esa-sceva/satcom-chunk-collection huggingface dataset:
# Service for Deploying Digital Twins of QKD Networks
## 2 Background
### QKD Networks  
Although QKD networks, and quantum networks in general, are still in an early development phase due to the relatively new appearance of equipment that supports them, they are expected to be an important advance that significant organizations, such as European Telecommunications Standard Institute (ETSI) or Internet Engineering Task Force (IETF), have created specific committees [24; 25] to develop standards that can serve as a framework to build future quantum networks. Several documents of this nature have already been published in the last years, like the Architectural Principles for a Quantum Internet RFC 9340 [26] by the IETF. These documents also include specifications on Application Programming Interfaces (APIs) for QKD nodes [27; 28], use cases for the Quantum Internet [29], or descriptions of the main communication resources involved in QKD systems [30].  
Apart from these standards, there are also several research lines where quantum network layered architecture is being developed. For example, in [31], the authors explore how QKD networks could be deployed in next-generation infrastructures, proposing an architecture for a Software-Defined QKD Network Node. There are also several surveys on QKD networks deploying techniques and practical implementation, which provide reviews of the different layered descriptions of QKD networks used [32; 33]. However, there is still not a widely accepted model for a quantum network architecture.  
A description of the main components of a QKD network node can be found in the ETSI GS QKD 004 Quantum Key Distribution (QKD) Application Interface document [27], along with an example of two sites in a QKD network, shown in Figure 1.  
In the scheme that can be seen in Figure 1, there is an upper layer corresponding to the client applications. Underneath, there is a component named a QKD module, marked with a dashed line in the same figure, which can communicate with the application layer through a QKD API defined in the same document [27]. The QKD module of each site is formed by a QKD Key Manager Peer in charge of managing the keys produced in the lower layer, the Quantum components layer of the node. This last layer is where the QKD protocol for forming quantum keys is implemented.
# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
## 4 Results Analysis
### Synergistic Development Characteristics of Logistics and Capital Flows
#### 4.4.3 Coupled Coordination of Logistics and Capital Flows
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**City**} & \multicolumn{4}{c}{**2016**} & \multicolumn{4}{c}{**2019**} & \multicolumn{4}{c}{**2021**} \\ \cline{2-13}  & **C-D** & **R** & **Type** & **Stage** & **C-D** & **R** & **Type** & **Stage** & **C-D** & **R** & **Type** & **Stage** \\ \hline Macao & 0.240 & 0.149 & I & Antagonist & 0.326 & 0.509 & I & Antagonist & 0.273 & 0.321 & I & Antagonist \\ Dongguan & 0.341 & 0.299 & I & Antagonist & 0.365 & 0.507 & I & Antagonist & 0.320 & 0.268 & I & Antagonist \\ Foshan & 0.316 & 0.747 & I & Antagonist & 0.379 & 1.117 & II & Antagonist & 0.295 & 0.493 & I & Antagonist \\ Guangzhou & 0.869 & 0.602 & VII & Coordination & 0.9179 & 0.744 & VII & Coordination & 0.844 & 0.443 & VII & Coordination \\ Huizhou & 0 & 0 & I & Antagonist & 0 & 0.177 & I & Antagonist & 0.195 & 0.795 & I & Antagonist \\ Jiangmen & 0 & 0 & I & Antagonist & 0.099 & 1.3018 & III & Antagonist & 0.017 & 0.020 & I & Antagonist \\ Shenzhen & 0.954 & 0.879 & VIII & Coordination & 0.9519 & 0.8248 & VIII & Coordination & 0.978 & 0.7639 & VII & Coordination \\ Hong Kong & 1 & 1.001 & VIII & Coordination & 0.9979 & 1.0148 & VIII & Coordination & 1 & 0.819 & VIII & Coordination \\ Zhaoqing & 0.018 & 0.065 & I & Antagonist & 0 & 0.9358 & II & Antagonist & 0 & 0.026 & I & Antagonist \\ Zhongshan & 0.246 & 2.108 & III & Antagonist & 0.192 & 1.008 & II & Antagonist & 0.085 & 0.030 & I & Antagonist \\ Zhuhai & 0.249 & 0.230 & I & Antagonist & 0.3752 & 1.358 & III & Antagonist & 0.2815 & 0.3175 & I & Antagonist \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of coordinative and harmonious development in space and time between logistics and capital flow in the Great Bay area. and coordination. Peripheral cities serve a supporting role, but their synergy effects require enhancement for improved integration and development.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 7 Secure Data-Sharing Protocols
### Secure APIs for Controlled Interaction  
Secure data-sharing gateways are crucial in the broader context of access control and authorization. These gateways act as guardians, ensuring only authorized entities traverse the healthcare data landscape. The use of secure APIs for controlled interaction aligns seamlessly with the tenets of Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC), underscoring the importance of managing permissions and privileges in the evolving healthcare ecosystem [17].  
