esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Substrate and Strain on Protein Quality of _Pleurotus ostreatus_
## 3 Results and Discussion
### Spectroscopic Analysis  
Figure 3 presents the spectral region (1840-840 cm\({}^{-1}\)) of the ATR-FTIR spectra of the mushroom flours 144 BOS, 150 BOS, 144 WS and 155 WS. The peaks and their assignments are shown in Tables S3 and S4. Shifts were observed in the 1560-1542 (150 BOS,150 WS), 1163-1139 (144 WS) and 925-905 cm\({}^{-1}\) (144 WS) spectral regions, which are probably due to intermolecular interactions. The highest peaks were at 1739-1738, 1367-1366 and 1218-1217 cm\({}^{-1}\). The first peak has been attributed to >C=O of alkyl esters [48; 49]. The second peak has been attributed to C-H deformation, and >C=O and C-O stretching of COO'. These absorption peaks have been linked to the presence of polysaccharides such as \(\beta\)-glucans and salts of uronic acids, particularly glucuronic acid [48; 50; 51]. The third peak has been assigned to P=O stretching of phospholipids [50; 52]. Nine other smaller peaks were also observed. The vibrations at 1666-1621, 1560-1542 and 1450-1440 cm\({}^{-1}\) have been attributed to the presence of proteins [48; 52; 53] and the C=C and C=O stretching of amino acids, carboxylates, phenols and chitin/chitosan [49; 54]. The absorption at 1163-1152, 1086-1082, 1043-1039 and 991-989 cm\({}^{-1}\) have been attributes to carbohydrates [51; 52; 55], while the absorption at 926-905 and 893-891 cm\({}^{-1}\) have been attributed to \(\beta\)- and \(\alpha\)- glycosidic bonds, mainly in \(\alpha\)- and \(\beta\)-glucans [49]. The observed shifts at 1560-1542 (150 BOS, 150 WS), 1163-1139 and 926-905 cm\({}^{-1}\) (144 WS) are most likely due to intermolecular interactions.  
Figure 3: The ATR-FTIR spectra (1840–840 cm\({}^{-1}\)) of (**a**) 144 BOS-F, (**b**) 150 BOS-F, (**c**) 144 WS-F, (**d**) 150 WS-F.  
Based on the spectroscopic analysis, the _P. ostreatus_ components found were polysaccharides (especially \(\alpha\)- and \(\beta\)-glucans), proteins, phospholipids, chitin/chitosan and amino acids. Several researchers have studied the components of _P. ostreatus_. Polysaccharides and, in particular, \(\alpha\)- and \(\beta\)-glucans [48; 49; 50; 54; 56], proteins [18; 49], phospholipids [56], chitin/chitosan [54] and amino acids [57] have been identified. Thus, the spectroscopic findings are in agree with the literature.
# Satellite-Based Wireless Sensor Development and Deployment Studies for Surface Wave Testing
## 2 Hardware Architecture
### Battery  
A USB portable charger power bank made by PISEN with a capacity of 20,000 mAh was employed to provide 5 V up to 2.0 A power supply for the computer and other electric components (Figure 1a). The size of the charger was 152.4 mm \(\times\) 78.7 mm \(\times\) 25.4 mm, and the weight was about 1.5 pounds. With six high-density Li-Polymer batteries, the power bank is a good device, retaining up to 70-80% of its full capacity even after 500 charge-discharge cycles. While considering the shipping issues, the sealed lead-acid rechargeable battery is also a good alternative for readers.  
