esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Experimental Results
### Voltage-Mode Multifunction Biquadratic Filter
The power consumption was approximately 180 mW under \(\pm\) 6 V of supply voltages and 15 mA constant output current. In Figure 23, the measured values of angular frequency were equal to 39.15 kHz, 76.34 kHz, and 142.21 kHz, respectively, by adopting C\({}_{1}\) = C\({}_{2}\) = 1 nF and equal resistors of R\({}_{1}\) = R\({}_{2}\) = R\({}_{3}\) with 4 k\(\Omega\), 2 k\(\Omega\), 1 k\(\Omega\). Selecting R\({}_{2}\) = R\({}_{3}\) = 4 k\(\Omega\) and different values for R\({}_{1}\) as 5.6, 4, and 1.6 k\(\Omega\), results in BP filters with theoretical angular frequency f\({}_{\rm o}\) = 39.79 kHz and quality factors of Q = 0.71, 1, and 2.5, respectively, as shown in Figure 15.0 The measured values of Q were Figure 15.1 Measured gain and phase responses at \(\rm V_{o2}\) when \(\rm V_{i1}=V_{in}\) and \(\rm V_{i2}=0\). Figure 15.2 Measured gain and phase responses at \(\rm V_{o3}\) when \(\rm V_{i1}=V_{in}\) and \(\rm V_{i2}=0\). Figure 15.3 Measured gain responses of HP (V\({}_{\rm o3}\)) filters by varying f\({}_{\rm o}\) while keeping Q (f\({}_{\rm o}\) = 39.15 kHz— black line; f\({}_{\rm o}\) = 76.34 kHz—red line; and f\({}_{\rm o}\) = 142.21 kHz—blue line). Figure 15.4 Measured gain and phase responses at V\({}_{\rm o1}\) when V\({}_{\rm i1}\) = 0 and V\({}_{\rm i2}\) = V\({}_{\rm in}\). Figure 15.5 Measured IBP filter of the two-tone test with the amplitude of 1.15 V\({}_{\rm P}\) voltage. Figure 15.6 Measured gain responses of BP (V\({}_{\rm o2}\)) filters by varying Q while keeping f\({}_{\rm o}\) (Q = 0.89—black line; Q = 1.06—red line; and Q = 2.12—blue line).
## 4 Discussion
### Comparison between Assistance Modalities with the Ekso  
We did not find changes in muscle activations between the two bilateral exoskeleton-assisted modalities (B-FA, B-AA), i.e., comparing maximal and adaptive assistance. This contradicted our original hypothesis. We believed that if the exoskeleton would provide a maximum assistance enabling to walk with the Ekso in a predefined manner, independently from the ability of the user to generate the walking patterns, then the users, being greedy optimizers [23], would allow the Ekso to do all the work, decreasing their voluntary control, and consequently their muscle activations. This did not happen in our experiment, contradicting the basis of the assistance-as-needed approach [23]. The comparison between unassisted and assisted gait further confirmed this finding, since subjects had higher activations in the presence of assistance, i.e., when these activations were not needed for walking, than in the absence of assistance. Indeed, both the B-NA and U-NA subjects had a reduced muscle activation of the BF, ST, RF, and VM when compared to the assisted conditions. The muscle synergies analysis confirmed these results, since the four synergies that were extracted were quite similar among all the walking conditions with the Ekso: in number, in their structure (weight coefficients), and in some activation profiles.  
The absence of the greedy optimization might depend on the limited experience of the users with the exoskeleton and because during testing, the overall walking distance was limited by the length of the walking path in the motion analysis lab, resulting in the anticipation of the unavoidable stops and turns at each side of the room. With longer training or with the possibility of walking in longer paths, subjects might exploit the assistive forces, decreasing their muscle activity.  
