esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Over the years, the concept of SMSI was also extended to non-oxidic materials to describe the influence exerted by the support on the catalytic performances of metal nanoparticles (NPs) [42; 43; 44; 45; 46]. Subsequently, [PERSON] et al. invoked the dopant-induced metal-support interactions (DIMSI) to rationalize the role of doping cations in mediating metal-support interactions in the case of noble metal crystallites deposited onto doped supports [42; 47; 48]. In an extreme simplification, the support can be envisaged as a supramolecular ligand strongly interacting with metal NPs. From this point of view, functional groups on the support surface act as preferential interaction sites and as electron-donating and electron-withdrawing substituents. The influence on the catalytic activity and selectivity can be essentially ascribed to three effects: (i) electronic effects, (ii) geometric effects, and (iii) dispersion effects (Figure 1) [46]. Electronic effects originate from the difference between the Fermi level (E\({}_{\text{F}}\)) of the metal and that of the support, resulting in an electron transfer aimed to equalize the E\({}_{\text{F}}\) at the metal-support interface [49; 50; 51]. The lattice mismatch between the support and the metal NPs induces changes in the shape of the nanoparticle to optimize the interaction with the support [52]. Consequently, these geometric effects are combined with variations in the exposed crystallographic planes and in the coordination number of surface metal atoms, which affect the catalytic performances. Finally, functional groups can act as anchoring sites, promoting the nucleation of metal NPs, and then, the uniform dispersion on the support [53; 54; 55; 56]. However, electronic, geometric, and dispersion effects are interrelated, and it is not possible to clearly discriminate each single contribution. Although the existence of metal-support interactions in carbon-supported metal catalysts has been confirmed by various characterization techniques, theoretical models, and catalytic results, their nature and the role of heteroatoms is still an object of discussion. The purpose of this review is to contribute to this ongoing debate by providing an overview of the main experimental and theoretical outcomes about heteroatom-mediated metal-support interactions. We begin by describing the main theoretical models proposed to account for strong metal-support interactions. Following this, the advances of characterization techniques in probing metal-support interactions are explored. We conclude with a collection of examples reporting the role of heteroatoms in enhancing metal-support interactions in catalysis.
## 2 Materials and Methods
### CNN Architecture, Training and Testing Procedures  
The preprocessed 3D PET data were converted into tensors and fed as input, together with their corresponding binarized \"label\" predictions as output, to a 3D CNN. The CNN architecture is illustrated in Figure 1.  
Briefly, we used a simplified convolutional neural network combining 5 blocks of a three-dimensional convolutional layer, a batch normalization layer and a 3D max-pooling layer, followed by a fully connected layer and a final 2-way activation layer (SoftMax function) for output binary classification purposes [13]. Rectified linear units (ReLUs) were used as activation functions at all the hidden convolutional layers to add nonlinear complexity. To verify the ability of the CNN to recognize normal scans, an ideal dataset was created by selecting all the strictly normal and highly pathological PET data, excluding injection hot spots, drug-related digestive hypermetabolism, urinary catheters, renal transplants and sophisticated surgical anastomoses. This ideal sample of strictly normal/highly pathological PET data (ratio approximately 50/50, respectively), named the \"unambiguous\" sample, was first used to train and test the CNN under optimal conditions. For this purpose, the \"unambiguous\" data sample was split into training and test sets (ratio 90/10). Eighty percent of the training set served to educate the CNN about the binary cross-entropy loss function for a maximum of 100 epochs, with a batch size of 4 per iteration, using the Adam optimizer [14] with a learning rate of 1\(e^{-3}\). To prevent overfitting, the training was stopped once the model loss reached the minimum on the validation set. To check the model's  
Figure 1: Architecture of the CNN used: five blocks composed of a 3D convolution (3 \(\times\) 3 \(\times\) 3), a 3D batch normalization and a 3D max-pooling (2 \(\times\) 2 \(\times\) 2) layer, followed by a fully connected layer (3 layers, from 12,288 neurons to 2 neurons), with a binary output for prediction.  
stability, the trained CNN was assessed on the remaining 20% of the training set by using a stratified 5-fold cross-validation procedure [15] and tested on the test set (10% of the unambiguous dataset). We ensured that no patients from the training set were included in the test set.  
In the second step, the CNN was trained by starting on the full dataset using a stratified 5-fold cross-validation procedure with the same training parameters, named the \"real-life\" data sample; it was then evaluated on the previously defined test set using the best fold. All the experiments were run inside the EDS dedicated environment on a GPU NVIDIA P40 (24 Go VRAM) and 48 CPU (188 Go of memory). All the CNN frameworks were built using the PyTorch backend.
