esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, the performance of satellite-based links for measurement-device-independent quantum key distribution has been further evaluated. The effect of weather conditions, the different wavelengths of signal light and the ground loss on the model is analyzed. All the results are reported by changing the satellite's height and the angle from zenith. Compared with previous work, it is more close to the reality. It combines the traditional optical-fiber-based links with free-space links. It is significant for long-distance quantum communication. Moreover, it also offers important reference to build long-distance quantum communication network with satellite-to-ground links. More progress can be made in transmittance and key rate and better results may be gotten with optimizing by machine learning.  
Conceptualization, R.J. and G.H.; methodology, G.H.; validation, R.J.; formal analysis, G.H.; investigation, Q.D. and W.C.; writing--original draft preparation, G.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities grant number 2019 XD-A02, Ministry of Science and Technology of China grant number 2016 YFA0301300 and National Natural Science Foundation of China grant number 61571060.  
Data Availability Statement: Data sharing not applicable.  
Figure 4: The secret key rates of the MDI-QKD protocol in two different weather conditions, as functions of the angle from zenith and the orbit’s height, are reported at \(L_{A}\) = 0 km, 50 km, 100 km respectively. \(\lambda\) is fixed at 785 nm.
# Investigation of Pre-Pulse Effects on Ultrashort-Pulse Laser Interaction with Structured Targets
## 3 Experiment Results
The error in proton energy measurement can be attributed to three main factors: (i) fluctuation in MP intensity due to alignment error on the morphology of the spike target (drawn schematically in Figure 1e), (ii) shot-to-shot instability of the MP and APP energies, and (iii) discrete proton energy measurement by aluminum filters (\(\Delta\approx\) 200 keV). Figure 3d shows the variation in mean (and corresponding maximum) proton energy with time delay. Figure 2: Plasma emission optical spectrum (intensity vs wavelength) plots at artificial pre-pulse (APP) time delays (\(\Delta\)t) of, (**a**) \(-\)2 ns, (**b**) \(-\)5 ns, (**c**) \(-\)14 ns, with fluence ranges: (I) no-APP (black dashed line), (II) 0.1–0.2 (orange dash-dot line), (III) 0.4–0.6 (violet solid line), and (IV) 2–5 (teal dash-dot-dot line) J/cm\({}^{2}\) in each plot (**a**–**c**). (**d**) Normalized intensity versus wavelength plot for the optimum APP condition of \(\Delta\)t \(\approx-\)5 ns in the fluence range \(\approx\)0.4–0.6 J/cm\({}^{2}\). The plot is generated by extracting the peak proton energy value (mean and maximum of 5 experiments) at each time delay. The plot shows a fast rise in the mean PE as the negative delay is increased, reaching a maximum at \(-5\) ns, and then slowly decreasing. At \(\Delta t\approx-\infty\), i.e., when the MP arrived long after the APP had interacted with the target, the mean PE was found to be \(\sim\)250 keV.
# Airborne GNSS Reflectometry for Water Body Detection
## 4 Segmentation of the GNSS Signal  
### Transition Model  
In our radar application, the amplitude of the reflected GNSS signal changes as a function of time. The GNSS signal amplitude is proportional to the ground reflectivity contained in the surface of the first Fresnel zone of the satellite footprint. The first Fresnel zone is an ellipse centered on the specular reflection point. The displacement of this ellipse on the ground follows the satellite trace. We show in Figure 3, the satellite footprints displacement from one area to another. We show in Figure 4 the signal model in the working window.  
We can observe in Figure 4 that when the mean value of the GNSS signal amplitude is equal to \(m_{1}\), the ellipse is on the first area (land), and when the mean value of the GNSS signal amplitude is equal to \(m_{2}\), the ellipse is on the second area (water body). We observe that at the border between different areas of reflection, the change in the signal level is not abrupt but rather transitional. That is why a transition model is adapted to characterize the changes in the amplitudes of the reflected GNSS signals. We assume that the GNSS measurements are piecewise stationary and the noise on the observations are additive, Gaussian and centered. The increasing evolution of the signal model between the mean values \(m_{1}\) and \(m_{2}\) in Figure 4 models a linear transition from a surface of low reflectivity (land) to a surface of higher reflectivity (water body). We define in Section 4.3 the start and end instants of the working window in order to optimize the estimation.  
