esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation and Evaluations
### Simulation Results
#### 6.3.4 Time to Reach Destination vs. the Number of Ships in the Region  
We further evaluated the sailing efficiency performances of greedy, greedy + risk, and the DP algorithms. By setting the maximum velocity of the MASS to 100 and the maximum acceleration to 100, we evaluated the time to reach a target at (5000 km, 5000 km), when the MASS started from (0, 0). The target was reached when the MASS was within a 100-m distance of the target. The sailing efficiency was evaluated by varying the density of surrounding ships. The surrounding ships had the maximum velocity of 100 km/h in the experiments. The range of 0-500 in the \(x\)-axis of Figure 12 maps to 0-100 m/h in the velocity of the ships. Figures 12 and 13 show the evaluation result of the time to reach the destination as a function of number of ships in the region. It can be seen that the greedy algorithm had better sailing efficiency, such that the MASS reached the target earlier than that of greedy + risk algorithm. When \(\lambda_{3}\) was changed from 10 to 100, the sailing efficiency of greedy + risk was reduced a little bit. DP provided overall the best sailing efficiency in the compared algorithms. DP was also less impacted by the parameter \(\lambda_{3}\). Note that in all of these evaluations, we counted only the trip in which the MASS successfully reached the destination without colliding with other ships.  
Figure 8: Collision probability vs. ship density when \(\lambda_{3}=10\).  
Figure 9: Collision probability vs. ship density when \(\lambda_{3}=100\).  
Figure 11: Collision probability vs. maximum velocity of other ships when \(\lambda_{3}=100\).  
Figure 12: Time to destination vs. density of ships when \(\lambda_{3}=10\).  
Figure 10: Collision probability vs. maximum velocity of other ships when \(\lambda_{3}=10\).
## 4 Conclusions  
The study presented in this paper led to the successful fabrication of a controllable Pd-Ni membrane filter, which is both biocompatible and durable, using lithography and an electroplating process. The result of elemental analyses of the Pd-Ni membrane filter indicated the Pd and Ni contents of the filter to be approximately 81.5 wt % and 18.5 wt %, respectively. In addition, the surface hardness of the Pd-Ni membrane filter was close to 553.5 Hv and had good biocompatibility. These results evidently showed that the as-prepared Pd-Ni membrane filter has excellent durability, and that it was, therefore, suitable for use in the nozzle filter of a high-frequency vibrating nebulizer system. Furthermore, this paper proposes a new micro-porous mesh nebulizer, including a piezoelectric element, micro-nozzle filter, and fluid cavity, to achieve a high-quality nebulizing effect. When the working frequency of the piezoelectric mesh nebulizer was set to 98.5 kHz, a spray rate of 0.34 mL/min and low power consumption of 2.5 W were obtained. The average size of the standard liquid droplet was 4.53 um (in the case of DI water). On the basis of these results, we are able to conclude that this micro-porous mesh nebulizer is highly promising for applications in oral drug delivery systems as a therapeutic tool in biomedical engineering.  
This work was supported financially by the National Research Foundation of Korea (NRF, #2015M3A9E2066855 and #2015M3A9E2066856) grant funded by the Korean government (MSIP).  
[PERSON] designed the study and wrote the manuscript. [PERSON], [PERSON] and [PERSON] performed the experiments and analyzed the data. [PERSON] and [PERSON] gave many suggestions during the project. All authors reviewed the manuscript.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Characterization of MWCNTs  
CNTs have been reported as adequate support for enzymes immobilization, mainly due to their high specific surface area and high enzyme loading capacity [29; 30; 31]. Additionally, CNTs' surface can be easily functionalized, tuning their properties towards specific applications, and enhancing their efficiency as enzyme support. In this way, the pristine MWCNTs were oxidized with HNO\({}_{3}\) (0.5 M) in order to generate materials with large amounts of surface oxygen groups, namely carboxylic acids, anhydrides, quinones,or phenols [30]. Two important characteristics of the support to the immobilization of enzymes are the \(pH_{PZC}\), i.e., the pH at which the net charge of total particle surface is equal to zero, and the \(S_{BET}\). The \(pH_{PZC}\) of the pristine MWCNTs was determined using a drift method, being the value around 7, a value close to other CNTs reported in the literature (7.3) [32]. The introduction of acid groups on the surface through oxidation of pristine MWCNTs lead to a \(pH_{PZC}\) of f-MWCNTs around values of 3-4.  
