esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 3 System Model of Optical Wireless Fronthaul-Enhanced Hybrid Space Network
### Hsn Timeline Management  
FC-AE-1553 uses a master-slave protocol initiated by the network controller. In this protocol, all terminals need to first report their transmission requests to the NC, and then the NC arranges network messages based on NT requests or higher-level requirements. FC-AE-1553 uses a transmission protocol that is more suitable for deterministic and centralized transmissions. On the other hand, the Li-Fi network uses a peer-to-peer protocol where terminals can directly initiate transmissions without sending them to the server for approval. Therefore, the Li-Fi network is more suitable for dynamic and high-throughput transmissions.  
In a hybrid network, terminals have the option to send transmissions through either the FC-AE-1553 network or the Li-Fi network. However, due to the distinct transmission control mechanisms of these networks, the scheduling methods employed will also differ.  
The FC-AE-1553 network commonly utilizes a time-sliced transmission scheduling scheme. This scheme divides the network scheduling into small fixed periods and assigns pending transmissions to each node within each small period (Figure 5). Different transmissions are converted into corresponding time-slot sizes based on network bandwidth. Thus, transmission allocation and management become the scheduling of time-slot sizes for different transmissions within small periods.  
To simultaneously consider the transmission characteristics of both the FC-AE-1553 and Li-Fi networks, the hybrid network adopts a dual-channel timeline management strategy: one timeline is used to manage the FC-AE-1553 transmissions, and another timeline is used to manage the Li-Fi transmissions (Figure 6). The allocation of these two transmission timelines is equal and synchronized, but due to the different bandwidths of the two networks, the number of transmissions carried at the same time is different.  
For the messages to be executed in the FC-AE-1553 network, they still need to comply with the FC-AE-1553 protocol. A proportion of the messages are periodic transmission time slots directly arranged by the NC, and another proportion is time slots obtained after dynamic requests from the NT. For each NT, one type of transmission can be scheduled per cycle. For the messages to be executed in the Li-Fi network, multiple types of transmissions can be served as long as the time slots are not fully utilized.  
Figure 5: Cycle period schedule.  
In this way, the hybrid network can simultaneously support both FC-AE-1553 and Li-Fi transmissions and perform reasonable time-based scheduling and allocation. Terminal devices can choose the appropriate network for communication based on their needs, thereby achieving more efficient and flexible data transmission.
## 2 Metric Spaces
Moreover, the number of pivots tends to increase with the size of the working set. Some hybrid approaches (e.g., [23; 24]) combine clustering techniques with pivoting techniques. Typically, clusters are used to prune the search at a high level of the hybrid data structure. Inside each cluster, a pivot data structure is used to reduce the number of comparisons. Metric spaces have been used in parallel and distributed environments. For instance, the work in [25] introduces three approaches for distributing a clustering-based index on a cluster of processors. In the first approach, each processor builds a local index using its respective local objects. The second approach involves building a single index and subsequently distributing its clusters among the processors. The third approach combines both local and global indices, determining the center and radius of each cluster using the entire database and then having each processor fill the clusters with its local objects. The work in [26] investigates the energy consumption between a GPU and a multicore platform when searching for the \(k\) nearest objects in metric spaces on a database of finger vein images. The work in [27] presents two methods to search for similarity objects using a distributed index structure which is built with several graphs. The first method is based on a controller-performer scheme and the second method is based on a pipeline. The work in [28] presents a framework to search similar objects during stream query processing. The framework uses a set of smaller tree-based indices rather than a single large index. The work in [29] proposes a new distributed index named the M-Index. It transforms metric objects to a certain M-Index hash. Some works also investigate the use of metric spaces on peer-to-peer (P2P) networks. In particular, the work in [30] presents a dynamic load balancing algorithm based on hypergraph partitioning. Also, the work in [31] presents the Asynchronous Metric Distributed System (AMDS), which uniformly divides the data using pivot-mapping to maintain load balance and utilizes a publish/subscribe communication model for the asynchronous processing of a large number of queries. This approach not only enhances the robustness and efficiency of the AMDS but also ensures load balancing across the system. Figure 1: Pre-calculated distances between the pivots (\(P1\dots PN\)) and every object (\(o1\dots oM\)) in the metric space database.
