esa-sceva/satcom-chunk-collection huggingface dataset:
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 3 System Design
### Federated Reinforcement Learning System  
As explained before, we utilized FRL to train UAVs' neural network, and Figure 2 shows the overall operations of FRL in the proposed system. To explain the FRL scheme for our system, we assume that there are \(n\) UAVs, \(U_{1},\ldots,U_{n}\), which have their own databases, \(D_{1},\ldots,D_{n}\). The FRL in the system includes the following major steps. First, a server, a satellite in our system, sends the initial global model to all of the UAVs, and then each UAV trains its local model using local information including states, actions, and rewards. After that, the UAVs send local model parameters, \(W_{1},\ldots,W_{n}\), back to the server, and the model parameters are aggregated into the global model in the server. The parameters of the aggregated global model, \(W_{G}\), are delivered to the UAVs again, and the above procedures are repeated until the global model is trained enough.  
Figure 1: The concept of the proposed system.
## 5 Discussion
### Visual Spatial Boundary and the Calibration of the Auditory Space  
The influence of visual divergence was studied by investigating differences between the CW and FW groups. Results in Section 4.4 demonstrate that the FW group, whose participants were exposed to a longer spatial boundary, presents a smaller compression effect. This result can be compared to past studies concluding that the presence of congruent visual cues could enhance the accuracy of auditory distance perception [13; 29; 41].  
In this experiment, a room divergence effect originates from the discrepancy between the reproduced room and the experimental environment specific to each participant. The geometrical characteristics of the reproduced room are, however, closer to the dimensions reported on average by the participants of the FW group than those reported in the CW group. The volume of the reproduced room is 144 m\({}^{3}\), while the mean volume is 81.7 m\({}^{3}\) in the FW group and 37.5 m\({}^{3}\) in the CW group. The presented stimuli intend to reproduce auditory distances up to 7 m, while the visual spatial boundary is limited to 3 m in the CW group and ranges from 5 to 10 m for the FW group. Therefore, the presence of more congruent visual cues might have enhanced the auditory distance perception accuracy of the FW group.  
It has been long argued that the visual context in which auditory judgments occur contributes to the organization of the auditory space [13; 15; 16]. This finding about the influence of the visual spatial boundary could be seen as an extension of the hypothesis discussed by [PERSON] et al. [13]. In their experiment, participants had to evaluate the distance to a real loudspeaker, presented frontally and emitting white noise bursts. As a result, no room divergence effect was present. Participants had access to an increasing quantity of visual cues, ranging from being blindfolded to having full vision of the room. Intermediate scenarios in which only two or four LEDs were lit during the reports were also examined. The authors demonstrated in this acoustically congruent situation that the presence of congruent visual cues increases the accuracy of auditory distance perception. They hypothesized that visual range information helped calibrate the reported distances of real sound sources, visual distance perception being a sensory modality far more accurate for distance estimations [29]. In the current study, the visual spatial boundary condition could also have acted as a calibration of auditory distance perception in an acoustically divergent situation. A long visual spatial boundary condition might have influenced the representation of the auditory space and incited participants to expand the range of the reported distances. This influence on the reporting strategy explains the significantly lower compression effect of the FW group.
## 3 ROR: Reliable Opportunistic Routing for CRSN
### System and Network Model
By default, the transceiver is tuned to CC and only changes during the data-routing phase. The scheme utilizes a fixed sensing period to sense the potential list of \(N\) channels. Given the accumulated PU interference level, \(\gamma_{i}\), on channel \(C_{i}\), \(i\in N_{c}\), at time instant \(t\), the channel quality of \(C_{t}^{i}\) is better than that of \(C_{t}^{j}\) if \(\gamma_{i}(t)<\gamma_{j}(t),j\in N_{c}\). This output of the sensing result, \(\gamma_{i}^{t}\), is mapped onto an absolute initiative scale, \(v_{i}^{t}\), as shown in Figure 1. Thus, over time, \(v_{i}(t)=v_{i}^{t_{1}}+v_{i}^{t_{2}}+....\). In this manner, the quality ratings for each channel, \(i\in N_{c}\), are graded according to the scale, \(V_{i\in N_{c}}(t)\). Then, by rearranging the channels in a descending order of \(v_{i}(t)\), the channel with the best quality is easily identified. Over time, this result is updated on the scale to grade the channel having the greater initiative to be selected for the routing operation depending on the routing requirements. This is because a higher value indicates a low PU activity on that channel and _vice versa_. It is important to note that, once a node has engaged in a successful route setup operation, all sensing operations cease to be performed, and the nodes uses its previous acquired knowledge of the spectrum for data forwarding. Upon this model, ROR is designed to perform routing based on its operational components, which are explained next.