The \(S_{BET}\) of pristine and f-MWCNTs determined by the BET method through N\({}_{2}\) adsorption-desorption isotherms are displayed in Table 2. As the MWCNTs diameter increases, there is a decrease in their surface area, as reported by [PERSON] et al. [33] and [PERSON] et al. [34]. On the other hand, the hydrothermal oxidation treatment led to an increase in the surface area of MWCNTs, explained by the creation of defects on the MWCNTs sidewalls along with the opening of the MWCNTs end caps, causing an increase in the porosity [15]. These additional apertures allow access to the MWCNTs cavity, increasing \(S_{BET}\). According to [PERSON] et al. [15], this type of materials functionalization produces MWCNTs mainly with carboxylic acid (-COOH) surface groups. Therefore, an acidity increase in the MWCNTs surface and a decrease in \(pH_{PZC}\) to lower values is expected after treatment with HNO\({}_{3}\).  
An easy separation from the aqueous solution is an intuitive advantage for protein immobilization. However, after centrifugation, some pristine MWCNTs, especially those with diameters of 20-40 nm and 60-100 nm, tended to remain in solution, hindering the separation process. This occurrence can be explained by the extreme hydrophobic characteristics of pristine MWCNTs with high diameters that may cause their aggregation [35]. f-MWCNTs demonstrated a more effortless and faster separation by centrifugation, as their different functional groups contributed to a decrease in hydrophobicity [36].
# GUI for Analysis of Parameters, Accurate Design and Optimization of Microstrip Filters
## 2 Mathematical Preamble to Microstrip Filters
### Steps of Design  
In summary, the process for designing a low-pass microstrip filter is as follows:  
1. Define the values for \(f_{c}\), \(h\), \(\varepsilon_{r}\) and type of filter.
2. Compute the width \(W_{0}\) from \(Z_{0}\), by using Equation (6) or Equation (7).
3. Propose a value for \(W_{L}\) and \(W_{C}\) and then compute their impedances \(Z_{0L}\) and \(Z_{0C}\), respectively, from Equations (1) and (2).
4. Compute the physical length of each line, \(l_{Li}\) and \(l_{Cj}\), for \(i=1,3,\ldots,n\) (\(i\) odd) and \(j=2,4,\ldots,n\) (\(j\) even), by using Equations (9) and (10).
5. Apply an adjustment in the physical lengths to include the effects of series reactance for the low-impedance line and shunt susceptance for the high-impedance lines, by solving the set of Equations (12) and (13).
# Numerical Validation of the Radiative Model for the Solar Cadaster Developed for Greater Geneva
## 2 Materials and Methods
### Case Study
#### 2.3.1 Study of the Influence of the Mesh Resolution on the Results  
The mesh sensitivity was tested on a simple configuration--presented in Figure 5--which consisted of two buildings that were 25 and 35 m high and separated by 10 m. Three grid sizes of 0.5, 1, and 2 m square meshes were chosen. The daily cumulative irradiation on the south facade of the highest building is given in Figure 6. It appears that the cumulative irradiation predicted by the solar cadaster increased along with the coarseness of the resolution.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Indicator** & **Definition** & **Value** \\ \hline Shape Factor & Ratio between the external building envelope & 0.23 \\  & surface and the building volume & 0.25 \\ Floor Area Factor & Ratio between the building gross floor area and the site area & 2.5 \\ Site Coverage & Ratio between the building footprint and the site area & 0.25 \\ Average Building Height & Average height (or rise of height) of buildings in an urban model & 30 m \\ Resolution & & 1 m \\ Number of meshes per roof & & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Urban morphology indicators of the homogeneous and heterogeneous districts.  
Figure 5: Geometry used for the study of the influence of the mesh.  
The influence of the mesh resolution on the results is detailed in Table 2 for the months of February and August. The mesh resolution had an influence on the irradiation predicted for every tool considered. Indeed, the coarser the resolution, the higher the discrepancy in terms of cumulative irradiation. Nonetheless, DIVA, which is based on a ray-tracing method, was less influenced by the mesh resolution, unlike ENVI-met and the solar cadaster, which are based on radiosity methods.  
On the other hand, the influence of the mesh resolution was more important in February than in August. This was due to the sun's lower course, which led to more shadowing.  
In the particular case of the solar cadaster tool, a resolution of 2 m is, therefore, coarse and is not recommended for an analysis of irradiation in an urban environment. A resolution of 1 m remains acceptable for large areas, and the deviation from 0.5 m is small. This difference is explained by the fact that the obstacles' mutual shadows between the two plots will be better detected and considered in every facade point with a finer resolution.  
