esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussion  
This section presents the simulation results and key findings about the proposed concept. The improvements of the proposed algorithm were compared using four criteria; the improvements in computation time, path lengths, path overlapping, and number of turns with the existing closely related algorithms. To benchmark the proposed algorithm, we compared the proposed algorithm results with decomposition-based CPP methods, BCDH-CPP [53] and CA-CPP algorithm [54]. The simulation results were produced and compared on a PC running Windows 10, with a CPU Intel Core i5 of 2.6 GHz and 8.00 GB of RAM, using MATLAB version 9.4.0.81 (R2018a). In simulations of the proposed CPP algorithm, we consider a 25-kg UAV similar to our previous study [55]. We considered both global constraints that are related to the UAV operating environment and local constraints that are related to the UAV. The numerical values related to the local constraints are: maximum steering angle: p/6 radius and wing span: 1 m. We assumed a zero-wind scenario in our simulations and assumed that there exists no external inference that can impact the established path. We assumed that a UAV has sufficient power to finish the mission successfully in one round. The minimum and maximum UAV flight height limits are 25 m and 150 m (\(h_{min}=25\) m, \(h_{max}=150\) m). The safe distance value for collision avoidance with obstacles is set to 10 m (\(D_{safe}=10\) m). The ACO parameters were specified considering the problem size (i.e., no. of sweeps). The sensor footprint sweep width is set to 20 m (\(F_{w}\) = 20 m) and footprint sweep length is set to 30 m (\(F_{l}\) = 30 m). We present the overview of the 3D maps used in the experiments and two exemplary coverage path results visually in Figure 7(a),b. We compared the proposed algorithm results with the existing methods on two grounds: the varying obstacles densities and shape of the AOI. The relevant details about AOI sizes, obstacles' densities, and the shape of the AOI used in experiments are explained in Sections 4.1 and 4.2.
# Gaussian-Distributed Spread-Spectrum for Covert Communications
## 4 Detectability of GDSS Signals  
This section investigates detectability of the GDSS waveform at low SNR using higher-order moments and modulation stripping and compares the performance with DSSS signals. Simulated noise is generated using a random number generator to create a sequence of values that mimic the behaviour of real-world noise. The noise power is adjusted based on the desired SNR value to create noisy signals at different SNR levels.
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
###### Abstract
In this paper, we present a systematic review of OBFN architectures using various TTDs and phase shifters, introduce typical architectures, discuss the scalability of different architectures, as well as propose several scalable techniques and application scenarios for OBFNs. The following sections are organized as follows: Section 2 shows the principles of PAAs, OBFNs with TTDs and phase shifter arrays, Section 3 introduces the representative architectures of five subclasses of OBFNs with TTDs and the corresponding two-dimensional (2D) schemes, Section 4 presents four classic categories of OBFNs with phase shifters and a promising combination of these with TTD architectures, Section 5 discusses the scalability, scalable techniques and application potential of OBFNs in next-generation wireless communication networks, and finally, Section 6 gives the conclusion and outlook.
## 4 Conclusions  
Green carbon nanostructures produced by the bio-pyrolysis of _Allium cepa_, L., [41] from India [39; 40] were studied through a combination of SEM/EDS, ED-XRD, and Raman spectroscopy, showing the self-assembly formation of metallic copper nanostructures. SEM images displayed for the first time the presence of rolled-up structures, called nano-roses for their evident shape, distributed in a fractal-like pseudo-morphism. The single nano-rose unit of 0.92 \(\upmu\)m in diameter revealed rolled petals of nanometric size, with a lateral dimension of about 172 nm. The single nano-roses are arranged in ordered groups, occupying ideal circular structures of about 2.63 \(\upmu\)m. These structures further unite to form a larger circle of 7.54 \(\upmu\)m in diameter, exhibiting a quite uniform distribution, although this description represents a geometric simplification. The global ensemble of observed nano-roses indeed consists of spherical three-dimensional objects. EDS microanalysis showed that the nano-roses are constituted by metallic Cu\({}^{0}\), as confirmed by ED-XRD and Raman spectroscopy measurements. The data excluded the presence of copper oxides and/or copper sulphides.  
