esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Vehicle Control Unit Description
### Failure Management  
The software on the VCU has hence been developed considering different control and failure modes. In particular, the VCU can receive a force reference from the high-level vehicle mission planner (MP), along with a speed reference or a position reference which is chosen by mission planner according to the mission task. The VCU software commands the vehicle powertrain components to track the references generated by MP considering  
Figure 4: Braking strategy that combines electric and pneumatic braking scheme (**a**). Distribution pneumatic and electric coefficients as function of vehicle speed (**b**). Electric distribution coefficient as function of battery \(SOC\) (**c**).  
the actual state of each subsystem and handling possible component failure, i.e., failure for the batteries, which are one of the most critical components in the case of failure [20]. A list of the possible failures that are managed by the VCU is schematically reported below:  
* limitation of electric braking at a high _SOC_;
- powertrain limitation at a low _SOC_;
- powertrain limitation for too high overload conditions (an I2T model is used);
- calculation of the range extender operating point;
* powertrain limitation for high motor or drive temperature;
- powertrain limitation for slippery conditions;
* management of the electric vs. mechanical braking operations;
* management of rear/front traction force distribution.  
Lastly, the VCU is also responsible of onboard energy management. For this reason, the range extender is activated both in the case of low battery _SOC_ and to reduce the overload level of the batteries. Different logic is considered for defining the generated power level. Algorithms that go beyond the thermostat or power follower controller are being analyzed at present. The main purpose of the system is to let the battery discharge properly while avoiding excessive fuel consumption. In conclusion, the RE can be used as a supplementary power supply due to the rather slow dynamic characteristics of the power demand request with respect to other applications, as in [21], in which a hybrid storage system has been considered.
# Multi-Topology Routing Algorithms in SDN-Based Space Information Networks
## 4 Multi Topology Routing Algorithm
### Integrated Link Weight  
After assigning the initial link weight to the link in the SIN, the routing algorithms is executed, the following situations may occur: although the selected link is of high quality, the two ends of the link are already performing other tasks and have a high degree of busyness, which will cause the burden of space nodes to increase, resulting in poor transmission quality and even failure of tasks. Therefore, in order to ensure the transmission efficiency of the SIN, a variable \(N\) is proposed to describe the busy degree of the SIN nodes, which is defined as the ratio of the acceptance rate of the spatial node (the rate of receiving packets) to the transmission rate of the spatial node (the rate of sending packets). The transmission state of space node is described according to the situation of receiving data packets by space node. Sending data packets will reduce the busy degree of the space node, while receiving data packets will increase the busy degree of the space node. Therefore, the larger the value of \(N\), the larger the busyness of the spatial node, the busy the spatial node, and the weaker the ability to accept the load, the smaller the value of \(N\), the stronger the ability of the spatial node to accept the load [25].  
Let \(N\) represents the node busy degree of a segment, and the formula of node busy degree is  
\[N=\frac{N_{current}+N_{neigh}}{2},\ N_{current},\ N_{neigh},\ N\in[0,1] \tag{8}\]  
In addition, when the nodes in the SIN are busy, after receiving the new routing query message, according to their busy degree, the nodes set a certain delay forwarding routing query time to reduce the possibility of competition and congestion. The delay forwarding routing query time is shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Busy Degree** & **Delay Forwarding Routing Query Time (s)** \\ \hline
[0, 0.3) & 0 \\
[0.3, 0.5) & 0.3 \\
[0.5, 0.8) & 0.6 \\
[0.8, 1) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Delay Forwarding Routing Query Schedule.  
The schematic diagram of the SIN nodes using delayed forwarding routing strategy is shown in Figure 4.  
When the routing from point A to point C is established, if point D wants to establish a routing to point G, when the routing query is initiated, the routing from point D to point E will be established prior to the routing from point B under the same initial link weight of the BD link and the ED link.  
