esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Instrument Performance
### Optimization of the Aperture Size, Pixel Size, and Magnetic Field Strength  
We conduct test-particle simulations to quantify the relative errors made on VDF parameters for different aperture sizes and pixel sizes. We assume an idealized homogeneous magnetic field inside the instrument and use Equation (4) to determine the hit position of the particles. We inject particles with a Maxwellian distribution and a \(\kappa\)-distribution starting with a uniform distribution of particle locations on the instrument's disk aperture. We represent both input distributions (Maxwellian and \(\kappa\)-distribution) in Figure 6. We obtain estimates of the VDF parameters such as number density (\(n\)), thermal speed (\(\upsilon_{T}\)) and bulk speed (\(U\)) by fitting, using a Levenberg-Marquardt algorithm, for every set of aperture and pixel size. For the \(\kappa\)-distribution, we require a fourth parameter, the \(\kappa\)-index. We run these simulations for different types of solar wind: slow, intermediate and fast. The instrument and solar wind parameters are summarized in Table 3.  
Figure 5: Maxwellian VDF with density 5 cm\({}^{-3}\), temperature 100,000 K and bulk speed 500 km/s. This distribution function serves as the input for our calculations in Section 3.2.  
Figure 6: Maxwellian and \(\kappa\) VDFs with parameters summarized in Table 3. These distribution functions serve as inputs for the following simulations.  
Figure 8: _Cont._  
Figure 7: Relative errors in percent made on measurements of an intermediate solar wind (bulk speed of 500 km/s, temperature of 100,000 K and density of 5 cm\({}^{-3}\)) as a function of aperture and pixel size. Panels (**a**) through (**g**) show the resulting relative errors for all of the fit parameters of the Maxwellian and \(\kappa\)-distributions.
# Advanced Metamaterial-Integrated Dipole Array Antenna for Enhanced Gain in 5G Millimeter-Wave Bands
## 4 Limitations and Future Work Directions  
This antenna provides broadband operation with stable gain and end-fire radiation. However, this work is limited to the exploration of a single-element design. For future communications, multiple-input multiple-output (MIMO) configurations are essential. To check the suitability of an MIMO configuration of the proposed metamaterial-integrated antenna, a two-port MIMO antenna is designed, as shown in Figure 13. Subsequently, the MIMO performance is studied. The distance between the two single antennas is kept as low as 3.8 mm.  
The simulated reflection coefficient (\(S_{ii}\)) and transmission coefficient (\(S_{ii}\)) results of the designed two-port MIMO antenna are presented in Figure 14a. It can be seen that with the MIMO configuration, the antenna shows similar reflection coefficient characteristics to the proposed single-unit antenna. Moreover, the proposed antenna with the MIMO configuration offers very low mutual coupling (\(S_{ii}\)) of less than \(-28\) dB within the operating frequency band.  
Figure 14: The results of the MIMO configuration of the proposed antenna: **(a)** S-parameters and **(b)** the ECC and DG.  
Figure 13: The 2-port MIMO configuration of the proposed metamaterial-integrated non-uniform dipole array.  
Other simulated MIMO performances metrics, such as the envelope correlation coefficient (ECC) and diversity gain (DG), derived from the S-parameters, are presented in Figure 14b. The MIMO configuration shows a very low ECC value, while the DG is close to the ideal value of 10 dB. Both the ECC and DG show good MIMO performance of the proposed antenna. Overall, the MIMO configuration of the proposed antenna shows an excellent performance, which makes it a viable candidate for 5G millimeter-wave MIMO technologies.
# On Performance Analysis of Protective Jamming Schemes in Wireless Sensor Networks
## 6 Conclusions  
Wireless sensor networks (WSNs) are serving as a crucial component in cyber-physical social sensing systems. The security of WSNs has received extensive attention recently. One of the serious security threats in WSNs is eavesdropping attacks. In this paper, a novel anti-eavesdropping scheme has been proposed to alleviate eavesdropping attacks in WSNs. In particular, we deploy a number of friendly jammers that emit artificial noise to mitigate the eavesdropping capability of adversaries. More specifically, we consider three types of jamming schemes, such as regular placement of jammers (FJ-Reg), random placement of jammers (FJ-Ran) and regular placement of jammers with power control (FJ-PC). We establish a theoretical model to evaluate the performance of these jamming schemes. Our results show that to introduce friendly jammers in WSNs can significantly reduce the eavesdropping probability without the significant influence on the legitimate communications with the appropriate placement of jammers and the proper assignment of emitting power of jammers.  