Mitigating Security and Privacy Risks in Data Sharing:  
1. Data Integrity and Authenticity: Applying cryptographic hash functions and digital signatures ensures the authentication and guarantees the integrity of healthcare data during transmission, as it prevents unauthorized tampering or modifications by verifying that the received data are derived from a legitimate source [48].
2. Access Revocation: Enabling timely and effective access revocation is a challenge in secure data sharing. If a stakeholder's access privileges change or if a breach is detected, revoking access should be immediate and irreversible. Implementing mechanisms for instant access revocation while preserving data consistency and minimizing disruptions requires careful planning [17].
3. Minimizing Data Exposure: While sharing data is essential for collaborative healthcare efforts, minimizing the exposure of sensitive information is crucial. Techniques such as data anonymization and minimal data disclosure help strike a balance between sharing insights and preserving patient privacy. Ensuring that shared data are stripped of unnecessary details without losing their analytical value poses a technical challenge [17].  
Our investigation of secure data-sharing protocols shows that they fit within the classification framework for privacy preservation solutions. These protocols contribute to a considerable number of the framework's categories, ranging from Data Protection Mechanisms, where encryption techniques play a pivotal role, to Privacy-Preserving Data Sharing, with tokenization as a cornerstone. The integration of these protocols is vital,providing a structured approach to addressing privacy concerns while guiding the design and implementation of secure IoT-based healthcare systems. In conclusion, the technical landscape of secure data-sharing protocols is intricate and essential for privacy preservation in IoT-based healthcare systems. By leveraging secure data-sharing gateways, tokenization, and secure APIs, healthcare stakeholders can effectively collaborate while ensuring patient privacy. Overcoming the data integrity, access revocation, and data exposure challenges requires innovative approaches and a holistic understanding of privacy and data utility considerations.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 2 Improved Double-Ended Traveling Wave Localization Method Based on Line Mode Component
### Improved Double-Ended Traveling Wave Ranging Method Based on the Line Mode Component  
According to Equation (9), the fault location accuracy is mainly determined by the wave head time \(t_{M}\) and the fault occurrence time \(t_{0}\). The random nature of the fault makes it difficult to accurately measure \(t_{0}\), which impacts the localization accuracy accurately. To solve this problem, this paper improves the localization equation by combining the line mode component to improve localization accuracy without measuring the time at the moment of fault occurrence.  
According to the three-phase decoupling above, it is known that the attenuation of the 0-mode component is inversely proportional to the frequency, the linear-mode element is more stable, and the linear-mode component can detect all fault types. Therefore, this paper analyzes the aerial mode component.  