## 3 Results
### Analysis of Actual Vibration Amplitude  
The actual vibration \(v_{a}\) amplitude is half the difference between the time-domain curve's maximum and minimum vibration velocity in one cycle [33; 34]. The relationship between the actual vibration amplitude at different depths and the driving speed is depicted in Figure 6. Figure 7 depicts the lateral attenuation of actual vibration amplitude at various driving speeds.  
\[v_{a}=\frac{v_{\text{max}}-v_{\text{min}}}{2} \tag{1}\]  
The driving speed and the actual vibration amplitude are about linear, as shown in Figure 6. Actual vibration amplitude tends to grow gradually as driving speed increases. Possible explanation: the geocell-reinforced aeolian sand produces a dense structural layer. The dense structural layer's sensitivity to the moving velocity of the vibration source differs. In addition, as the depth increases, the real vibration amplitude reduces, with 120 km/h being the most noticeable. The amplitude decay is nonlinearly proportional to depth [35; 36].  
According to Figure 7, the actual vibration amplitude decreases continually as the lateral distance increases. When the driving speed is between 20-120 km/h, the actual vibration amplitude decays more quickly in the 0-2.9 m range for both schemes than in the 2.9-4.8 m range. In addition, the actual vibration amplitude decay in the horizontal direction is faster in scheme 1 than in scheme 2. The geocell increases the horizontal bending moment and shear force to resist deformation in the lower portion, hence promoting lateral  
Figure 6: Actual vibration amplitude longitudinal decay curve.  
vibration diffusion. The vibration decay rate in the lowest portion of the gravelly soil is greater than that of the geocell-reinforced aeolian sand.
## 4 Model Verifications
### Verification of Ammonia-Water AHP Model  
Finally, the ammonia-water AHP model is validated by using the reported results in [PERSON] and [PERSON]'s work [50]. The same assumptions and conditions are adopted in the simulation platform:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Parameters**} & \multicolumn{2}{c}{\(\mathbf{x}\)} & \multicolumn{2}{c}{\(\mathbf{\eta}_{th}\)} \\ \hline \(\mathbf{T_{\min}}\) (\({}^{\circ}\)C) & \(\mathbf{T_{\max}}\) (\({}^{\circ}\)C) & \(\mathbf{P_{\max}}\) (MPa) & \(\mathbf{PRc}\) & **Present** & **Published** & **Present** & **Published** \\ \hline
32 & 550 & 20 & 2.64 & 0.3332 & 0.334 & 41.18 & 41.18 \\
32 & 550 & 30 & 3.86 & 0.3546 & 0.355 & 43.32 & 43.32 \\
32 & 750 & 20 & 2.65 & 0.2212 & 0.223 & 46.07 & 46.07 \\
32 & 750 & 30 & 3.94 & 0.2809 & 0.281 & 49.84 & 49.83 \\
50 & 550 & 20 & 2.40 & 0.1842 & 0.184 & 36.71 & 36.71 \\
50 & 550 & 30 & 2.80 & 0.2533 & 0.254 & 38.94 & 38.93 \\
50 & 750 & 20 & 2.88 & 0.0962 & 0.109 & 43.50 & 43.50 \\
50 & 750 & 30 & 3.08 & 0.1745 & 0.175 & 45.28 & 45.28 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between the simulated results and the published data for the sCO\({}_{2}\) cycle.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**State Points**} & \multicolumn{2}{c}{\(\mathbf{T}\) (\({}^{\circ}\)C)} & \multicolumn{2}{c}{\(\mathbf{h}\) (k/kg)} \\ \cline{2-5}  & **Present** & **Published** & **Present** & **Published** \\ \hline A7 & 164.85 & 162.75 & 369.49 & 365.80 \\ A4 & 142.63 & 140.76 & 313.86 & 310.30 \\ A2 & 91.99 & 92.94 & 216.03 & 217.87 \\ A6 & 106.75 & 106.90 & 280.22 & 265.63 \\ A8 & 160.52 & 162.75 & 2798.07 & 2797.36 \\ A1 & 51.85 & 51.85 & 2594.55 & 2595.19 \\ A9 & 96.85 & 96.85 & 405.88 & 404.90 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between the simulated results and the published data for LiBr-H\({}_{2}\)O AHP.  