# The Resilience of Vegetation to the 2009/2010 Extreme Drought in Southwest China
## 4 Discussion  
### Vegetation Resistance, Recovery, and Resilience  
Previous studies showed opposing trends in tree resistance and recovery to extreme drought, that is, the higher the resistance, the lower the recovery, which was possibly due to the changes in the strategies of trees to extreme drought [12; 14]. This phenomenon was also observed in southern Germany and Austria [41] and central Spain [42]. In our study, the value of recovery was relatively high with low resistance values, especially in Yunnan and Guizhou, where vegetation suffered from the most intensive drought (Figures 4a-f, S2a-f and S3a-f).  
The resistance and recovery of different vegetation types to extreme drought are divergent. In our study, we found that vegetation resistance and recovery to the extreme drought varied with vegetation types in SWC (Figures 5a,b, S4a,b and S5a,b). Forests, especially evergreen broadleaf forest, displayed a higher resistance than other vegetation, whereas their recovery process was slow from the extreme drought. Cropland was susceptible to the extreme drought, but its level of recovery was higher. Evergreen broadleaf shrubland and deciduous broadleaf shrubland showed a moderate resistance and recovery, based on NDVI, LAI, and GPP, which was consistent with other studies [14]. Previous studies suggested that evergreen broadleaf forest could accelerate the loss of old leaves and maintain the growth of young leaves in the dry season to maintain photosynthetic utilization efficiency [43; 44], which could be the reason why evergreen broadleaf forest displayed a higher resistance. Grassland demonstrated a strong ability to regrow from extreme drought [45] and it could recover quickly as long as the precipitation and temperature returned to the pre-drought conditions [46]. In comparison with grassland, the higher level of recovery of cropland was related with human cultivation and irrigation [3]. The values of NDVI and LAI after the extreme drought were lower than those before the extreme drought (Figures 5c, S4c and S5c), which may be because the extreme drought affected xylem growth and reduced water transport rates, leading to low growth [10].
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 4 Parallel Edge Deblocking Filter Architecture (PEDBF)
### Resource Sharing Architecture  
Depending on the deblocking filtering technique used in [9], which follows the HEVC standard, the weak and strong filter units are implemented. The filter architecture is constructed in such a way that it shares the common resources that are used in these equations by taking advantage of the similarity in the filtering equations. This technique thenceforward brings down the area and the power utilization. The pixels of the two neighboring blocks are updated for the strong filter using the third parameter of the clip3 function, which is an equation involving two or more of the pixel values of the adjacent P and Q blocks. Adders and shifters are utilized to implement these equations in the hardware. It is identified that \(p_{0}+q_{0}\) is employed in all the expressions and \(p_{0}+q_{0}+2\) is used in most of the expressions. In addition, \(p_{1}+2\) and \(q_{1}+2\) are used twice. Hence, to add \(p_{0}+q_{0}\), an eight-bit binary adder is employed and the output is fanned out to all the equations. This adder's output is also used as an input for another binary adder, whose other input is 2, producing the result \(p_{0}+q_{0}+2\), which is then utilized in the appropriate expressions. The same mechanism is used to implement the addition operations \(p_{1}+2\) and \(q_{1}+2\), and the output of these adders is reused wherever necessary. As a result, the shared resources are distributed over several equations, and an area-optimized filter is used. Figure 11 depicts the resource sharing architecture used for the third argument of the clip3 function for the strong filter.  
Figure 10: Mapping of internal memory to buffer before horizontal filtering for chroma block.