Figure 1: \(Cont.\)
# Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
###### Abstract  
Aiming at the poor effect of traditional denoising algorithms on image enhancement with strong noise, an image denoising algorithm based on improved whale optimization algorithm and parameter adaptive array stochastic resonance is proposed in the paper. In this algorithm, through dimensionality reduction scanning, coding, modulation and other processing, the noise-containing gray image becomes a one-dimensional aperiodic binary pulse amplitude modulation signal suitable for a bistable stochastic resonance model. Then, the traditional whale optimization algorithm is improved in the initial solution distribution, global search ability and population diversity generalization. The improved whale optimization algorithm is applied to select the parameters of the stochastic resonance, which effectively improves the parameters self-adaptive of the array stochastic resonance model. Finally, the denoised image is obtained by demodulating, decoding and anti-scanning the stochastic resonance output. The experimental results show that compared with the array stochastic resonance method with fixed parameters and the classical image denoising method, the algorithm proposed in this paper has better performance in terms of visual effect and peak signal-to-noise ratio index, which proves the advantages and effective application of the method in image denoising.  
image denoising; improved whale optimization algorithm; array stochastic resonance; peak signal-to-noise ratio Article
## 3 Experimental Techniques
### Dynamic Tensile Testing  
The dynamic testing of the nonwoven fabric was conducted on a Split-Hopkinson Tensile Bar (SHTB) device specially designed for this project. The requirements were set at: (i) high sensitivity of the output bar to register the mechanical response of soft fabrics; (ii) long time duration of the stress pulse, to facilitate the force equilibrium between bar ends and increase the applied strain; and (iii) large gauge length to characterise the fibre network response and avoid the influence of the material constituents.  
The present experimental set-up comprised of hollow input and output bars connected to a loading bar actuated by a striker; see Figure 1a. The loading bar was surrounded by a striker with a U shaped cross-section supported through brass railings to freely slide and strike the impact flange of the loading bar. The striker was fired by a hook connected to a pulling rod and piston propelled by a low-pressure chamber. The hook accelerated the striker towards the impact flange generating the stress pulse that propagated towards the loading and input bars. Further details of this SHTB equipment are available in [39]. For the current experiment, a titanium Ti-6 Al-4V loading bar of 3.6 m in length and 20 mm diameter was used, surrounded by a titanium U shaped striker of 35 mm diameter and 2.5 m in length providing a pulse duration \(T~{}\approx~{}1\) ms [45]. The input and output bars comprised of Aluminium 7075-T6 alloy hollow tubes of 50.8 mm outer diameter, 1.651 mm wall thickness and 2.7 m in length. A thread adaptor manufactured in steel was included to connect the loading and input bar. The fabric was gripped by conical clamps allowing a maximum specimen width of 35 mm, see Figure 1b. The gauge length of the specimens, equivalent to the distance between clamps, was set to 35 mm for all experiments to ensure the mechanical response was representative of the homogeneous macroscale. All the components of the grips were manufactured in Aluminium except the threads that were produced in brass to avoid welding during the loading process; see Figure 1c.  
The input and output bars were instrumented with three strain gauges, as in Figure 1a. The amplifiers were used to accurately record the signal on the order of millivolts. All the signals were registered by a high frequency oscilloscope and were post-processed with a Matlab script. The main outcomes were the forces and the velocities of the bars and the average applied strain rate. The specimens were speckled with a random pattern and the full-field displacement measurements were carried out via high-speed photography, employing an high-speed Kirana camera operated at 50,000 fps. Acquisition rate was limited by the time duration of the experiment. 2D Digital Image Correlation analysis was performed using the commercial software Vic2D.
# Collaborative Multi-Robot Formation Control and Global Path Optimization
## 3 Improved Global Path Planning with Ant Colony Algorithm
### Experimental Analysis of Global Path Planning Simulation
#### 3.4.2 10 \(\times\) 10 Static Environment Simulation Results Analysis  
In this experiment, Matlab was used as the experimental platform, and 10 \(\times\) 10 scale simple and complex grid obstacle environments were set respectively. The related parameter settings are shown in Table 2. The image shows that the obstacle distribution in simple environment is less, and the optimal path can be obtained before and after the ant colony algorithm is improved. However, a comparison between Figures 2 and 3 shows that the improved optimal path is smoother and shorter, the path length is 12.7280, and the algorithm tends to stabilize faster by 46.4%.  