The moments when the \(\alpha\)-mode and \(\beta\)-mode components arrive at the ends of the lines M and N, respectively, are \(t_{M1}\), \(t_{N1}\), \(t_{M2}\), and \(t_{N2}\), which can be obtained by substituting into Equation (9).  
\[\left\{\begin{array}{l}d_{M1}=\frac{t_{M1}-t_{0}}{t_{M1}+t_{N1}-t_{N2}}L_{ MN}\\ d_{M2}=\frac{t_{M2}-t_{0}}{t_{M2}+t_{N2}-2t_{0}}L_{MN}\end{array}\right. \tag{10}\]  
Since \(d_{M}=d_{M1}=d_{M2}\), it follows that  
\[d_{M}=\frac{t_{M1}-t_{M2}}{t_{M1}-t_{M2}+t_{N1}-t_{N2}}L_{MN} \tag{11}\]  
Comparing (10) and (11), the improved double-ended traveling wave ranging method proposed in this paper does not need to collect the time of short-circuit fault occurrence and can quickly locate the short-circuit point by accurately detecting the arrival time of the traveling wave head at both ends of the line.  
Using the CEEMDAN-TEO algorithm to detect faulty traveling warheads, this paper proposes an improved method of correctly identifying faulty traveling wave heads.
## 7 Conclusions  
The simultaneous detection of methane, ethylene, and ethane is of great significance in the power industry, the petroleum industry, agriculture, and other fields. The photoacoustic sensor in this paper is constructed from a Helmholtz resonance cell equipped with a single ICL light source, and can simultaneously detect methane, ethylene, and ethane at normal pressure. With an integration time of 2 s, the detection limits of the sensor for methane, ethylene, and ethane can reach 98.8 ppb, 252 ppb, and 33 ppb, respectively. Due to the large-range cancellation of ethylene and methane 2f demodulation signals, the 1f demodulation with a certain detection window is applied. By fitting the multiwavelength data with the least squares method, the mutual interference in the three-component mixture is effectively reduced, and the concentrations of the three components are successfully and accurately derived. This Helmholtz resonance photoacoustic system has good stability and can effectively adapt to complex application environments. In the future, thanks to the special structure of the Helmholtz resonator, another laser can be directly added to another channel to further increase the number of types of gases that can be simultaneously detected without using auxiliary tools such as a beam combiner.  
Figure 11: (a) Multi-wavelength fitting profiles of the mixture 3 in Table 2; (b) The fitting residual plot.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13053169/s1](https://www.mdpi.com/article/10.3390/app13053169/s1)]([https://www.mdpi.com/article/10.3390/app13053169/s1](https://www.mdpi.com/article/10.3390/app13053169/s1)), Figure SI: PA cell left and right microphone signals and differential signals; Table S1: Average and standard deviation of the left and right microphone signals and differential signals; Figure S2: Broadband 1f spectra of the seven gas mixtures listed in Table 2 and their multi-wavelength fittings.  
**Author Contributions:** Designed/modified the experimental setup and writing--original draft preparation, Z.W.; performed the experiments and analyzed the data, Z.W. and Y.S.; data curation, Z.W., Y.S. and Y.H.; writing--review and editing, Y.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data underlying the results presented in this paper are not publicly available at this time, but may be obtained from the authors upon reasonable request.  
**Acknowledgments:** The authors are grateful to [PERSON], [PERSON] and [PERSON] for valuable discussion and help.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experimental Results
Figure 6: Washing results of MF010-Min using NW as solvent: variation in washing duration and agitation (S is not stirred; D is stirred; DH is stirred and heated at 40 \({}^{\circ}\)C). considering the chemical composition range and measurement accuracy, increasing the citric acid concentration does not necessarily lead to a substantial improvement in reducing the concentration of observed elements. Adopting the result of Ca to explain the washing effectiveness and balancing the resulting cost, citric acid concentrations between 0.5 and 1.25 M should deliver a reasonable result. Considering the results in Figures 5-8, in addition to the citric acid concentration range mentioned earlier, it is also suggested that a six-hour washing process has already been a convincing starting point for further exploration to determine the optimal washing duration. Accordingly, the last set of experiments was conducted to determine the effects of the L/S ratio and other possible influencing parameters, primarily after a six-hour washing duration. The results in Figure 9 indicate that an L/S ratio of ten (standard setup) is the optimal L/S value for the washing process using a citric acid concentration of 0.5 M. This finding is based on the concentration reduction results of LS10 (6H-D), which is nearing the washing process at a higher temperature (6H-DH) and can also affect the Fe content. Two additional exciting results are provided in Figure 9. The first is 6H-S, which represents a trial without stirring and delivers similar results with DW-12H-S and NW-12H-S in Figures 5 and 6, where a reduction in the Ca and Mn contents was detected. These findings  