## 3 Results
### Differences of F760 tot, F760 toc, and VIs in Responding to Different Drought Levels
F760 toc and the PAR of four plots (r value = 0.736 for P1) was found, and the correlation coefficients declined with the drought levels (P1-P4, r value: 0.7-0.4). After being obtained by normalizing F760 toc for fesc, F760 tot appeared to have a stronger correlation relationship with the PAR (r value: \(\sim\)0.8 for P1), and compared with F760 toc, there was only a minimal drop of the r value from 0.799 (P1) to 0.75 (P4), which seemed to suffer fewer effects from the drought for the relationship between F760 tot and the PAR. In terms of the APAR, the better correlations between them of P1-P3 were found, whereas in P4, F760 tot and the APAR exhibited a weak positive relationship. Apart from the PAR, LAI and Chl are two growth parameters that will dramatically reduce when crops experience water stress, and these two parameters also influence the emission of SIF. Therefore, partial correlation analysis was then applied to explore the relationship between the SIF and VIs with LAI and Chl in Figure 8. Here, the LAI and Chl measurements seemed to be insufficient for rigorous analysis, but it is noted that our analyses were considered in the global perspective and focused on the results that had been tested for significance. First, the average correlation of LAI with SIF (r values > 0.6) was greater than that of Chl with a significant positive correlation, indicating that LAI could have had much more of an impact on SIF emissions than Chl over the growing season under different drought levels. Then, as depicted in Figure 8a for LAI, the impact of LAI on F760 toc usually exceeded that of F760 tot from P1 to P3. This suggested that SIF was less prone to vegetation  
Figure 7: Relationship between hourly PAR, APAR, and F760 toc (blue) or F760 tot (golden) in the field campaign collected from four plots (P1–P4). The blue or golden lines with band denote the regression line and 95% confidence interval, respectively. Significance was performed according to two-tailed test (ns: \(p\) > 0.05. * \(p\) < 0.05. ** \(p\) < 0.01. ** \(p\) < 0.001). canopy structures after downscaling to the leaf level, whereas for P4 in an extreme drought, a similar impact from LAI was observed on F760 toc and F760 tot. As with AF760 toc and AF760 tot, the former was more related to LAI from P1 to P2, and the latter was more linked to LAI from P1 to P2. Among them, AF760 toc showed the most significant correlation with LAI (r value: 0.92, \(p<\) 0.001). As depicted in Figure 8b for Chl, F760 tot was more related to Chl than F760 toc from P1 to P3, indicating that the former was closely integrated with the plant physiology. F760 tot showed the most significant correlation with Chl, whereas the impact of Chl on F760 toc was less than that of F760 tot for P4 under an extreme drought. As for AF760 toc and AF760 tot, the impact of Chl on AF760 toc was usually superior to that of AF760 tot for P1, P2, and P4 instead of P3.
# Dynamic QoS Management for a Flexible 5G/6G Network Core: A Step toward a Higher Programability
## 5 Testbed Design
### Testbed Setup  
In order to test and verify the capabilities of the proposed solution, it is necessary to implement an appropriate multi-slice environment that would adequately reflect the environment of 6G networks. Therefore, we have set up a virtual environment using the EVE-NG platform [53] to represent a simplified multi-slice environment for future networks. As shown in Figure 4, the design of the testbed environment consists of three parts:  
* The 6G provider's infrastructure consists of core and distribution layer devices and control plan management devices of its SDN network (SDN controllers and SDN base stations);
* Users (e.g., smart vehicles) access the 5G/6G infrastructure through a base station;
* Connections with third-party service providers.  
We will verify the proposed methodology by using three independent slices. For testbed simplicity, we limit our service groups to one service per slice. Each slice is in  
Figure 4: The design of the testbed environment.  
charge of one service group (Table 1 in Appendix A). It is important to emphasize that we dedicate a separate SDN controller for managing each slice or a particular group of services. Moreover, to perform a more realistic testing environment, we arrange that one user obtains services from different providers. Table 1 presents the slice arrangement with each service group's associated services and bandwidth requirements.  
For each slice, we implement two components on the smart vehicle's side. The first is the CarSDN component (an SDN switch, an integrated part of a 5G/6G modem) that provides user-side programmability, multi-slicing segmentation, and intelligent queue and traffic management. Applying the dynamic creation of queues and the ability to change their parameters enables the automated management of QoS mechanisms. Multi-slice segmentation is provided up to the CarSDN component (5G/6G modem). The example of practical use is presented in Appendix A, Figure 1. One of the roles of this component is to optimally manage the QoS of outgoing communications and to allow dynamic allocation of available upstream bandwidth. The client applications are the second component. They are embedded into the car's ECUs (Electronic Control Unit) or user's terminal equipment within the car.  
As depicted in Figure 4, we have placed another manageable device between the SDN-aware station (BasestationSDN) and the car (CarSDN). This device has the role of introducing bandwidth availability limitations and, thus, enabling congestion into the virtual system (the congestion represents variable air transmission capability).  
We include a separate service management server (QoSDB) in the testbed environment to enable dynamic changes of the QoS parameters and to provide necessary synchronization between slice controllers. This server contains an SQL database that specifies the configuration parameters of the desired QoS policies. The SDN controller in charge of a specific slice fetches the QoS policy from the SQL database and provisions the queues.