As shown in Figures 4 and 5, the search difficulty increases as the density of obstacle distribution in complex environment increases, and the path branch points become more frequent. The improved ACO still performs better than the classical ACO with the shortest path of 14.4854, and the speed of algorithm convergence to stability is improved by 41.4%. The above experimental results show that the improved ACO based on artificial potential field has significant advantages in the speed, and the results of optimal path search compared with the traditional ACO.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & \multicolumn{4}{c}{**Parameter Experimental Comparison Values**} \\ \hline \(\alpha\) & 0 & 0.5 & 1 & 2 & 4 \\ Path length mean/m & 20.28 & 17.36 & 15.32 & 17.28 & 15.48 \\ \(\beta\) & 0 & 3 & 6 & 7 & 10 \\ Path length mean/m & 19.34 & 18.13 & 15.64 & 17.41 & 19.85 \\ \(\rho\) & 0.1 & 0.3 & 0.5 & 0.7 & 0.9 \\ Path length mean/m & 16.10 & 18.43 & 19.68 & 19.80 & 21.32 \\ \hline \end{tabular}
\end{table}
Table 1: Parameter selection.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameter** & \(N_{max}\) & \(M\) & \(\alpha\) & \(\beta\) & \(\rho\) & \(Q\) \\ \hline Value & 50 & 30 & 1 & 6 & 0.1 & 10 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental parameter setting.  
Figure 4: Path planning using basic ACO in the complex grid map.  
Figure 3: Path planning using improved ACO in the simple grid map.  
Figure 2: Path planning using basic ACO in the simple grid map.  
The comparison of the results before and after algorithm improvement in two environments are shown in Table 3.
# Signals Intelligence System with Software-Defined Radio
###### Abstract  
In this paper, we present the implementation of a system that identifies the modulation of complex radio signals. This is realized using an artificial intelligence model developed, trained, and integrated with Microsoft Azure cloud. We consider that cloud-based platforms offer enough flexibility and processing power to use them instead of conventional computers for signal processing based on artificial intelligence. We tested the implementation using a software-defined radio platform developed in GNU Radio that generates and receives real modulated signals. This process ensures that the solution proposed is viable to be used in real signal processing systems. The results obtained show that for certain modulation types, the identification is performed with a high degree of success. The use of a cloud-based platform allows quick access to the system. The user is able to identify the signal modulation using only a laptop that has access to the internet.  
software-defined radio; artificial intelligence; Azure; signal modulation; modulation detection; GNU Radio; automatic detection; signal classification +
Footnote †: journal: Applied Sciences
# Modeling Pedestrian Motion in Crowded Scenes Based on the Shortest Path Principle
## 3 Model Learning
### Connectivity between Trajectories  
In crowd scenes, pedestrians are often occluded by each other. When an occlusion occurs, the old feature point will stop tracking, and a new feature point will be detected and tracked. The trajectories tracked for the same pedestrian at different times should be connected. However, we cannot explicitly determine which trajectories belong to the same pedestrian. Therefore, we define the connectivity between trajectories to indicate whether there is a possible connection between two trajectories. Since the motion of pedestrians is continuous in space and time, the connectivity between trajectories depends on the travel times and relative positions of trajectories.  
Suppose that there are trajectories \(ab\) and \(a^{\prime}b^{\prime}\), and trajectory \(ab\) is before trajectory \(a^{\prime}b^{\prime}\) in time. We define that there is a possible connection between trajectories \(ab\) and \(a^{\prime}b^{\prime}\) if they satisfy the following two conditions:  
1. \(t_{e}^{ab}\leq t_{s}^{ab^{\prime}b^{\prime}}\leq t_{s}^{ab}+T\),
2. \(d\left(X_{e}^{ab}+v_{ab}\cdot\left(t_{s}^{a^{\prime}b^{\prime}}-t_{e}^{ab} \right),X_{s}^{a^{\prime}b^{\prime}}\right)\leq D\) or \(d\left(X_{e}^{ab},X_{s}^{a^{\prime}b^{\prime}}-v_{a^{\prime}b^{\prime}}\cdot \left(t_{s}^{a^{\prime}b^{\prime}}-t_{e}^{ab}\right)\right)\leq D\).  