## 3 Results  
### Correlation of Velocity Magnitude between the 4D Flow MRI and CFD Measurements  
The blood flow velocities at the comparison plane for all 4D flow MRI simulations (\(n\) = 12) were compared to the CFD models. The velocity waveforms at the bifurcation plane for each subject provided by the 2 methods revealed a close similarity of the shape (Figure 3A). The mean maximum and minimum velocities of the 12 subjects measured by the 4D flow MRI and CFD were not significantly different (p \(\geq\) 0.05), as shown in Table 1. While none of these results reached statistical significance, velocity measurements in 4D flow MRI were higher compared to CFD for both values at the carotid bifurcation plane (maximum velocity: 50.29 \(\pm\) 11.66 centimeters/second (cm/s) on the 4D flow MRI vs 47.21 \(\pm\) 11.02 cm/s on CFD; minimum velocity: 23.06 \(\pm\) 11.84 cm/s on the 4D flow MRI vs 17.19 \(\pm\) 3.85 cm/s on CFD). There was a 6.12% difference in maximum velocity and 25.46% difference in minimum velocity between the 4D flow MRI measurement and CFD calculation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Velocity at the carotid Bifurcation (\(n\) = 12)**} & **4D Flow MRI** & **CFD** & **Difference** \\ \cline{2-5}  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** \\ \hline Maximum velocity (cm/s) & 50.29 \(\pm\) 11.66 & 47.21 \(\pm\) 11.02 & 6.12\% & 0.05 \\ \hline Minimum velocity (cm/s) & 23.06 \(\pm\) 11.84 & 17.19 \(\pm\) 3.85 & 25.46\% & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Blood velocities measured in 12 subjects at the bifurcation planes by four-dimensional (4D) flow magnetic resonance imaging (MRI) and computational fluid dynamics (CFD).  
Bland-Altman plot analysis of velocity data at the carotid bifurcation plane comparing the results of the maximum and minimum velocities obtained in the 4D flow MRI and CFD is presented in Figure 3B. Most of the data points lie in the 95% limit band, showing good agreement between the 4D flow MRI and CFD measurements.
## 4 FL-Based Controller: Formulation, Constraints and Algorithm
### Constraints and Formulation of Charging and Discharging Control
#### 4.2.3 On/Off Peak Hours Membership Function  
Integrating ESSs into the grid is considered to be the best strategy for peak shaving, which consists in providing or absorbing the power surplus, thereby shaving the total exchanged power [43]. In this way, the coordination of charging and discharging of batteries offers the opportunity to participate in Demand Side Management (DSM) to reduce the peak load demand in the grid and avoid the surplus of PV generation injection into the grid [44]. Thus, the on/off-peak hours and electricity prices should be considered and varied with the load demand to allow for the scheduled operation of the battery and reduce the gap between the power generation and the load demand. For these reasons, the implemented controller considers the on-peak and off-peak times and it considers an input membership 'On/Off peak hours', which is fuzzified into on-peak hours: from 7 a.m. to 10 a.m. and from 5 p.m. to 9 p.m., off-peak hours: from 10:01 a.m. to 4:59 p.m., and from 9:01p.m. to 6:59 a.m., as illustrated in Figure 8.
## 4 Result and Discussion
### Port Injection of Ammonia
However, the port injection of ammonia, up to 10%, leads to a CO increment of nearly 30% at various engine speeds, respectively, which is mainly because of the decrease in in-cylinder temperature and the spread of unburnt zone. Figure 4:3 Nitrogen oxides production at various engine speeds at different fractions of ammonia. Figure 4:4 Peak cylinder temperature at various engine speeds at different fractions of ammonia. The impact of ammonia port injection on engine HC emission production is shown in Figure 4:5 Similar to CO, HC production increased by the injection of ammonia. With the port injection of ammonia increased up to 5% and 10%, the HC emission increased by 6% and 10%, respectively. However, the increase in HC and CO emissions caused by the injection of ammonia can be controlled by the employment of efficient catalytic converters in vehicles [37; 38; 39]. The minimum required fuel octane number for preventing engine knock with ammonia injection is presented in Figure 4:6 While ethanol addition did not affect the minimum octane number parameter significantly [3], the increase in port injection of ammonia up to 5% and 10% led to a reduction in the minimum octane number, from 84 (A0) to 82 (A5) and  
Figure 4:7 HC production at various engine speeds at different fractions of ammonia. Figure 4:8 CO production at various engine speeds at different fractions of ammonia. 81 (A10), respectively. Therefore, the port injection of ammonia can be considered as being beneficial for engine knock.