## 3 Photon and Phonon Tunneling through a Vacuum Gap
### Photon Tunneling through a Vacuum Gap
The efficiency of heat transfer by evanescent modes depends on the dielectric properties of the interacting materials. For example, resonant surface waves (surface phonon polaritons) make a significant contribution to heat transfer between polar dielectric materials with complex dielectric constants \(\varepsilon_{1}(\omega)\) and \(\varepsilon_{2}(\omega)\) at \(d\ll\lambda_{T}\). In this case, the spectral density \(G_{nf}(\omega)\) of the interfacial thermal conductance \(G_{nf}\) can be approximately represented by Equation (8) [38; 40; 41]. \[G_{nf}(\omega)\simeq\frac{k_{B}}{d^{2}}\frac{\text{Im}(\varepsilon_{1})\text{ Im}(\varepsilon_{2})}{\left|1+\varepsilon_{1}\right|^{2}\left|1+\varepsilon_{2} \right|^{2}}(\frac{\hbar\omega}{k_{B}T})^{2}\frac{\exp\left(\frac{\hbar\omega} {k_{B}T}\right)}{\left[\exp\left(\frac{\hbar\omega}{k_{B}T}\right)-1\right]^{2 }}, \tag{8}\]\[\text{Denote B}=\int\frac{\text{Im}(\varepsilon_{1})\text{Im}(\varepsilon_{2})}{|1+ \varepsilon_{1}|^{2}||1+\varepsilon_{2}|^{2}}\frac{x^{2}e^{x}}{(e^{x}-1)^{2}}dx,\]  
where \(x=\hbar\omega/k_{B}T\), then  
\[G_{nf}(d)\simeq\frac{k_{B}^{2}T}{\hbar d^{2}}\text{B}, \tag{9}\]  
where the coefficient B depends on the complex dielectric constants \(\varepsilon_{1}(\omega)\) and \(\varepsilon_{2}(\omega)\). Thus, heat transfer by photon tunneling is very efficient at frequencies close to surface polariton resonances, at which \(\text{Re}\varepsilon_{1}(\omega)\) or \(\text{Re}\varepsilon_{2}(\omega)\) approaches \(-1\); see Equation (8). Surface wave resonances are the main channels of heat transfer in the near-field. In fact, the energy transfer mediated by evanescent tunneling modes was significantly enhanced due to surface phonon polaritons in dielectrics and semiconductors [37; 38; 39; 40; 45; 46; 47], surface plasmon polaritons (which can be observed at interfaces containing metals or doped semiconductors) [37; 45; 46; 47], and vibrational states of adsorbates on the surface of various types of materials [57]. The near-field photon tunneling is significant at distances \(d\) below 100 nm. However, at sub-nanometer distances, low-frequency acoustic phonons can tunnel though the vacuum gap by coupling with evanescent electric fields.
## 3 Results and Discussion
### Technical Parameters
#### 3.6.1 Filtration Parameters  
Filtration parameters, expressed by the air permeability, present one of the major properties of textile materials and are governed by factors like the fabric structure, density, thickness and surface characteristics. The comparisons of air permeability of PETE-Cu\({}^{(0)}\) composites vs. PETE fabric are shown in Table 4. The obtained results indicated that the modification of PETE fabrics with copper coating moderately (16%-18%) decreases the air permeability from 65.5 mm/s to 54.5 mm/s at 100 Pa and from 147 mm/s to 123 mm/s at 200 Pa. Moreover, a slightly further reduction in the air permeability (51.8 mm/s at 100 Pa and 117 mm/s at 200 Pa) was observed for the sample coated with copper on both sides. Therefore, it may be concluded that the coating of PETE with copper minimally affected filtration parameters. Observed results may be associated with the changes of porosity of the sample due to the presence of the copper coatings on the surface.  
The obtained results indicate 16%-18% decreases of the PETE air permeability accompanied its metallization (PETE\(\rightarrow\)PETE-Cu\({}^{(0)}\)-1: from 65.5 mm/s to 54.5 mm/s at 100 Pa and from 147 mm/s to 123 mm/s at 200 Pa and PETE\(\rightarrow\)PETE-Cu\({}^{(0)}\)-2: from 65.5 mm/s to 51.8 mm/s at 100 Pa and from 147 mm/s to 117 mm/s at 200 Pa, respectively) and 4%-5% decrease between air permeability PETE-Cu\({}^{(0)}\)-1 and PETE-Cu\({}^{(0)}\)-2 (from 54.5 to 51.8 at 100 Pa and from 123 to 117 at 200 Pa, respectively). These results may be associated with changes of porosity of the samples caused by formation of copper layers on the PETE surface during sputtering process (PETE \(\rightarrow\) PETE-Cu\({}^{(0)}\)-1 \(\rightarrow\) PETE-Cu\({}^{(0)}\)-2).