Here, \(t_{e}^{ab}\) represents the end time of trajectory \(ab\), \(t_{s}^{a^{\prime}b^{\prime}}\) represents the start time of trajectory \(a^{\prime}b^{\prime}\) and \(T\) is the threshold constant of time interval. \(X_{e}^{ab}\) represents the end position of trajectory \(ab\); \(X_{s}^{a^{\prime}b^{\prime}}\) represents the start position of trajectory \(a^{\prime}b^{\prime}\); \(v_{ab}\) and \(v_{a^{\prime}b^{\prime}}\) represent the velocities of trajectories \(ab\) and \(a^{\prime}b^{\prime}\); \(d\left(X_{i},X_{j}\right)\) represents the distance between position \(i\) and position \(j\); and \(D\) is the threshold constant of space interval. Conditions (1) and (2) guarantee the continuity of the possible trajectory connection in time and space, respectively, but they are not sufficient for the connection of trajectories.
# The Effect of Silica-Filler on Polyurethane Adhesives Based on Renewable Resource for Wood Bonding
## 3 Results and Discussion
### Pull-Off Adhesion Test  
In order to assess the applicability of adhesives in veneering, a good bond in pull-off test is even more important than that in the lap shear strength. It showed that a significant increase in bonding between a veneer and a substrate can be induced by silica addition. The effect is apparent as the investigated adhesives that exhibit innate low strengths. It is worth to mention that the adhesives for veneering are not required to render substrate failure (MDF/particleboard). It is desirable, but not required by any European standard. The bonding strengths of both investigated PURs (A2% vs. A0% and C6% vs. C0%) are shown in Figure 6. The revealed improvement in pull-off adhesion tests remains in accordance with the increased work of adhesion and surface free energy of the adhesive with SiO\({}_{2}\) (Table 4). Thus, the enhanced bonding between wooden materials and the silica-filled PURs has been clearly demonstrated. The values shown in Figure 6 are comparable or slightly lower to those achieved for commercial non-renewable-based PUR (2.50 MPa) [22].  
One can notice a poor performance of the C0% PUR in the pull-off test (Figure 6), while the strengths observed in the lap shear tests were satisfactory (100% substrate failure) (Figure 4). A possible explanation can low viscosity (1.2 Pa\(\cdot\)s at 25 \({}^{\circ}\)C) of the adhesive, which, in contact with porous substrates (MDF) and oak veneer, intensely penetrated both materials. Subsequently, it is likely that too thin bondline was formed. Unlike for MDF substrate, C0% PUR penetration into less porous solid wood in lap shear specimens was weaker, which resulted in an improved bondline performance. Viscosity increase to 19.7 Pa\(\cdot\)s due to silica addition impeded overpenetration of the adhesive into substrates.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multicolumn{2}{c}{**-**} & \multicolumn{3}{c}{**Polyol Used**} \\ \cline{2-7}  & **-** & **A0\% SiO\({}_{2}\)** & **A2\% SiO\({}_{2}\)** & **C0\% SiO\({}_{2}\)** & **C6\% SiO\({}_{2}\)** \\ \hline contact angle \(\theta\) [\({}^{\circ}\)] & water & 56.9 \(\pm\) 2.3 & 23.1 \(\pm\) 4.0 & 53.5 \(\pm\) 1.6 & 27.7 \(\pm\) 0.7 \\ diiodomethane & 45.3 \(\pm\) 2.4 & 17.8 \(\pm\) 0.3 & 38.1 \(\pm\) 0.9 & 38.0 \(\pm\) 2.2 \\ surface free energy [m]/m\({}^{2}\)] & & 51.7 \(\pm\) 2.0 & 75.0 \(\pm\) 1.3 & 55.0 \(\pm\) 1.7 & 70.7 \(\pm\) 1.8 \\ work of adhesion [m]/m\({}^{2}\)] & & 112.0 \(\pm\) 1.5 & 139.7 \(\pm\) 1.8 & 116.5 \(\pm\) 1.6 & 137.4 \(\pm\) 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Wetting, surface energy and work of adhesion of the studied PURs.  