Also, we should consider an alternative explanation: overground walking is different with respect to the treadmill walking (as in [23]), since in the latter case, the motor commands are generated mostly  
Figure 9: First row: Mean activation profiles of the four muscle synergies for all the walking conditions. Unilateral assistance is indicated with dashed lines, while bilateral assistance is indicated with continuous lines, and the type of assistance is indicated by the color of the lines: B-FA (red), B-AA, U-AA (blue), B-NA, U-NA (green). Second row: SPM[F] statistic as a function of the percentage of gait cycle. The threshold for significance is indicated in red. Third row: Statistic results for the pre-planned comparisons between normal walking and the five Ekso-assisted modalities (NW vs. B-NA; NW vs B-FA; NW vs. B-AA; NW vs. U-AA; NW vs. U-NA ) and for the comparisons between Ekso-assisted modalities (B-FA vs. B-AA; B-AA vs. U-AA; B-NA vs. U-NA; –A vs. -NA). Black intervals \(p<0.001\) and grey intervals \(p<0.05\).  
at the spinal level, while walking overground is more mediated by voluntary activity, especially in the early stage of the movement. A future study must further investigate this point.
## 4 Simulation Analysis of Roughness on Shot Peening Residual Stress and Surface Deformation
### Analysis of Residual Stress and Deformation of Different Impact Positions in Rough Surface
#### 4.2.2 Analysis of Different Impact Positions on Surface Residual Stress Distribution  
The distribution of residual stress on the surface of the target material after impact is shown in Figure 10. As can be seen, with the increase of surface roughness, the residual stress produced by impact changes obviously, and at the position of the peak and the pit, the change is the most obvious. When the surface roughness is 16 \(\upmu\)m, the residual stress caused by shot peening is the largest at the pit while the smallest at the peak. This is because the contact area between the pellet and pit of the small roughness target material surface is relatively small compared with the high roughness surface, and the contact form is on only one side. During the contact process, the deformation on the surface caused by continuous in-depth contact and the one side accumulation caused by the scratch effect on one side strengthens the deformation and stress concentration behavior. Although the contact area at the peak is relatively small, the peak with a small rough surface has an obvious deformation effect through impact. The deformation increases the actual contact area between the pellet and surface, which reduces the stress caused by impact and makes the residual stress relatively small. When the surface is 24 \(\upmu\)m and 29 \(\upmu\)m, the situation is opposite; the topography of the surface is more complex with the increase of surface roughness, and the contact form between the pellet and peak converts into point contact. Therefore, the maximum residual compress stress appears at the impact center, and the residual stress becomes larger. In addition, the residual compress stresses at the ridge are normally larger than that at the half slope.  
By comparing Figures 9 and 10, the surface roughness and surface topography have a noticeable effect on surface material deform displacement and residual stress. It mainly reflects that the values of surface material displacement and surface residual stress change obviously, which is caused by the pellet impacting the peak and the pit, two typical surface topographies. By comparing analysis, we can also see that when the surface roughness is relatively large, the pellet impacted on the surface would have a secondary impact on other surface areas, affecting other pellets, and changing the moment tracking of non-impacted pellets, thus finally affecting the processing effect of shot peening. The phenomenon shows that it is not that the larger the initial surface roughness, the better effect of shot peening it will be. However, in order to achieve a better shot peening processing effect, the initial surface roughness of workpieces should also be considered. It should be processed with relatively small surface roughness and simple surface topography before shot peening, which means the roughness of the workpiece should be controlled within a reasonable range.
## 4 Numerical Modelling
### Verification of Numerical Results  
In order to validate the accuracy of the numerical model, a comparison was conducted regarding pile foundation settlement and lateral load. This comparison aimed to assess the consistency between the numerical and physical models. Figure 10a illustrates the piled foundation settlement curve. Throughout the simulation process, noticeable fluctuations of approximately 15 mm were observed, gradually converging to a stable value. The final settlement results from the physical model deviated by only 4% from the stable settlement values obtained in the numerical model. From Figure 10b, it can be observed that the two models exhibited a similar variation pattern in horizontal loads, with the maximum difference in peak points being 9%. The reasons for the observed differences include boundary effects in the physical model and the absence of seismic wave reflection effects in the physical model. It is evident from these results that the consistency between the experimental and numerical findings is satisfactory.  
Figure 10: Result validation: (**a**) settlement of the foundation; (**b**) transverse load of the foundation.