## 5 System Equilibrium Analysis under Arbitrary Transmission Limit
### QoS Guaranteed Resource Allocation and Dynamic Pre-Backoff Algorithm  
For Poisson distributed H2H traffic, a backoff scheme can only increase delays without improving the other performance metrics. Hence, in this paper the backoff scheme is not used for H2H traffic. We will first satisfy the resource requirement of H2H traffic due to its high priority. Then according to the prior equilibrium analysis, the reserved resource blocks \(L_{TP}\) for a given QoS requirement can be computed by Equations (34), (48) and (49).  
As to the M2M traffic, the maximum supportable arrival rate for a given available resources \(L_{SP}\) can be computed by Equation (39):  
\[\lambda_{th}^{\text{M2M}}=\frac{L_{SP}}{\delta_{2}e\Big{[}1-(1-e^{-1})^{Q} \Big{]}} \tag{50}\]  
The pre-backoff scheme should be enabled to balance the system load for beta distributed M2M traffic if \(\lambda\geq\lambda_{th}^{\text{M2M}}\). Therefore in this paper, we design an aggressively increase conservatively decrease based dynamic pre-backoff scheme as stated in Algorithm 1, in which \(\eta\) is a pre-defined time parameter to control the pre-backoff window size. In the view of the base station, \(X_{l}[1]\) is the actual rate of new arrivals. Each new arrival in the \(i\)-th cycle would randomly select a backoff interval \(\omega_{PBO}\in[0,W_{PBO}]\) and send its first access request in the \(i+\omega_{PBO}\)-th cycle.  
```
1. Input:\(L,\lambda,N_{\text{M2M}},T,\alpha,\beta,\theta,\gamma,\tau,\delta_{1},\delta_{2}, \eta,Q,W_{\text{BO}}\), QoS requirements of H2H traffic
2. Initialization:
3. Solve Equation (13) and return \(I_{\text{max}}\);
4.\(X_{i}^{\text{M2M}}[1]=0,\quad\forall i\in[1,I_{\text{max}}]\);
5.\(W_{PBO}=0\);
6. Step1: Solve Equations (34), (48) and (49) and return \(L_{TP}\);
7. Solve Equation (10) and return \(L_{SP}\);
8. Solve Equation (50) and return \(\lambda_{th}^{\text{M2M}}\);
9. Step2:for each cycle \(i=1\): \(I_{\text{max}}\)do
10. Solve Equation (12) and return \(N_{i}^{\text{M2M}}\);
11. # Newly arrived M2M devices execute pre-backoff procedure
12. for\(k=1\): \(W_{PBO}+1\)do
13.\(X_{i+k-1}^{\text{M2M}}[1]=X_{i+k-1}^{\text{M2M}}[1]+\frac{1}{W_{PBO}+1}N_{i}^{ \text{M2M}}\);
14. endfor
15. # Adjust the pre-backoff window size dynamically
16. if\(X_{i}^{\text{M2M}}[1]\geq\lambda_{th}^{\text{M2M}}\)
17.\(W_{PBO}=W_{PBO}+1\);
18. elseif\(X_{i}^{\text{M2M}}[1]<\lambda_{th}^{\text{M2M}}\) continuously in time interval \([i-\eta,i]\)
19.\(W_{PBO}=W_{PBO}-1\);
20. endif
21.\(I_{\text{max}}=\text{max}[i+W_{PBO}+(Q-1)(W_{BO}+1),I_{\text{max}}]\)
22. endfor
```  
**Algorithm 1**Dynamic pre-backoff algorithm (DPBO)
# Post Quantum Cryptographic Keys Generated with Physical Unclonable Functions
## 3 Public Key Infrastructure  
### Public-Private Key Pairs  
As part of a PKI, the public-private key pairs can be used to securely transmit shared secret keys though KEM and to digitally sign messages with DSA (see Figure 4). The public key \(\mathbf{Pk}_{(2)}\) of Client 2 encapsulates the shared secret key of Client 1, that can only be viewed by the client (2), thanks to their private key \(\mathbf{Sk}_{(2)}\) that reverses the encapsulation. Client 1 uses their private key \(\mathbf{Sk}_{(1)}\) to digitally sign a message that is verified with the public key \(\mathbf{Pk}_{(1)}\), providing non-alteration and non-repudiation in the transaction. The trust and integrity of such an architecture relies on the following:  
1. The secure generation and distribution of the public-private key pairs to the client devices that are participating in the PKI.