Figure 5: Water contact angle for non-SiO\({}_{2}\)-filled and SiO\({}_{2}\)-filled PURs 60 s after droplet deposition.
## 3 Results and Discussion
### Decision Tree  
A confusion or error matrix allows an assessment of the performance of the algorithm and is often used to assess model accuracy [40]. The accuracy of the decision tree shows absences (\(n=24\),\(832\)) had an overall 70.3% accuracy; whereas presences (\(n=69\),\(999\)) have a higher overall accuracy of 88%. According to the confusion matrix of predicted and actual presence/absence, an internal model agreement of predicted _versus_ actual presence/absence, known as the kappa index, is calculated to be 0.60.  
The predicted distribution of Chinese fir under current climate conditions is shown in Figure 5a, matching field observations that the species grows between approximately 21\({}^{\circ}\) to 35\({}^{\circ}\) N and 101\({}^{\circ}\) to 121\({}^{\circ}\) E in mainland China as well as the central area of Taiwan. This result agrees with other distributional studies of the species in both the mainland and Taiwan natural Chinese fir forests [2, 41, 42]. Given the accuracy of the model and the confidence from this agreement, we applied the decision tree rules to the two future climate projections to assess the Chinese fir distributions under projected climate conditions. Figure 5b and c indicate that under climate projection A1B and A2, the species is likely to experience a northward shift with minor changes in the south resulting in an overall increase in species distribution area compared to current climate conditions. Comparing the two projected climate projections, Figure 5b shows that for projection A1B, regions in the central region of China are likely to become more suitable for Chinese fir, however the northward shift is not as marked as under the A2 projection.
# Improved Multi-GNSS PPP Software for Upgrading the DEMETRA Project Time Monitoring Service
## 3 Processing and Results
### Atomium Galileo PPP  
For both the CCD experiments in ORB and INRIM, the common clock was compared through their two GNSS stations with the Atomium PPP using GPS signals and GPS + Galileo signals. As shown in Figure 1, the two solutions in ORB show a good coherence, however, due to the presence of daily boundary jump, the performances of the GPS-only solution and GPS + Galileo solution are hard to compare.  
For a better comparison, the daily batch boundaries were minimized by aligning the daily results using 2 nd order extrapolation at the border [6]. This method will cause an accumulated error in the long-term, so only 3 days of data from Figure 1 were aligned and compared, as displayed in Figure 2.  
Figure 1: Common clock difference (CCD) Galileo results between BRUX and RTBS.  
From Figure 2, it is observed that the daily PPP clock comparison noise was generally within 0.1 ns, and with GPS + Galileo signals, the PPP obtained slightly more precise results, and a similar conclusion can be made from the CCD experiments in INRIM. To better elaborate the improvement by adding Galileo signals, they were compared in the form of Allan DEVIation (ADEV). The ADEVs for the CCD results in ORB and INRIM are plotted in Figures 3 and 4.  
The ADEVs of GPS + Galileo PPP results were generally lower than the GPS-only ones, as shown in Figures 3 and 4. The improvement in the frequency stability at medium and short averaging times was generally between 10% and 25%. However, due to the discontinuity at the batch boundary, as shown in Figure 1, it is hard to compare the long-term stability.  
