esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 2 Overview of V2X Protocols
### IEEE 802.11 bd  
The IEEE 802.11 Next-Generation V2X Study Group was formed in March 2018 to define a protocol to support next-generation wireless access applications for autonomous driving and to expand the transmission services used in the IEEE 802.11 WLAN. In January 2019, the IEEE 802.11 bd Task Group defined the main objectives of this new standard in terms of interoperability and access to communication channels for devices still using the 802.11p protocol.  
The 802.11 bd protocol is based on the physical layer of the 802.11 ac PHY protocol. However, the performance of the 802.11 ac PHY, in decoding the frame by the receiver, is currently poor due to channel variations within the duration of the frame. The 802.11 bd PHY proposal aims to solve this problem with the use of midambles. Midambles are reference signals that help vehicles synchronize and detect transmissions from other vehicles, ensuring more robust and accurate V2V communication.  
To increase the efficiency of OFDM in 6G transmissions, TGbd (Task Group 802.11 bd) members are also studying the use of OFDM numerologies to vary the spacing between sub-carriers in such a way that the number of sub-carriers increases even when they occupy a 10 MHz channel [22; 23].  
Table 1 shows a summary of the main differences between 802.11p and 802.11 bd.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **802.11p** & **802.11 bd** \\ \hline Frequency band & 5.9 GHz & 5.9 GHz, 60 GHz \\ \hline Subcarrier spacing & 156.25 kHz & 312.5 kHz, 156.25 kHz, 78.125 kHz \\ \hline Channel coding & BCC & LDPC \\ \hline Cyclic prefix time & 1.6 \(\upmu\)s & 1.6 \(\upmu\)s and 3.2 \(\upmu\)s \\ \hline PHY layer & N/A & OFDM \\ \hline Relative vehicle speed & 252 km/h & 500 km/h \\ \hline Spatial streams & one & multiple \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between 802.11p with 802.11 bd.
## 2 Materials and Methods
### In Vitro Study Design  
We tested EA and Cur antigenotoxicity on zebrafish blood cells _in vitro_. Maintenance and experimental procedures were in accordance with the recommendations reported in the Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes [26]. Zebrafish (_n_ = 60) obtained from a local source (CARMAR sas, San Giorgio a Cremano, Italy) were bred in a large tank containing \(\sim\)80 L of water (temperature \(\geq\) 25 \({}^{\circ}\)C, pH = 7.6), and respecting the photoperiod of 10 h (h) of darkness and 14 h of light, fed every two days (Tetramin Tropical Flakes, Spectrum Brands, Blacksburg, VA, USA). Adult fish were anesthetized with ethyl 3-aminobenzoate methanesulfonate (E10521, Sigma-Aldrich) and the blood collected by using a heparinized syringe. Zebrafish blood cells obtained from 60 zebrafish were pooled and washed in phosphate buffered saline (PBS) 1X. Samples were split into six different aliquots (10 \(\times\) 10\({}^{6}\) cells/mL) according to the study design: 100 \(\upmu\)m EA (EA group); 40 \(\upmu\)M Cur (Cur group); 100 \(\upmu\)M EA plus 10 \(\upmu\)L/mL benzene (EA+ BZ group); 40 \(\upmu\)M Cur plus 10 \(\upmu\)L/mL benzene (Cur + BZ group); positive control (benzene 10 \(\upmu\)L/mL) and negative control groups. In the negative control group, ethanol was added at the same volume used to dissolve Cur and EA. Experiments were performed at 30, 60, and 90 exposure minutes (min).
# Influence of Load Dynamics on Converter-Dominated Isolated Power Systems
## 2 Isolated Power System Modeling
### Grid-Forming Operation of Power Converters
#### 2.1.4 Transient Virtual Impedance  
Converter interfaces typically do not have a large overcurrent capability, contrarily to conventional SM, which are able to provide large short-circuit currents [27; 28]. With respect to converter interfaces operating in the grid-forming mode, current control during voltage sags is a challenging task as the current is only limited by the impedance seen at the converter terminals. In order to properly control the grid-forming converter output current during grid faults, an adequate current-limiting strategy is required. For this purpose, transient virtual impedance (TVI) was adopted, consisting in a virtual impedance at the output of the converter that is modulated as a function of the output current, aiming to achieve appropriated current limitation during grid faults [28; 29]. Therefore, in case of an overcurrent, a virtual voltage drop effect will be included in the calculated voltage reference, which will represent the voltage across the virtual impedance, as follows:  
\[v^{*}\ =\ v-\Delta v_{TVI} \tag{3}\]  
where \(v\) is the converter voltage reference, \(v^{*}\) is the compensated voltage amplitude to be synthetized by the converter interface and \(\Delta v_{TVI}\) represents the voltage drop across the  
Figure 3: Reactive power–voltage control loop.  
