esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Party Controlled Semi-Quantum Dialogue Protocol Based on Hyperentangled Bell States
## 1 Introduction
first proposed the Semi-Quantum Key Distribution (SQKD) protocol in 2009 [20], successfully achieving quantum-classical heterogeneous communication. In 2017, [PERSON] et al. [21] introduced an SQD protocol based on entangled Bell states for the first time. The classical party, Bob, had to perform permutations on photon sequences and publish permutation operators, which not only increased investment costs for the classical side but also reduced communication efficiency. Therefore, reducing resource consumption and improving communication efficiency became primary concerns for researchers. In terms of conserving quantum resources, [PERSON] et al. [22] proposed a mediated semi-quantum secure direct communication protocol in 2021, enabling two classical users to transmit secret information with the assistance of a fully quantum third party. This significantly reduced the reliance on quantum devices. To enhance efficiency, [PERSON] [23] constructed an efficient SQD protocol in 2023 based on polarization-spatial mode hyperentangled Bell states, achieving a transmission efficiency of 50%. In 2025, [PERSON] et al. [24] proposed an SQD protocol based on four-particle Omega states, which achieves significantly enhanced transmission efficiency while ensuring security, opening new dimensions for SQD research. That same year, [PERSON] et al. [25] designed a d-dimensional single-particle-state-based SQD protocol through groundbreaking simplification of semi-quantum operations. This approach requires only particle permutation operations, eliminating the need for quantum state preparation or measurement steps essential in conventional protocols. Based on the analysis provided, this paper proposes a novel protocol designated as multi-party controlled semi-quantum dialogue (MCSQD) based on hyperentangled Bell states. In this protocol, multi-party controllers are capable of simultaneously controlling the participating parties, preventing communication without their permission. The classical party employs Huffman compression technology for encoding before sending information to the quantum party. Similarly, [PERSON] uses compression techniques to encode source information into binary strings. Leveraging unitary operations, [PERSON] can encode string onto the code particles. [PERSON] decodes to obtain the secret message based on the measurement results announced by [PERSON]. By introducing Huffman compression coding technology, the storage space complexity for the participants can be significantly optimized. Furthermore, the protocol exhibits exceptional robustness against prevalent external and internal attack vectors, providing strong support for complex-scenario CSQD. The structure of the paper is organized as follows: Section 2 introduces the relevant theoretical knowledge and details the design of the MCSQD protocol; Section 3 analyzes the security of the MCSQD protocol; Section 4 calculates and compares the communication efficiency of the protocol; and Section 5 concludes the findings and contributions of the work.
# JSSTR: A Joint Server Selection and Traffic Routing Algorithm for the Software-Defined Data Center
## 1 Introduction
However, when selecting s4 as the destination server, the optimal routing path is u5-sw2-sw3-s4, which clearly occupies less network resource than the routing path u5-sw2-sw3-sw4-s2. In a word, the sequential destination server and routing path calculation method may affect the efficiency of the network. That is caused by the fact that the destination server calculation is carried out without considering its effect on the routing path. Thus, the destination server calculation often makes the routing path calculation unable to achieve a global optimal result. To address these shortcomings, we first propose the joint server selection and traffic routing problem in SDDC. Then, a Joint Server Selection and Traffic Routing algorithm (JSSTR) is designed to  
Figure 1: Example of the shortcoming caused by sequential server selection and routing path calculation. solve that problem based on the Shuffled Frog-Leaping Algorithm (SFLA) [12]. The main contributions of the paper are summarized below. * The joint server selection and traffic routing problem is proposed and formulated. * A joint server selection and traffic routing algorithm is presented to solve that problem, which is designed based on the SFLA algorithm. * Several metrics are proposed and used to evaluate the performance on the routing path length, network load, network load balancing, server load, server load balancing and computational complexity of the proposed and compared algorithms. * The proposed JSSTR algorithm and two compared algorithms are implemented and evaluated in SDN. The effectiveness of the proposed algorithm was proven using the above performance metrics. This paper is organized as follows: Section 2 gives the description of the background. Section 3 lists and discusses the related work. Section 4 describes the system model and problem. The proposed algorithm and its implementation are illustrated in Section 5. The evaluation results are presented in Section 6 followed by the conclusions in Section 7.
