esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Size Estimation
In estimation of depth, the effect of environmental condition was significant, F (1.1, 24.17) = 7.37, \(p=0.01\), \(\mathrm{\SIUnitSymbolFace{\Pi}}_{\mathrm{P}}\) = 0.251; Greenhouse-Geisser correction. Simple pairwise comparisons with Bonferroni adjustment revealed that estimation of depth was significantly more accurate in the environment with minimum cues (\(\mathrm{M_{relative\;error}}\) = 0, SD = 0) than in environments with moderate (\(\mathrm{M_{relative\;error}}\) = 0.21, SD = 0.23; \(p<0.001\)) and maximum (\(\mathrm{M_{relative\;error}}\) = 0.23, SD = 0.23; \(p=0.001\)) cues. However, estimation of depth was equally accurate under the environments with maximum and moderate visual and gravitational cues. In estimation of height, the effect of environmental condition was significant, F (1.47, 32.32) = 5.62, \(p=0.014\), \(\mathrm{\SIUnitSymbolFace{\Pi}}_{\mathrm{P}}\) = 0.204; Greenhouse-Geisser correction. Simple pairwise comparisons with Bonferroni adjustment showed significantly more accurate estimation of the height in the environment with maximum cues (\(\mathrm{M_{relative\;error}}\) = 0.50, SD = 0.60) than the environment with minimum visual and gravitational cues (\(\mathrm{M_{relative\;error}}\) = 1.50, SD = 1.27; \(p=0.006\)). None of the other pairwise comparisons reached significance. Nevertheless, in estimation of length, the effect of environmental condition was not significant (\(p>0.017\)). In conclusion, the interaction of environmental condition and estimated dimension of the cuboid was dependent on target proximity. This interaction was only significant in size estimation of the near target. Further analyses on estimation of the near target revealed an effect of environmental condition in estimation of the depth and height, but not in estimation of length. Namely, the accuracy of estimation of depth was substantially better in the environment without any visual or gravitational cues than the other two environments. However, estimation of height was more accurate in the environment with maximum cues than the environment with minimum visual and gravitational cues. On a descriptive level, estimation of depth overall was more accurate than the other two dimensions, regardless of the target proximity and environmental condition (see Figure 5).
# A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data
## 3 Results
### Cross-Comparison with Other Method  
To further demonstrate the accuracy of this analysis, four MERSI orbits were selected, and the identification method proposed by [PERSON] (2022) was implemented in this section for a cross-comparison ([PERSON], 2023). On one hand, the pixel identification can provide the distri  
Figure 10: True color map and identified distribution overlaid with the national PM\({}_{2.5}\) monitoring data (unit: \(\upmu\)g/m\({}^{3}\)). PM\({}_{2.5}\) values belonging to excellent, good, little pollution, medium pollution, heavy pollution and serious pollution are marked as green, yellow, orange, red, purple and dark purple, respectively.  
bution of haze regions from a qualitative perspective, and on the other hand, this study's identification results can be used as a haze mask for the retrieval of aerosol quantitative data. That is to say, we should pay more attention to the pixels classified as haze and the number of the pixels that are to be retrieved. Therefore, the cloud, ice/snow and inland waterbody pixels were combined in a background mask, and the remaining pixels were used for the comparison. Figure 11 shows the true color map and the comparisons of two recognition methods on UTC0540 8 November 2019, UTC0445 29 January 2020, UTC0545 31 January 2020 and UTC0550 31 January 2020, covering clear regions over bright surfaces, haze regions over the China North Plain and clear regions in southern China, respectively. It is obviously seen that, both this study and [PERSON]'s study (2022) can identify the certainly cloudy pixels well, but there have some discrepancies between them in the identification of cloud edges, heavy haze and clear regions over bright surfaces. For example, the recognition procedure of cloud pixels in [PERSON]'s study (2022) is stricter than that of this study, as shown in 29 January 2020, when most of the areas were covered by a haze layer, and the east of Shandong Province was covered by a mixture of haze and clouds [23]. By contrast, our identification method is very sensitive to these phenomena, which is beneficial to increase the number of retrieved pixels and improve the detection ability of the MERSI instrument during haze days. At UTC0550, on 31 January 2020, the number of clear pixels in outer Mongolia detected by [PERSON] (2022) was smaller than that of this study, which was closer to the true color map [23]. It is also found that this study is very sensitive to the identification of cloud edges/clear sky edges, such as those in the eastern part of Yunnan province shown in UTC0545 on 31 January 2020. Although it performs well in the identification of haze or clear or cloud edges, large errors will be introduced in the aerosol retrievals. This is a double-edged sword that needs to be balanced in different application scenarios.  