Figure 2: Virtual synchronous machine control loop.  
virtual impedance, which is zero when the current magnitude is lower than a predefined threshold value, being given by:  
\[\Delta v_{TVI}\ =\ i\times(R_{VI}+jX_{VI}) \tag{4}\]  
where \(i\) corresponds to the converter output current, and \(R_{VI}\) and \(X_{VI}\) correspond to the virtual resistance and reactance, respectively. In order to achieve a smooth transition between states, i.e., between the normal and the current-limiting control mode, as soon as the current amplitude exceeds the predefined threshold value (\(i_{th}\)), which is considered to be equal to the nominal current of the converter interface, the virtual impedance will present a linear increase depending on the current magnitude value. This is presented in Figure 4, where the impedance reaches the maximum value (\(Z_{max}\)) at the maximum admissible current magnitude (\(l_{max}\)).
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 5 Web3602--Interactive Application Applying SDM Ontology
### Implementation
#### 5.3.3 Audio Processing Using Web Audio API  
Web360\({}^{2}\) performs audio processing, as shown in the left dashed box of Figure 11 and renders the audio from each audio object in the ON state in real time according to the viewing position and angle. In addition, Web360\({}^{2}\) extracts each audio source data from the AudioSprite ([[https://github.com/tonistiigi/audiosprite](https://github.com/tonistiigi/audiosprite)]([https://github.com/tonistiigi/audiosprite](https://github.com/tonistiigi/audiosprite)) (accessed on 1 June 2023)) file. It then inputs it into the Buffer Source node of the Web Audio API. The next Gain node expresses the ON/OFF state by setting the audio gain in the OFF state to 0. A-Frame can automatically render appropriate images by specifying each display object's viewing  
Figure 17: Overview of obtaining viewing information. When the query shown in Figure 16 is issued, a search is performed based on VideoPlayer1, AudioPlayer1 and the properties (blue text) that match the search criteria are returned.  
position, viewing angle, and position. However, the position of the audio object played by the Web Audio API does not automatically follow the viewing position and angle changes. Web3602 computes the relative coordinates of audio objects sequentially based on the viewing position and viewing angle and passes them to the Panner node to track the position of the audio object. The frequency spectrum of each audio source data is calculated using the Analyzer node and used for the A-Frame audio visualization process. Web3602 mixes these audio data via Gain and Dynamics Compressor nodes, which serve as master gains, and outputs them as binaural audio. The user can experience spatial acoustic reproduction synchronized with the application video by listening to this binaural audio through headphones or earphones.
## 2 Related Concepts
### ACDE Algorithm Superiority Verification  
In order to verify the excellent global optimization performance of the proposed ACDE algorithm, which is compared with the current state-of-the-art differential evolution algorithm, the LSHADE algorithm [41]. The ACDE algorithm and LSHADE algorithm are evaluated using a set of problems presented in the CEC2017 competition on single objective bound constrained real-parameter optimization. This benchmark contains 30 test functions with multiple characteristics. \(T\) is the dimensionality of the problem and the functions are tested on 50\(T\). In short, functions 1-3 are unimodal, functions 4-10 are multimodal, functions 11-20 are hybrid functions and 21-30 are composition functions. More details can be found in Reference [42].  
The parameters values of ACDE algorithm are set as follows: population size \(D\) of the ACDE algorithm set to 50, maximum iteration number \(M\) set to 10,000*\(T\), the upper and lower limits of the scaling factor are 1.2 and 0.3 and crossover probability \(cr\) set to 0.9. While the parameters of the LAHADE algorithm are set as shown in Reference [41].  
The two algorithms perform 51 simulation tests on 30 benchmark problems, respectively. The statistical results of the comparisons on the benchmarks with ACDE algorithm and LSHADE algorithm are summarized in Table 1. It includes the mean and the standard deviations of error. The best results are marked in bold for all problems. As can be seen from Table 1, the ACDE algorithm is significantly better or flatter than the LSHADE algorithm on 18 functions. The ACDE algorithm is less accurate than the LSHADE algorithm on 12 functions. In general, the optimization performance of the ACDE algorithm is slightly higher than the LSHADE algorithm.
