esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Computation Overhead Analysis
### Proposed SCA Method  
In the case of the SCA method, the signature generation time of \(\sigma_{C}=N|\sigma_{i}|\), where \(\sigma_{C}\) is n concatenated signatures per message that consumes n(TM2P + TMG1) = n (0.479 + 0.135) = 0.614n ms, where a single signature generation time is 0.614 ms. On the other hand, the signature verification consumes two pairing operations and one HashAndMapTo operation = 2 TP+TM2P = 4.9 + 0.135 = 5.035 ms. Verification time incurs only a constant overhead of 5.035 ms, since the individual receiver verifies only its corresponding signature and ignores the other signatures. We also assume that concatenation operations of n signatures at the transmitter side is neglected, as compared to the signature generation time. We assume that the searching time for the correct zone parameters is negligible, as compared to the verification time. Thus, the total computation time for the SCA method is the concatenated signature generation time, and the individual verification time at each receiver is (0.614n) ms + 5.035 ms. The computation time of SCA depends on the number of destination zones \(n\).
# DDPG-Based UAV-RIS Framework for Optimizing Mobility in Future Wireless Communication Networks
## 1 Introduction
### Related Work
\begin{table}
\begin{tabular}{p{12.3 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline
**Ref.** & **System Configuration** & **Optimization Technique** & **Mobility Consideration** & **Remarks** \\ \hline \multirow{3}{*}{[23]} & \multirow{3}{*}{RIS-assisted UAV} & RIS location optimization for gain maximization & \multirow{3}{*}{UAV users} & Does not consider GU mobility; focuses only on RIS deployment in buildings. \\ \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[24]} & \multirow{3}{*}{RIS-assisted UAV} & DRL-based rate enhancement & \multirow{3}{*}{Mobile GUs} & Limited joint design for RIS and UAV trajectories. \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[29]} & \multirow{3}{*}{IRS-based UAV-NOMA} & DRL-based beamforming and UAV optimization & Mobility clustering not considered & Lacks user mobility clustering despite IRS integration. \\ \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[30]} & \multirow{3}{*}{UAV-RIS-SWIPT} & SWIPT-based energy optimization & \multirow{3}{*}{Static users} & Does not considered mobile users, focus on static users with energy-harvesting RIS. \\ \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[31]} & \multirow{3}{*}{RIS-UAV Mobile Vehicle} & Successive convex approximation-based secrecy rate maximization & UAV is assumed to be static & No dynamic UAV mobility; RIS for secrecy enhancement. \\ \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{**This Work**} & UAV-RIS with user clustering & DDPG-based joint UAV trajectory and RIS phase optimization & Dynamic mobility management of mobile GUs & Fully dynamic, adaptive UAV-RIS integration with user clustering. \\ \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the proposed work with existing related studies.
## 4 Experimental Results and Discussion
### Performance of the Machine Learning Model  
The results of SVM is worse than KNN. While the KNN is suitable for low dimensional problems. The advantage of SVM is that it can handle high-dimensional features using linear functions [38] but requires a large dataset. Therefore, additional features were experimented with, such as adding the distance from ball to basket and the movement vector of players whose defensive relationship has not changed. The highest accuracy of the SVM algorithm has increased by 2% after increasing the number of features. However, as the number of additional features increases, the accuracy of SVM and other algorithms is gradually decreasing. Thus, the algorithm is experiencing curse of dimensionality. Furthermore, there may be several potential factors affecting the training model: the features are less effective, and the amount of data is insufficient. Therefore, we can optimise the training model by two methods: optimising the quality of features or expanding the amount of data.  
In the present experiments, the KNN shows the highest accuracy levels. However, it should be noted KNN algorithm works by comparing a new data point against all the past data points, and assigning it for the majority voted class within the k-number of nearest neighbours. Therefore, for the KNN algorithm to be used, all the data points (e.g., 32,377) need to be stored on the computer. This is a significant disadvantage, especially when the dataset is extremely large. Therefore, considering both the computational complexity and the classifier performance, the SVM with the movement vectors and distance features, would be the best method to classify defensive strategies.  