# Nonlinear Control Design of a Half-Car Model Using Feedback Linearization and an LQR Controller
## 5 Conclusions  
Effective road handling and better ride quality are two conflicting requirements in design of a suspension system. In active suspension system, additional design freedom is provided by  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**States** & **Without Controller (m)** & **With Controller (m)** & **Improvement (\%)** \\ \hline Vertical Displacement & 0.05 & 0.016 & 68 \\ Vertical Acceleration & 4.4 & 1.3 & 70.4 \\ Pitch Displacement & 0.03 & 0.01 & 66.7 \\ Pitch Acceleration & 2.3 & 0.69 & 70 \\ Front Suspension Travel & 0.022 & 0.013 & 41 \\ Rear Suspension Travel & 0.025 & 0.016 & 36 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMS values comparison with previous techniques.  
the active element (linear actuator) present therein. In this paper, the objective of minimizing the road disturbances of a half-car model is achieved successfully using the Feedback Linearization technique and LQR controller. A nonlinear transformation is defined which transforms the nonlinear model of the active suspension system into normal form, which is then linearized using a nonlinear control law. Linearizing the nonlinear system and then applying an LQR greatly reduced the complexity of implementation in Simulink/Matlab as well as in real time. The technique proposed in this research solved the problem a great extent by maintaining a trade-off between the good quality ride and road handling performance by not letting the suspension travels to rise so much in presence of road disturbances. Simulation results of case 1 and case 2 presented in this paper revealed that compared with other techniques, Feedback linearization technique applied along with LQR controller has a better response in reducing the amplitudes of states, overshoot and good dynamic performance.  
In this work, it has been assumed that states of the system are measurable. As a future work, an LQG controller may be designed to achieve better ride quality when all the states are not measurable. Furthermore, driver dynamics can be added in the model of Half Car Active Suspension system to make the system more accurate.  
[PERSON] proposed the concept, [PERSON] and [PERSON] did formal analysis and designed methodology, [PERSON] performed mathematical model, [PERSON] performed simulations, [PERSON] and [PERSON] supervised the research work. [PERSON] wrote the original draft of paper and [PERSON] supervised it. H.P. did funding acquisition. A.W. provided resources and administered the manuscript submission process. [PERSON] and [PERSON] review and supervised the manuscript for final submission. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A3B03035693).  
The authors declare no conflict of interest.
## 3 Problem Definition and Formulation
### Constraints
#### 3.2.3 Bandwidth Resource Constraint  
Bandwidths ensure that each node is not overloaded, so they are critical for efficient task scheduling.  
**Assumption 2**.: _The bandwidth availability is finite and may vary dynamically based on network conditions._  
The bandwidth resource constraint is given as  
\[\sum_{j=1}^{M}X_{ij}t_{j}^{b}\leq b_{i},\ \forall i \tag{8}\]  
This constraint ensures that the total bandwidth usage at node \(i\) for task \(j\) does not exceed the bandwidth capacity \(b_{i}\) of node \(i\), where \(t_{j}^{b}\) represents the bandwidth resource requirement of task \(j\), and \(b_{i}\) is the bandwidth capacity of node \(i\).
## 4 Results and Discussions  
### Implementation Details  
This study used the Berkeley Segmentation Dataset (BSD) of size 180 \(\times\) 180 [8] and 100 pristine natural images from the Waterloo Exploration Database [39] for training. We introduced three known noise levels (\(\sigma\) = 15, 25, and 50) into the gray image datasets for  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Noise Level** & \multicolumn{2}{c}{**Set12**} & \multicolumn{2}{c}{**BSD68**} \\ \hline
**Methods** & **L1 Loss (MAE)** & **L2 Loss (MSE)** & **L1 Loss (MAE)** & **L2 Loss (MSE)** \\ \hline \(\sigma\) = 15 & 32.92 & 32.96 & 31.73 & 31.76 \\ \(\sigma\) = 25 & 30.50 & 30.54 & 29.22 & 29.26 \\ \(\sigma\) = 50 & 27.29 & 27.33 & 26.25 & 26.29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average PSNR (dB) results of MSE and MAE on Set12 and BSD68 with noise levels of 15, 25, and 50.  
image denoising. For color image denoising, we introduced five known noise levels (\(\sigma\) = 15, 25, 35, 50, and 75). We set the patch size as 50 \(\times\) 50 and the stride as 40.  
The test set and the training set are also divided into gray images and color images. We used McMaster [40], Kodak24 [41], and CBSD68 as the test datasets of color images. McMaster and Kodak24 are 18 image sets and 24 image sets, respectively, with a size of 500 \(\times\) 500. CBSD68 is 68 natural images with a size of either 481 \(\times\) 321 or 321 \(\times\) 481.  