**Acknowledgments:** The work described in this paper was supported by Macao Science and Technology Development Fund under Grant No. 096/2013/A3 and the National Natural Science Foundation of China under Grant No. 61672170. The authors would like to thank [PERSON] for his excellent comments.  
**Author Contributions:** [PERSON] derived the results, conducted the numeral results and wrote the paper. [PERSON] supervised the work and revised versions. [PERSON] gave valuable suggestions on the motivation of conducting analysis on protective jamming schemes in wireless sensor networks and assisted in revising the paper. [PERSON] contributed to revising and proofreading of the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results
### Annual Variability and the Seasonal Cycle  
Annual variability is significant in areas with low precipitation. In order to further test the ability of satellites to describe the temporal variability of precipitation in the TSM, we analyzed the annual precipitation changes in different subregions based on the GPM/TRMM datasets, as shown in Figure 6. The seasonal distribution of precipitation in the TSM is uneven. The precipitation is mainly concentrated in summer and reaches its minimum in winter, especially in TS3 and TS4. Compared with the ground observations, the satellite datasets portray the annual variability of precipitation well, especially in TS2 and TS3, achieving high correlations (above 0.9). However, both of the satellite products underestimate the annual precipitation. Overall, GPM and TRMM have similar statistical behaviors, but GPM has a lower RMSE in the four subregions and performs better.  
To show the statistical comparison of the seasonal variation of precipitation in the TSM and its subregions in 2014, we plotted a standardized Taylor diagram [38] and provide a summary of the corresponding degree between the estimated and observed precipitation by considering three statistics: standard deviation (SD), R and RMSE. The normalized Taylor diagram uses the SD and RMSE of  
Figure 5: Relative deviation percentage of GPM, TRMM and CGDPA from 1 April 2014 to 31 March 2015.  
Figure 6: Annual variation in precipitation in four subregions of the Tianshan Mountains.  
the reference value and variable value divided by the SD of the reference value, which eliminates physical-unit quantities. The result is shown in Figure 7. Among these, the radial coordinate represents the standard deviation, shown in black dashed lines, and the green semicircle represents the RMSE. Meanwhile, the angular coordinate represents R value, which is shown in blue dashed lines. In these figures, CGDPA represents the ground reference dataset (CGDPA is the reference point), and the other values were derived from the satellite precipitation datasets in different seasons. The closer the points of satellite precipitation are to the reference points, the better the product is.  
As shown in Figure 7, overall (Figure 7a), except for winter, the correlation between the two satellite datasets and the ground reference dataset shows the same trend as the seasonal variations, both of which show lower levels in spring and higher levels in summer and autumn. Among the four subregions (Figure 7b-e), TS2 shows significant seasonality, and the estimated summer spatial patterns of precipitation in TS3 and TS4 are better than the estimates for the other three seasons, with a lower RMSE and a higher correlation coefficient (R). All the products tend to underestimate the spatial variability of seasonal precipitation.
## 6 Speech Quality and Intelligibility Evaluation
### Evaluation Procedure  
As described in (Section 4), the proposed approach includes two stages, i.e., the Wiener filter and a composite-DDAEs, and the DDAE has two phases, namely the training phase and the testing phase. For each overlapped windowed frame (16-ms window and 8-ms frameshift), the LPS feature \(Y_{i}^{LPS}\) calculated through the short-time Fourier transform (STFT), was computed during the MFCC feature \(Y_{i}^{MFCC}\) by the following steps:(1) Pre-emphasis; (2) windowing; (3) FFT; (4) Mel filtering; (5) nonlinear transform; (6) discrete cosine transform (DCT). Wiener enhanced speech \(W_{i}^{LPS+MFCC}\) and the clean speech \(X_{i}^{LPS+MFCC}\) were then added as the input and output of the composite-DDAEs, separately, to train the model instead of noisy-clean pairs. Moreover, the Wiener filter and DDAE methods were used separately to compare the performance with the proposed hybrid-composite approach.