## 1 Introduction
One of its primary advantages is its ability to achieve zero-voltage switching (ZVS) and zero-current switching (ZCS) during the switching transitions, reducing switching losses and increasing overall efficiency. This advantage makes it well-suited for high-power applications, where minimizing power losses is crucial. The full-bridge LLC converter also provides isolation between the input and output, ensuring safety and reducing noise interference in sensitive applications. Additionally, its full-bridge configuration allows bidirectional power flow, making it suitable for applications where power can flow in both directions, such as in renewable energy systems. Its complex topology and control requirements can make the design and implementation more challenging compared to simpler converters, potentially increasing development costs and time. In [5], a newly developed power electronic converter that combines elements of both the flyback and Cuk converter topologies is introduced. The converter is designed to provide multiple output voltages with synchronization between the outputs. Combining flyback and Cuk topologies in a hybrid configuration, allows for the generation of multiple output voltages while maintaining synchronization between them. The design and control of the synchronized multiple output DC-DC converter improve the performance of the converter, including efficiency, voltage regulation, and transient response. This converter can be applied in various fields, including power supplies, telecommunications, electric vehicles, and renewable energy systems. It offers a compact and efficient solution for generating multiple synchronized output voltages from a single input source. The flyback-SEPIC Converter also offers galvanic isolation between the input and output, ensuring safety and reducing ground loop issues in sensitive systems. Moreover, it can achieve continuous input and output current, enabling it to handle fluctuating load demands efficiently [6]. Different hybrid DC-DC converter topologies based on different references are presented in Table 1 with their advantages. A type of DC-DC power converter called a Zeta converter, see Figure 1, can change voltage in both step-up and step-down directions. They were created by combining buck and boost converters, and they have characteristics in common with the cuk converter [31]. Because of their versatility and high efficiency, Zeta converters are utilized in a wide range of applications, including telecommunications, solar energy harvesting, and battery-powered systems. One of the key benefits of the Zeta converter is its ability to maintain consistent input and output currents. These properties have the advantages of lower
## 4 Discussion
### Limitations  
While the results we present show that DSPEB is capable of reconstructing bathymetry, there are limitations to the current method. Specifically, we highlight the limitations of data pre-selection based on dates and the requirement to train on application sites.  
The accuracy of W-DSPEB was found to be sensitive to the dates selected, as was S2 Shores, indicating that the necessary wave kinematics information was lacking for certain dates. Currently, S2 Shores is used as a date-selection method to dictate which images can be used for training W-DSPEB at a certain site, requiring large amounts of computing time before dataset construction. A possible solution to this limitation could be the use of a CNN as a binary classifier to dictate whether a Sentinel-2 image contains the necessary information for W-DSPEB. Such a model would greatly minimize the amount of computing power required for date selection, in addition to providing insight into this issue. Breaking this limitation is important in order to achieve the operational requirements of the International Hydrographic Organization (IHO).  
Another limitation of DSPEB is that it currently requires local training before application, limiting the model to sites with existing survey data. A future direction of this work is to use trained models from individual sites and fine-tune them to unseen sites. Applying a network to a site for which no survey data is available is also a goal but requires further study in developing models capable of zero-shot learning [51].
## 4 Performance Evaluation  
### Tools Used  
The set of tools used for traffic simulation is composed of two main components: Simulation of Urban MObility (SUMO) and TraCI4J. SUMO [21] is an open source program for urban traffic simulation. It is a microscopic simulator; that is, each vehicle is modeled explicitly, has its own route and moves independently through the network. It is mainly developed by the Institute of Transportation Systems. The version used in this work was version 0.25.0, published in December 2015. TraCI4J [22] is an Application Program Interface (API) developed for SUMO that allows communication in time of execution between the proposed system and the simulator. TraCI4J was developed by members of ApPeAL (Applied Pervasive Architectures Lab) of Politecnico di Torino. This API offers a higher level of abstraction and better interoperability than other APIs developed for the same task.  
Figure 4: Flowchart illustrating the selection of congested streets and sending notices from the Zone Agent.  
Keras was used for the implementation and training of deep architecture. Keras [23] is a high-level neural network API, developed in Python and capable of running over TensorFlow, CNTK, or Theano. In our case, and intending to take advantage of a server with two NVidia GTX 1080 cards, we chose to use TensorFlow [24] as a backend.
# Continuous Latent Spaces Sampling for Graph Autoencoder
## 5 Experiments
### Training and Evaluating Setups  
The objective of the node classification task is to predict labels for unknown nodes in a given network. In this study, we evaluated the performance of colaGAE on eight standard benchmarks that cover both transductive and inductive scenarios. These benchmarks include Cora, Citeseer, PubMed, and Ogbn-arxiv, which are citation networks in which nodes represent documents and edges represent citations.  
The network architecture of colaGAE comprises a series-encoder with the same structure as illustrated in Figure 1. The first layer of the series-encoder is GCN, followed by [1; 2; 3; 4; 5] layers of linear mappings, each equipped with batch normalization [41] and a sigmoid activation function. We used different numbers of hidden units for each dataset. The dropout rate between each layer was carefully tuned within the range of [0.01, 0.2, 0.3, 0.4].  