Remarkably, in this work, the Cu nano-roses showed a morphology with an evidently increased surface/bulk ratio. This observation suggests, as also mentioned in [32] for protein-inorganic hybrid Cu\({}^{2+}\) phosphate nanoflowers, the possibility of novel featured applications for these Cu nano-roses. These include (1) efficient electrodes in catalytic processes for fuel cells or (2) sensors for revealing the oxidation of fruits in the agri-food sector, thanks to their metallic unoxidized ground state. Indeed, in the particular conditions of a pH environment, the Cu\({}^{0}\) nano-roses can be oxidized, revealing the ripeness of the fruits, i.e., the enzymatic polyphenols to quinones' oxidation process.  
Conceptualization and methodology, P.D.P.; investigation and analysis, P.D.P., A.G., B.O., L.P., B.P.; data curation, P.D.P.; writing--original manuscript preparation--review and editing, P.D.P.; samples synthesis, S.S., G.H.; data discussion, P.D.P., A.G., B.O., L.P., B.P., L.S., F.D.P., G.D.V., C.O., S.S., G.H. All authors have read and agreed with the published version of the manuscript.  
This research received no external funding. G.D.V. was financially supported by MIUR-Italy \"Dipartimenti di Eccellenza, ARTICOLO 1, COMMI 314-337 LEGGE 232/2016\".  
The authors have the pleasure to acknowledge [PERSON] for his invaluable technical support in XRD and Raman spectroscopy measurements and [PERSON] for Zeiss FE-SEM data collection. G.H. would like to acknowledge the DST-Nanomission, Gov. of India for providing the grant N. SR/NM/NT-1026/2017.  
The authors declare no conflict of interest.
# Privacy Framework for the Development of IoT-Based Systems
## 4 Related Work
### Zero Trust Approaches for IoTS Security and Privacy  
Zero Trust (ZT) approaches have emerged to address IoT security and privacy from an access control and trust management perspective. Traditionally, security measures focused on protecting the perimeter, assuming that everything inside the network could be trusted. However, ZT operates on the assumption that no entity, internal or external, should be trusted by default. Every access request must be authenticated, authorized, and continuously validated [35]. This is particularly critical in IoT environments where devices are often interconnected, and the risk of vulnerabilities from both inside and outside the network is high.  
[PERSON] et al. [36] conducted a comprehensive analysis to evaluate the current landscape of ZT research, with a particular focus on its practical applications within IoT environments. Their study further investigates existing IoT vulnerabilities and examines how ZT can effectively address these risks through a detailed assessment of contemporary security mechanisms. One key aspect highlighted by the authors is the critical issue of data security in IoT systems, where unauthorized access can lead to data theft, manipulation, or destruction. The ZT model effectively addresses this challenge by categorizing and monitoring sensitive data based on its importance and ensuring access is granted solely to authorized users and devices, thereby safeguarding IoT data. This approach helps protect sensitive data and ensures compliance with privacy regulations like GDPR.  
While the research presented above highlights the critical role of ZT in enhancing security within IoT environments, it is also important to consider its future integration into privacy frameworks. Given its potential to safeguard sensitive data and its effectiveness in preventing unauthorized access, ZT should be further explored as an essential element of privacy strategies, future research, and recommendations for developers.