2. The identification of the client devices, and trust in their public keys.
3. The sharing of the public keys among participants.  
Most PKIs rely on certificate authorities (CA) and registration authorities (RA) to offer such an environment of trust and integrity. The architecture is vulnerable to several threats, including loss of identity, man-in-the-middle attacks, and side channel attacks in which the private keys are exposed during KEM, and DSA.
## 4 Results and Discussion
### Performance Evaluation
The results show that NEAT and MSCPSO have the best performance for road safety applications, whilst GA and isotropic antenna have much worse performance due to their larger standard deviation and lower average value in the case of isotropic antenna. Figure 5: Heat map for the isotropic antenna (**top left**), beamforming with mutation strategy-based particle swarm optimization algorithm (MSCPSO) (**top right**), beamforming with the GA (**bottom left**), and beamforming with neuroevolution of augmenting topologies (NEAT) (**bottom right**).
## 4 Simulation Validation
### Dynamic Response to Torque Load Change  
This subsection provides detailed comparisons of the control performance between conventional FCS-MPC and the proposed ESN-MPC under nominal conditions. Figure 5a,b depict the dynamic response of the PMSM when subjected to a step change in load torque. Initially, the PMSM operates at a stable speed of 2100 rpm with a load of 40 N\(\cdot\)m, and the torque is subsequently stepped up to 80 N\(\cdot\)m at 0.5 s. The waveform reveals that the proposed ESN-MPC achieves comparable steady state performance to the conventional FCS-MPC and superior transient performance, exhibiting an improved transient time (_Ts_ = 4.64 ms vs. 6.28 ms in FCS-MPC). The transient superiority is further corroborated by a smaller speed deviation (42.6 rpm drop vs. 67.1 rpm in FCS-MPC) during load transitions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline \multicolumn{3}{c}{Inverter parameter} \\ inverter type & / & 2L-3P VSI \\ sampling delay & / & 1.5 μs \\ dead time delay & / & 2.2 μs \\ DC-side capacitor & \(V_{\text{dc}}\) & 400 V \\ control frequency & \(f_{\text{sw}}\) & 20 k Hz \\ \multicolumn{3}{c}{PMSM parameter} \\ stator resistance & \(R\) & 0.1 \(\Omega\) \\ q-axis inductance & \(L_{\text{q}}\) & 0.8 mH \\ d-axis inductance & \(L_{\text{d}}\) & 0.6 mH \\ pole pairs & \(p\) & 4 \\ flux & \(\psi_{f}\) & 0.985 Wb \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strategies** & **Estimate FLOPs** &
\begin{tabular}{c} **Estimate Resource Utilization** \\ **(Control Frequency: 20 kHz)** \\ \end{tabular} \\ \hline \multicolumn{3}{c}{Conventional FCS-MPC} \\
1 prediction step & 272 & 1.36\% \\
2 prediction step & 2176 & 10.88\% \\
3 prediction step & 17408 & 87.04\% \\ \multicolumn{3}{c}{Proposed ESN-MPC} \\ \(\text{N}_{\text{H}}\) = 5, with/without & 708/453 & 3.54\%/2.26\% \\ activate online learning & 1615/1080 & 8.07\%/5.40\% \\ \(\text{N}_{\text{H}}\) = 15, with/without & 3078/2163 & 15.39\%/10.81\% \\ \(\text{N}_{\text{H}}\) = 20, with/without & 4863/3468 & 24.31\%/17.34\% \\ \(\text{N}_{\text{H}}\) = 30, with/without & 9441/6786 & 47.21\%/33.93\% \\ activate online learning & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison on computation burden of different strategies.  
This improved transient performance results primarily from the reservoirs in ESNs that enable the ESN to effectively adapt to abrupt disturbances. Furthermore, steady-state torque ripple and current THD for both methods remain nearly identical, highlighting that the proposed ESN-MPC transient improvements are achieved without compromising steady-state waveform quality.
