esa-sceva/satcom-chunk-collection huggingface dataset:
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 4 Experiments
### Implementation Details
#### 4.3.2 Experimental Settings  
For each baseline method, VSLNet follows the configurations reported in their original paper, adopting the C3D-feature version of VSLNet. As for 2D-TAN, the components of the framework likewise follow the same parameters as presented in its original documentation. The hyperparameters \(\alpha\) and \(\beta\) (refer to Section 3.4) regulate the weighting for the transfer losses \(L_{trans}^{A}\) and \(L_{trans}^{B}\). Based on validation tuning, we choose \(\alpha=0.5\) and \(\beta=0.5\) to strike a balance between supervision and transfer objectives in model optimization. The IoU threshold \(\lambda\) (see Section 3.5) evaluates predictive alignment between models, set at 0.5 to maintain a reasonable level of discriminatory power. The factor \(\gamma\) (see Section 3.5), responsible for dynamically modulating weights, is fixed at 0.8 to attenuate the transfer loss under scenarios of high model consistency. The number of video frames \(n_{f}\) in the CLIP-Guided Module (see Section 3.3.1) is set to 32 to balance semantic representational power and computational efficiency. The similarity threshold \(\tau\) (see Section 3.3.1) is set to 0.4, filtering out highly relevant frames to generate a more precise guiding temporal range. The learning rates \(\eta_{A}\) and \(\eta_{A}\) are both set to 0.0001, and the number of iterative optimization cycles K (see Algorithm 1) is set to 10.
## 4 Model Validation and Result Analysis
### Security Resource Scheduling Optimization Implementation
#### 4.3.1 Movement Control Procedures Implementation
In terms of resource utilization, the Plan B model achieves a 4.82% reduction in equipment and labor costs compared to the fixed-shift model and a 2.65% reduction compared to the static scheduling model. These findings demonstrate that the MHALSTM-MPC model effectively optimizes resource allocation, significantly improves operational efficiency, and reduces overall costs.
# Low-Temperature and Low-Pressure Silicon Nitride Deposition by ECR-PECVD for Optical Waveguides
## 3 Characterization and Results
### Surface Roughness  
To quantify the surface roughness of the films, we used AFM to inspect 1 \(\times\) 1 \(\upmu\)m\({}^{2}\) areas on multiple locations for selected samples on Si substrates. Films 2 and 3 were chosen due to their drastically different optical losses for visible wavelengths. It is anticipated any surface roughness will increase scattering losses, especially in the visible regime with the known 1/\(\lambda^{4}\) relationship. Surface homogeneity was also inspected and used to quantify uncertainty on the root mean square (RMS) reported roughness and peak-peak amplitude values. A correlation between annealing and decreased peak-peak height is observed when measuring Film 3 before and after annealing; however, it is noted that for films of similar RMS roughness, drastically different (\(\sim\)10 dB/cm at 638 nm) optical loss is reported. A decrease in the peak-peak amplitude of 1.65 \(\pm\) 0.40 nm is observed after annealing Film 3, which is attributed to the densification of the film. After annealing out the hydrogen in Film 2, it demonstrates a higher optical loss at 1550 nm compared to the unannealed Film 3, while still demonstrating less surface roughness (RMS and peak-peak) as shown in Table 2 which summarizes the AFM amplitude plots in Figure 4. This points to stoichiometry-dominated optical loss mechanisms for the ECR-PECVD SiO\({}_{\text{x}}\)N\({}_{\text{y}}\):H\({}_{\text{z}}\) films. This can also be attributed to the low percentage of optical intensity in the upper air cladding, on the order of \(\sim\)3-9% for most films measured. Moving forward, this is an important merit for multilayer systems, where interface losses may be exacerbated by higher surface roughness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Film \#** & **2, Annealed** & **3, As-Deposited** & **3, Annealed** \\ \hline RMS roughness (nm) & 0.95 \(\pm\) 0.01 & 1.10 \(\pm\) 0.01 & 1.01 \(\pm\) 0.05 \\ Peak–peak amplitude (nm) & 9.55 \(\pm\) 0.16 & 9.58 \(\pm\) 0.12 & 7.93 \(\pm\) 0.38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Surface roughness from atomic force microscopy.