BSD68 and Set12 [42] were used as the test set of gray images. BSD68 is the gray version of the color test dataset CBSD68. Set12 is a natural square image with a size of 256 \(\times\) 256, and there are 12 images in total.  
The data used in this study do not need to be anonymous before use.  
The network depth of DRFENet is 23 layers. Our initial learning rate started from \(10^{-3}\) to train 20 epochs, then every 10 epochs eps was 1/10 of the previous one and finally stopped when eps = \(10^{-8}\). The batch size of the training was 128, \(\beta\)1 = 0.9, \(\beta\)2 = 0.999. Our experimental environment was all completed in Windows 10, the Python environment was 3.8, the PyTorch version was 1.7.1, and the main hardware configuration of the PC is shown in Table 2.
# Antimicrobial and Odour Qualities of Alkylpyrazines Occurring in Chocolate and Cocoa Products
## 3 Antimicrobial Activity of Pyrazines
### Antifungal and Antioomycete Activity
The presence of pyrazine compounds was at least 90% in those strains; in addition, they had high in vitro mycelial growth inhibition activity against Phaeomoniella chlamodspora, the most common fungus responsible for Esca disease, which majorly impacts the world wine industry by infecting grapes (inhibition rates of 71 and 74.3%, respectively, by _B. pumilus_ and _Paenibacillus_ sp. ), highlighting the crucial function of pyrazine derivatives in fungal antagonism [51]. In research by [PERSON] et al. [55], mass spectrometric identification was conducted on microbial volatiles of strains associated with antagonistic effects on _Scherichia scleroorum_ and _Rhizoctonia Solani_. Out of 41 microbial volatiles, seven were pyrazine derivatives: 2-methyl-5-(1-methylethyl)-pyrazine, 2-isobutyl-3-methylpyrazine, 2-methoxy-3-(2-methylpropyl)-pyrazine, 2,6-dimethyl-3-sec-butylpyrazine, 2,3-dimethyl-5-(2-methylpropyl)-pyrazine, 2,5-dimethyl-3-(2-methylpropyl)-pyrazine and 2-ethylpyrazine, produced mainly by _Bacillus cereus_ (RS-MS53) and Bacillus aerius (RS-So365). 2-Ethylpyrazine and 2,3-dimethyl-5-isobutylpyrazine applied separately or combined were found to significantly lower the germination level of sclerotia of Sclerotinia scleroorum after 96 h of incubation on potato-dextrose agar plates. The testing of 2-isobutyl-3-methylpyrazine applications for the treatment of the fungus _Candidida albicans_ H5 showed the inhibition (MIC) and then total destruction (MFC) of fungal colonies on a 4 mg/mL concentration application. Slightly better results out of all the tested pyrazines showed 5-isobutyl-2,3-dimethylpyazine, with MIC = 3 mg/mL and MFC = 4 mg/mL [35]. VOCs bioactivity, which was produced by _Lysobacter capsici_ AZ78, was evaluated against soil-borne fungal plant pathogens, such as _Pythium ultimum_, _Rhizoctonia solani_ and _Sclerotinia minor_[56]. The most consistent compounds found in the PDA medium of the volatile profile were 2,5-dimethylpyrazine, 2-isopropyl-3-methoxypyrazine and 2-ethyl-3-methoxypyrazine, out of which the first and second are present in chocolate. The MIC was measured on three strains of previously mentioned pathogens. To three different pathogen strains, all three compounds exhibited antibacterial activity. The most vulnerable to the action of pyrazine derivatives was _S. minor_, as it was inhibited by the addition of 7.5 mg of 2-ethyl-3-methoxypyrazine and 2-isopropyl-3-methoxypyrazine for more than 80% and by the addition of 7.5 mg of 2,5-dimethylpyrazine for more than 50%. R. solani turned out to be the most stable: a maximum of more than 50% of growth was inhibited by relatively high doses of 2,5-dimethylpyrazine (10 mg) and was inhibited for more than 10% by 2-ethyl-3-methoxypyrazine (7.5 mg).