For each dataset, we used Adam as the optimizer for 500 fixed training iterations. In addition, we performed a hyperparameter search for the learning rate within the range  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Nodes** & **Edges** & **Classes** & **Features** & **Density** \\ \hline Cora & 2708 & 10,556 & 7 & 1433 & 0.288\% \\ Citeseer & 3327 & 9228 & 6 & 3703 & 0.167\% \\ Pubmed & 19,717 & 88,651 & 3 & 500 & 0.046\% \\ CS & 18,333 & 327,576 & 15 & 6805 & 0.195\% \\ Computer & 13,752 & 574,418 & 10 & 767 & 0.608\% \\ Photo & 7650 & 287,326 & 8 & 745 & 0.982\% \\ Arxiv & 169,343 & 1,166,243 & 40 & 128 & 0.008\% \\ MAG & 1,939,743 & 21,111,007 & 349 & 128 & 0.001\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of benchmark datasets.  
of [0.001, 0.01, 0.05, 0.1] and a weight decay within the range of [\(5\times 10^{-4}\), \(5\times 10^{-3}\)]. We employed an early stopping strategy with a patience of 50, i.e., we stopped training when the validation metric did not improve for 50 epochs.  
To ensure consistency with previous research in the field [9,15,18,22], we followed standard evaluation protocols in our experiments. We used publicly available data splits for the Cora, Citeseer, PubMed, Arxiv, and MAG datasets. For the remaining three datasets, we used a 1:1:8 training, validation, and testing split. We trained a linear classifier based on the best-performing model as determined by the validation results and kept the classifier parameters fixed while generating embeddings for all nodes. Finally, we report the average test node accuracy over 20 random initializations.
## 3 Results  
Figure 6 provides the history of back pressure values at the compressor outlet (\(p_{out}\)) during the iteration process through the dichotomy method. The outlet pressure was made dimensionless by using a reference pressure, where the reference pressure (\(p_{n\
eq}\)) is 138,500 Pa. Thus, in the subsequent comparisons, when the result (\(p_{out}/p_{n\
eq}\)) approached one, the fewer the iterative steps required, the higher the efficiency. As can be seen from the figure, it is evident that after six cycles of stall margin searching and the subsequent eight iterations for the stall point searching, the boundary point was ultimately determined, resulting in a pressure value of 138,476 Pa at that moment.  
The optimization algorithm process outlined in Section 2.2.2 presents multiple options as mentioned in the introduction. It suffices to select one from the iterative methods available. This study has opted for the Hooke-Jeeves method [33]. Named after its creators, [PERSON] and [PERSON], this algorithm is a pattern search technique that does not require gradient information, which makes it suitable for problems where derivatives are not readily available or are difficult to compute. It is apt for both linear and nonlinear design spaces and has been applied to many real-life optimization problems [33, 34, 35]. Specifically, the size of variable perturbations is established by the relative step size, with a value of 0.5. As for the convergence, it is gradually reduced by applying the step size reduction factor until convergence is detected, retaining a default value of 0.5. Concurrently, the termination step size is set at 0.008. Other parameters, including max evaluation, remain at their default settings.  
Figure 7 showcases the iterative solution process of the outlet boundary condition adopting the optimization algorithm, which, in comparison to the dichotomy method, also first undergoes six stall margin searching cycles. However, the optimization algorithm computes additional 17 iterations during the optimization process to fulfil the criteria for optimal solution judgment, culminating in a total of 23 iteration steps in this instance. Moreover, the resultant stall point pressure is 138,437 Pa, which bears significant proximity to the solution ascertained by the former method.  
Comparative analysis indicates that the optimization algorithm autonomously resolves stall point pressure within the designated interval, but its iteration count does not have a  
Figure 6: Iteration history of the outlet boundary condition using the dichotomy method.
## 8 Routing Protocols of Multi-UAV Systems
### Topology-Based Routing Protocols
#### 8.1.4 Hybrid  
This sub-category of protocols [88; 90] is both proactive and reactive and is suitable for large mobile networks. It takes advantage of both to overcome the problems of bandwidth saturation, long delays, and collisions. Its operating principle consists in dividing the network of UAVs into sub-networks, commonly called zones. Intra-zone communications are based on the proactive protocols, while inter-zone communications are handled by the reactive protocols. The most popular hybrid protocols are the Zone Routing Protocol (ZRP) [90] and Temporarily Ordered Routing Algorithm (TORA) [88].  
ZRP [90] divides the network into several zones of relatively equal radius and uses a dynamic routing table for the exchanges within the zones. Messages exchanged between UAVs from different zones are based on on-demand routing protocols. UAVs should always stay within the radius of their zones in order to maintain network stability. To address this limitation, researchers [88] have proposed TORA. TORA is a distributed version of a hybrid protocol that, instead of dividing the network, distributes the routing table in such a way that each UAV contains only information about its neighbors, rather than the entire network.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 4 Experimental Evaluation and Results Analysis
### Computation Time  
This section performs a local computation performance comparison and analysis in each node using three security approaches--No security, DLAPP [13] and MFSPV [14]. This allows to draw insights into how each security approach performs with real hardware in different execution environments and a slightly different context of mobility (with smartphones). To achieve this, the evaluation procedure consists of two modes.  
