effluent was 20 and 30 \(\mu\)g/L for MIRESORB\({}^{\text{TM}}\) and GFH, respectively. This indicates that the adsorption by the AMDs derived adsorbent, MIRESORB\({}^{\text{TM}}\), is an effective solution for treating As-contaminated wastewater such as mine drainage.  
The spent adsorbent from the column experiments was analyzed for adsorbed arsenic. The sample was subjected to an acid digest assisted by microwaves, and the arsenic in the sample was determined by ICP-AES. The arsenic concentration in the spent MIRESORB\({}^{\text{TM}}\) and GFH was 0.052% and 0.020%, respectively as compared with the arsenic concentration in the fresh adsorbent which was less than 0.001%, establishing substantially higher arsenic content in the spent adsorbent than the fresh adsorbent.  
The SEM/EDS images of fresh and spent adsorbents are presented in Figures S4 and S5. The morphology was not changed during the column experiments, but the elemental composition obtained by EDS showed different results. On the one hand, the fresh GFH and MIRESORB\({}^{\text{TM}}\) consisted of Fe, O, Al, and C, similar to the composition obtained by XRF. On the other hand, As was detected in spent adsorbents, the wt.% of As was 0.41% for GFH and 1.86% for MIRESORB\({}^{\text{TM}}\). The higher wt.% was obtained in the EDS analysis because it analyzed the elemental composition at the surface. This data unambiguously establishes that both materials adsorb arsenic during the column experiments.
# Independent Double-Boost Interleaved Converter with Three-Level Output
## 2 Converter Topology Analysis
### DCM Operation of the Proposed Converter
#### 2.3.2 Case 2  
This DCM working case is illustrated in Figure 8, in which the converter is working at a duty cycle smaller than 0.5, where four different time intervals (D, \(\mathrm{D_{1}}\), \(\mathrm{D_{2}}\), and \(\mathrm{D_{3}}\)) can be observed, and the inductor current has two falling slopes. For this operation mode, the average current of the diode D1 can be expressed using the current represented by the area A and the output capacitor \(\mathrm{C_{1}}\) charge balance.  
Using the average inductor voltage equation, the expression of the time interval \(\mathrm{D_{2}}\) defined in Figure 8 is obtained:  
\[\mathrm{D_{2}}=\frac{\mathrm{V_{in}}}{\mathrm{V_{C1}}}(\mathrm{D+D_{1}})- \mathrm{D_{1}} \tag{10}\]  
Figure 6: inductor voltage and current steady-state waveforms—DCM Case 1.  
Figure 7: DC conversion ratio M (D, K) of the boost converter—DCM Case 1.  
Using the output C\({}_{1}\) capacitor charge balance, the converter load resistance R, and the current area A from Figure 8, the diode D\({}_{1}\) average current equation can be expressed as:  
\[\langle\mathrm{i_{D1}}\rangle=\frac{2\mathrm{V_{C1}}}{\mathrm{R}}=\frac{1}{ \mathrm{T_{S}}}\mathrm{A} \tag{11}\]  
By substituting the time interval defined by D\({}_{2}\) into the capacitor C\({}_{1}\) charge balance (11), the result is:  
\[2\mathrm{KV_{C1}^{2}}+\mathrm{V_{C1}V_{in}D_{1}^{2}-V_{in}(D+D_{1})}=0 \tag{12}\]  
Knowing that the time interval D\({}_{1}\) is 0.5, the voltage ratio of the converter becomes:  
\[\frac{\mathrm{V_{out}}}{\mathrm{V_{in}}}=\frac{-0.5^{2}+\sqrt{0.5^{4}+8 \mathrm{K}(D+0.5)^{2}}}{\mathrm{K}} \tag{13}\]  
Using (13) and different values of the parameter K, Figure 9 shows the DC conversion ratio, M(D, K), of the IDBIC boost converter for DCM Case 2.  
Figure 8: Inductor voltage and current steady-state waveforms—DCM Case 2.  