Figure 2: Airborne GNSS-R system.  
In the presence of a large amount of data that need to be processed, an on-line change detection process needs to be implemented. In this regard, we use the CUSUM algorithm [45; 46; 47] to detect a change on-line. The CUSUM change detector is capable of detecting changes associated to the model of transition that we use in this work. After the changes are detected on-line, an off-line localization approach is proposed to localize the detected change points. We propose a maximum likelehood estimate for change localization. This approach is close to the optimal estimation because we maximize in this case the size of the working window in which the detected change point is localized.
## 3 Passive Systems for the Stadium's Envelope--Heating and Cooling Loads
### Heating and Cooling Loads Calculation
#### 3.3.1 Existing Envelope's Condition  
Given the annual final thermal energy needs for indoor space heating and cooling with the existing envelope's condition (381.5 MWh and 436.3 MWh, respectively) and the conditioned space covered  
Figure 6: The introduced Stadium’s profile in TRNSYS (**a**) and the computational calculation model (**b**) for the indoor space conditioning loads calculation. area (10,637.7 m\({}^{2}\)), the specific thermal energy consumption per unit of conditioned space covered area is calculated at 35.87 kWh/m\({}^{2}\) and 41.02 kWh/m\({}^{2}\) for heating and cooling, respectively. The total thermal energy specific consumption for indoor space conditioning equals 76.88 kWh/m\({}^{2}\). In Table 1, the monthly analysis of the electricity consumption for the indoor space heating and cooling is also presented. This calculation is based on the following assumptions:  
* The indoor space cooling is totally covered with an air-to-air central cooling device (Carrier 30 GX 267, 924 kW, Carrier, Farmington, CT, USA). * As already stated in Section 2.3, according to the Stadium's Management, the existing oil burners are exclusively employed for hot water production. Hence, no oil is consumed for indoor space heating. This means that the indoor space heating needs are only partially covered with autonomous split units (air-to-air heat pumps), installed mainly in offices. Based on the on-site inspection, the percentage of these heated indoor space covered area over the total conditioned space area was calculated at 14.7%. * Based on the above approach, the electricity consumption calculation for the indoor space heating and cooling in the existing operation was executed by assuming that the cooling loads are 100% covered, while the heating loads are 14.7% covered annually. * The electricity consumption annual time series was calculated on the basis of the existing heating and cooling loads coverage as described above, the ambient temperature (Figure 3b, Section 2.2) and the Coefficient of Performance (COP) and the Energy Efficiency Ratio (EER) typical fluctuation curves for air-to-air heat pumps versus the ambient temperature, presented in Figure 7. These curves are retrieved from real, commercial heat pumps models. Given the heating \(\dot{\mathrm{Q}}_{\mathrm{h}}\) or the cooling \(\dot{\mathrm{Q}}_{\mathrm{c}}\) load, the electrical power consumption \(\mathrm{P}_{\mathrm{el}}\) is simply calculated with the following relationship:  
\[\mathrm{P}_{\mathrm{el-c}}= \frac{\dot{\mathrm{Q}}_{\mathrm{c}}}{\mathrm{EER}}\ \mathrm{or}\ \mathrm{P}_{\mathrm{el-h}}=\frac{\dot{\mathrm{Q}}_{\mathrm{h}}}{\mathrm{COP}}, \tag{4}\]  
where the subscripts \"c\" and \"h\" stand for cooling and heating, respectively. The heating and cooling loads annual fluctuation in the existing operation and the corresponding electricity consumption are presented in Figure 8. The indoor space inadequate heating is characteristically revealed by comparing Figure 8a,b.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 2 Related Work