**Total Computation Time**: Measures all the local computation times (CTs) from the beginning of a transmission or reception processing until completion. This evaluation may be used with any security approach.
**Security Computation Time**: Measures the CTs for security protocol protection and deprotection. This mode must be used with a security protocol (DLAPP or MFSPV). Protection involves applying a security protocol to a message and encapsulating it with the protocol bytes. Deprotection entails verifying a message according to the configured protocol. If the message is valid, the security bytes are then removed.  
In summary, the evaluation objective is to measure the total and security CTs in each computing node without considering the network latency, only the local computing.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Equipment** & **Specifications** \\ \hline \multirow{3}{*}{Laptop} & Windows 10 \\  & Processor Intel core i7-4710 HQ CPU @ 2.50 GHz \\  & 16 GB RAM \\ \hline \multirow{3}{*}{Unex OBU} & Linux Yocto \\  & Dual 600 MHz ARM Cortex-A7 32-bit CPU cores \\  & 128 MB RAM \\ \hline \multirow{3}{*}{Siemens RSU} & Linux \\  & Dual-Core ARM-Cortex A9 @800 MHz \\  & 1 GB RAM \\ \hline \multirow{3}{*}{Smartphone X} & Android 13 \\  & CPU Octa-core Max 2.96 GHz \\  & 8 GB RAM \\ \hline \multirow{3}{*}{Smartphone Y} & Android 8.0 \\  & Qualcomm Snapdragon 425 \\ \cline{1-1}  & 2 GB RAM \\ \hline \hline \end{tabular}
\end{table}
Table 6: Characteristics of the computational environment where the prototype was tested.  
The extraction of the necessary timestamps in each mode is illustrated in Figure 12. These are used in the computation time calculation, as given by Equations (6) and (7).  
\[t=TF\ -\ TI \tag{6}\]  
\[s=SF\ -\ SI \tag{7}\]  
where:  
\(t\)--total CT measurement;  
\(T\)--total CT initial timestamp;  
\(TF\)--total CT final timestamp;  
\(s\)--security CT measurement;  
\(SI\)--security CT initial timestamp;  
\(SF\)--security CT final timestamp.  
Note that the processing results obtained by the RSU will be given less emphasis as it does not fully represent the actual RSU execution environment. Therefore, regarding the computation performance evaluation, the OBU and smartphone results are more relevant. From the set of all combinations--computing node, evaluation mode, and security approach--about 2000 samples were extracted to make the obtained values more accurate.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 6 Reception Results
### Reception of Terrestrial and LEO Satellite Antenna System
#### 6.2.4 ADS-B and Air Band Voice Communication with Discone Antenna  
A discone antenna is an omnidirectional antenna with low gain but has the advantage of being a wide-band antenna capable of receiving frequencies ranging from 25 to 3000 MHz. This makes it suitable for receiving a variety of signals, particularly those from terrestrial sources and aircraft. It can receive signals from all directions without needing to know or point the antenna towards the precise direction of the signal source. In this research, the discone antenna was installed at a height of approximately 10 m above the ground.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Satellite & Immarsat-F2 \\ Mode & STD-C/ Immarsat Aero \\ SNR & \(\sim 10\) dB SNR \\ Az/Ele of antenna & \(\sim\)100, 39 \\  & (satellite has inclination) \\ Receiving antenna & Grid 60 cm \(\times\) 90 cm \\ Decoding software & SattDump \\ Spectrum & - \\ Duration of receiving & - \\ Image size & - \\ Spatial resolution & 150/ UTC \\ Receiving time & 143.5 E \\ Satellite location & 1541.5 MHz/ \\ Frequency & 1545.211 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 10: Parameters for the reception of Immarsat4-F2.  
Figure 22: Inmarsat Aero from Inmarsat4–F2. The waterfall graph represents signal intensity over time. Brighter colors represent stronger signals, while darker colors indicate weaker or no signals.  
An example of ADS-B signal reception at a frequency of 1090 MHz and the process with SDRangle software is shown in Figure 23, as well as air band signals in the frequency range of 118-136.975 MHz. It was found that the antenna could receive ADS-B signals from aircraft flying at an altitude of 40,000 feet (approximately 12.2 km) and at a distance of 300 km. Additionally, voice communications from ATC (air traffic control) could be heard from the same position. This demonstrates the effectiveness of the discone antenna for studying aircraft communication.