# Maritime Over the Horizon Sensor Integration:
## 5 Field Results
In this way, multiple false (duplicated) targets are eliminated, even though with each reappearance of the radar 0 target a new radar id was assigned. Figure 5: Update” (red trace: long radar track provided by radar 1, orange trace: fused track, yellow trace: new radar track provided by radar 0). Figure 6: “Tracks” and “Unstable target” (red trace: radar 0 data, green trace: radar 1 data, brown trace: fused track, white trace: AIS data). In the end, it is important to emphasize that the presence or absence of AIS data does not influence the fusion process. The AIS data presented here are used only to justify the decisions made during the fusion process.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 2 Background
### Related Work
For example, [PERSON] et al. [39] introduced an ECC-based authentication and key agreement protocol that addressed the issue of secure smart meter registration over open communication channels, though its reliance on a completely trusted registration center presents a potential point of vulnerability. Similarly, [PERSON] et al. [40] proposed an Shangyong Mima 2 (SM2)-based AKA protocol that offers user anonymity and reduced computational demands. However, its requirement for secure channels during registration restricts its applicability in environments where such channels are unavailable. Certificateless cryptographic schemes have gained significant attention due to their efficiency and reduced reliance on traditional certificates, thereby lowering key management overhead. Early certificateless public key cryptography (CPKC) models, such as [PERSON] and [PERSON]'s work in 2003 [41], introduced a framework that eliminates the need for traditional certificate authorities. However, early implementations faced scalability issues and dependency on centralized or semi-centralized key generation centers (KGCs), making them susceptible to single points of failure and trust issues. Recent advancements have explored certificateless cryptographic techniques in smart grid applications. For instance, [PERSON] et al. (2023) [42] proposed a certificateless multi-dimensional data aggregation scheme leveraging Paillier homomorphic encryption within a fog computing architecture. This approach ensures secure data aggregation and key negotiation among users while mitigating collision risks from control centers and fog nodes. Additionally, it enhances user privacy protection against potential threats from malicious KGCs and significantly reduces computational overhead for smart meters and aggregators compared to conventional aggregation techniques. Certificateless aggregate signatures (CLASs) have also evolved to improve security and bandwidth efficiency by aggregating signatures from multiple participants. However, existing certificateless methods still face scalability constraints and implementation challenges in real-world smart grid environments. While blockchain-based certificateless architectures [43] offer decentralization and transparency, they impose significant computational overhead, making them less practical for low-power, resource-constrained smart grid devices.
# Reinforcement Learning Based Relay Selection for Underwater Acoustic Cooperative Networks
## 1 Introduction
### Motivations and Contributions
Figure 2: The impact of obstacles and frequency selective fading on relay selection. effective and applicable for UWA networks. Furthermore, the parameters of the learning process for relay selection strategy will be dynamically optimized for less convergence time and better convergence value. The main contributions of this paper are as follows:  
* We propose a RL based relay selection strategy for UWA cooperative communication with the knowledge of feedback CSI and transmission delays of relay nodes. Effective state, action and reward expression have been formulated to better reveal the relationship between agent and environment. The combination of outdated CSI and the system mutual information (MI) is defined as the state of RL. The selection of different relay nodes is set as the action. The joint function of system MI and access delay is established as the reward. The proposed scheme selects relay nodes with good link quality and low access delay. * We exploit the simulated annealing (SA) algorithm to improve the convergence performance of UWA relay selection strategy. In the RL process, exploration rate is dynamically adapted by SA optimization through the temperature decline rate. In turn, the temperate decline rate will regulate the probability to accept the new solution of Q-values. The proposed strategy can improve the convergence speed and value in comparison with RL with constant exploration rate. * We propose the fast reinforcement learning (FRL) scheme for implementation of the proposed relay selection scheme in practical UWA networks. Before experimental implementation, the similar simulated channel model will be utilized for pre-training of SA-RL scheme. So the possible action in different channel state will be explored. And then, the on-line RL stage will be implemented with the pre-trained parameters. Evaluated by experimental data, it shows that the proposed FRL scheme can accelerate the convergence and can improve data rate UWA network. The remainder of this paper is organized as follows. Section 2 formulates the system model and channel model in details. Section 3 models the UWA dynamic cooperative problem by RL, and has proposed SA-FRL method for performance improvement. Section 4 presents the simulation and experimental results in comparison with other RL based UWA relay selection methods. Section 5 provides a discussions. Finally, Section 6 concludes the research work.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 4 Applications
### Drug Discovery and Development  
In the field of drug discovery and development, AI has made significant contributions, including target identification, virtual screening, structure-activity relationship modeling, de novo drug design, optimization of drug candidates, drug repurposing, and toxicity prediction [68]. Two common AI techniques are employed: supervised learning (e.g., classification and regression) and unsupervised learning (e.g., clustering and dimensionality reduction). Supervised learning uses labeled data to train models for classifying or predicting outcomes, while unsupervised learning identifies recurring patterns and clusters unlabeled data without prior knowledge [69].  