## 2 Related Works
### [PERSON] et al.'s Scheme  
[PERSON] et al. (2018) [22] proposed a prediction error-based RDH technique in encrypted AMBTC compressed images. In this technique, for each _trio_, the two quantization levels \(Q_{1}\) and \(Q_{2}\) are encrypted with a bit-based XOR operation using the same random binary code. Bitmap \(b\) is encrypted in a similar way. Then, we can obtain the prediction error values by calculating the difference between the encrypted \(Q_{1}\) and \(Q_{2}\). The RDH procedure is as follows: First, the 16-bit secret message is replaced directly with the bitmap in the \(i\)-th block when \(Q_{2}\) equals \(Q_{1}\), and \(f^{i}\) is set to '1' at the same time. Otherwise \(f^{i}\) is set to '0'. Assume here that the two peak bins are \(P^{1}\) and \(P^{2}\), the two zero bins are \(Z^{1}\) and \(Z^{2}\), and \(Z^{1}<P^{1}<P^{2}<Z^{2}\). The secret message \(s\) can be embedded into \(Q_{1}\) by changing \(Q_{1}\) to \(Q_{1}^{\prime}\) according to Equation (9):  
\[Q_{1}^{\prime}=\begin{cases}Q_{1}+1,\text{ if }(Z^{1}<PE<P^{1}),\\ Q_{1}+s,\text{ if }(PE=P^{1}\text{ and }f\
eq Q_{1}),\\ Q_{1}-s,\text{ if }(PE=P^{2}\text{ and }f\
eq Q_{1}),\\ Q_{1}-1,\text{ if }(P^{2}<PE<Z^{2}),\end{cases} \tag{9}\]  
where \(PE\) is the prediction error value between two quantization levels in the \(i\)-th block. Here, some auxiliary data including two zero-bins, two peak-bins, and a location map are embedded together with the messages.
# GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
## 4 Simulation results
### Parameter Analysis

Table 3: Error parameters of the different localization verification thresholds of forwarding spoofing. Figure 8: The trend of APE under different localization verification thresholds of forwarding spoofing. Figure 9: The trend of APE under different localization verification thresholds of inducing spoofing. It can be seen that the change of \(E_{s}\) affects the trend of APE under different localization verification thresholds. With the increase of \(E_{s}\), the rmse of the APE changes from large to small and then increases. When \(E_{s}\) is small (for example \(E_{s}=8.2\) m), the cumulative error calculated by PTC-SLM exceeds the threshold due to the influence of sensor cumulative measurement errors. When there is no induced spoofing, false alarms occur and cause normal GNSS frames to be discarded and the localization results of visual SLAM system will produce cumulative errors because there is no GNSS frame to optimize the absolute position. When \(E_{s}\) is large (for example, \(E_{s}=82\) m), the PTC-SLM method cannot detect GNSS spoofing in time, and missed detection occurs, so that the localization result of PTC-SLM is induced by the GNSS spoofing and large errors are generated. Among them, when \(E_{s}=14.7\) m, PTC-SLM can detect GNSS induced spoofing in time, and its absolute error is the smallest, which is consistent with the theoretical analysis. Therefore, we set the localization verification threshold as \(E_{s}=\mathrm{W}\cdot\sigma_{\mathrm{V}}=14.7\) m in subsequent experiments.
## 3 Evolution Towards Next Generation Networks (NGN)
### Overview of NGN Features, Compared to PSTN and Internet  
Table 2 shows some of the main parameters and features of network concepts: NGN, PSTN/IN and internet (simplified and generalized interpretation).  
\begin{table}
\begin{tabular}{|l|c|c|c|} \hline  & **PSTN/IN** & **Internet** & **NGN** \\ \hline
**Multimedia services** & NO & YES & YES \\ \hline
**QoS support** & YES (Voice) & NO & YES \\ \hline
**Network intelligence** & YES & NO & YES \\ \hline
**Intelligent terminal equipment** & NO & YES & YES \\ \hline
**Integrated supervision and control** & YES & NO & YES \\ \hline
**Reliability** & high & low & high \\ \hline
**Service creation** & complex & ad-hoc & systematic \\ \hline
**Simplicity of services use** & medium & high & high \\ \hline
**Modularity** & low & medium & high \\ \hline
**Time of service introduction** & long & short & short \\ \hline
**Openness of architecture** & small & high & high \\ \hline \end{tabular}
\end{table}
Table 2: Comparing the features of PSTN/IN, Internet and NGN.  
Figure 2: Principle for layered architecture of NGN (integrated services, control and packet routing over wide variety of access technologies).
## 3 Results
### AIGC Input-Output Classification
#### 3.3.7 Text-Driven 3D Content Generation  
Generative AI models have revolutionized the field of image synthesis, allowing for the generation of visually compelling 2D images based on textual descriptions. However, the potential of these models extends beyond 2D images, as they can also be applied to the domain of 3D content generation. In this section, we will discuss various text-to-3D techniques.  