Figure 9: DC conversion ratio M (D, K) of the boost converter—DCM Case 2.
## 3 Numerical Experiments
### Inversion with Different Initial Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Initial Model** & \begin{tabular}{c} **Homogeneous** \\ **(None)** \\ \end{tabular} & \begin{tabular}{c} **Homogeneous** \\ **(TV+MSG)** \\ \end{tabular} & \
\begin{tabular}{c} **Smoothed** \\ **(None)** \\ \end{tabular} &
\begin{tabular}{c} **Smoothed** \\ **(TV+MSG)** \\ \end{tabular} \\ \hline \(\lambda_{c}\) in TV & / & 0.01 & / & 0.01 \\ \(\lambda_{r}\) in MSG & / & 0.0025 & / & 0.0025 \\ \(\beta_{r}^{2}\) in MSG & / & 0.5 & / & 0.5 \\ Total iterations & 51 & 51 & 51 & 51 \\ PDE solvers & 106 & 106 & 108 & 108 \\ Time (min) & 303.357 & 306.080 & 319.631 & 318.061 \\ \(RRMS_{m}\) of \(\varepsilon_{r}\) & 0.785 & 0.668 & 0.807 & 0.766 \\ \(RRMS_{m}\) of \(\sigma\) & 1.122 & 0.927 & 0.984 & 0.925 \\ \(SSIM_{m}\) of \(\varepsilon_{r}\) & 0.366 & 0.447 & 0.523 & 0.607 \\ \(SSIM_{m}\) of \(\sigma\) & 0.156 & 0.196 & 0.324 & 0.374 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of TV+MSG-constrained inversion using different initial models. the recovery of block anomalies was not ideal compared to the results when using initial models with accurate background information.
## 3 The Production of a Noise Study for the Evaluation of Construction Site Noise
### The Content of a Noise study and the Principles Needed for Its Production
#### 3.1.2 Legislation, Maximum Permissible Noise Values for the Construction Site  
The statement of parts, paragraphs and sections of the government regulation which will be applied in relation to the investigated noise case study, specifying the terminology related to issues concerning construction noise. The statement of whether the performance of construction work is being considered for other times than daytime. The description of noise indicators and the designation of evaluated periods. The explanation of the method of calculating the limit values for construction noise on the basis of the sum of basic sound pressure levels adjusted by corrections. At the end of this chapter, it is necessary to clearly state the sound pressure level value that is the limit against which the calculated values will be assessed.  
1.3 Description of the Area in Terms of Acoustically Significant Elements and the Designation of Objects and Structures in Protected Outdoor Spaces around Buildings  
A verbal description of the construction site in question is given here in relation to the map document which will be used; the verbal description supplements the information about the area from the perspective of the acoustics of significant elements which may affect the results of the calculation. Of particular importance is significant vegetation, changes in terrain elevation and structural elements which function as passive barriers against noise propagation, such as walls or fencing. In accordance with the legislation, a protected outdoor area is exactly defined as a line positioned 2 m in front of the windows of buildings, or as individual risk points. A list of points is provided for which the model will require sub-calculations in which the exact locations of objects are stated.
# An Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization
## 2 Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization
#### 2.1.5 Double Threshold Algorithm to Select and Connect Edges  
The traditional wavelet modulus maxima algorithm uses a single threshold algorithm to select the edge. The single threshold algorithm determines that the edge is retained when the gradient is greater than the threshold, and is set to 0 when the gradient is less than the threshold. This method is easy to cause edge breakage. The double threshold algorithm selects two thresholds to remove false edges and retain edge information effectively. Multiple studies have shown that, when two thresholds are used to judge an edge, the edge detection effect is the best when the low threshold is half of the high threshold [16; 17].  
Figure 3: Flow chart of the iterative threshold method.  