### Proxy Re-Encryption  
A Proxy Re-Encryption (PRE) scheme is a scheme that converts the ciphertext so that a proxy server can decrypt the ciphertext encrypted with user A's public key using user B's private key. In 1998, [PERSON] et al. [22] proposed the first two-way proxy re-encryption scheme. This scheme was designed using the ElGamal encryption scheme [23]. In 2007, [PERSON] et al. [24] proposed an ID-based proxy re-encryption scheme using ID-based encryption for the first time to solve the certificate management problem of the existing Public Key Infrastructure (PKI) based proxy re-encryption. ID-based encryption is a scheme of using the user's identity as a public key [25]. In this scheme, the user's identity itself is owned, so unlike in PKI-based environments there is no need to issue and manage certificates. In addition, since the KGC generates a private key corresponding to the identities and issues them to the users, it has the advantage of performing verification of the user through KGC in case of a dispute. However, the KGC issues all users' private keys, which causes a key escrow problem in which KGC knows the private keys. Therefore, to solve this problem, a Certificateless Public Key Cryptography (CL-PKC) system was developed. The CL-PKC scheme was proposed by [PERSON] et al. [26], and it solves the key escrow problem by issuing partial private keys to the users by combining the user's identity and a random number. Building on these feature, in 2010, [PERSON] et al. [27] proposed Certificateless Proxy Re-Encryption (CL-PRE) using CL-PKC. CL-PRE is currently a representative form ofsecure PRE because it can perform the purpose of proxy re-encryption without suffering the PKI certificate management problem or IBE key escrow problem [28; 29; 30].
# Hurdles on the Way to Sustainable Development in the Education Sector of China
## 4 Result and Discussion  
### Measurement Model  
The two-step approach identified by [PERSON] and [PERSON] [69] was applied and tested in SEM. Regarding the first step, for evaluation of relationships among the variables and latent constructs, CFA was tested. Findings after applying CFA show that all values and ranges were acceptable in the whole scenario, which further reflects the goodness of the model (\(\chi^{2}\) (\(1144.453\)) = 94, \(\chi^{2}/\mathrm{df}\) = 1.834, SRMR = 0.042, GFI = 0.941, AGFI = 0.901, NFI = 0.913, RFI = 0.969, CFI = 0.946; TLI = 0.936; RMSEA = 0.052). While in the second strand, for confirming measurement model CR (composite reliability), convergent and discriminant validity [70] tests were applied for further proceedings. All CR scores, shown in Table 2, were between 0.87 (0.70) and 0.88, and above the cut-off value of 0.70. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Constructs** & **Statements** & **SFL** \\ \hline \multirow{3}{*}{Sustainable public procurement} & To what extent sustainable procurement is implemented in your organization? & 0.842 \\ \cline{2-3}  & To what extent environmental criteria are included in the tender documents? & 0.869 \\ \cline{2-3}  & \multirow{2}{*}{To what extent your organization asks the suppliers to commit to waste} & \multirow{2}{*}{0.825} \\  & & reduction goals?
# Modeling and Analyzing Transmission of Infectious Diseases Using Generalized Stochastic Petri Nets
## 5 Conclusions  
Infectious diseases are a major health problem throughout the world. Mathematical models based on an ordinary differential equation (ODE) system have an important role in predicting the outcome of infectious diseases. Markov models for analyzing the dynamics of the spread of epidemics and information have been studied by many researchers. The models based on stochastic Petri nets and their variants are the important evolution of Markov models, which are suitable for modeling the complex and large-scale systems.  
In this paper, a new general transmission model of infectious diseases based on the generalized stochastic Petri net (GSPN) is proposed. The advantage of this method is that, with the concurrency and state analysis methods of Petri net, it can analyze the relationship between the associated attribute variables of the development and evolution processes for different events in the infectious disease epidemic event chain. It provides a new analysis model and tool for the infectious disease experts. By setting relevant parameters of different infectious diseases to the firing rates in this Petri model, they can analyze the spread of various diseases, as well as the effect and trend analysis of vaccines, specific drugs, response levels, and restrictions on epidemic control and so on. The experimental results have shown that the proposed GSPN model is an attractive tool and can provide decision support for effective surveillance and response to epidemic development.  