## 3 Results
### Principal Component Analysis and GPP Correlations  
PCA shows that GPP and ET are positively correlated in the wet season (Figure 5b), but they are not correlated during the dry season (Figure 5a). Furthermore, it clearly shows that WUE and the variables related to heating (H) and drying (VPD) of the atmosphere are positively correlated and near each other in the unit circle. This means that these variables have a similar temporal behavior. This correlation is particularly strong in the  
Figure 4: (**a**) Monthly averages of net ecosystem CO\({}_{2}\) exchange (NEE), gross primary production (GPP Tower and GPP MODIS), ecosystem respiration (Reco), and (**b**) water use efficiency (WUE) during 2014–2015 in the Caatinga (ESEC-Seridó). Orange bars (**b**) indicate the total number of rain days (days with registered rainfall) in each month.  
dry season (Figure 5a). A similar relationship can also be observed between Rn and Tair, probably due to soil moisture depletion, with most of available energy being directed to the heating of the air, increasing air temperature, and, consequently, VPD. MODIS GPP satisfactorily represents tower-measured GPP both in the dry season (Figure 6a) and the wet (Figure 6b) season (R\({}^{2}\) = 0.76 and R\({}^{2}\) = 0.62, respectively). We also assessed the biophysical performance of vegetation indices in the Caatinga regarding GPP dynamics. Figure 7 shows the correlation between experimentally derived GPP and the remaining meteorological and carbon variables, while Figure 8 shows the linear fit between GPP and vegetation indices (NDVI and EVI). Tower GPP was better correlated with the EVI (r = 0.81, \(p\) < 0.01) than with the NDVI (r = 0.69, \(p\) < 0.01).  
Figure 5: Principal component analysis (PCA) biplots for the (**a**) dry season and (**b**) wet season, showing the dependency between meteorological and carbon variables. Vertical and horizontal coordinates represent the correlation between variables and principal components 1 (Dim1) and 2 (Dim2), respectively. The proportional representativeness of each variable in each component is shown by the colors associated with the cos2 value.  
Figure 6: Relationship between measured gross primary production (GPP Tower) and MODIS gross primary production (GPP MODIS) in the dry season (**a**) and wet season (**b**) for the ESEC-Seridó site. Gray bands represent the 95% confidence interval for the fitted linear model.
# MinHash-Based Fuzzy Keyword Search of Encrypted Data across Multiple Cloud Servers
## The Proposed Fuzzy Keyword Search Scheme Based on MinHash
### Authorization and Revocation User Privileges  
Before performing an encrypted search, the user needs to be authorized by the data owner. For user \(U_{L}\), the data owner randomly selects a key \(SK_{U_{L}}\in Z_{p}^{*}\) and assigns it to the user as the query key. The user generates a re-encryption key \(RK_{U_{L}\to DO}\) and transmits the user ID \(U_{L}\) and the re-encryption key over a secret channel to the cloud server S. The authorized user \(U_{L}\) generates a legal query threshold based on \(SK_{U_{L}}\) and uses the re-encryption key \(RK_{U_{L}\to DO}\) to re-encrypt the query threshold to carry out the query correctly.  
\[RK_{U_{L}\to DO}=g^{SK_{DO}}_{SK_{U_{L}}} \tag{7}\]If the data owner wants to revoke the query permission of user UD, it needs to send the user's identity UD and revocation command to the cloud server S. S deletes the necessary attributes, complete legal re-encryption thresholds and query operations.
## 5 Conclusions  
In this work, we proposed a novel approach for CO abatement in H\({}_{2}\)-rich streams to be fed to PEM fuel cells. The main concept is a dual-stage process operating with a copper/ceria based catalyst, consisting of a reactive adsorption phase, allowing for CO abatement under the PEM limits, and an oxidation phase, restoring the catalyst and converting the adsorbed CO into CO\({}_{2}\). By this approach it should be possible to simultaneously get CO lower than the limiting value and avoid any H\({}_{2}\) consumption with no O\({}_{2}\) in the feed stream to the PEM. Parameters of a kinetic model including CO adsorption and oxidation to CO\({}_{2}\), CO\({}_{2}\) desorption, and catalyst re-oxidation by molecular oxygen, developed on the basis of previous results, were obtained from data of suitably programmed experimental tests. Experimental and model simulations are in good agreement, especially for the CO-reactive adsorption phase.  