Text to 3D Image  
The field of text-to-3D-image generation has witnessed significant advancements in recent years. One notable approach, DreamFusion [133], utilizes a diffusion-based method to generate high-quality 3D images from textual descriptions. By employing a pre-trained image diffusing model and score distillation sampling, DreamFusion [133] achieves impressive results. However, challenges such as low-resolution image synthesis and the slow optimization process of NeRF (neural radiance fields) have been identified. To address these limitations, NVIDIA proposed a two-stage optimization framework called Magic3D [167]. This framework incorporates both low-resolution and high-resolution optimization steps, leading to the generation of detailed and high-resolution 3D images.  
Text to 3D Animation  
The synthesis of animated 3D images from textual descriptions has also gained significant attention in the research community. One notable model, AvatarCLIP [66], leverages the power of CLIP (contrastive language-image pre-training) to generate animated avatars guided by text prompts. This two-step process involves generating a static avatar based on the textual description and then animating it using motion guidance. Another approach, known as DreamFace [102], focuses on generating animated 3D faces. It involves a three-step process: geometric generation using ICT-FaceKit shape space, texture diffusion, and  
Figure 17: Input to the StyleCLIP (output image is generated using [[https://replicate.com/orpatashnik/styleclip](https://replicate.com/orpatashnik/styleclip)]([https://replicate.com/orpatashnik/styleclip](https://replicate.com/orpatashnik/styleclip)), accessed on 25 July 2023) is baby image with prompt “A baby boy with smiley face.” The output is a smiley baby image.  
animatability empowerment. Textual descriptions are utilized to generate geometric details, hair, face color, and other personalized features. Additionally, the generated faces are further animated using a neural animation approach. Addressing the need for human avatars in virtual reality (VR) and augmented reality (AR) applications, the AvatarCraft [55] model offers a comprehensive solution. It involves a two-stage process: encoding a base mesh and utilizing a text-conditioned diffusion model to generate the desired human avatar. Furthermore, the avatar can be animated using the SMPL (skinned multi-person linear) model, enabling realistic and customizable animations.
# Research on Blockchain Transaction Privacy Protection Methods Based on Deep Learning
## 2 Preparatory Knowledge  
### Literature Review  
Blockchain features include decentralized storage, data immutability, and consensus mechanisms. These features ensure the transparency and security of blockchain data, but they also create challenges for users' privacy protection. Researchers have introduced numerous privacy protection technologies to safeguard privacy.  
Coin mixing is a significant privacy protection scheme that obscures the relationship between inputs and outputs in blockchain transactions to protect privacy. CoinJoin is a specific type of coin mixing scheme, its core idea is to merge transactions from multiple users into a single transaction to hide the source and destination of each user's funds. Dash [9] uses CoinJoin technology to ensure privacy by facilitating coin mixing through network master nodes. This process involves master nodes in chain mixing, where the output of one node becomes the input of another, undergoing multiple rounds of mixing to enhance anonymity.  
Besides coin mixing, cryptographic privacy protection mechanisms are also a key research direction. Researchers use technologies like ZKP and ring signatures to secure transaction data confidentiality. Zerocoin [10] employs non-interactive ZKP and RSA accumulators within a cryptocurrency framework that allows Bitcoin conversion into Zerocoin, concealing the identities of both the sender and the receiver during transactions. However, Zerocoin faces issues like high costs and low transaction efficiency. Building on Zerocoin, Zerocash [11] introduces improvements using zk-SNARKs technology to reduce the transaction verification time required by Zerocoin, enhancing transaction efficiency. Moreover, Zerocash enables private transactions of differing amounts and allows direct transfers to user addresses. Monero focuses on privacy protection, it uses a ring signature framework to keep transaction senders anonymous. Additionally, it employs stealth address technology, generating a one-time address for each transaction to prevent address reuse.  
Beyond privacy protection, regulatory technology is another vital aspect of blockchain transactions, which can effectively prevent illegal activities. Thus, balancing transaction regulation with privacy protection is an essential research direction. [PERSON] et al. [12] proposed a traceable Monero system that adds an accountability mechanism to the original system. It can trace the flow of funds and infer users' long-term addresses from one-time anonymous addresses. [PERSON] et al. [13] proposed an MBDC framework for CBDC, which employs permissioned blockchain technology and utilizes a multi-blockchain structure and ChainID to improve scalability. However, MBDC focuses on regulatory features and has limitations in privacy protection. [PERSON] et al. [14] introduced Gemini-Chain, which adopts a dual chain structure to store and access complete transaction and verification information, maintaining a balance between privacy security and regulatory functions. But its structure is relatively complex.