In the future work, further cooperations with epidemiologists are needed to map the transmission parameters of various infectious diseases into this model.  
Conceptualization, L.P. and P.X.; methodology, P.X.; software, Z.T.; validation, L.P. and P.X.; formal analysis, P.X.; investigation, F.L.; resources, F.L.; data curation, Z.T.; writing--original draft preparation, L.P. and P.X.; writing--review and editing, L.P. and Z.T.; visualization, P.X.; supervision, L.P.; project administration, L.P.; funding acquisition, L.P. All authors have read and agreed to the published version of the manuscript.  
No external funding has been received.  
Figure 11: Relationship between duration of specific drugs development and the number of infected people.
## 2 Materials and Methods
### Adaptive Evolution Analysis of M. longifolia TPSs  
Based on the phylogenetic tree and duplication gene analysis of the _M. longifolia_ TPS gene family, 14 paralog pairs were selected to calculate the nonsynonymous-to-synonymous substitution ratio (Ka/Ks). The calculation was conducted using a KaKs-Calculator 2.0 [38] with the sliding window method (90 bp window and 30 bp slide). Then, the site model of EasyCodeML [39] was used to conduct adaptive evolution analyses on each subfamily of _M. longifolia_ TPSs. Three pairs of models (M0 (one-ratio) vs. M3 (discrete), M1a (neutral) vs. M2a (positive selection), and M7 (\(\beta\)) vs. M8 (\(\beta+\omega\))) were chosen to test positive selection using the likelihood ratio test (LRT) and the Bayes empirical Bayes (BEB) method [40; 41].
# Resolution Enhancement of Spherical Wave-Based Holographic Stereogram with Large Depth Range +
## 5 Increasing Focal Length \(f\) with Aperture Filtering and Moving Array Lenslet Technique  
Increasing focal length f will decrease the image spot size near the CDP, resulting in better reconstruction quality. However, according to Equation (2), increased \(f\) will cause increased pixel number \(N\) of EI. That is, both sizes of hogel and EI increase and cause wider light rays, making larger defocused spots away from the CDP. It can be easily understood by the second term in Equation (5). The above aperture filtering technique can also be used here to narrow the light ray width, as shown in Figure 10b. However, dark gaps among hogels will appear. To fill in the dark gaps, the moving array lenslet technique [23,26] is introduced in Figure 10c. That is, two groups of EIA are captured with lateral shift of \(p/2\). Each group of EIA is converted into the corresponding sub-hologram, and the two sub-holograms have relative shift in real space. Then, the two holograms are superimposed with relative shift of \(p/2\). The dark gaps of one hologram will be filled up by the other hologram. Note that considering the two-dimensional case, four groups of EIA are needed.  
Figure 11a shows the same simulation results as Figure 8a, with focal length of 3 mm. In Figure 11b, the focal length \(f\) is increased to 6 mm, and the sizes of both hogel and EI increase to 0.6 mm, which means the number of hogels or EIs decreases to 30 \(\times\) 30. Figure 11b shows that increasing\(f\) improves the reconstruction quality near the CDP, at the price of quality degradation at the marginal depth. The aperture filtering is applied to the right half hogels in Figure 11c, but dark gaps appear. Note that the dark gaps do not appear at the school badge plane. However, when observing the 3D image, our eye will tend to focus on such periodic structures, causing severe image quality degradation. Figure 11d shows the simulation results with MALT, in which the dark gaps disappear. The image quality is improved without decreasing the depth range. Figure 12 shows the optical reconstruction results, which verifies the conclusion in Figure 11.  
Figure 10: (**a**) A conventional case with a small focal length. (**b**) Increased focal length with aperture filtering, resulting in dark gaps. (**c**) Increased focal length with aperture filtering and MALT.
## 5 Results and Discussion
### Volumetric Properties  
The volumetric properties of each mix configuration are presented in Table 9, as per MRWA [23; 24; 25].  
_Marshall stability_ is an indicator of the resistance of bituminous materials to rutting and shearing stresses, whereas _flow_ is defined as the vertical deformation occurring under the maximum load applied by an apparatus. An ideal asphalt mixture has low flow and high Marshall stability, making it resistant to permanent deformation.  