Figure 11: True color map and comparison of identified results of [PERSON] (2022) [23] and the MHAM algorithm at UTC0540 on 8 October 2019 (the first line), UTC0445 on 29 January 2020, UTC0545 on 31 January 2020 and UTC0550 on 31 January 2020.
# Suboptimal Analysis of the Differential System of the Conceptual Trailer Air Brake Valve
## 2 Materials and Methods  
### Object of Analysis  
The realization of a conceptual brake valve with a differential part was decided by modifying a commercial solution of the Visteon trailer relay valve 44100110 (Figure 1a). In the original version, this valve can work with both single- and dual-line tractor braking systems. In the case of a single-line system, the connection \(M\) is connected to the power and control port (black line), while the connection \(Zm\) is left unconnected and sealed. In a two-line system, connector \(M\) connects to the power port (red line of the tractor connection), and \(Zm\) connects to the control port (yellow line of the tractor connection). In both systems, connection \(V\) connects to the trailer's compressed air reservoir, and connection \(Z\) connects to the brake force regulator or directly to the actuator. A detailed description of the valve's operation can be found in [12].  
The extent of the modification of the Visteon trailer relay valve 44100110 is shown in Figure 1b,c. In the first step, the interlock \(a\) was removed from the upper body and the channel between the bodies \(b\) was plugged. In addition, the \(f\) and \(g\) connections \(M\) and \(Zm\) were blanked off. Necessary for the realization of the differential function was the drilling of a hole \(c\) in the piston and the permanent immobilization of the piston on the pusher \(d\). The next step involved replacing the one-way seal \(e\) with a seal that works both ways. It  
Figure 1: Analyzed brake valve: (**a**) view of the valve; (**b**) and (**c**) proposed modifications to the valve; and (**d**) diagram of the valve (description in text).  
was decided to use the vent \(W\) as a functional channel. Schematically, the conceptual valve is shown in Figure 1d.  
When the control pressure \(p_{in}\) (blue line in Figure 1b) feeding \(W\) and channel \(a\) builds up with a small gradient, the hole in the piston does not allow the pressure difference above and below the piston to be large enough to overcome the force coming from the spring. Then, the brake actuators, by connection \(Z\), are supplied with a pressure \(p_{in}\) in tracking mode. In the case of a large gradient \(p_{in}\), the piston is displaced, changing the supply to the actuators from connection \(W\) to \(V\). The \(V\) connection is supplied from an additional reservoir close to the valve, so this mode is referred to as acceleration mode. Depending on the stiffness of the spring under the piston controlling the valve modes and the diameter of the hole in the piston, the timing of the appearance of the accelerating mode and its disappearance can be controlled. While accelerating action can be achieved with various valve configurations, such as a relay valve, guaranteeing a smooth transition to the tracking function is difficult. The basic problem of differential valve operation is the selection of the stiffness of the spring under the control piston and the diameter of the piston bore, which formed the basis of the analyses in this study.
## 4 Conclusions  
A numerical study shows that the hub is crucial in streamlining the flow at the centrifugal fan's inlet and improving performance. The results of the current numerical analysis and comparative investigation are summarized in the following sentences.  
* It is found that, compared to the hub-less base model, both hemispherical and ellipsoidal hub geometry model configurations lead to relatively better overall efficiency at all volume coefficients considered in the analysis.
* An ellipsoidal hub design with an ellipsoidal hub ratio of 1.10 improved head coefficient by about 8.4% and relative theoretical efficiency by about 8.6% when compared with those of a hub-less base model for the operating range of volume coefficients employed in the current numerical studies.