# Development of a GNSS Buoy for Monitoring Water Surface elevations in Estuaries and Coastal Areas
## 2 Methodology
### Determination of Directional Wave Spectra Using GNSS Data  
During the field tests, GNSS data, including the altitude (\(\eta\)) and velocities in the east (\(u\)), north (\(v\)), and upward (\(w\)) directions of the GNSS antenna, was obtained. For analyzing waves, this work assumes that the motion of the GNSS antenna is identical to that of water particles on the free surface. Both \(\eta uv\) and \(uvw\) data can be used to obtain the directional wave spectrum. In this work, the \(\eta uv\) data were utilized to exploit the available water surface elevations because the altitude (\(\eta\)) is related directly to waves. For simplification, the velocities in the east and north directions are referred to as the \(x\)- and \(y\)-velocity components, respectively. Appendix B provides detailed information on the method for determining the directional wave spectrum and the peak wave direction from the \(\eta uv\) data provided by the GNSS buoy.
## 3 Warehouse Delivery Problem
### Formalization
#### 3.1.2 Solution Formulation
Figure 1) are given in Table 2, and formally captured by function \(f_{W}\), viz. \[f_{W}(r_{1})=\langle(h_{1},0,0),\ldots,(h_{1},405,\infty)\rangle\text{ and }f_{W}(r_{2})=\langle(h_{2},0,0),\ldots,(h_{2},383,\infty)\rangle \tag{4}\] \begin{table}
\begin{tabular}{c c l l l l} \hline \(f_{T}(r_{1})\) & \(t_{1}\) & pickup from \(l_{1}\) & \(f_{T}(r_{2})\) & \(t_{5}\) & pickup from \(l_{2}\) \\  & \(t_{2}\) & putdown at \(s_{1}\) & & \(t_{6}\) & putdown at \(s_{2}\) \\  & \(t_{3}\) & pickup replacement from \(p_{1}\) & & \(t_{7}\) & pickup replacement from \(p_{1}\) \\  & \(t_{4}\) & putdown replacement at \(l_{1}\) & & \(t_{8}\) & putdown replacement at \(l_{2}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Task assignment for robot \(r_{1}\) and \(r_{2}\). Both robots start at time point zero at their respective home docking stations as starting locations and return there at time point 405 and 383, respectively. In our scenario, most nodes are passed instantaneously as indicated by the same arrival and exit time. We use \(\infty\) as the exit time for a terminal vertex to indicate that a robot has reached its final destination and will not subsequently leave this vertex. We now consider additional definitions and criteria that make for a solution to a warehouse problem. Firstly, we need to ensure that all specified tasks are assigned to robots and every task must be assigned to exactly one robot. **Complete and Non-Overlapping Assignment**. Given a task execution graph \((T,D,f_{D},f_{V})\), we define a robot assignment \((f_{T},f_{W})\) as _complete_ and _non-overlapping_ with respect to \(T\) iff:  
\(\bigcup_{r\in R}f_{T}(r)=T\); and  
\(f_{T}(r)\cap f_{T}(r^{\prime})=\emptyset\) for distinct \(r,r^{\prime}\in R\). Clearly, the task assignment in (3) is complete and non-overlapping with respect to the eight tasks \(T=\{t_{1},\ldots,t_{8}\}\) in our example (cf. Figure 2) since the first four are taken care of by robot \(r_{1}\) and the second four by \(r_{2}\). Secondly, we ensure that each robot must return to its home base once it has finished its assigned tasks. In particular, each robot's timed walk must start at its assigned starting location and end at its home vertex. **Starting and Homing Assignment**. Given a warehouse \((V,E,f_{E},C,R,f_{H},f_{S})\), we define a robot assignment \((f_{T},f_{W})\) as _starting_ and _homing_ iff for all robots \(r\in R\), \(f_{W}(r)\) is a non-empty sequence \(\langle(v_{0},a_{0},\epsilon_{0})\ldots,(v_{n},a_{n},\epsilon_{n})\rangle\) such that:  
\(v_{0}=f_{S}(r)\);  
\(v_{n}=f_{H}(r)\);  
\(a_{0}=0\); and  
\(e_{n}=\infty\). Recall that we use \(\infty\) as exit times at terminal vertices to indicate that robots stay put. It is worth noting that an empty sequence does not constitute a starting or homing walk as it does not place the robot on any vertex at any point in time. In contrast, a robot \(r\) that starts and remains stationary at its home vertex corresponds to the singleton walk \(\langle(f_{H}(r),0,\infty)\rangle\) and trivially satisfies the criteria for a starting and homing assignment. For