Therefore, we set the high threshold of threshold segmentation to: \(T_{\max}=T\), and the low threshold to: \(T_{\min}=0.5\times T_{\max}\). The specific steps are as follows:  
1. When the gradient value of the point is greater than the high threshold, it is reserved as an edge;
2. When the gradient value of the point is less than the low threshold, it is not an edge point and is set to 0;
3. When the gradient value of the point is between the high and low thresholds, judge whether there are pixels in the eight-neighborhood that are greater than the high threshold. If there are pixels larger than high thresholds, the point is reserved as an edge; otherwise, it is not an edge.
## 5 Simulation Results  
An improved IEEE 33-bus system is employed to simulate the UDN incorporating VPP, as shown in Figure 3. The rated voltage of the distribution network system is 12.66 kV (as a representative case), and the permissible range of bus voltages in the distribution network system is 0.95-1.05 p.u. The communication channel type of the model in this article adopts optical fiber communication for large-scale data transmission between base stations and core networks and wireless communication for communication between distributed terminal devices and base stations. The data transmission rate needs to meet the transmission requirements of business and operational data, with a peak rate of 20 Gbps for the downlink and 10 Gbps for the uplink. The update frequency of bus voltage information is approximately once every 15 min in areas without important load equipment or DG access and once per second in areas with important load equipment or DG access. A distributed control architecture is adopted to improve reliability and response speed. PMU will be deployed at buses 6, 8, 11, 13, 1, 6, 23, 28, and 30, while SCADA will be deployed at other buses. The resulting load examples are normalized to match the power requirements in the simulation system. By using the MATPOWER7.0 toolbox on the MATLAB2020A platform, the power flow equation is solved by using the normalized load sequence as input through the cow method. The computer processor used in the test calculation is an Intel Core i5-6300 [EMAIL_ADDRESS] GHz, an Nvidia GeForce GTX1660 Ti (4 GB) (NVIDIA, California, USA), and the memory is 8 GB.  
Figure 2: Flowchart of the ITLCO algorithm for solving the UDNs incorporating VPPs reconfiguration model.  
The model includes two distributed photovoltaic (PV) sources, two energy storage systems, one 5G base station, one IDC, and two EV charging stations. The distributed PV considers the correlation and temporal characteristics of solar irradiance between the two power stations. The load model is primarily composed of commercial and residential loads combined to form the load curve for the entire community with weights of 55.8% and 44.2%. Moreover, the model uses typical days of four seasons to approximate the annual load curve, with the number of days for spring, summer, autumn, and winter specified as 91, 90, 91, and 93, respectively [29; 30]. The fundamental loop parameters of the UDN incorporating VPP are shown in Table 4.
## 3 The PSOTSC Algorithm
### NARJ Particle Update Strategy
An example is given to illustrate various situations in the process of particle update, as shown in Figure 4. Only one feature point is adjusted for each evolution, and the neighborhood adjustment span is 1. For a high-quality particle, neighborhood adjustment will produce two possible results. One is the evolution into a better particle, which is obviously the expected result. The other is that the quality becomes worse. The solution is to restore the particle to its original state, so that in the next round of evolution it will try to adjust again based on a good state. In Figure 4, the segment feature points of high-quality particle are \(P_{2}\) and \(P_{5}\), and adjusting \(P_{2}\) to \(P_{3}\) may lead to a better solution. However, adjusting \(P_{5}\) to \(P_{6}\) will generate a bad solution, so it is necessary to restore \(P_{6}\) to \(P_{5}\). In the next evolution, \(P_{5}\) will try to adjust to \(P_{4}\) with a certain probability. For a low-quality particle, the neighborhood adjustment of feature points cannot change the bad state fundamentally, but random jump can bring the possibility of \"reversal\". Of course, it may also make the quality worse. In Figure 4, the selection of segment feature point for a low-quality particle is \(P_{4}\) and \(P_{7}\). After evolution, \(P_{7}\) jumps to \(P_{5}\), which brings a better solution. The subsequent evolution can adjust the feature points in the neighborhood, such as the high-quality particle. The jump from \(P_{4}\) to \(P_{2}\) further worsens the quality, and the particle will directly enter the next evolution to reflect the randomness and globality.