This algorithm can be improved in multiple ways. A player-specific dataset that contains information about players' abilities can be combined with the current trajectory dataset, which will strengthen the system's ability to define each defensive person and determine the defensive relationship between players. For example, the algorithm of this paper determines the defensive zone of players  
Figure 7: The receiver operating characteristic (ROC) curves and confusion matrices for different classification algorithms compared for defensive strategy classification.  
based on the distance relationship. However, in reality, different players have different defensive capabilities. So, a player-specific model that takes into account the defensive ability of players or quantifies the defensive capability, will enable segmentation of the defensive zones according to the individual defensive capabilities of the players.  
Another avenue of work would be to manually identify the one-on-one defensive relationships data, to develop a labelled dataset which can be used to classify the defensive strategy. Furthermore, to optimise the features and using a Recurrent Neural Network to classify temporal activity would be an intuitive way forward.
# Therapeutic Applications of Halloysite
## 3 Halloysite Structure and Applications
### HNTs in Cancer Therapeutics  
HNTs have been studied for multiple applications. Currently, modified surface HNTs are being researched as an efficient delivery system for cancer drugs. For example, Chitosan oligosaccharide modified HNTs (HNTs-g-COS) demonstrated the ability to enhance the therapeutic efficacy of the anticancer drug doxorubicin (DOX) [53]. In vitro, DOX loaded HNTs-g-COS released in cell lysate in a controlled manner and increased the apoptosis effects of MCF-7 cells in flow cytometry results [53]. In vivo, the tumor inhibition ratio of DOX loaded HNTs-g-COS was two times higher than free DOX and no apparent systemic toxicity in DOX loaded HNTs-g-COS groups [53].  
Synthesized chitosan grafted HNTs (HNTs-g-CS) also showed great potential as nanovhelics for anticancer drug delivery in cancer therapy [54]. The research found that HNTs-g-CS had a significantly enhanced curcumin loading capability and good serum stability. However, the curcumin-loaded HNTs-g-CS show specific toxicity to various cancer cell lines, including HepG2, MCF-7, SV-HUC-1, E], Caski, and HeLa demonstrate an inhibition concentration of IC50 at 5.3-192 mM as assessed by cytotoxicity studies [54]. In addition, this nanocomposite has a too high anticancer activity in E] cells compared to the other cancer cell lines [54].  
Folate-conjugated HNTs can be an efficient drug carrier for targeted breast cancer therapy via intravenous injection [55]. HNT conjugated with polyethylene glycol and folate (HNTs-PEG-FA) is designed as a targeted drug delivery system [55]. Doxorubicin (DOX) loaded HNTs-PEG-FA shows significant inhibition of proliferation and induction of death in MCF-7 cells with a positive folate receptor [55]. DOX-loaded HNTs-PEG-FA leads to more mitochondrial damage and apoptosis than the same dose of DOX [55]. In contrast to DOX, DOX-loaded HNTs-PEG-FA effectively reduces heart toxicity and inhibits substantial tumor growth with higher cleaved caspase-3 protein levels in tumor tissue of 4T1-bearing mice [55]. DOX-loaded HNTs-PEG-FA reveals more DOX in tumor tissue than in other normal tissues, including the heart, spleen, lung, and kidney.
## 3 Field Measurement
### Decay Rate  
The standard EN15461: 2008+A1: 2010 [23] presents a general measurement method of the DR. However, this method does not perform the experimental wavenumber decomposition, as a result of which only one DR curve encompassing the attenuation behaviors of all waves is obtained. In order to obtain the separate DR of each wave, the experimental analysis method of wave propagation in the reference [24] was adopted for the DR measurement. After placing an accelerometer on the mid-span railroad, a number of hammer impacts were applied on the railroad in the longitudinal direction at equal intervals of \(\Delta x\) = 0.625/4 m = 0.15625 m. The number of impact points was \(Q\) = 21. The positions of the accelerometer and hammer impacts are shown in Figure 9.  
Figure 8: The field measurement of the rail accelerance.  