Let \(IIW\) to indicate the comprehensive transmission weight of the link, \(t\) represents the delay forwarding routing query time of neighbor nodes. \(\alpha,\beta,\varepsilon\) respectively represent the calculation coefficient of the initial weight of the link, the busyness of the spatial node, and the delayed forwarding route query time. The formula for calculating the integrated link weights is  
\[IIW=\alpha\times E+\beta\times N+\varepsilon t,\alpha+\beta+\varepsilon=1 \tag{9}\]
## 1 Introduction
In the present study, a bacterial strain capable of growing in a saline medium containing PET as the sole carbon source was isolated from landfill soil. This strain was identified as _Stenotrophomonas maltoophilia_. _S. melophilia_ is a gram-negative bacterium and belongs to the \(\gamma\)-proteobacteria. It is present in water, soil, and plant rhizosphere and is considered an opportunistic pathogen [18]. This species has previously been reported to play a role in the bioremediation of various contaminants, including xenobiotics, oils, dyes,and polyaromatic hydrocarbons (PAHs) [19; 20; 21]. However, to our knowledge, this is the first report of the PET-depolymerizing activity of an _S. melophilia_ strain, and the purification of an enzyme associated with this activity. The _S. melophilia_ PRS8-degrading activity of the plastic was assessed using plastic weight loss, Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). An enzyme with a cutinase-like activity was purified from a bacterial enzyme production culture optimized for nutrients and physical parameters using the Plackett-Burman and central composite design. The enzyme was purified for homogeneity and characterized to determine its stability and activity, using _p_-nitrophenyl butyrate as the substrate. The enzymatic depolymerization of the PET flakes and powders was assessed by detecting and quantifying the PET hydrolysis products using high-performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC-MS).
## 3 Results  
### General Descriptions  
The values obtained in the nine subjects studied ranged from 329.90 to 443.69 \(\upmu\)V for the dominant leg and 318.61 to 356.17 \(\upmu\)V for the non-dominant leg. Table 2 details the activation of both legs. The data show the averages of the records of the three series of 50 m run, with the values of the electrical activity of the biceps femoris.  
Regarding the accelerations obtained in the anterior-posterior, lateral, and superior-inferior axes, their results in G (1 G = 9.80 m/s) are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subject** & **Dominant (\(\upmu\)V)** & **Non-Dominant (\(\upmu\)V)** \\ \hline
1 & 350.46 & 354.13 \\
2 & 388.84 & 318.61 \\
3 & 434.12 & 320.08 \\
4 & 443.69 & 345.98 \\
5 & 393.18 & 339.83 \\
6 & 344.64 & 328.80 \\
7 & 329.90 & 341.98 \\
8 & 353.05 & 325.88 \\
9 & 384.62 & 356.17 \\ Average (SD) & 380.279 (39.715) & 336.829 (14.095) \\ \hline \hline \end{tabular} Note: \(\upmu\)V = Microvolt.  
\end{table}
Table 2: EMG values in the dominant and non-dominant legs of each participant.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subject** & **AP (G)** & **AP (m/s2)** & **ML (G)** & **ML (m/s2)** & **SI (G)** & **SI (m/s2)** \\ \hline
1 & 1.55 & 15.20 & 1.86 & 18.24 & 1.38 & 13.53 \\
2 & 1.47 & 14.42 & 1.81 & 17.75 & 1.40 & 13.73 \\
3 & 1.39 & 13.63 & 1.76 & 17.26 & 1.37 & 13.44 \\
4 & 1.26 & 12.36 & 1.67 & 16.38 & 1.28 & 12.55 \\
5 & 1.24 & 12.16 & 1.67 & 16.38 & 1.18 & 11.57 \\
6 & 1.24 & 12.16 & 1.68 & 16.48 & 1.09 & 10.69 \\
7 & 1.24 & 12.16 & 1.68 & 16.48 & 1.00 & 9.81 \\
8 & 1.31 & 12.85 & 1.68 & 16.48 & 0.99 & 9.71 \\
9 & 1.32 & 12.94 & 1.68 & 16.48 & 1.01 & 9.90 \\ Total & 1.33 & 13.04 & 1.68 & 16.48 & 1.19 & 11.65 \\ \hline \hline \end{tabular} Note: AP = Anterior–posterior. ML = Mediolateral. SI = Superior–inferior. G = Gravity force. m/s2 = meters per second squared.  
\end{table}
Table 3: Acceleration in the anterior–posterior (AP), mediolateral (ML), superior–inferior (SI), and superior–inferior (SI) axes.