## 2 Results
### Statistical Analysis to Determine Compounds Consumed and Produced by S. Aureus
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Identification**} & \multicolumn{3}{c}{**Characterisation**} \\ \cline{2-7}
**Volatile/Semi-Volatile Metabolite** & \multicolumn{2}{c}{**LRI 1  & 803 & 714 & 12.5\% & \(\diagup\) & \(3.12\times 10^{-3}\) & Consumed \\ Propan-2-one 3  & 843 & 818 & 3.1\% & \(\diagup\) & \(4.80\times 10^{-5}\) & Produced \\
3-methylbutanal & 866 & 876 & 1.1\% & \(\diagup\) & \(6.38\times 10^{-5}\) & Consumed \\ Ethanol 4  & 938 & 940 & 0.2\% & \(\diagup\) & \(5.32\times 10^{-6}\) & Produced \\ (methyldisulfanyl)methane & 1059 & 1039 & 1.9\% & \(\diagup\) & \(2.92\times 10^{-4}\) & Consumed \\
1-methyl-1-propylhydrazine & 1066 & - & \(\diagup\) & & & Produced \\ (methyltrisulfanyl)methane & 1376 & 1376 & 0.0\% & \(\diagup\) & \(3.38\times 10^{-2}\) & Produced \\
3-ethyl-2,5-dimethylpyrazine & 1454 & 1450 & 0.3\% & & & Produced \\ Acetic acid 5  & 1464 & 1479 & 1.0\% & \(\diagup\) & \(7.54\times 10^{-3}\) & Produced \\ Formic acid & 1531 & 1510 & 1.4\% & \(\diagup\) & \(4.89\times 10^{-6}\) & Produced \\ Benzaldehyde 6  & 1542 & 1534 & 0.5\% & \(\diagup\) & \(1.97\times 10^{-4}\) & Consumed \\
3-methylbutanoic acid 6  & 1645 & 1655 & 0.6\% & & & Produced \\
2-hydroxybenzaldehyde & 1702 & 1680 & 1.3\% & \(\diagup\) & \(2.41\times 10^{-7}\) & Produced \\ Acetamide & 1767 & 1773 & 0.3\% & & & Produced \\
1,3,5,7-Tetraazatricylcolo[3.3.1.137]decane & 1946 & - & \(\diagup\) & & \(1.88\times 10^{-3}\) & Produced \\
4-methylquinoline & 2152 & 2108 & 2.1\% & = & \(6.90\times 10^{-1}\) & No variation \\ Isoquinoline-1-carbonitrile & 2991 & - & & = & \(6.12\times 10^{-1}\) & No variation \\ Quinoline-4-carbaldehyde & 2500 & - & & = & \(5.37\times 10^{-1}\) & No variation \\
1H-indole & 2502 & 2451 & 2.1\% & \(\diagup\) & \(2.76\times 10^{-2}\) & Produced \\ \hline \hline \end{tabular}  
* LRI: Linear retention index. \(\triangleright\): LRI not available in the literature. \(\cdot\): Metabolites that have additionally been confirmed by analysis of standards. Percentage ALRI: Percentage change between theoretical and calculated values. \(\diagup\): Positive area variation of deconvoluted peaks (i.e., mean peak area detected in bacterial culture above that of the control). \(\diagup\): Negative area variation of deconvoluted peaks (i.e., mean peak area detected in bacterial culture below that of the control). \(=:\) No significant area variation of deconvoluted peaks.  
\end{table} 
Table 1: Compound tentatively identified in a _S. aureus_ culture by SBSE/HSSE or SPME-GC-MS and their statistical variation between culture and control under the same conditions.
## 2 Results and Discussion
### The effects of Selected Key Odorants on the Sensory Profiles of Doubanjiang  
Sensory evaluation tests were used to access the effect of selected key odorants on the overall aroma of doubanjiang. The effects of spiking certain volatiles alone on flavor descriptors are shown in Table 5. Significant difference was observed compared with the unspiked control, which further confirmed that these key active-aromas played an important role in the flavor of doubanjiang. Doubanjiang has a quite complex food matrix, from which flavor release not only depends on the nature of the aroma compounds but also the interaction between sensory attributes of aroma compounds or with the nonvolatile constituents of the food matrix. For example, spiking acetic acid, 3-methylbutanoic acid and methional separately from doubanjiang did not impart the overall aroma, although methional has a relatively high OAV (160), whereas adding sotolone to doubanjiang not only imparted a caramel-like aroma but also increased the fruity aroma.
# Broadband Holography via Structured Black Silicon Nano-Antennas
## Material and Methods
### Optical Setup  
The optical setup for imaging reconstruction of the fabricated CGH is illustrated in Figure 1c. In this study, the reconstruction beam was continuously illuminated by diode lasers with different wavelengths of 440 nm, 532 nm, and 632 nm, respectively. The laser source beam was collimated using a lens of 10 cm focal length, which guarantees the right beam-spot dimension. The beam then illuminates the hologram by passing through a beam-splitter in transmission. The object wavefronts were captured via the imaging screen after passing back to the beam-splitter in reflection. The reconstruction process was carried out in a darkroom. The efficiency of the BSi hologram was calculated as the ratio between the power in the holographic image and the total power illuminating the holographic plate.