* In comparison with the hub-less base model configuration, a hemispherical hub design with a spheroidal hub ratio of 0.50 produced a higher head coefficient of around 2.3% and a greater relative theoretical efficiency of about 3.2% at all the values of volume coefficients.
* The performance characteristics of the fan only slightly improved for hub designs with ellipsoidal hub ratios above 1.1 and spheroidal hub ratios over 0.5.
* The values of average error between CFD and ANN results for head coefficient and relative theoretical efficiency are found to be 0.0008% and 0.0002%, respectively, and are well within the range of acceptable limits.
* The significant improvement in performance indicators primarily targets energy efficiency, thereby ensuring access to affordable, reliable energy for all as per one of the sustainable development goals, SDG 7.  
Conceptualization: K.V.K.; methodology, M.N.; software, M.N.; validation, M.N.; formal analysis, K.V.K.; investigation, K.V.K.; resources, M.N.; data curation, M.N.; writing--original draft preparation, M.N.; writing--review and editing, K.V.K.; visualization, M.N.; supervision, K.V.K.; project administration, K.V.K. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by Manipal Academy of Higher Education (MAHE), Manipal 576104, Karnataka, India.  
The data may be available from the authors upon a reasonable request.  
Manipal Institute of Technology, Manipal, provided the computational resources needed to carry out this research. The authors would like to thank the institute for helping them with their research project and acknowledge their assistance.  
The authors certify that none of their known financial conflict of interest or close personal ties might have appeared to have influenced the research presented in this study.
## 3 Data Preparation and Pre-Processing
### Selection of Data
\begin{table}
\begin{tabular}{c c c c c} \hline
**Feature Source** & **Feature Coding** & **Feature** & **Feature Type** & **Unit** \\ \hline \multirow{6}{*}{Geologic parameter} & c\_1 & Main roof type & Character & - \\ \cline{2-5}  & c\_2 & Immediate roof type & Character & - \\ \cline{2-5}  & c\_3 & Floor type & Character & - \\ \cline{2-5}  & c\_4 & Gas content & Numerical value & m\({}^{3}\)/t \\ \cline{2-5}  & c\_5 & Coal burial depth & Numerical value & m \\ \cline{2-5}  & c\_6 & Immediate roof thickness & Numerical value & m \\ \cline{2-5}  & c\_7 & Coal seam thickness & Numerical value & m \\ \cline{2-5}  & c\_8 & Coal bulk density & Numerical value & t/m\({}^{3}\) \\ \cline{2-5}  & c\_9 & Maximum thickness of caved & \multirow{2}{*}{Numerical value} & mm \\  & & false roof & & \\ \cline{2-5}  & c\_10 & Total thickness of gauge & \multirow{2}{*}{Numerical value} & mm \\  & & under the floor & & \\ \cline{2-5}  & c\_11 & Roof rock bulk density & Numerical value & t/m\({}^{3}\) \\ \hline \multirow{6}{*}{c\_12} & c\_12 & Daily planned output & Numerical value & t \\ \cline{2-5}  & c\_13 & Fully mechanized mining face & \multirow{2}{*}{Numerical value} & m \\  & & length & & \\ \cline{2-5}  & c\_14 & minimum mining height of & \multirow{2}{*}{Numerical value} & mm \\  & & coal seam & & \\ \cline{2-5}  & c\_15 & maximum mining height of & \multirow{2}{*}{Numerical value} & mm \\  & & coal seam & & \\ \cline{2-5}  & c\_16 & Average mining height & Numerical value & m \\ \cline{2-5}  & c\_17 & Fully mechanized mining face & \multirow{2}{*}{Numerical value} & - \\  & & recovery rate & & \\ \cline{2-5}  & c\_18 & Equipment utilization rate & Numerical value & - \\ \cline{2-5}  & c\_19 & Shearer slope cutting entry & \multirow{2}{*}{Numerical value} & m \\ conditions & c\_20 & Shearer slope cutting entry & \multirow{2}{*}{Numerical value} & min \\  & & time & & \\ \cline{2-5}  & c\_21 & Shearer coal cutting imbalance & \multirow{2}{*}{Numerical value} & - \\  & & coefficient & & \\ \hline \multirow{6}{*}{c\_22} & c\_22 & Coal seam cutting specific & \multirow{2}{*}{Numerical value} & kW\_h/t \\  & & energy consumption & & \\ \cline{2-5}  & c\_23 & Rock thickness coefficient & Numerical value & - \\ \cline{2-5}  & c\_24 & Initial step distance of main & \multirow{2}{*}{Numerical value} & m \\  & & roof pressure & & \\ \hline \multirow{2}{*}{c\_25} & c\_25 & Maximum roof control distance & Numerical value & m \\ \hline \multirow{2}{*}{c\_26} & Hydraulic support center & \multirow{2}{*}{Numerical value} & m \\  & & distance & & \\ \hline \end{tabular}
\end{table} 
Table 1: Input feature variables.