Figure 8: Washing results of MF010-Min after 6 h in different concentrations of citric acid using an L/S ratio of ten. Figure 7: Washing results of MF010-Min after 3 h in different concentrations of citric acid using an L/S ratio of ten. indicate that certain substances containing Ca or Mn are susceptible to dissolution in a citric acid solution. In contrast, the aluminum content is consistently not (notably) affected by a six-hour washing duration, regardless of any variations addressed in the present study. The stability of the aluminum-containing phases was also substantiated in Figure 10, where an approximately constant or a fluctuation of less than 10% was measured compared with the initial aluminum content. In this instance, it is also indicated that only the L/S ratio of ten could affect the iron content (6H-4M and 6H-6M). Nonetheless, regardless of the result for Fe, this L/S ratio of ten can still be considered generally beneficial compared with the other variations in Figure 10. On the other hand, it is also worth noting that the effect of citric acid concentration is relatively minor, as previously reported in Figures 7 and 8. Specifically, this result demonstrated that increasing the concentration from 4 M to 6 M did not directly lead to a notable performance increase.
# An Energy Centric Cluster-Based Routing Protocol for Wireless Sensor Networks
## 4 The Protocol Details
### Data Transmission Phase
#### 4.3.1 Intra-Cluster Communication  
During intra-cluster communication, member nodes send the local data directly to their respective cluster head. A cluster head aggregates the received data from the associate member nodes. Each member node piggybacks the value of its rank and residual energy along with the local data.
###### Abstract  
Telemedicine has emerged as a vital tool for expanding healthcare access, particularly in underserved areas, yet its effectiveness is often hindered by inefficient queuing systems, fluctuating patient demand, and limited resources. This study addresses these challenges by developing a hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) model aimed at improving the performance of telemedicine queuing systems. A simulation-based dataset was generated to represent patient arrivals, service rates, and queuing behaviors. An ANN was trained to predict key performance metrics, including queue intensity, system utilization, and delays. To further enhance the model's predictive capabilities, PSO was applied to optimize critical ANN parameters, such as neuron count, swarm size, and acceleration factors. The optimized ANN-PSO model achieved high predictive accuracy, with correlation coefficients (R\({}^{2}\)) consistently exceeding 0.90 and low mean squared errors across most outputs. The findings show that optimal parameter configurations vary depending on the specific performance metric, reinforcing the value of adaptive optimization. The results confirm the ANN-PSO model's ability to accurately predict queuing behavior and optimize system performance, providing a practical decision-support tool for telemedicine administrators to dynamically manage patient flow, reduce waiting times, and enhance resource utilization under variable demand conditions.  
Article
## 4 Measurement Results  
Table 3 summaries the measurement results which demonstrate the ET system performance. The ET transmitter obtains the measured efficiency of 60.8%, 53.7%, 44.1% and error vector magnitude (EVM) of 0.32%, 0.60%, 0.67% at an average output power of 34.4 dBm, 34.2 dBm, 34.1 dBm for 6.7 dB PAPR 16 QAM LTE signals with 5 MHz, 10 MHz, and 20 MHz bandwidths, respectively. Figure 10 shows the representative measured waveforms (20 MHz bandwidth (BW) LTE at 2.1 GHz) for RF output, drain voltage, and switching state in the ET experimental system. The drain supply voltage closely tracks the RF PA output envelope, which indicates that the proposed PA is working at high-efficiency ET mode.  