(1) The temperatures of generator, condenser, absorber and evaporator are set to be 120 \({}^{\circ}\)C, 45 \({}^{\circ}\)C, 45 \({}^{\circ}\)C, 45 \({}^{\circ}\)C, and 10 \({}^{\circ}\)C; (2) The minimum temperature approach of SHE is set to be 5 K; (3) The solution leaving the absorber is assumed to have a subcooling of 3 K. Then, the comparison is shown in Table 8, which indicates a high agreement between two groups of results.  
Table 8 shows the comparison between the simulated results and the published data for ammonia-water based AHP.
## 2 Materials and Methods
### Raman Spectroscopy Measurements  
The nonpolarized Raman spectra were recorded in the backscattering geometry using a Renishaw inVia micro-Raman system (Wotton-under-Edge, Gloucestershire, UK). An infrared solid-state laser operating at \(\lambda\) = 785 nm was used as the source of excitation light, with a power of less than 20 mW. The incident laser beam was tightly focused on the sample surface through a Leica 50\(\times\) LWD (long working distance) microscope objective. Five spectra were collected for each sample in the range of 200 to 3200 cm\({}^{-1}\), with a spectral resolution of 2 cm\({}^{-1}\). Then, the average spectrum was calculated based on these five origin spectra, followed by additional calculations. All measurements were performed in air at room temperature two months after curing.
# A Transfer Reinforcement Learning Approach for Capacity Sharing in Beyond 5G Networks
## 2 System Model and Problem Definition  
The considered system consists of a RAN infrastructure that is owned by an Infrastructure Provider (InP). It is initially composed of \(N\) cells that use the 5G New Radio (NR) technology with diverse deployment characteristics (i.e., cell radius, transmission power, frequency of operation). Cell \(n\) has a total of \(W_{n}\) Physical Resource Blocks (PRBs), each with bandwidth \(B_{n}\), providing a total cell capacity \(c_{n}\) (b/s), expressed as:  
\[c_{n}=W_{n}\cdot B_{n}\cdot S_{n} \tag{1}\]  
where \(S_{n}\) is the average spectral efficiency of the cell. The total system capacity \(C\) is obtained by aggregating \(c_{n}\) for all cells \(n=1\ldots N\). The InP shares its RAN infrastructure among \(K\) tenants by providing each tenant with a RAN Slice Instance (RSI). The provisioning of RSIs to each tenant is subject to the definition of certain Service Level Agreements (SLAs) between each tenant and the InP (see [11] for details).  
To dynamically adapt the capacity assigned to the different tenants to their spatial and temporal traffic variations among the different cells, minimize SLA breaches (i.e., violations) in the system, and optimize the resource utilization of the different cells in the system, the DQN-MARL capacity sharing solution in [11] is considered. In this solution, a DQN agent learns the policy \(\pi\) that tunes the capacity share of a tenant in the \(N\) cells in time steps of duration \(\Delta t\) (in the order of minutes). Specifically, at time step \(t\) the DQN agent provides the capacity share \(\mathbf{\sigma}(\mathbf{t})\), which is defined as:  
\[\mathbf{\sigma}(\mathbf{t})=[\sigma_{1}(t),\ldots,\sigma_{n}(t),\ldots,\sigma_{N}(t)] \tag{2}\]  
where each component \(\sigma_{n}(t)\) is the proportion of the total PRBs \(W_{n}\) in cell \(n\) assigned to the tenant during time step \(t\).  
In this context, let us assume that the InP decides to deploy \(\Delta N\) new cells to improve the coverage and capacity of the RAN infrastructure in the area so that the resulting number of deployed cells in the system is \(N^{\prime}=N+\Delta N\). To be able to distribute the resulting capacity \(C^{\prime}\) in the scenario considering the new cells, the DQN-MARL capacity sharing model needs to be updated to learn the new policy \(\pi^{\prime}\), since the previous policy \(\pi\) can only distribute the capacity among \(N\) cells. This constitutes a practical problem since re-training the policies for each tenant from scratch can be highly consuming both in terms of time and computing resources. To deal with this problem, this paper proposes the use of a Transfer Reinforcement Learning (TRL) approach for capacity sharing, which is described in further detail in the following section.