A mesh composed of square cells with a resolution of 1 m was retained for the following study. This constituted a good compromise between the accuracy of the results and the computation time.
# A Packet Collision Reduction Based on Reselection for LTE V2X Mode 4
## 2 LTE V2X Mode 4  
In the sidelink communication, the UEs transmit a packet in synchronization with each other by using the Global Navigation Satellite System (GNSS), eNB, and UEs [35]. The sidelink communication supports a repeated transmission up to two times using Hybrid Automatic Repeat Request (HARQ) as an option. For Mode 4, which does not require the eNB control, it is optimized to autonomously avoid the packet collisions for periodic transmissions.
# Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
## 2 Fault Types
### Mechanical Faults  
Mechanical faults mainly involve damage to the magnet, bending of the shaft, loosening of the bolts, bearing faults, and air gap eccentricity.  
Bearing faults, which contribute to almost 40-50% of all motor faults [16], occur due to environmental mechanical vibrations, shaft misalignment, poor lubrication, overload, corrosion and so on. Notably, bearings are inevitably fatigued, even during normal operation [17]. As the fatigue crack begins beneath the metal surface and propagates to the surface, a piece of metal breaks away and leave a small pit or flaking. This will result in bearing damage and failure [18], which can include a cage fault, inner raceway fault, outer raceway fault, and ball fault of the bearing [19]. The bearing damage can also cause other types of faults, such as air gap eccentricity, increased friction, and inter-turn short-circuit fault.  
Eccentricity faults refer to the inconsistent air gap between the rotor and the stator. It is mainly caused by improper installation, lack or missing of the bolts, misalignment of the shaft, and rotor imbalance. Eccentricity faults include static eccentricity (SE) [20], dynamic eccentricity (DE) and mixed eccentricity (ME) [21]. When static eccentricity occurs, the centerline of the shaft is offset from the center of the stator by a constant value. When dynamic eccentricity occurs, the minimum distance of the air gap rotates with the rotation of the rotor. In the actual situation, these two phenomena often coexist, which leads to the mixed eccentricity.  
Eccentricity faults can cause additional vibration, noise, torque ripple and so on [22]. And when the eccentric fault becomes severe, it will cause friction between the stator and the rotor, and damage the stator or rotor core [23], affecting the normal operation of the motor.
# Small Satellite Tools for High-Resolution Infrared Fire Monitoring
## 9 Innovative Tools to Increase the Agility of Small Satellites
### The BIROS RGB Matrix Camera  
The Red-Green-Blue (RGB) CMOS matrix camera shown in Figure 21 is a secondary payload of BIROS that was initially used for snap-shooting of a pico satellite launch from BIROS within the experiment named Autonome Visuelle Anflug--Navigation und Target Identifikation (AVANTI) [15]. This RGB camera was also used for Earth imaging when the BIROS satellite was operated in the \"Target-pointing\" attitude mode, i.e., the camera LoS was fixed to a certain AoS on the Earth's surface.  
Figure 20: A thermal image sequence acquired on 19 March 2018 at night local time over the Lausitz region in East Germany during attitude maneuvers conducted by the BIROS HTWs and AOCS.  
Generally, cameras with a matrix detector offer in the push-broom mode interesting advantages compared with a camera equipped with linear array detectors:  
* The maximum sampling time Ts-matrix-max may be larger by a factor of N, compared with the Ts-line-max of a linear detector array, where N is the number of detector lines in the matrix array;
* Using, for example, a sampling time of Ts-matrix = Ts-matrix-max/m >> Ts-line-max, m overlapping scene samples can be acquired, digitized, and processed by so-called binning, i.e., an m-fold digital signal accumulation, to improve the signal-to-noise ratio (SNR) by a factor of (m)\({}^{1/2}\); and
* Stripped spectral filters may be aligned in front of the matrix array segments, which are read out in parallel, to create a multi-spectral camera based on one matrix array detector.