The digital pre-distortion (DPD) technique can compensate the nonlinear distortion well enough to ensure high signal quality of the transmitter. Measured ET transmitter output spectra (20 MHz BW LTE at 2.1 GHz) before and after DPD are presented in Figure 11. It is apparent that the output spectrum shows less spectral re-growth and a 20 dB reduction in adjacent channel power ratio (ACPR) can be achieved after using the DPD technique.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Bandwidth** \\ **(BW) (MHz)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Digital** \\ **Pre-Distortion** \\ **(DPD)** \\ \end{tabular} } & \multirow{2}{*}{**Pout** \\  & & & & & **(dB)** \\ \hline
5 & W/O & 34.4 & 11.0 & 61.3 & \(-\)26.7 & 7.50 \\
5 & With & 34.4 & 11.0 & 60.8 & \(-\)49.7 & 0.32 \\
10 & W/O & 34.6 & 11.1 & 56.7 & \(-\)26.8 & 8.10 \\
10 & With & 34.2 & 10.7 & 53.7 & \(-\)46.3 & 0.60 \\
20 & W/O & 34.3 & 11.3 & 46.4 & \(-\)26.4 & 8.90 \\
20 & With & 34.1 & 11.1 & 44.1 & \(-\)46.0 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of measurement results of the ET system performance.  
Figure 9: Measurement setup of the envelope tracking (ET) experimental system diagram.  
In addition, without DPD, ET PA shows significant AM-AM and AM-PM distortion with memory effects. Figure 12 shows the detailed AM-AM and AM-PM characteristics of the ET PA, before and after DPD. Obviously, the linearity of the ET PA in this work can be improved with the DPD technique.  
Figure 11: Measured ET PA output spectra before and after digital pre-distortion (DPD).  
Figure 10: Representative waveforms of the ET experimental system.  
Figure 12: (**a**) AM-AM characteristics of ET PA before and after DPD; (**b**) AM-PM characteristics of ET PA before and after DPD.  
ET PA can support multi-band operation since the EA circuit is independent of the carrier frequency. To show the broadband performances of ET PA, experiments were also performed at different frequencies. Tested by 20 MHz BW, 6.7 dB PAPR 16 QAM signal, 41.8% to 49.2% overall efficiency of ET PA is achieved at an average output power of 33.5 dBm to 35.1 dBm from 1.9 GHz to 2.2 GHz, as shown in Figure 13. Evidently, the proposed PA is quite suitable for a high-efficiency ET mode, and it has great potential for upgrading the broadband characteristics in a dynamic supply operation.
# Tiny Machine Learning Battery State-of-Charge Estimation Hardware Accelerated
## 7 Experiments and Accuracy Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **Average MSE** & **Standard Deviation MSE** \\ \hline Dense & \(3.88\times 10^{-4}\) & \(9.64\times 10^{-9}\) \\ LSTM Stateless & \(28.0\times 10^{-4}\) & \(2.84\times 10^{-6}\) \\ LSTM Stateful & \(335.9\times 10^{-4}\) & \(11.9\times 10^{-4}\) \\ GRU Stateless & \(35.47\times 10^{-4}\) & \(3.03\times 10^{-6}\) \\ GRU Stateful & \(525.8\times 10^{-4}\) & \(43.0\times 10^{-4}\) \\ Parallel TCNs & \(\textbf{2.09}\times\textbf{10}^{-4}\) & \(\textbf{2.06}\times\textbf{10}^{-9}\) \\ LMU & \(80.9\times 10^{-4}\) & \(2.12\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: K-fold cross validation results. measured quantity was current so the unit of measurement was ampere (A). By dividing two values with the same unit of measurement, a dimensionless result was obtained. The Table 4 shows the Mean Absolute Percentage Error (MAPE) values of the ANN architectures achieved using the test data which confirms the TCN lowest error on both test cycles. Looking at the Table 1, the stateful LSTM model was characterised by the lowest number of parameters yet its test and validation MSE was higher compared to other developed networks. The approach incorporating TCNs was the third largest in terms of the number of parameters, the LMU layer network being the first. Based on Tables 3 and 15, the parallel TCN architecture achieved the best results in both test sets, with the best average MSE and standard deviation MSE achieved during the K-fold CV. The LSTM Stateless architecture exhibited a good trade-off between MSE and the number of parameters.