Entropy_**2015**, _17_, 2919-2931; doi:10.3390/e17052919  
_Artcle_  
**Three-Stage Quantum Cryptography Protocol under Collective-Rotation Noise**  
**[PERSON] \({}^{\dagger}\) and [PERSON] \({}^{\dagger,\ast}\)**  
Department of Electrical and Computer Engineering, N308 Engr. Bldg. 1, University of Houston, Houston, TX 77204-4005, USA; E-Mail: [EMAIL_ADDRESS] (L.W)  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\ast}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-713-743-4441.  
_Received: 31 March 2015 / Accepted: 4 May 2015 / Published: 7 May 2015_  
**Abstract:** Information security is increasingly important as society migrates to the information age. Classical cryptography widely used nowadays is based on computational complexity, which means that it assumes that solving some particular mathematical problems is hard on a classical computer. With the development of supercomputers and, potentially, quantum computers, classical cryptography has more and more potential risks. Quantum cryptography provides a solution which is based on the Heisenberg uncertainty principle and no-cloning theorem. While BB84-based quantum protocols are only secure when a single photon is used in communication, the three-stage quantum protocol is multi-photon tolerant. However, existing analyses assume perfect noiseless channels. In this paper, a multi-photon analysis is performed for the three-stage quantum protocol under the collective-rotation noise model. The analysis provides insights into the impact of the noise level on a three-stage quantum cryptography system.  
**Keywords:** quantum cryptography; three-stage quantum protocol; multi-photon; collective-rotation noise
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 5 E-Health Sustainability Model
### E-health Sustainability Model
#### 5.2.4 Social  
The social dimension consists of all elements that are related to the users' perceptions towards the e-health solution. These elements were grouped into two aspects, which are the continuous usage intention and the user behavior. The intention of the users to continue using e-health must be highly considered, especially if the regulations do not enforce the use of the e-health solution. This interrelates with the human resources aspect in the resources dimension, having enough users using the system to achieve its outcomes. Furthermore, users' behavior towards e-health influences the work process and e-health productivity. The users who trust the use of e-health and have the requisite skills and training are most likely to be ready to make the necessary adjustments corresponding to the adoption of e-health, which contributes to the achievement of the outcomes and goals of the e-health solution.
## 4 Analysis of PC Member Quality Inspection Process and Major Inspection Items  
### Analysis of On-Site Quality Inspection Process of PC Members  
With the goal of developing an automated PC member crack detection system, this study conducted interviews with PC member production companies and experts to understand and to analyze the overall PC member production process and quality inspection flow as well as the PC member quality inspection process currently performed at OSC sites.  
Factory-manufactured PC members that passed the quality inspection are shipped to each site by means of transportation. PC members transported to the site are moved to the storage yard and go through the unloading procedure. Since defects may occur during transportation, loading, and unloading, a sampling inspection based on a visual check is performed to find any defects after unloading. When defects in a member are found, a return or repair plan is reviewed after consulting with the person in charge of the site. Figure 2 shows the life cycle of PC members from the production factory to the construction site.  
As shown in Figure 3, the on-site quality inspection of PC members is performed by the following procedure. First, a quality inspection checklist and any needed tools for quality inspection are prepared. Second, a sample PC member to be inspected is selected. Third, visual inspection is performed on the sample member based on the quality inspection checklist. Fourth, after completing the inspection, the quality inspection checklist is filled according to the inspection result. Finally, after the final review of the inspection results, the quality inspection process is completed.  
However, problems are commonly encountered during this on-site quality inspection process due to the extensive time it takes to prepare the quality inspection checklist and any needed inspection tools and to perform inspection based on one's visual and experience. Moreover, the inspection accuracy can be diminished due to human errors, such as errors due to handwriting checklists and the possibility of loss during checklist storage.