## 2 Experimental Methods  
### Experimental Setup  
Figure 1 shows the schematic of the experimental setup and the structure of the nozzle. The fuel passes through the flow meter and the pressure relief valve into the nozzle. The structure of the nozzle is shown in Figure 1b. Methane and air flow go into the nozzle through the inner and outer tubes, respectively. There is a blunt body above the nozzle, which helps to stabilize the flame. On the air side, a ring with evenly distributed holes is used to laminate the air flow. The dimensions of the ring and the holes are shown in Figure 1b. The flow rate of air and methane is set at 41.1 L/min and 3 L/min, with the corresponding Reynolds number at 1110 and 480, respectively. The main focus of the present study is to reveal the influence of acoustic excitation on the transient ignition process. Thus, the flow rates of air and methane are kept as a constant, which correspond to a lean burn condition normally favorable for most applications.  
The igniter consists of a lead-acid battery, an ignition coil and a pair of steel electrodes, which can supply a pulsed spark with the energy around 200 mJ. When the igniter starts working it will produce a continuous pulse spark at a frequency of 250 Hz. The ignition probe is located approximately 20 mm above the nozzle. Acoustic excitation is applied on the air side by a loudspeaker. Different acoustic signals are generated and controlled by a signal generator. On the outer tube (the air side), two microphones are mounted to measure the velocity pulsation caused by acoustic excitation, as shown in Figure 1a. The schlieren system (LUFTVIS-250, Luftvis Schlieren Co. Ltd., China) includes a point-light source, two concave mirrors, a knife edge and a high-speed camera (Camera 1, M220, Agile Device Co. Ltd., Shenzhen, China). The schlieren images are taken with a frame rate of 2000 fps, exposure time of 1/6600 s and image size of 1024 \(\times\) 920 pixels. The flame image was  
Figure 1: (**a**) Schematic of experimental system; (**b**) nozzle structure and parameters.  
captured by the other high-speed color camera (Camera 2, IDT NXA7S1, Integrated Design Tools Inc., Pasadena, CA, USA), with a frame rate of 2000 fps, exposure time of 1/2000 s and image size of 1024 \(\times\) 1024 pixels. The two cameras are synchronized to capture the flame and schlieren images simultaneously. The test conditions and specific parameter settings are shown in Table 1. During the experiments, the variation of sound pressure amplitude was recorded by microphones and converted into velocity pulsation amplitude by a two-microphone random-excitation algorithm [31], where the relative amplitude denotes the velocity amplitude over the average velocity.
# Quorum Sensing and Quorum Quenching in _Agrobacteriumum_: A \"Go/No Go System\"?
###### Abstract  
The pathogen _Agrobacteriumum_ induces gall formation on a wide range of dicotyledonous plants. In this bacteria, most pathogenicity determinants are borne on the tumour inducing (Ti) plasmid. The conjugative transfer of this plasmid between agrobacteria is regulated by quorum sensing (QS). However, processes involved in the disturbance of QS also occur in this bacteria under the molecular form of a protein, TraM, inhibiting the sensing of the QS signals, and two lactonases BlcC (AttM) and AiiB that degrade the acylhomoserine lactone (AHL) QS signal. In the model _Agrobacteriumum fabrum_ strain C58, several data, once integrated, strongly suggest that the QS regulation may not be reacting only to cell concentration. Rather, these QS elements in association with the quorum quenching (QQ) activities may constitute an integrated and complex \"go/no go system\" that finely controls the biologically costly transfer of the Ti plasmid in response to multiple environmental cues. This decision mechanism permits the bacteria to sense whether it is in a gall or not, in a living or decaying tumor, in stressed plant tissues, etc. In this scheme, the role of the lactonases selected and maintained in the course of Ti plasmid and agrobacterial evolution appears to be pivotal.  