# Amplitude and Phase Information Interaction for Speech Enhancement Method
## 6 Conclusions  
This paper presents improvements on the FullSubNet model by introducing a pMix unit, which enables the model to utilize phase spectrum information and facilitate information interaction between magnitude and phase spectra, leading to the extraction of more comprehensive features from the noisy speech. The parameters of the original model were also optimized, resulting in improved performance as demonstrated by the ablation experiments. The proposed approach achieves superior results compared to the baseline model on the utilized dataset and outperforms the majority of existing deep learning-based speech enhancement models. Additionally, it was experimentally verified that MSE loss achieves the best performance when training the proposed model in this study. However, there are still areas for further improvement in this method, such as exploring better ways to leverage full-band phase information and incorporating phase features into the sub-band units.  
Conceptualization, Q.Y. and R.Z.; methodology, Q.Y.; software, Q.Y.; validation, Q.Y. and R.Z.; formal analysis, Q.Y.; investigation, Q.Y.; resources, R.Z.; data curation, R.Z.; writing--original draft preparation, Q.Y.; writing--review and editing, Q.Y.; visualization, R.Z.; supervision, R.Z.; project administration, R.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The Interspeech 2021 Deep Noise Suppression Challenge dataset used in this paper can be accessed by the following link: ([[https://github.com/microsoft/DNS-Challenge/tree/master/datasets](https://github.com/microsoft/DNS-Challenge/tree/master/datasets)]([https://github.com/microsoft/DNS-Challenge/tree/master/datasets](https://github.com/microsoft/DNS-Challenge/tree/master/datasets)), accessed on 12 January 2023).  
The authors declare no conflict of interest.
###### Abstract  
Underwater wireless sensor networks (UWSNs) comprise numerous underwater wireless sensor nodes dispersed in the marine environment, which find applicability in several areas like data collection, navigation, resource investigation, surveillance, and disaster prediction. Because of the usage of restricted battery capacity and the difficulty in replacing or charging the inbuilt batteries, energy efficiency becomes a challenging issue in the design of UWSN. Earlier studies reported that clustering and routing are considered effective ways of attaining energy efficacy in the UWSN. Clustering and routing processes can be treated as nondeterministic polynomial-time (NP) hard optimization problems, and they can be addressed by the use of metaheuristics. This study introduces an improved metaheuristics-based clustering with multihop routing protocol for underwater wireless sensor networks, named the IMCMR-UWSN technique. The major aim of the IMCMR-UWSN technique is to choose cluster heads (CHs) and optimal routes to a destination. The IMCMR-UWSN technique incorporates two major processes, namely the chaotic krill head algorithm (CKHA)-based clustering and self-adaptive glow worm swarm optimization algorithm (SA-GSO)-based multihop routing. The CKHA technique selects CHs and organizes clusters based on different parameters such as residual energy, intra-cluster distance, and inter-cluster distance. Similarly, the SA-GSO algorithm derives a fitness function involving four parameters, namely residual energy, delay, distance, and trust. Utilization of the IMCMR-UWSN technique helps to significantly boost the energy efficiency and lifetime of the UWSN. To ensure the improved performance of the IMCMR-UWSN technique, a series of simulations were carried out, and the comparative results reported the supremacy of the IMCMR-UWSN technique in terms of different measures.  
underwater sensor networks; energy efficiency; metaheuristics; network lifetime; communication; routing +
Footnote †: journal: Sensors
# Effect of Thermo-Mechanical Processing on the Corrosion Behavior of Fe-30 Mn-5 Al-0.5C TWIP Steel
## 4 Conclusions  
Recrystallized TWIP steel specimens showed homogeneous monophasic austenite microstructure consisting of equiaxial grains with the presence of recrystallization twins. On the TMP TWIP steel specimens, the hot-rolling process generated oriented grains with zones including grain boundary sliding and dislocations glide.  
Electrochemical measurements indicated that the TMP TWIP steel presented lower corrosion resistance than the recrystallized specimens. The stability of the passive layer is lower for TMP specimens than for recrystallized specimens, due to the thermo-mechanical processing (TMP) which introduces discontinuities in the alloy matrix. The beneficial effect of Al was also observed on the TWIP steel, promoting a protective passive layer. In general, the hysteresis loop that was defined in the CPP curves decreases for the TMP TWIP steel. Furthermore, an improved corrosion resistance is observed for the recrystallized specimens compared to the TMP TWIP steel.  