The rail accelearance \(h(x)\) at the position \(x\)= (\(q\)-1)\(\Delta x\), where \(q\) = 1, 2,..., \(Q\), was obtained by processing the signals of the accelerometer and the hammer. At each frequency, the rail accelearance \(h(x)\) can be approximated as a sum of \(N\) waves:  
\[h(x)=h((q-1)\Delta x)\approx\sum_{n=1}^{N}A_{n}\mathrm{e}^{s_{n}(q-1)\Delta x} \tag{23}\]  
where \(s_{n}\) = -\(\beta_{n}\)-ik\({}_{n}\) is the complex propagation coefficient, \(\beta_{n}\) is the decay coefficient, \(k_{n}\) is the wavenumber, and \(A_{n}\) is the complex amplitude. The Prony method [25] was used to solve the Equation (23). After obtaining the unknown \(s_{n}\), we calculated the DR of each wave according to:  
\[DR_{n}=-201\mathrm{g}\mathrm{\left|e^{s_{n}}\right|}=8.686\beta_{n} \tag{24}\]  
In order to make a better comparison between the measurement and the simulation, let \(N\) be given by 2. Therefore, the results of the simulation and measurement each include two DR curves corresponding to two different waves. As same with the measurement of the rail accelearance, the result of the DR is comprised of two parts: the result obtained by the heavy hammer for the frequency range of 30-90 Hz and the result obtained by the light hammer for the frequency range of 90-2000 Hz.
# Quantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems
## 4 Numerical Experiments
### Results Analysis  
The performances of each model on the Lorenz system data are summarized in Tables 7 and 8. These tables detail the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for training and testing datasets across three dimensions.  
According to the error metrics, quantum-based RNNs exhibit a marked increase in accuracy, particularly the QGRU model in comparison to the classical GRU. Moreover, the LSTM model demonstrates notably poorer performance compared to the other models, potentially caused by its slower rate of convergence across epochs (again, only 20 epochs were used in this case).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Quantum** & **Classic** \\ \hline Optimizer & RMSprop & Adam \\ Loss Function & MSE & MSE \\ Backend & default.qubit & - \\ Number of Qubits & 4 & - \\ VQC Layer & 4 & - \\ Percentage of Train Set & 67\% & 67\% \\ Percentage of Test Set & 33\% & 33\% \\ Epochs & 20 & 20 \\ Learning Rate & 0.01 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Hyperparameter configuration for classic models and quantum models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **X** & **Y** & **Z** \\ \hline LSTM & 2.0361 & 2.1232 & 2.2119 \\ QLSTM & 0.8820 & 0.9473 & 1.0206 \\ GRU & 1.1684 & 1.1719 & 1.1710 \\ QGRU & 0.4864 & 0.4723 & 0.4555 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mean Absolute Error (MAE) for each model on test set.  
Firstly, regarding to the three-dimensional predictions, it is evident that all models correctly capture the dataset's trend. Nonetheless, a closer look at the figure scales reveals difficulties in predicting the spike at the figure's center for all the models (for LSTM in \(y\) and \(z\) dimensions, QLSTM in \(z\), GRU in \(y\) and \(z\), and QGRU in \(z\)). We believe this challenge could be solved by increasing the number of epochs, though the performance is deemed satisfactory for the current study.  
Furthermore, the QGRU outperforms other models in trajectory plotting, showing a consistent decline in both training and test losses across epochs. In contrast, the classical RNNs display slight fluctuations in their loss plots. Although a spike is observed in the  
Figure 19: QGRU predictions for dimensions X (**up**), Y (**middle**), and Z (**bottom**).  
Figure 18: GRU predictions for dimensions X (**up**), Y (**middle**), and Z (**bottom**).
## 3 Fundamentals of RF Energy Harvesting  
RF energy harvesting is a transformative technology that enables the collection and conversion of ambient electromagnetic waves into usable electrical energy, allowing wireless electronic devices to operate without relying on traditional battery power sources. This approach is particularly beneficial for powering low-energy applications such as wireless sensor networks, IoT devices, and biomedical implants, thereby reducing dependence on conventional energy storage solutions. By capturing and converting RF signals from sources like mobile networks, Wi-Fi, television broadcasts, and radio stations, RFEH presents a promising alternative for sustainable and autonomous power solutions. A comprehensive understanding of the fundamental principles, core components, available energy sources, and key performance metrics of RFEH systems is essential for optimizing their design and implementation.