Agrobacteriumum; Ti plasmid; quorum sensing; quorum quenching; lactonase; GABA; proline; (P)ppGpp
## 2 Results
### Optimization of the HS-SPME Technique
#### 2.1.2 Extraction Time  
The optimization of the extraction time is a central aspect of this study, and its detailed analysis provides an illuminating perspective on the optimal value to consider. Figure 3 shows the graph's asymptotic behavior, starting at 46 min of extraction. This phenomenon suggests that the differences in extraction yields were insignificant when the extraction time exceeded 50 min.  
The influential statistical correlation associated with extraction time is based on the chemical equilibria during extraction. This central characteristic makes extraction time a critical variable for optimization. During this period, volatilization processes and the interaction of analytes with the coating of the extraction fiber were triggered. Figure 4 illustrates the relationship between extraction time and chemical processes. This figure keeps all of the variables constant except for the extraction time. The substantial changes observed in the interaction graphs during the different extraction times clearly show the  
Figure 3: The optimization of the extraction time for HS-SPME was analyzed based on the response factors (total area and number of peaks).  
significant influence of this variable by causing notable adjustments in the quantity and concentration of the extracted compounds.  
Understanding the chemical equilibria involved in the extraction process highlights the need to consider this variable carefully. The complex relationship between absorption, volatilization, and interaction with the extraction fiber directly influences the quantity and concentration of the extracted compounds. This delicate process seeks to reach the point where extraction is optimally balanced, thus ensuring the effective retrieval of volatile compounds in the CB.  
In summary, extraction time plays a critical role in optimizing the HS-SPME technique, and its influence on the results and intricate relationship with the underlying chemical processes demonstrate its fundamental role in obtaining representative and precise samples.
# Grousers Effect in Tracked Vehicle Multibody Dynamics with Deformable Terrain Contact Model
## 5 Discussion and Conclusions  
A multibody model of a tracked vehicle for off-road application is presented. A specifically designed algorithm to model track-deformable terrain interaction was developed. Bekker and Janosi-Hanamoto soil mechanics equations were implemented to properly simulate both sinkage and traction behaviour of the tracked machine. Moreover, a passive earth failure model was adopted to also consider the contribution of grousers in terms of drawbar pull. The two flexible tracks were modelled as groups of rigid links constrained by bushing elements able to replicate the elastic behaviour of the track. On each track link, Single component Force elements were applied and related to the soil mechanics equation solved only for those links in contact with the terrain. The use of small rigid spheres kinematically constrained to the link motion, allowed to have at each integration step the local shape of the terrain body in contact with their corresponding link. A simple terrain modelling procedure was adopted to obtain indicative results about tracked vehicles behaviour on deformable soil, in favour of computational times. Vehicle sinkage, developed traction force and pressure distribution under tracks on different type of soil were highlighted in the presented results. The type of terrain was varied into the MTB environment changing the soil characteristic coefficients. In particular, traction force-slip curves were compared with the analytical ones and good agreement is shown. Pressure peaks under road-wheels were correctly evaluated and the actual pressure under each links contributed to the overall traction force computation. Drawbar pull developed by smooth tracks on different type of soil was compared with drawbar pull developed by tracks with grousers. The influence of grousers height and contact surface amplitude was also analysed highlighting the higher impact in traction force on soil when compared with smooth links. As expected, a much smaller impact of the grousers on compact terrain model was highlighted due to the limited sinkage.  
Conceptualization, F.M., A.S. and A.N.; methodology, F.M., A.S. and A.N.; software, F.M. and A.N.; validation, F.M., A.S. and A.N.; formal analysis, F.M. and A.N.; investigation, F.M., A.S. and A.N.; data curation, F.M. and A.N.; writing--original draft preparation, F.M. and A.N.; writing--review and editing, F.M., A.S. and A.N.; visualization, F.M., A.S. and A.N.; supervision, A.S.; project administration, A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
The authors declare no conflict of interest.  
Figure 20: Traction force-slip curves on soil.