###### Abstract  
The performance of speaker recognition systems is very well on the datasets without noise and mismatch. However, the performance gets degraded with the environmental noises, channel variation, physical and behavioral changes in speaker. The types of Speaker related feature play crucial role in improving the performance of speaker recognition systems. Gammatone Frequency Cepstral Coefficient (GFCC) features has been widely used to develop robust speaker recognition systems with the conventional machine learning, it achieved better performance compared to Mel Frequency Cepstral Coefficient (MFCC) features in the noisy condition. Recently, deep learning models showed better performance in the speaker recognition compared to conventional machine learning. Most of the previous deep learning-based speaker recognition models has used Mel Spectrogram and similar inputs rather than a handcrafted features like MFCC and GFCC features. However, the performance of the Mel Spectrogram features gets degraded in the high noise ratio and mismatch in the utterances. Similar to Mel Spectrogram, Cochleogram is another important feature for deep learning speaker recognition models. Like GFCC features, Cochleogram represents utterances in Equal Rectangular Band (ERB) scale which is important in noisy condition. However, none of the studies have conducted analysis for noise robustness of Cochleogram and Mel Spectrogram in speaker recognition. In addition, only limited studies have used Cochleogram to develop speech-based models in noisy and mismatch condition using deep learning. In this study, analysis of noise robustness of Cochleogram and Mel Spectrogram features in speaker recognition using deep learning model is conducted at the Signal to Noise Ratio (SNR) level from \(-5\) dB to \(20\) dB. Experiments are conducted on the VoxCeleb1 and Noise added VoxCeleb1 dataset by using basic 2 DCNN, ResNet-50, VGG-16, ECAPAPA-TDNN and TitaNet Models architectures. The Speaker identification and verification performance of both Cochleogram and Mel Spectrogram is evaluated. The results show that Cochleogram have better performance than Mel Spectrogram in both speaker identification and verification at the noisy and mismatch condition.  
speaker identification; speaker verification; Mel Spectrogram; Cochleogram; 2 DCNN; ResNet-50; VGG-16 2022
# Feasibility Analysis of Adopting the Hydrogen Hydrostatic Thrust Bearing
## 5 Dynamic Characteristics  
Inevitably, the rotor vibrates during this application, which considerably affects the performance of the HHTB. To explore the dynamic characteristics of HHTBs, the effects of harmonic disturbance amplitude, frequency and gas film equilibrium point positions on the dissipated energy, equivalent damping and dynamic stiffness were studied.  
Figures 12 and 13 show the load capacity-gas film clearance curve and dynamic parameters of HHTBs for different disturbance amplitudes, disturbance frequencies and gas film equilibrium values. Other parameters remained unchanged, as shown in Table 3. The minimum amounts of bearing load capacity during disturbance decreased with increasing amplitude. As a result, the region enclosed by the load capacity-gas film clearance curve (the dissipated energy) was greater. The dynamic stiffness factor reduced slightly with increasing amplitude. However, the large amplitude led to a low minimum load capacity that did not meet the requirements of this application. The equivalent damping of the HHTB varied irregularly with disturbance amplitude. The counterclockwise load capacity-film clearance curve shows that the positive damping dissipated the system disturbance energy and improved reliability [35].  
The maximum and minimum load capacity values were almost identical at different disturbance frequencies. The dissipation energy of the HHTB increased as the disturbance frequency increased. The dynamic stiffness and equivalent damping of the HHTB decreased as the disturbance frequency increased.  
The increase in the film equilibrium value caused a decrease in the overall load capacity. In addition, the dissipated energy and equivalent damping of the HHTB decreased as the air film equilibrium value increased. The dynamic stiffness of the bearing increased and then decreased.  
Figure 11: Bearing load capacities for various groove widths (\(P_{in}\) = 1.0 MPa, \(P_{0}\) = 0.5 MPa).  