## 2 Design of Low-Pass Filter
### Calibration and Bandwidth Tuning
#### 2.2.2 Bandwidth Tuning  
Similarly, the minimum number of MOS-switched capacitors \(\text{m}\), which is needed to meet the requirements of the tuning range for maximum and minimum bandwidth, with a desired maximum step \(\varphi_{\text{C}}\) can be calculated from (11).  
\[m\ =\ \lceil log_{2}(\omega_{\text{max}}/\omega_{\text{min}}\times\ 100\%/\varphi_{\text{C}})\rceil \tag{11}\]  
where \(\omega_{\text{max}}\) and \(\omega_{\text{min}}\) are the maximum and minimum bandwidth, respectively, with bandwidth margins taken into account; \(\varphi_{\text{C}}\) is the maximum allowed step size from \(\omega_{\text{max}}\), expressed in percentage. The value of the smallest capacitor \(C_{0}\) can then be found as a ratio to \(C_{\text{fixed}}\), using (12), while the values of the remaining capacitors are calculated in the same way as \(\text{R}_{\text{n}}\) using (10).  
\[C_{\text{fixed}}/C_{0}=\ \omega_{\text{min}}\times\ (2^{m}-1)/\omega_{\text{min}} \tag{12}\]  
For both proposed filter paths, the capacitor banks are designed with 8-bit control and \(C_{\text{fixed}}/C_{0}\) ratio of 22. The corresponding tuning ranges are 2.5-25 MHz and 20-200 MHz for the LP and HP path, respectively, with a 16% \(\omega_{\text{max}}\) margin, and a 4.6% \(\varphi_{\text{C}}\).
## 2 Non-Integer Oversampling Receiver Architecture in the Frequency Domain
A 2 \(\times\) 2 equalizer separates the two polarizations and equalizes the signal. A carrier recovery corrects for frequency and phase offsets. Finally, demapping converts the complex symbols to binary information. DFT: discrete Fourier transform, IDFT: inverse discrete Fourier transform. Further insights on the tracking of chromatic dispersion (CD), polarization, and coarse carrier frequency offset (CFO) can be found in [13]. Here, we observed that the clock tone gives insight on how to synchronize these impairments.
## 4 Experimental Validation and Discussion  
### Experimental Prototype and Experimental Platform  
In order to verify the correctness of the simulation results, an experimental prototype is made and an experimental platform is built in this paper. Figure 16 shows the experimental prototype and experimental platform. On this basis, magnet C and magnet D are removed as the BPEH system test platform. The IBPEH system test platform is mainly composed of a computer, voltage stabilizer, exciter controller (VT-9008, ECON, Hangzhou, China), power amplifier (VSA-1000A, ECON, Hangzhou, China), exciter (LT-50 ST, ECON, Hangzhou, China), oscilloscope (DSOX3024T, KEYSIGHT, Santa Rosa, CA, USA), laser vibrometer (LV-S01, SDPTOP, Shanghai, China), laser vibrometer controller (LV-S01, SDPTOP, Shanghai, China), and vibration signal analyzer (COCO80X, CRYSTAL, Silicon Valley, CA, USA). The experimental prototype consists of a base, a linear-arch composite beam, a PVDF film, and a magnet. PVDF is evenly bonded to the surface of the composite beam. One end of the composite beam is fixed on the top of the base, and the other end is bonded with magnet A. Magnet B is placed on the fixed end of the base, and magnet C and magnet D are placed symmetrically on both sides of magnet B.  