A specific example is the application of AI in anticancer drug discovery. Through machine learning algorithms, researchers can identify gene mutations associated with specific types of cancer from large amounts of genomic data. Subsequently, this information is used to virtually screen compound libraries to find compounds that are most likely to bind to these mutated genes and inhibit their function. This approach significantly shortens the time for drug discovery and increases the success rate [70]. In toxicity prediction, AI can predict the potential toxicity of new drugs by analyzing chemical structures and biological activity data. For instance, using supervised learning models, researchers can predict the toxicity of candidate drugs on the liver or kidneys, thereby excluding unsafe drugs in the early screening stages and reducing the risk of clinical trial failures [71]. Another example is the application of AI in optimizing drug compounds. Using AI-driven molecular generators, researchers can design new compounds that meet specific efficacy and safety requirements. This process involves using deep learning techniques, such as generative adversarial networks (GANs), to generate molecular structures with high activity and low toxicity from scratch. This approach greatly accelerates the design and optimization process of new drugs [72]. During clinical trials, AI enhances the quality of trial design and patient selection by reducing population heterogeneity and implementing prognostic and predictive enrichment strategies. For example, AI algorithms can analyze electronic health records (EHRs) and genomic data to select the most suitable patients for trials, thereby increasing the success rate of the trials. Additionally, AI-supported wearable devices can monitor the health status of trial participants in real time, providing continuous data streams that help to understand the effects and safety of the drugs more comprehensively [73].
## 1 Introduction
The scope of this paper is to propose a novel, high-speed implementation of a pitch detection algorithm based on a modified version of the autocorrelation, and to assess the performances of the most highly regarded algorithms in terms of speed and accuracy, on a suitable dataset purposefully built as a test bench. For the purposes of testing pitch detection algorithms on sheer speed or recognition capabilities, a custom dataset named SYNTHPITCH was built by producing synthetic signals so that the original pitch/F0 is objective and priorly known. Other algorithms such as YAAPT [23], SHRP [24] or the CREPE [25] CNN approach have been experimented with, but their preliminary results were not notable with respect to the others considered, especially for the speed vs. accuracy tradeoff. With speed being the main characteristic to search for, non-notable algorithms that provide high accuracies but poor speed have not been included in the present assessment although experimentations were made on them in order to rule them out, and synthetic signals are employed to evaluate the sheer computational complexity, while not forgoing the ability to infer pitch. The main contributions of this paper lie in the presentation of a novel pitch detection algorithm, based on a partially unexplored approach focused on high-speed and low bit depth, very suitable for hardware implementations. All of these characteristics make it a good candidate for live performance applications or MIDI instruments, which rely on real-time note detection. The mathematical and signal processing theories behind our novel algorithm explore the characteristics of the autocorrelation function, its maximization and its approximations, as well as the effect of quantization on the fundamental frequency of a signal. Along with the new algorithm, a testing paradigm for evaluating the speed and computational complexity of pitch detection algorithms is proposed, and a custom, synthetic dataset is produced and made available to the public. State-of-the-art, pre-existing pitch detection algorithms, especially those focusing on speed, are thoroughly explored and tested. The article is organized as follows. Section 2 presents the OneBitPitch algorithm along with its mathematical discussion, theoretical derivation and implementation, as well as three other algorithms (YIN, NLS, SWIPE) used for comparison and the custom SYNTHPITCH dataset produced and used in this paper. Section 3 presents the numerical results obtained from the simulations, as well as a statistical analysis. Then, Section 4 provides an in-depth discussion of the obtained results, especially focusing on real-time implementation and runtime speed, which are the main focuses of the analyses. The strengths and weaknesses of every algorithm are analyzed along with practical situations where each algorithm is best suited. Limitations and future works, especially regarding hardware solutions, are presented at the end of the Discussion and before the Conclusions.
# Four-Qubit Cluster States Generation through Multi-Coin Quantum Walk
## 3 Generating Cluster States Using Quantum Gates Formed by Multi-Coin Quantum Walk
### Generating Cluster States  
So far we have implemented the required CZ gate and Hadamard gate by mapping the corresponding quantum walk onto a 3D cube. Now we can combine the CZ gates and the Hadamard gates and carry out the 4-step operations mentioned in Equations (17)-(21). Figure 9 shows the equivalent quantum walk scheme for 4-qubit quantum circuit. The system state remains the same from \(|\Phi(1)\rangle\) to \(|\Phi(2)\rangle\) and from \(|\Phi(3)\rangle\) to \(|\Phi(4)\rangle\), so the corresponding cube do not change during these two steps. We can see that 4-qubit cluster state is achieved after 5 coin operations: the qubit at \(|000\rangle\), \(|100\rangle\), \(|011\rangle\) and \(|111\rangle\) are \(|0\rangle\), \(|1\rangle\), \(|0\rangle\) and \(|1\rangle\), respectively, which gives \(|0\rangle_{1}|000\rangle_{234}\), \(|1\rangle_{1}|100\rangle_{234}\), \(|0\rangle_{1}|011\rangle_{234}\) and \(|1\rangle_{1}|111\rangle_{234}\).  
Our improved method of simulating quantum gates using multi-coin quantum walk can potentially be used to generate higher entangled qubits. However, the mapping scheme can be different depending on the targeted qubits. Some possible schemes have been shown in [15].