According to Figure 4 and Table 9, the Marshall stability of the asphalt mixtures improved with the addition of GGBFS because this filler reduces air voids. It should be noted that the effect of voids is crucial to the Marshall results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Test Item** & **Conditions** \\ \hline Peak tensile strain & 400 \(\pm\) 10 \(\mu\)\(\varepsilon\) \\ Frequency & 10 \(\pm\) 0.1 Hz \\ Loading mode & Continuous haversine \\ Air void content & 5 \(\pm\) 0.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Four-point bending testing condition as per AG:PT/T233 [27].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mix** & **Air Voids (\%)** & **VMA (\%)** & **Flow (mm)** & **Stability (kN)** & **Conformance** \\ \hline Control (1.5\% HL) & 4.79 & 15.49 & 2.44 & 16.16 & \(\checkmark\) \\
1 (1.5\% GGBFS) & 4.44 & 15.21 & 2.80 & 16.96 & \(\checkmark\) \\
2 (3\% GGBFS) & 4.15 & 14.98 & 2.82 & 19.16 & \(\checkmark\) \\
3 (5\% GGBFS) & 4.02 & 14.87 & 2.79 & 17.04 & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Marshall properties of the asphalt mixtures.  
More bitumen will be required to coat these particles and allow them to adhere to the aggregates [4]. A higher amount of bitumen, on the other hand, results in pavement with significantly less stiffness and a greater tendency to bend at higher temperatures, making it more prone to rutting [30]. Accordingly, an optimal concentration of GGBFS should be added to a mixture to improve its Marshall properties.  
When 1.5% GGBFS was added to the asphalt mixture, the Marshall stability was 16.96 kN, while the control mixture with 1.5% HL had a Marshall stability of 16.16 kN. It increased by 4.95%. Figure 4 shows that there was an increase in flow, although it was not significant. The flow of Mixture 1 with 1.5% GGBFS was 2.80 mm.  
The Marshall stability of the mixture with 3% GGBFS addition was 19.16 kN. This is 18.56% higher than that of the control mixture containing 1.5% HL. The flow of Mixture 2 with 3% GGBFS was 2.82 mm, which is similar to that of Mixture 1 (1.5% GGBFS).  
Finally, the Marshall stability of the mixture with 5% GGBFS was 17.04 kN, which is 11.06% lower than that of Mixture 2 (3% GGBFS). The flow of Mixture 3 (5% GGBFS) was 2.79 mm, which is roughly equivalent to those of Mixtures 1 and 2.  
Despite having a higher flow than Mixtures 1 and 3, the peak values in these datasets reflect the ability of each mix to prevent rutting and fatigue distress. Mixture 2 had a peak stability of 19.16 kN, indicating that this configuration may have the best wheel tracking performance.
# DAG Hierarchical Schedulability Analysis for Avionics Hypervisor in Multicore Processors
## 5 Hierarchical Scheduling in Hypervisor
### Overview of Virtualization  
The work of this section is different from scheduling tasks to physical cores directly but uses a hierarchical scheduling method to schedule tasks. Furthermore, the hierarchical algorithm schedules every task to the unique virtual processor of this task and then schedules whole virtual cores to the physical core. This method involves an offline design part and an online scheduling part [24; 25]. Scheduler-1 schedules the workload of every DAG task to VCPUs. Scheduler-2 schedules the VCPUs on whole the PCPUs. Illustration of Hierarchical scheduling in Figure 4.
## 3 Queueing Analysis
### Analytical Results
Thus we suggest that such analytical models are particularly useful for worst case estimation, which is very important in the design and evaluation of emergency procedures. As an additional benefit of the queueing analysis, we have also considered ranking the criticality of the nodes based on their utilization (\(\rho\)) rates and compared this list with the results of the graph-based methods presented in the previous section. It appears that the same nodes appear in the top 10% ranking list, however the precise order is different across all three (two graph-based and one queueing-based) methods.