During the experiment, the experimental prototype was installed on the exciter, and the computer control software issued control commands to control the exciter through the exciter controller and power amplifier. In particular, the stable output of the power amplifier was ensured by connecting a voltage stabilizer. The experimental prototype  
Figure 16: Experimental prototype and experimental platform.  
Figure 15: Simulation results of time-domain velocity versus the displacement and voltage–time diagram when \(f=11\) Hz and \(A=14\) m/s\({}^{2}\). (**a**) Simulation results of time-domain velocity versus the displacement; (**b**) voltage–time diagram.  
vibrated under the action of the exciter, and the generated voltage data were collected by the oscilloscope. The vibration speed of the composite beam passed through the laser vibrometer and its controller and was finally recorded by the vibration signal analyzer.
## Material and Methodology
### Mechanical Properties Testing  
Before conducting mechanical testing, five miniplates were prepared for each sample group. The testing utilized an MCT-2150 universal testing machine (UTM) (A&D Company, Tokyo, Japan), with a maximum force capacity of 500 N and a crosshead speed set at 10 mm/min. The tensile test, in accordance with ASTM D638 standard [33], was employed to determine the tensile strength (TS), Young's modulus (E) and elongation at break (EB). Simultaneously, the bending test, following the ASTM D790 standard [34], was performed to assess the flexural strength, flexural modulus and elongation at break.
# Mapping Phonation Types by Clustering of Multiple Metrics
## 3 Results
### Description of the Classification Voice Maps  
We first consider some basic examples of clustering to better understand the voice map representation. In Figure 6, a male's (a), a female's (b), and a child's (c) voice maps with the same number of clusters \(k=5\) are displayed. The participant number is aligned with the original order in the data set. Each group was trained separately, i.e., the male group was trained with the voice and EGG signals of 13 male participants. In this map, each cell in the map represents a minimum of five phonatory cycles.  
Figure 4: BIC value, the maximum of which indicates the optimum number of clusters when using all six voice metrics. The circle marker represents the optimum for male voices of 6 clusters. For both female (star marker) and child voices (square marker), the optimum is 4 clusters.  
Figure 5: BIC values for models with (**a**) acoustic metrics only and (**b**) EGG metrics only.  
Inspecting broadly the overall contours, even though they are from individuals, it can be seen that the male participant has a lower pitch than the female and child; the child has a smaller pitch and SPL range than the adults, fully as might be expected. The adults exhibit a greater diversity of phonation types than the child. One can assume that every participant uses his/her strategy so that the phonation type distribution would inevitably vary. People will vary their phonation type in order to be able to phonate without undue effort at different \(f_{\text{o}}\) and SPLs. In the map of the male voice (Figure 6a), it can be seen that there are three main regions in the higher, middle, and lower parts, which could be named, from top to bottom, as loud, transition, and soft regions, together with some subgroups around these regions. The map (Figure 6b) of the female voice shows similar partitions, with an extra split in the soft region. Except for the child (Figure 6c), the phonation type as classified by clustering is fairly consistent throughout the whole vocal range. Notice that the outliers near the contours are also classified as a cluster. In the softest regions, relatively breathy voices appear in the male and female participants.
## 2 Related Work
### AUV Navigation and Localization  
Using AUVs to fetch data underwater might enable much higher data rates but the potential of such a solution needs to be evaluated realistically, including not only data transfer simulation but also realistic motion of the AUVs under practical circumstances. The motion of an AUV is affected by the control algorithms and by the localization information or more generally by state estimation. While long-range navigation is a modest problem under the data muling scenario, short-range navigation might have a strong impact on the overall data rates, depending on the employed technology, for ensuring adequate proximity between the DMUs and the SU. Some recent works have looked into the latter problem mostly in the context of AUV docking [47], inspection, intervention [48], and maintenance. However, the tracking performances are hard to model, as they depend on (1) many variables related to control and estimation, (2) random errors corrupting the data from sensors, and (3) often unpredictable environmental disturbances.