###### Abstract  
Neural network models, such as BP, LSTM, etc., support only numerical inputs, so data preprocessing needs to be carried out on the categorical variables to convert them into numerical data. For unordered multi-categorical variables, existing encoding methods may produce dimensional catastrophes and may also introduce additional order misrepresentation and distance bias in neural network computation. To solve the above problems, this paper proposes an unordered multi-categorical variable encoding method O-AE using orthogonal matrix for encoding and encoding representation learning and dimensionality reduction via an autoencoder. Bayesian optimization is used for hyperparameter optimization of the autoencoder. Finally, seven experiments were designed with the basic O-AE, Bayesian optimization of the hyperparameters of the autoencoder for O-AE, and other encoding methods to encode unordered multi-categorical variables in five datasets, and they were input into a BP neural network to carry out target prediction experiments. The results show that the experiments using O-AE and O-AE-b have better prediction results, proving that the method proposed in this paper is highly feasible and applicable and can be an optional method for the data processing of unordered multi-categorical variables.  
unordered multi-categorical variables; orthogonal matrix; autoencoder; encoding method; dimensionality reduction; Bayesian optimization; BP neural network; target prediction
# Metallic Nanoparticles Obtained via \"Green\" Synthesis as a Platform for Biosensor Construction
## 3 Results and Discussion
### Biosensor Evaluation
#### 3.2.1 Laccase-Based Amperometric Biosensors
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Bioelectrode** & **Substrate** & \(K_{M}\)**\({}^{app}\)**, mM** & \(I_{max}\)**, \(\mu\)A** & \begin{tabular}{c} **Linear Range, up** \\ **to mM** \\ \end{tabular} & \
\begin{tabular}{c} **Sensitivity,** \\ **A**-\(\mathbf{M}^{-1}\)**-\(\mathbf{m}^{-2}\)** \\ \end{tabular} &
\begin{tabular}{c} **LOD 1, mM** \\ \end{tabular} \\ \hline Laccase/GE & BP & \(0.73\pm 0.05\) & \(1.85\pm 0.04\) & \(0.35\) & \(232\) & \(0.050\) \\ Laccase-gPdNPs/GE & BP & \(1.80\pm 0.16\) & \(1.86\pm 0.05\) & \(0.50\) & \(137\) & \(0.145\) \\ Laccase/GE & ABTS & \(0.28\pm 0.03\) & \(19.16\pm 0.91\) & \(0.20\) & \(5780\) & \(0.008\) \\ Laccase-gPdNPs/GE & ABTS & \(0.32\pm 0.02\) & \(14.29\pm 0.28\) & \(0.25\) & \(3620\) & \(0.014\) \\ \hline \end{tabular} \({}^{1}\) LOD—limit of detection.  
\end{table} 
Table 1: Analytical characteristics of the developed laccase-based amperometric biosensors. Figure 5: Chronoamperograms (**a**), dependence of the amperometric response on increasing concentrations of ABTS (**b**) and calibration curves (**c**) for laccase-based GE electrodes: control (1) and modified with gPdNPs (2). Conditions: working potential \(-100\) mV vs Ag/AgCl (reference electrode), \(50\) mM acetate buffer, pH 4.5. Numerous biosensors based on increase from _T. versicolor_ have been constructed [63; 64; 65; 66; 67; 68; 69; 70]. Different methods of enzyme immobilization on multi-walled carbon nanotube electrodes were tested [63; 64]. All tested bioelectrodes were rather sensitive to ABTS, had a limited linear range (up to 0.060 mM) and a low I\({}_{\text{max}}\) (up to 11 \(\upmu\)A). Laccase from _T. pubescens_ immobilized on an electrode [66] had a 2-fold higher sensitivity and a similar \(K_{M}\)\({}^{app}\) value, but a 2.5-fold lower top of the linear range compared with the currently proposed laccase-gPdNPs/GE (Table 1). \(K_{M}\)\({}^{app}\) values of laccase from _T. versicolor_ immobilized on control or modified graphite electrodes are comparable to the \(K_{M}\) of laccases from other fungi [67]. It should be noted that laccase can communicate with the electrode through direct electron transfer, mediated by enzyme substrates. The similarity of the \(K_{M}\) obtained for different substrates with free laccases in solutions and immobilized on the electrode surface confirms that the physical binding of the enzyme on electrodes does not have a fundamental effect on the native structure of the enzyme.