The simulations were performed at varying operating parameters. The results showed that high contact times and low CO inlet concentration are beneficial in order to obtain CO concentration lower than 100 ppm for long period, as expected. However, due to the non-linear behavior of the CO reactive adsorption, the effect is quantitatively different from that expected for a classical adsorption, in particular, the effect of contact time is more than linear, while that of CO inlet concentration is less than linear.  
The simulations of the complete system, varying the time length of the oxidation phase (adsorption phase ending when CO at the outlet reaches 100 ppm followed by the oxidation phase), highlighted the importance of carrying out a deep oxidation in order to fully regenerate the material, restoring the initial oxidation state. Even if CO\({}_{2}\) buffering on the catalyst causes an increase of the mean oxidation state, related to larger and larger adsorption phase duration, by a proper choice of the duration of the oxidation phase, conditions to operate always under the target exit CO concentration (100 ppm) can be found. Consequently, a suitable equal time length can be chosen for the two phases in order to synchronize them and assure a cyclic operation which could be carried out by using only two reactors (one acting as adsorber, the other in regeneration). In this work, we chose 200 s as working time and demonstrated that a stable periodic operation was achieved.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the experiments and the simulations; [PERSON] and [PERSON] performed the experiments; [PERSON] developed the model and performed the simulations; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] wrote the paper.  
**Acknowledgments:** The present work was financially supported by MIUR (Italy) under FIRB projects (project code: RBFR1054 OW--Novel catalytic systems for H\({}_{2}\)-rich streams purification).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Research Method
### New Mathematical Scheme for Improving the GPS and GLONAS Solution in the PPP Method
The last element of the calculations of the presented research method is the external control of the proposed algorithm, i.e., determining the accuracy of the presented algorithm, as shown below [35]:  
\[\left\{ \begin{array}{c}DX=X_{k}-X_{RTK}\\ DY=Y_{k}-Y_{RTK}\\ DZ=Z_{k}-Z_{RTK}\end{array} \right. \tag{11}\]  
where:  
\((DX,DY,DZ)\)--position errors, and  
\((X_{RTK},Y_{RTK},Z_{RTK})\)--reference trajectory of aircraft based on RTK-OTF solution [35]. Position errors are determined independently along the XYZ coordinate axes for the entire flight path for each measurement epoch. Accuracy determination will confirm whether the proposed weighing scheme is optimal for improving the PPP GPS and PPP GLONASS positioning performances. Furthermore, the accuracy analysis will also show which weighting scheme is better, whether using weighting in the function of the number of satellites or using the inverse square of the error of the mean of the determined coordinates. Considering the entire algorithm of the research methodology, it can include elements of calculations carried out in real time and post-processing mode. Starting from the beginning of the algorithm, the classic PPP method, i.e., Equations (1)-(3), can be implemented both in real time and post-processing. The boundary condition is, of course, the use of a dual-frequency dual-system GPS/GLONASS receiver. Next, the new proposed algorithm, i.e., Equations (4)-(8), can also be performed in real time and post-processing mode. However, it should be noted that, in Equation (4), there are measurement weights from Equations (5) and (6). In this case, parallel programming must be performed for both weighting processes. Of course, this weighting condition should first be tested in post-processing to determine the best measurement weight-fitting model for GPS + GLONASS data integration. Equation (9), in turn, determines the aircraft positioning accuracy for the PPP measurement technique. The algorithm allows for numerical analysis in post-processing mode. However, it is possible to perform it in real time, but it involves sending RTK corrections from the reference station to the GNSS on-board receiver, which requires an appropriate internet connection, transmission technique and data transmission power. Therefore, it is a difficult task in real time, when the aircraft's position changes dynamically every 1 s.
## Materials and Methods
### SEM Measurements  
SEM measurements on C nanostructures were carried out by using two Zeiss systems: (1) FEG-SEM, LEO 1530 (Oberkochen-Germany), equipped with an in-lens secondary electrons detector (SE), a Centaurus back scattered electron (BSE) detector, and an EDS X-ACT (detector of 10 mm\({}^{2}\)) Oxford with an AZTEC software microanalysis system at ENEA-Casaccia, S. Maria di Galeria Rome, Italy; and (2) a Gemini 300 FE-SEM (field emission SEM), equipped with two detectors for secondary electrons, a BSE detector with five sectors, and an EDS Bruker system (detector of 60 mm\({}^{2}\)) at LIME, Science Department, University of Roma Tre. For all experiments, we used the C powder provided by the Centre for Nano-materials & Displays, B. M. S. College of Engineering, India, lying on freshly deposited Au films on Si slabs.