## 3 Evaluation and Application of PWV Derived from CPRAP
### Performance of CPRAP-Derived PWV
#### 3.1.3 Accuracy Analysis of Satellite-Derived PWV Product
Table 5 gives the statistical result of averaged RMS, Bias and MAE of Sentinel-3A/OLCI in China and the corresponding values are \(2.47/2.95\) mm, \(-0.63/0.01\) mm and \(1.58/1.37\) mm, respectively, which are similar to those from [PERSON] and [PERSON] [21]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Value**} & \multirow{2}{*}{**Index**} & \multicolumn{2}{c}{**Comparison with RS**} & \multicolumn{3}{c}{**Comparison with GNSS**} \\ \cline{2-7}  & & **RMS** & **Bias** & **MAE** & **RMS** & **Bias** & **MAE** \\ \hline Mean & 2.47 & \(-0.63\) & 1.58 & 2.95 & 0.01 & 1.37 \\ Max & 8.40 & \(-6.88\) & 6.88 & 10.82 & 0.84 & 7.51 \\ Min & 0.09 & \(-0.42\) & 0.01 & 0.24 & \(-0.89\) & 0.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical results of RMS, Bias, MAE of PWV differences between Sentinel-3A and GNSS (260)/RS (87) over the period from 1 March 2019 to 4 March 2020. Figure 7: RMS and Bias distributions of PWV differences between FY-3A and GNSS/RS over the period of 2012 to 2013. Figure 8: Two-dimensional image of PWV distributions at four seasons, where the first column is obtained from FY-3A, the second column is from the GNSS/RS and the last column is the PWV difference between the FY-3A and GNSS/RS over the period of 2012 to 2013.
# Effective Dereverberation with a Lower Complexity at Presence of the Noise
## 3 Proposed Dereverberation Method
### Application of Kalman Filter
According to the following cost function:  
\[\mathbf{J}\big{\{}\mathbf{w}_{\rm r}(l),\hat{\mathbf{w}}_{\rm r}^{+}(l)\big{\}}= \text{tr}\Big{\{}\text{E}\Big{[}\text{e}^{+}(l)\text{e}^{+}(l)^{H}\Big{]}\Big{\}} \tag{30}\]  
the update process and the estimation of gain \(\mathbf{k}(l)\) of the Kalman filter are expressed as follows [12]:  
\[\hat{\mathbf{w}}_{\rm r}(l)=\mathbf{A}(l)\hat{\mathbf{w}}_{\rm r}^{+}(l-1) \tag{31}\]  
\[\mathbf{\Phi}_{\text{w}}(l)=\text{E}\Big{[}\mathbf{w}_{\text{r}}(l)\mathbf{w} _{\text{r}}{}^{H}(l)\Big{]} \tag{23}\]0  
\[\mathbf{\Phi}_{\text{w}}(l)=\text{E}\Big{[}\mathbf{w}_{\text{r}}(l)\mathbf{w} _{\text{r}}{}^{H}(l)\Big{]} \tag{23}\]1  
\[\mathbf{\Phi}_{\text{w}}(l)=\text{E}\Big{[}\mathbf{w}_{\text{r}}(l)\mathbf{w} _{\text{r}}{}^{H}(l)\Big{]} \tag{23}\]2  
\[\mathbf{\Phi}_{\text{w}}(l)=\text{E}\Big{[}\mathbf{w}_{\text{r}}(l)\mathbf{w} _{\text{r}}{}^{H}(l)\Big{]} \tag{23}\]3  
\[\mathbf{\Phi}_{\text{w}}(l)=\text{E}\Big{[}\mathbf{w}_{\text{r}}(l)\mathbf{w} _{\text{r}}{}^{H}(l)\Big{]} \tag{23}\]4  
\[\mathbf{\Phi}_{\text{w}}(l)=\text{E}\Big{[}\mathbf{w}_{\text{r}}(l)\mathbf{w} _{\text{r}}{}^{H}(l)\Big{]} \tag{23}\]5  
where Equations (31) and (32) are used to update the predictive coefficients and the covariance matrix of the errors in the current frame based on the information from the previous frame. In the following, the complex conjugate error signal \(s^{*}\) (\(l\)) (i.e., the final enhanced signal) and its power spectral density \(\phi_{\rm s}\) (\(l\)) are calculated using Equations (33) and (34) by combining the observation Equation (22) for calculating the Kalman gain \(\mathbf{k}\) (\(l\)) in Equation (35). The final predictive coefficients and covariance matrix of the errors in the current frame are finally obtained by Equations (36) and (37) with the Kalman gain \(\mathbf{k}\) (\(l\)). The final results are also used to calculate the information needed for the next frame. In the calculation of the next frame, however, the error signal \(s\) (\(l\)) and the Kalman gain are reset, which means that they only represent the current frame and do not iterate through the frames. The complex conjugate error signal \(s^{*}\) (\(l\)) is the signal we need, which is given by the Kalman filter. The initialization of the Kalman filter is introduced in the following content.