Figure 3: Allan DEVIations (ADEVs) for the CCD global positioning system (GPS)-only and GPS + Galileo precise point positioning (PPP) results in the Observatory of Belgium (ORB) from Figure 2.  
Figure 2: 3 days aligned CCD Galileo results from Figure 1.
# Organic Crystals for THz Photonics
## 4 Organic Crystalline THz Sources
### THz Sources Based on Optical Rectification
To avoid gaps in the spectrum, a solution based on exchanging between different crystals at different THz frequencies as proposed for the systems based on difference-frequency generation (see Figure 10c) is here not applicable because the whole spectrum is generated and detected simultaneously through the time-domain traces \(E_{\text{THz}}(t)\)  
Figure 12: (**a**) THz time-domain signal and (**b**) the corresponding amplitude and power spectrum achieved in a compact terahertz setup based on DSTMS (0.3 mm thick) as THz generator and photoconductive antenna as THz detector and a pump laser with pulse length of \(<\)100 fs, \(>\)200 mW average power, 80 MHz repetition rate and 1560 nm central wavelength [79]. The red lines represent signals measured in air with a humidity of 44% and black lines are those obtained in dry-air environment. Reprinted from Ref. [79]. Licensed by Creative Commons CC BY 4.0. Therefore, one important challenge for future development of novel organic NLO crystals for THz photonics is to reduce the strength of these vibrations. This can be done through molecular engineering, but this is unfortunately still extremely difficult to control because even a slight chemical change of the constituting molecules can considerably change the crystalline packing and the crystal growth ability [172]. Another recently proposed approach combines two complementary organic NLO crystals in a combined structure as a tandem THz generator [54]. Using a tandem consisting of HMQ-TMS and OH1, it was shown that a certain compensation of phonon-absorption gaps in the generated THz spectra is possible for optimized crystal combinations [54].
## 3 Results and Discussion
### Transient Performance under High Heat Flux Millisecond Pulses  
#### 3.2.1 Transient Responses under Millisecond-Pulsed Heat Sources without PCM  
Based on the results under a constant heat load, the three-dimensional transient model with the structure of \(d\) as 2 mm and \(H\) at 2 mm was built. The heat flux boundary condition of the 3 \(\times\) 3.5 mm\({}^{2}\) chip was changed from constant heat flux to millisecond heat pulses to obtain the temperature responses of the heat sink without a phase change temperature control unit. To reduce the solution time, the transient model takes the steady-state result as the initial value. Figure 8 shows the variations in \(T_{\rm max}\) under heat pulses. It can be seen that \(T_{\rm max}\) fluctuates in the same period as the heat pulses. During the heat pulse interval, \(T_{\rm max}\) decreases. When the heat pulse comes, \(T_{\rm max}\) rises rapidly and reaches 66.9 \({}^{\circ}\)C, 4.7 \({}^{\circ}\)C higher than the steady state value of 62.2 \({}^{\circ}\)C. The high response temperature caused by high heat flux pulses is detrimental to the reliability of electronic devices. Therefore, it is necessary to add a phase change temperature control unit to reduce the response temperature.  
The 2D axisymmetric model was used to calculate the transient responses with phase transition and its validity was verified first before calculation. The volumetric flow rate of the cooling medium and average inlet velocity of the 2D axisymmetric model are consistent with the 3D model. The chip area is equal to the chip area of 3 \(\times\) 3.5 mm\({}^{2}\), and the other boundary conditions are similar to the 3D model. For the 2D axisymmetric model, the \(T_{\rm max}\) under basic heat flux is 62.2 \({}^{\circ}\)C, which is the same as the 3D model. The transient result under the heat pulses is shown in Figure 8, and the initial value is also the result of the steady state. It can be seen from the figure that the variation trend of \(T_{\rm max}\) of the 2D axisymmetric model is consistent with the 3D model. Therefore, the 2D axisymmetric model is effective and can be used to further study the temperature responses of the heat sink with metallic PCM.