The pit density decreased with the recrystallization process, as seen from the IFM results, being in good agreement with the CPP analysis. Nevertheless, the pit depth was kept constant at 6 \(\upmu\)m for both the TMP and the recrystallized TWIP steel specimens.  
XPS analysis for both specimens showed that their passive layers were mainly composed by Fe and Al oxides, metallic Al, and bound water. A small peak of Mn oxide and hydroxide has also been detected.  
Conceptualization, D.M.B.; methodology, U.M., J.R., J.B. and D.M.B.; formal analysis, U.M., J.R., J.B. and D.M.B.; investigations, U.M., J.R., J.B. and D.M.B.; resources, D.M.B.; data curation, U.M., J.R., J.B. and D.M.B.; writing--original draft preparation, U.M., J.R., J.B. and D.M.B.; writing--review and editing, U.M., J.R., J.B. and D.M.B.; visualisation, D.M.B.; supervision, D.M.B.; project administration, D.M.B.; funding acquisition, D.M.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Firestone Research, grant number 639430 and The University of Akron.  
**Acknowledgments:** The authors acknowledge the technical support and facilities from The National Center for Education and Research on Corrosion and Materials Performance (NCERCMAP-UA), The College of Engineering and Polymer Science and The University of Akron.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research on Path Planning for Robots with Improved A* Algorithm under Bidirectional JPS Strategy
## 2 Traditional A* Algorithm  
### Modeling of Grid Maps  
Establishing a map model of the environment is the primary prerequisite for path planning, and commonly used methods for map modeling include the grid method, visual graphic method, topological map method, Voronoi diagram, and others [17]. The grid method is widely adopted for its intuitive representation, simple operation, and ease of implementation. In this paper, 2D grids of uniform size are used to abstractly represent information about the actual environment, with each grid corresponding to a unique coordinate. Each grid is classified into two states based on whether it is occupied by an obstacle [18]. One is the free state, represented by white grids in Figure 2, indicating areas where the robot can freely pass; the other is the occupied state, represented by black grids in Figure 2, indicating areas impassable for the robot.  
Commonly used neighborhood expansion patterns in A* algorithms include four-neighborhood expansion and eight-neighborhood expansion [19]. In actual robot operations, the model's complexity is increased due to uncertainties in the robot's operational state, which affect its movement direction. To mitigate this complexity, this paper selects an eight-neighborhood expansion pattern when there are no boundaries or obstacles around the robot, as depicted in Figure 3.
## 3 Results and Discussion
### Simulation Results and Analysis of S-U-S  
#### 3.1.1 Forces as Affected by the Different Biomimetic Structure Parameters  
According to the scheme listed in Table 2, 15 groups of discrete element simulations were performed with different structure parameters of S-U-S. The total force, draft force in the forward direction and the vertical force were used as the test indicators to compare the corresponding forces of O-P-S. The simulation results are listed in Table 4.  
From Table 4, it is clear that the structure of No. 14 was the bionic structure with the best drag reduction performance, with results of TF (total force) = 2159.02 N, DF (draft force) = -1728.91 N (the negative sign indicates that the direction of the force is opposite to the direction of the movement) and CF (compressive force) = 2100.03 N. The scalelike unit structure with the best drag reduction was labeled as S-U-S-_I7h_3.5, for which the structural parameters were \(l\) = 7 mm and \(h\) = 3.5 mm and the arranged spacing was \(S_{1}\) = \(S_{2}\) = 1 mm. For the O-P-S, the corresponding values were TF = 2930.81 N, DF = -2275.92 N, and CF = 2100.03 N, and so the force values of the S-U-S-_I7h_3.5 were reduced by 26.33%, 24.03% and 26.91%, respectively.  
To explore the influence of the scalelike unit structure parameters on the drag reduction performance, a preliminary analysis of the above 15 groups of results was performed. Variations of the total force changing with the height for different lengths were investigated, as shown in Figure 11.  
Figure 10: Field test system.  
As shown in Figure 11, when the length of the scalelike unit structure was 3 mm, the total force had a tendency to decrease significantly as the height (\(h\)) increased from 2 mm to 4 mm. However, \(h\) = 3.5 mm was a turning point. When \(h\) > 3.5 mm, the total force showed an increasing trend.  