## 3 Materials and Methods
### Analysis of the Volatile Fraction  
#### 3.4.1 GC-FID Analysis of EOs  
The quantified EO marker compounds were \(\alpha\)-pinene, \(\beta\)-pinene, myrcene, limonene, 1,\(\beta\)-cineole, (\(E\))-\(\beta\)-ocimene, terpinolene, (\(E\))-caryophyllene, \(\alpha\)-humulene, caryophyllene oxide, CBD and THC. Their analytical standards, provided by Sigma Aldrich (Milan, Italy), were injected to build the calibration curves in the range 0.005-10 mg/mL. The EOs obtained from the 9 commercial hemp varieties were diluted 1:100 in analytical-grade \(n\)-hexane; 0.5 \(\upmu\)L of this solution was analyzed in split mode (1:30), by employing an HP-5 coated capillary column (HP-5, 30 m l, 0.32 mm i.d., 0.25 \(\upmu\)m f.t., Agilent Technologies), placed in an Agilent 6850 Gas-Chromatograph (GC). A generator PGH2-250 (DBS Analytical Instruments, Vigonza, Italy) was used to produce hydrogen flowing at 3.7 mL/min. The injector temperature was set at 300 \({}^{\circ}\)C, while that of the GC oven was programmed as follows: 60 \({}^{\circ}\)C for 3 min, then 350 \({}^{\circ}\)C at 25 \({}^{\circ}\)C/min for 1 min. The FID detector temperature was 360 \({}^{\circ}\)C, and the hydrogen and air flow were 40 and 400 mL/min, respectively.
# Quality Enhancement of MPEG-H 3 DA Binaural Rendering Using a Spectral Compensation Technique
## 6 Conclusions  
In this paper, we proposed an effective algorithm to improve the sound quality of MPEG-H 3 DA binaural rendering. The goal of our algorithm is to reduce the spectral artifacts caused by the comb filter effect. In low-frequency bands, the proposed algorithm allows independent compensation of the panning gain for each object at a different position. The panning gains are adjusted with the consideration of avoiding spectral notches and boosts occurrence during downmixing. In high-frequency bands, the proposed algorithm uses a more straightforward method of compensating the downmixed binaural signal. It provides a computationally affordable solution while preserving the sound quality in both timbral and spatial aspects. In the experiment, the proposed algorithm was implemented on the FD-BR  
Figure 7: The MUSHRA test results.  
of the MPEG-H 3 DA reference software. We could confirm empirically that the additional complexity of the proposed algorithm was minor, allowing it to be used even on low-power mobile devices. Finally, objective and subjective tests showed that the proposed method achieves the goal--improving the subjective quality of MPEG-H 3 DA BR by reducing the spectral distortions significantly. In the future, we intend to extend the proposed method to the case where the transfer function of a virtual loudspeaker is BRIR. This will improve binaural audio experience even further by taking early reflection and late reverberation into consideration.  
Conceptualization, H.M. and Y.-c.P.; methodology, H.M.; software, H.M.; validation, H.M. and Y.-c.P.; formal analysis, H.M. and Y.-c.P.; investigation, H.M. and Y.-c.P.; resources, H.M.; data curation, H.M.; writing--original draft preparation, H.M.; writing--review and editing, H.M. and Y.-c.P.; visualization, H.M.; supervision, Y.-c.P.; project administration, Y.-c.P.; funding acquisition, H.M. and Y.-c.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technology Innovation Program (10080057, MPEG-I Binaural Audio Codec Standards for Immersive Virtual Reality Contents) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea).  
The authors declare no conflict of interest.
# Vocabulary Retention Under Multimodal Coupling Strength Index (MCSI): Insights from Eye Tracking
## 4 Results
### Analysis of MCSI Calculation Results  
#### 4.5.1 MCSI Specific Calculation Process  
Based on the previously proposed Multimodal Coupling Strength Index, we collated the collected experimental data and calculated the corresponding MCSI values. The specific values are shown in Table 12.  
The specific calculations for Groups B/D are as follows:  
\[\Delta\text{ASC}=265.04-236.52=28.52\]  
\[\text{MCSI}=\frac{28.52}{265.04}\times\frac{9.93}{\sqrt{3.91}}=0.1076\times 4.4813=0.54\]  
The specific calculations for Groups C/D are as follows:  
\[\Delta\text{ASC}=259.40-236.52=22.88\]  
\[\text{MCSI}=\frac{22.88}{259.40}\times\frac{16.00}{\sqrt{3.91}}=0.0882\times 7.2207=0.71\]