Figure 12: Variation in dynamic load capacity at (**a**) different amplitudes, (**b**) different frequencies and (**c**) different film equilibrium values.  
Figure 13: Variation in dynamic parameters of HHTBs at (**a**) different amplitudes, (**b**) different frequencies and (**c**) different film equilibrium values.
## 2 Methodology
### Equivalent Circuit Model  
In this study, we selected Thevenin model, which simplifies battery behavior through essential circuit components. According to research by [PERSON] and [PERSON] et al., when using the Thevenin model as the battery equivalent circuit model, a comparison among first-order, second-order, and third-order RC circuit models revealed that the second-order RC circuit model offers an optimal balance between model accuracy and complexity. Therefore, we employed the second-order RC ECM in this study [24; 25]. Figure 2 illustrates the schematic diagram of the second-order RC Thevenin ECM.  
The output voltage of the 2 RC model, \(\mathrm{U_{t}}\), can be described by the following equation, where \(\mathrm{U_{t}}\) represents the terminal voltage of the battery, \(\mathrm{U_{ocv}}\) is the ideal voltage of the battery, \(\mathrm{I}\) is the battery current, and \(\mathrm{R_{0}}\) is the internal resistance of the battery.  
\[\mathrm{U_{t}}=\mathrm{U_{ocv}}-\mathrm{IR_{0}}-\mathrm{U_{p1}}-\mathrm{U_{p2}} \tag{1}\]  
\(\mathrm{U_{p1}}\) and \(\mathrm{U_{p2}}\) represent the voltage drops across each RC circuit, calculated as follows, where \(\mathrm{R_{p1}}\) and \(\mathrm{R_{p2}}\) are polarization resistances, and \(\mathrm{\tau 1}\) and \(\mathrm{\tau 2}\) represent the time constants, calculated as the products of \(\mathrm{R_{p1}}\times\mathrm{C_{p1}}\) and \(\mathrm{R_{p2}}\times\mathrm{C_{p2}}\), respectively.  
\[\mathrm{U_{p1}}(\mathrm{t})=\mathrm{IR_{p1}}\Big{(}1-\mathrm{e}^{-\frac{\mathrm{ t}}{\mathrm{\tau 1}}}\Big{)} \tag{2}\]  
\[\mathrm{U_{p2}}(\mathrm{t})=\mathrm{IR_{p2}}\Big{(}1-\mathrm{e}^{-\frac{ \mathrm{t}}{\mathrm{\tau 1}}}\Big{)} \tag{3}\]  
By substituting these results into the overall voltage equation of the battery, the final output voltage can be expressed as follows:  
\[\mathrm{U(t)}=\mathrm{U_{ocv}}[\mathrm{soc(t)}]-\mathrm{IR_{0}}-\mathrm{IR_{p1 }}-\mathrm{IR_{p2}}+\mathrm{IR_{p1}}\mathrm{e}^{-\frac{\mathrm{t}}{\mathrm{ \tau 1}}}+\mathrm{IR_{p2}}\mathrm{e}^{-\frac{\mathrm{t}}{\mathrm{\tau 2}}} \tag{4}\]  
The open-circuit voltage is an expression that depends on SOC, while the parameters \(\mathrm{R_{0}}\), \(\mathrm{R_{p1}}\), \(\mathrm{R_{p2}}\), \(\mathrm{C_{p1}}\), and \(\mathrm{C_{p2}}\), which describe the dynamic behavior of the battery, are related to the OCV, as discussed previously. Furthermore, these parameters are also related to SOC. In this work, the formula fitting of the battery data is performed based on Equation (4), enabling the identification of parameters to facilitate the construction of the equivalent circuit. Parameter fitting is conducted using experimental data, calibrated with least squares or other fitting algorithms to improve model accuracy.  
Figure 2: Equivalent Circuit Diagram with a Second-Order RC Circuit.