It is clear that, when the length of the scalelike unit structure was 5 mm, the total force had a tendency to decrease as the height \(h\) increased from 2 mm to 3 mm. However, \(h\) = 3 mm was a turning point. When \(h\) > 3 mm, the total force showed an increasing trend.  
When the length of the scalelike unit structure was 7 mm, the total force had a tendency to decrease significantly as the height \(h\) increased from 2 mm to 4 mm. However, \(h\) = 3.5 mm was a turning point. When \(h\) > 3.5 mm, the total force showed an increasing trend. When \(l\) = 7 mm and \(h\) = 3.5 mm, the total force reached the minimum value TF = 2159.02 N.  
From Figure 11, both the height and the length parameters of the scalelike unit structure had an important influence on the drag reduction performance. It is worth mentioning that when the height of the structure exceeded 3.5 mm, the total force increased significantly.  
After the preliminary analysis, to obtain a better drag reduction performance, the range of structural parameters we recommend is 5 mm \(<l<\) 8 mm, 2 mm \(<h<\) 4 mm. Based on this finding, the structural parameters can be further optimized.
## 2 Materials and Methods
### Nano HPLC-HRMS Analysis  
To further purify the peptide mixtures, a protein precipitation step has been performed by adding to 100 \(\upmu\)L of each sample (SM-substrate, SM-TCA, SM-beads) 5 volumes (500 \(\upmu\)L) of glacial acetone; after 30 s of sample vortexing they were incubated at \(-20\)\({}^{\circ}\)C for 1 h, centrifuged (Eppendorf 5804R refrigerated centrifuge with A-4-44 rotor, Eppendorf SrI, Milan, Italy) for 20 min at 14,500 g, and the supernatant containing the peptide mixture was transferred in a new Eppendorf tube. The remaining pellets were washed by adding additional 500 \(\upmu\)L of glacial acetone and another centrifugation step was performed together with the recovery of the supernatant. The two aliquots of supernatant were dried in a vacuum concentrator (Vacuum Concentrator RVC 2-18 CD plus, [PERSON], Osterode am Harz, Germany) and the new pellets obtained were resuspended in 30 \(\upmu\)L of 5% ACN, 0.1% FA. Peptide mixtures were extracted by using two C18 Ziptips with a binding capacity of 5 \(\upmu\)g each (10 \(\upmu\)g) and eluted in 80% ACN, 0.1% FA. The hydrolysates were further dried in a vacuum concentrator and finally dissolved in an appropriate volume (30 \(\upmu\)L) of mobile phase (0.1% TFA) for mass spectrometry (MS) analysis.  
Peptide mixtures were analyzed in duplicate by nano-LC-HRMS as described by [PERSON] et al. with few modifications in the multi-step separative gradient [26]. Briefly, for each sample, 5 \(\upmu\)L of solubilized peptides in 0.1% TFA were injected onto the Acclaim PepMap C18 column (75 \(\upmu\)m \(\times\) 15 cm, 100 A pore size), protected by a pre-column (Acclaim PepMap, 100 \(\upmu\)m \(\times\) 2 cm, 100 A pore size), both from Thermo Scientific (Milan, Italy). After the sample loading, peptide separation was performed by the nanoflow pump with a linear gradient of buffer B (0.1% FA in ACN) from 1% to 40% (70 min), followed by a further 5 min of linear gradient from 40% to 95% (buffer B); then 5 min at 95% of buffer B served to fines the column before the re-equilibration to initial conditions (7 min at 99% of buffer A, 0.1% FA in H\({}_{2}\)O). The nano-chromographic system was connected to an LTQ-Orbitrap XL mass spectrometer equipped by a Thermo Scientific (Milan, Italy) dynamic Nanospray ion source, and operated in data-dependent acquisition mode (DDA) to acquire selected full MS spectra, and MS/MS spectra. Xcalibur software (version 2.0.7, Thermo Scientific Inc., Milan, Italy) was used to control the mass spectrometer. A Pierce LTQ ESI Positive Ion Calibration Solution was used for positive calibration of Thermo Scientific orbitrap instrument.