###### Abstract
We can combine diverse factors to design rewards, in order to achieve optimization of these indicators. Therefore, learning-based routing algorithms are more suitable for CBTC networks. Existing DRL-based algorithms are often used in relay selection for VANET and MANET [15; 16]; these algorithms consider more focus on single-hop delay, outage probability, and power consumption as the criteria for next-hop selection. However, they ignore the whole-link delay and throughput. In this paper, our objective is to design a low-latency and high-throughput routing method in an ad hoc network for a CBTC system. However, packet transmission still faces challenges in multi-hop relay selection. For example, due to the high-speed train, the transmission distance between the train and wayside node is limited. In order to decrease the outage probability, we set strict distance limitations when transmitting with trains. In addition, we comprehensively consider the transmission delay, queuing delay, and channel quality, aiming to optimize the overall performance of the link by selecting the next-hop node. Moreover, the process of selecting the next-hop node can be formulated as a Markov decision process (MDP) [17; 18]. We propose a multi-agent DRL method to solve the problem. The main contributions of this paper are summarized as follows:  
1. We formulate the next-hop relay selection problem in a CBTC system. The goal is to select relay nodes with low transmission delay and high throughput in both the train and the wayside node communication range. Meanwhile, in order to balance the single-hop transmission delay and the whole-link hop count, we propose the concept of \"hop tradeoff\" to minimize the entire link latency. To handle the time-varying channel state and node congestion, we propose a DRL algorithm to optimize the long-term system reward. Using a multiagent approach [14], all nodes are trained centrally with dueling DQN [19], and then each node makes the next-hop decision individually, in order to avoid nodes with a long queuing delay and poor channel quality. 3. Lastly, we conduct simulations with a different number of nodes between two trains and different buffer sizes. Meanwhile, the proposed algorithm is compared with several existing algorithms in terms of whole-link delay, packet loss rate, and throughput. The simulation results indicate that the proposed scheme works well against congested networks. In particular, it also significantly superior to other routing algorithms in the aspects of whole-link delay, throughput, and packet loss rate. The remainder of this paper is organized as follows: in Section 2, some related work about routing selection in ad hoc networks is introduced; in Section 3, we present a multi-hop relay selection model for ad hoc networks in CBTC systems; the joint optimization problem of channel throughput and total-link delay is formulated in Section 4; then, we introduce the multiagent deep reinforcement method to solve the formulated problem in Section 5; some simulation results and analyses are presented in Section 6; in Section 7, we conclude the paper and propose some future work.
## 2 Numerical Setup
The mesh generation was performed by an OpenFoam block-mesh utility that reads a dictionary where points, faces, cells, and boundary are defined. A similar meshing strategy as that in [PERSON] et al. [17] was followed in order to achieve a high quality grid. The computation domain was normalized with respect to the conventional VG height H, which it is equal to the local boundary layer thickness at the leading edge of the VG. Thus, its width is 64 times, its height 10 times, and its length 75 times the local BL thickness (Figure 1). In the present study, four incident angles were analyzed, \(\beta=10^{\circ}\), \(15^{\circ}\), \(18^{\circ}\), and \(20^{\circ}\) Six VG heights were considered, 0.2H, 0.4H, 0.6H, 0.8H, H and 1.2H; for every case, the VG length is 2H. The free stream velocity of the fluid is \(U_{\infty}=20\) m \(\cdot\) s\({}^{-1}\), the kinematic viscosity has a value of \(\
u\ =\ 1.85185\times 10^{-04}\) m\({}^{2}\)s\({}^{-1}\), and the Reynolds number \(Re=27\),000. The Reynolds number definition can be seen in Equation (1),  
Figure 1: Computational domain. Figure 2: Refined mesh sections near the vortex generator (VG). where \(U_{\infty}\) represents the free stream velocity, \(\delta\) the boundary layer thickness where the VG leading edge is located, and \(\
u\) the kinematic viscosity. \[Re\ =\ \frac{U_{\infty}\delta}{\
u} \tag{1}\]  
The domain was automatically decomposed in eight subdomains as shown in Figure 3 in order to solve in parallel with all the workstation cores available, making the solving process faster and more efficient. Simulations were performed on a personal server-clustered parallel workstation with Intel(r) Xeon(r) E5-2630 v3 CPU 2.40 GHzx16 cores and 32 GB RAM on 64 bits Linux. The simulations were completed when the residuals of the velocity, pressure, and turbulence fields reached previously defined threshold values. A qualitative convergence was considered for residuals decay to \(10^{-5}\) during the simulation. Taking into account the six VG heights and the four incident angles considered in this parametric study, twenty-four simulations were carried out. The average computational cost of each simulation took approximately 194 h.  