# La-CTP: Loop-Aware Routing for Energy-Harvesting Wireless Sensor Networks
## 6 Evaluation  
We compare the network performances of CTP and La-CTP with multiple experiments which consist of real wireless sensor motes but realistically simulate the operation of EH-WSNs. We implemented La-CTP based on TinyOS 2.1.2 and neSC codes; so it can support any hardware platforms that can run TinyOS. In this section, we first describe the testbed and evaluation metrics in experiments; second we introduce how the experiments were carried out to simulate the typical EH-WSN operation; finally, we demonstrate and analyze the experimental results.
# Study of Basalt Fibers and Graphene Enriched Polymers on Bond Behavior of FRP Bars in Concrete
## 3 Numerical Model  
In order to simulate the bond process, a FE model was used. The model was defined on the bar scale (see [50; 51; 52]), i.e., the reinforcement and concrete are modeled as volumetric FEs and the discretization of the ribs or details of the reinforcement is not done. A bond stress-slip law is applied to the model considering cohesive interface elements between concrete and reinforcement. In this work the model developed by [PERSON] et al. [53] was used as a bond stress-slip law. This model was chosen in this work because it was specifically proposed for FRP applications and it is also a complete model in the sense that ascending, descending branch of the bond-slip law, as well as residual bond, are all included in the model. Classical models as in ref. [9; 12] only consider the ascending branch. The model also presents a greater generality than others permitting a very good fit with experiments. Bond stress \(\tau(s)\) is calculated as a function of the slip \(s\), considering three different zones according to Equation (2) and Figure 4. The model is governed by seven parameters, \(\alpha\), \(\beta\), \(s_{0}\), \(\tau_{0}\), \(\tau_{\infty}\), \(\tau_{1}\) and \(s_{1}\).  
In the first part of the model, the parameters \(s_{0}\) and \(\tau_{0}\) define an ascending linear phase. Subsequently, when the slip is greater than \(s_{0}\), there is an ascending hyperbolic curve governed by the \(\alpha\) parameter, which goes up to the maximum bond stress \(\tau_{1}\) (bond strength) for a corresponding slippage \(s_{1}\). Then, in the residual phase for \(s>s_{1}\), the curvature of the descending hyperbolic branch is controlled by the \(\beta\) parameter and, finally, the horizontal asymptote is governed by the \(\tau_{\infty}\) parameter. \(\tau_{1}\) and \(s_{1}\) are obtained experimentally through the pull-out test and the other parameters are here obtained through an optimization process, which is detailed below.  
Figure 3: Test specimen in the Universal Testing Machine.  
\[\tau(s)=\tau_{0}\frac{s}{s_{0}}\ \ :\ \ 0\leq s\leq s_{0} \tag{2}\] \[\tau(s)=\tau_{0}+(\tau_{1}-\tau_{0})\Big{(}1+\frac{1}{a}\Big{)} \Bigg{(}1-\frac{1}{1+a\left(\frac{s-s_{0}}{s_{1}}\right)}\Bigg{)} \ :\ \ \ s_{0}<s\leq s_{1}\] \[\tau(s)=\tau_{\infty}+(\tau_{1}-\tau_{\infty})\Bigg{(}\frac{1}{1+ \beta\left(\frac{s-s_{1}}{s_{1}}\right)}\Bigg{)} \ :\ \ \ s>s_{1}\]
# Position Control of a Mobile Robot through Deep Reinforcement Learning
## 5 Experiments and Results
### Experiments  
#### 5.1.1 Khepera Robot Position Control  
The first experiment corresponds to the problem of controlling the position of the mobile robot. The two deep reinforcement learning agents, DQN and DDPG, were used and compared with the two control algorithms: Villela and IPC. Figure 7a corresponds to an image of the simulated environment for the position control used in this experiment.