# Optimal Cluster Expansion-Based Intrusion Tolerant System to Prevent Denial of Service Attacks
## 3 Proposed ITS System  
In this section, we present an optimal cluster expansion scheme for ITS. Figure 2 shows the architecture of the proposed scheme. The considered system consists of a robust cluster controller (RCC) and multiple hosts. The RCC controls the whole system in order to provide a guaranteed QoS under any circumstances, including a massive DoS attack. The RCC also determines the cluster size and redistribution of packets in queues by monitoring the VMs in every host and checking overall system performance with the incoming packets. Detailed RCC operations are addressed in Section 3.4. The proposed scheme is based on the following assumptions:  
* The maximum number of VM processing packets is constant
* The main target scenarios are massive DoS attacks
* The existing attacks are prevented by IDS and IPS
* The considered cloud system has sufficient VMs and hosts  
The entire system process can be divided into three cases: cluster size expansion, unprocessed packet distribution, and cluster size reduction. To preserve QoS service without degradation, it is important to expand the cluster size at the right time. The expansion and reduction timing should be determined by considering service response time, VM queue size, and central processing unit (CPU) usage.
## 4 Improvements in PRI Implementation
### Modeling  
Models of radiative transfer have been applied to explore the changes in PRI influenced by a variety of detection geometries, illumination conditions and canopy structures in conjunction with in situ measurements [4; 31; 69; 75; 82; 83; 109; 117; 128; 133; 151; 154]. The accuracy of the simulated PRI from ACRM (A Markov chain Analytical two-layer Canopy Reflectance Model) was affected by  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Formulation** & **References** \\ \hline \multirow{6}{*}{Different formulations} & \multirow{2}{*}{\begin{tabular}{c} \(\mathrm{APRI}=\mathrm{CPRI}-\mathrm{PRI}\) \\ (cPRI is dark-state PRI) \\ \end{tabular} } & [PERSON] and [PERSON] [67] \\ \cline{2-3}  & \multirow{2}{*}{\begin{tabular}{c} \(\mathrm{PRIc}=\mathrm{PRI}-\mathrm{PRI}\) \\ (\(\mathrm{PRI}_{\mathrm{Ref}}\) is the minimum PRI near midday) \\ \end{tabular} } & [PERSON] et al. [123] \\ \cline{2-3}  & & \multirow{2}{*}{\begin{tabular}{c} [PERSON] et al. [115] \\ [PERSON] et al. [25] \\ [PERSON] et al. [129] \\ [PERSON] et al. [109] \\ \end{tabular} } \\ \cline{2-3}  & \multirow{2}{*}{\begin{tabular}{c} \(\mathrm{APRI}=\mathrm{PRI}_{\mathrm{midday}}-\mathrm{PRI}_{\mathrm{pre-down}}\) \\ \end{tabular} } & [PERSON] et al. [30] \\ \cline{2-3}  & \multirow{2}{*}{\begin{tabular}{c} \(\mathrm{APRI}=\mathrm{PRI}-\mathrm{PRI}_{\mathrm{Ref}}\) \\ (\(\mathrm{PRI}_{\mathrm{Ref}}\) is the minimum PRI near midday) \\ \end{tabular} } & [PERSON] et al. [110] \\ \hline \multirow{6}{*}{\begin{tabular}{c} \(\mathrm{PRInorm}=\mathrm{PRI}/(\mathrm{RDVI}\times\mathrm{R2V00}/\mathrm{R670})\) \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} [PERSON] et al. [75] \\ \end{tabular} } \\ \cline{2-3}  & Chlorophyll index & \multirow{2}{*}{\
\begin{tabular}{c} [PERSON] et al. [105] \\ [PERSON] et al. [129; 136] \\ [PERSON] et al. [117] \\ \end{tabular} } \\ \cline{2-3}  & \multirow{2}{*}{
\begin{tabular}{c} \(\mathrm{APRI}\mathrm{a}\mathrm{a}\mathrm{a}\mathrm{a}\mathrm{a}\mathrm{a}\mathrm{a} \mathrm{a}\mathrm{a}  
canopy structural change and thus led to inaccurate estimates of RUE and GPP [83]. Empirical regression models were effectively used to assess PRI and copophysiological variation [87; 109; 113; 124]. Devising a valid model is therefore important for accurately estimating PRI and providing a continuous assessment of GPP.