## 6 Discussion
### Application for the Rock Brittleness
By considering the brittleness indexes, engineers can assess the potential for brittle failure and identify areas prone to rock fracturing. This information helps in designing appropriate reinforcement measures and slope stabilization techniques, minimizing the risk of catastrophic failures and ensuring the safety of infrastructure [29]. (c) Prediction of Rock Failure Rock brittleness evaluation methods aid in predicting rock failure, providing early warnings and allowing for proactive measures to mitigate potential hazards. By quantifying the brittleness characteristics of rocks, engineers can assess their susceptibility to failure under different loading conditions. This information is vital for designing monitoring systems, implementing preventive measures, and developing reliable risk management strategies [30]. (d) Challenges and Future Directions The practical implementation of rock brittleness evaluation methods faces several challenges, including the accurate quantification of brittleness indexes, the heterogeneity of rock formations, and the need for advanced testing equipment. Future research should focus on developing standardized testing protocols, integrating multiple evaluation methods, and improving the applicability of these methods to diverse geological conditions. Additionally, advancements in non-destructive testing techniques and numerical modeling can enhance the accuracy and efficiency of rock brittleness assessments.
## 3 Results
#### 3.2.1 Simulation Experiment  
The system used in the simulation experiment is MEMPHIS, an airborne multi-baseline SAR system from Germany. The relevant parameters of the system and simulation are shown in Table 1. It can be seen that the theoretical resolution of the simulation system in the height direction is about 42 m, the theoretical minimum mean square deviation in height direction is about 1.2 m, and the maximum unambiguous distance in height direction is 210 m. Since the theoretical resolution of the elevation direction is 42 m, in order to verify its super-resolution performance, the height of the scatterers selected in the simulation experiment is 0 m and 20 m, the amplitude of the scatterers is the same, and the SNR is set as 20 dB. Due to the L1 norm solution of the traditional layover separation method in scattering point height not being stable enough to estimate, this subsection uses the L1 norm solution based on multi-look for comparison with the proposed method, The method proposed in this subsection also adopts multi-look processing, and the number of views is five. The simulation results are shown in Figure 7.  
Figure 6: Height distribution of scattering points. (**a**) Traditional L1 norm compressive sensing method; (**b**) Multi-look compressive sensing method. The horizontal axes represent pixels from 600 to 800 in the slant range, and the vertical axes are heights in the elevation direction with the unit m.  
Figure 7: Simulation results. (**a**) Multi-look compressive sensing method; (**b**) Separable approximate sparse reconstruction algorithm. The horizontal axes are heights in elevation direction with the unit m, and the vertical axes are normalized amplitudes.  
It can be seen from Figure 7 that the algorithm based on separable approximate sparse reconstruction has lower sidelobes, and the height resolution is not significantly reduced compared with the multi-look compressive sensing method. However, from the perspective of operation time, the processing time of the multi-look compressive sensing algorithm is 2.635 s, while the operation time of the algorithm based on separable approximate sparse reconstruction is 0.7427 s. It can be seen that the approximate separable sparse reconstruction algorithm can effectively reduce the operation time while maintaining the height resolution of scatterers.
## 4 Results and Discussions
### Result Validation  
The results presented in this study were validated using CST software. In this section, we present the comparison of some of the results obtained from HFSS with those obtained from the CST software when plotted together in Microsoft Excel. While the operating band of UWB under this study is 3.1 GHz to 10.6 GHz, the graphs plotted show a frequency range from 2 GHz to 14 GHz only to observe what happens outside the UWB band and is not relevant to the present study. Figures 14 and 15 show the S-parameters (\(\mathbb{S}_{11}\) and \(\mathbb{S}_{12}\)) of the MIMO-UWB antenna with and without the MEBG decoupling structure for both HFSS and CST software. From these figures, it can be observed that the results presented by HFSS are not out of range when the antenna is validated using CST software. The MIMO-UWB antenna with and without the MEBG structure designed for the HFSS platform gave a similar response to that of the CST simulation software within the UWB band of 3.1 GHz to 10.6 GHz.  
Figure 14: MIMO\(-\)UWB antenna S\(-\)parameter (\(\mathbb{S}_{11}\)) using HFSS and CST software (**a**) with MEBG, (**b**) without MEBG.  
Figure 13: Envelop correlation coefficient with and without MEBG decoupling structure.