All variables needed to estimate the vortex features were extracted from eleven spanwise planes placed downstream of the VG (Figure 1) in each simulation. The planes were normal to the main flow direction and located from 1 to 25 times the local boundary layer thickness \(\delta\) downstream of the VG trailing edge (TE).
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Size (mm\({}^{2}\))** & **Operating Bands (GHz)** & **Technique** & **Max. BW (\%)** & **Max. Gain(dBi)** \\ \hline \multirow{3}{*}{[30]} & \multirow{3}{*}{\(45.0\times 45.5\)} & (0.898–0.929), & \multirow{3}{*}{CPW-Fed} & \multirow{3}{*}{02.47} & \multirow{3}{*}{2.72} \\  & & (1.540–1.580), & & & \\  & & (1.956–2.005) & & & \\ \hline \multirow{3}{*}{[31]} & \multirow{3}{*}{\(30.0\times 30.0\)} & (0.918–0.927), & \multirow{3}{*}{U, L, F-shaped} & \multirow{3}{*}{09.50} & \multirow{3}{*}{5.37} \\  & & (2.440–2.460), & (3.520–3.620), & & \\  & & (5.700–6.270) & & & \\ \hline \multirow{3}{*}{[32]} & \multirow{3}{*}{\(18.0\times 35.0\)} & (1.660–2.710), & \multirow{3}{*}{CPW-Fed} & \multirow{3}{*}{–} & \multirow{3}{*}{5.27} \\  & & (2.950–4.540), & (5.020–6.100) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of a designed antenna with others in the state of the art.
# Improving Eight-State Continuous Variable Quantum Key Distribution by Applying Photon Subtraction
## 2 Eight-State CVQKD with Photon Subtraction Operation  
This section is divided into two parts. The original eight-state CVQKD protocol is first explained. Then, we introduce a modified eight-state protocol, based on the entanglement-based (EB) scheme.
###### Abstract
Therefore, in this work, we analyze the technologies and the methodologies proposed in the last years in the context of cultural heritage concerning four main aspects that we consider as the pillars of our vision: (a) _User Localization_: the user must be localized in the environment, the knowledge of his position and of the objects surrounding him enables the possibility of providing a contextualized and personalized information; (b) _Multimodal Interaction_: the user should have the possibility of interacting in a variety of ways, such as through spoken language, gestures and glances; (c) _User Understanding_: understanding how people make use, interact, and catch the essential elements of a cultural heritage site allows improving their knowledge and comprehension; and (d) _Gamification_: a closer link between the visitor and the environment can be created by playing serious games supporting cultural heritage learning. It should be underlined that all the four pillars aim to enforce the overall interaction and interest between users and cultural heritage. In the following sections, after positioning our paper compared to the state-of-the-art in Section 2, we provide an overview of the main systems dealing with the aspects mentioned above by explaining why they are particularly relevant for our vision. In particular, in Section 3, we tackle user localization both in indoor and outdoor spaces by summarizing how it is possible to sense the user's presence in the environment and obtain its position. In Section 4, we outline the existing technologies to perceive and interact with the museum visitors through different verbal and not verbal modalities. In Section 5, we deepen the methodologies used to make deductions about the user's profile, needs and emotions. In Section 6, we give an overview of the gamification approaches currently used in cultural heritage to increase visitors' attention and engagement, which could inspire the formalization of strategies for our museum intelligent agent. Finally, in Section 7, we provide some concluding remarks about the survey and a description of our vision, inspired by technologies and solutions identified in the literature. References are then categorized by an overview table based on their relevance with one or more of the four pillars we focused in the paper.