# A Multiscale Approach to the Numerical Simulation of the Solid Oxide Fuel Cell
###### Abstract  
The models of solid oxide fuel cells (SOFCs), which are available in the open literature, may be categorized into two non-overlapping groups: microscale or macroscale. Recent progress in computational power makes it possible to formulate a model which combines both approaches, the so-called multiscale model. The novelty of this modeling approach lies in the combination of the microscale description of the transport phenomena and electrochemical reactions' with the computational fluid dynamics model of the heat and mass transfer in an SOFC. In this work, the mathematical model of a solid oxide fuel cell which takes into account the averaged microstructure parameters of electrodes is developed and tested. To gain experimental data, which are used to confirm the proposed model, the electrochemical tests and the direct observation of the microstructure with the use of the focused ion beam combined with the scanning electron microscope technique (FIB-SEM) were conducted. The numerical results are compared with the experimental data from the short stack examination and a fair agreement is found, which shows that the proposed model can predict the cell behavior accurately. The mechanism of the power generation inside the SOFC is discussed and it is found that the current is produced primarily near the electrolyte-electrode interface. Simulations with an artificially changed microstructure does not lead to the correct prediction of the cell characteristics, which indicates that the microstructure is a crucial factor in the solid oxide fuel cell modeling.  
solid oxide fuel cells; mathematical modeling; numerical simulation 2019
# RF Energy Harvesting Wireless Communications:
## 7 Potential Applications  
The energy harvesting technology is penetrating into many hot areas, such as the IoT, BAN, and 5G communications, providing realistic solutions for large-scale, sustainable, and low-cost environment monitoring and wireless information exchange system. In this section, we present several existing and potential applications of energy harvesting wireless communications.
## 3 Results and Discussion
### Leaf Interception and Water Redistribution in the Soil  
#### 3.6.1 Throughput (Ta) and Stemflow (Sa)  
Throughfall was directly related to both the depth collected and differences in height of sprinklers, catch cans and plants (H, Equation (9)). The best fit was obtained with a quadratic equation (each of the terms had a significance level of \(p\leq 0.0001\)):  
\[\mathrm{Ta}=-1.28-1.79\times\mathrm{H}+1.82\times\mathrm{Dc}-0.04\times \mathrm{Dc}^{2} \tag{12}\]  
where \(\mathrm{Dc}\): depth collected (mm); H: vertical distance between sprinkler height and catch can height (m). To make the application of Equation (12) easier, and considering that \(\mathrm{Dc}\) estimation requires field measurements, a regression was performed to determine this variable (each of the terms had a significance level of \(p\leq 0.0001\)):  
\[\mathrm{Dc}=2.83\times 10^{0.05\times\mathrm{Id}} \tag{13}\]  
where \(\mathrm{Dc}\): depth collected (mm) and Id: depth applied (mm) and  
\[\mathrm{Sa}=-1.47-2.07\times\mathrm{H}+2.09\times\mathrm{Dc}-0.05\times \mathrm{Dc}^{2} \tag{14}\]  
where \(\mathrm{Dc}\): depth collected (mm); H: vertical distance between sprinkler height and catch can height (m).  
Stemflow had similar behavior to that of throughfall, with the participation of \(\mathrm{Dc}\) and H (each of the terms had a significance of \(p\leq 0.0001\)). The equations proposed by [4] did not have an appropriate fit because we used i-Wob sprinklers (rotating, oscillating and low-pressure), whereas [4] used high-impact, high-pressure sprinklers. Additionally, the distances between sprinklers and crop canopy at different stages were also different. For this reason, we incorporated a new variable (H, Equation (9)) that considers this aspect and resulted in a better fit of Ta and Sa. H allows us to evaluate pivot systems with sprinklers located at different heights.