## 3 Numerical Analysis  
It is difficult to predict how the soil changes over time because it is an anisotropic material that behaves nonlinearly and constantly changes over time. Additionally, soil-structure interactions are extremely complex, making it impossible to determine building behaviors without performing a numerical analysis on the ground. This study examined the effects of vibrations generated during TBM pilot operations and NATM widening excavation on the TBM, TBM backup equipment, and surrounding ground using FLAC 3D [17]. FLAC 3D was developed by ITASCA as a three-dimensional continuum analysis program for the mechanical assessment of rock, soil, and structures; and it is capable of performing continuum analysis, discontinuous surface analysis, and mechanical-hydraulic coupling analysis considering fluid flow, as well as executing time-domain methods. This program performs well in dynamic analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Building Type** &
\begin{tabular}{c} **Permissible** \\ **Vibration** \\ **(cm s\({}^{-1}\))** \\ \end{tabular} \\ \hline \multirow{4}{*}{United Kingdom} & Tunnel blasting in densely populated areas & 1.0 \\  & Tunnel blasting in areas with low population density & 2.5 \\  & Open-pit blasting at a frequency of 12 Hz or less & 1.2 \\  & Other & 0.5 \\  & Vibration with a frequency of 40 Hz or more & 5.0 \\  & Vibration with a frequency of 15 Hz or more & 1.0 \\  & Vibration with a frequency of 1 Hz or less & 0.5 \\  & No damage caused & 0.2 \\  & Vibration can be detected, but no structural & 0.2–0.5 \\  & damage occurs & 0.2–0.5 \\  & Microscopic damage occurs only in areas where the & 0.5–1.0 \\  & structure is particularly vulnerable & 1.9 \\  & Vibration with a frequency below 15 Hz & 0.02 \\  & Historical relics or sensitive structures at 10 to 60 Hz & 0.762 \\  & Historical relics or sensitive structures at 60 to 90 Hz & 1.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overseas blasting vibration standards [15].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**TBM Type** & **Soil** & \multicolumn{2}{c}{**PPV**} \\ \hline Small diameter & Bedrock & 0.03048 at 7.62 m & 0.0381 at 30.48 m & 0.0013462 at 60.96 m \\  & Bedrock & 0.5588 at 4.88 m & 0.2286 at 10.06 m & 0.0762 at 20.12 m \\  & Bedrock & 0.0508 at 24.99 m & 0.0381 at 30.48 m & 0.013208 at 60.96 m \\ Five-to-six meter & Glacial till/ & 0.3048 at 4.88 m & 0.127 at 10.06 m & 0.0508 at 20.12 m \\ diameter & dense sand & 0.0254 at 24.99 m & 0.02032 at 30.48 m & 0.00762 at 60.96 m \\  & Soft river silt/clay & 0.01016 at 10.06 m & 0.002032 at 20.17 m & 0.00254 at 24.99 m \\  & 0.002032 at 30.48 m & 0.000762 at 60.96 m & \\ \hline \hline \end{tabular}
\end{table}
Table 5: TBM vibration levels [16].
## 3 Results and Discussion
### The Effect of Operation Parameters on AC-CoPcS Catalytic Performance  
#### 3.2.1 The Performance Comparison between Free CoPcS and AC-CoPcS Catalysts  
In order to show the advantages of the supported catalyst, the catalytic performance of free CoPcS and AC-CoPcS catalysts were compared in Table 2 and Figure 5. As is shown in Figure 5, the free CoPcS catalyst is so highly soluble that it runs into not only the water layer but also the organic layer of DMDS product, resulting in post-treatment of DMDS by AC absorption and an inability to reuse the catalyst. However, the supported AC-CoPcS catalyst could be a good solution to these problems: as is shown in Figure 5b, the AC-CoPcS catalyst only stays in the water layer and can be reused through simple filtration and washing treatment. In addition, the AC-CoPcS also exhibits better catalytic performance than the free CoPcS catalyst: as is shown in Table 2, the _Yield_DMDS and _Purity_DMDS using the AC-CoPcS catalyst are higher than the one using the free CoPcS catalyst of 86.8% to 81.4% and 99.8% to 98.7%, respectively. The better catalytic performance of the AC-CoPcS catalyst could be attributed to the following reasons: (1) Large specific surface area and flourishing pore structure of AC support, which can not only absorb the reagents of SMM and oxygen to react on its surface, but also remove the byproduct of the reaction to improve the yield and purity of DMDS; or (2) AC itself can be used as the catalyst in the Merox process, which can also help to improve the catalytic performance of the new AC-CoPcS catalyst [35].