esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Problem Statement
### Secure Transmission Based on Adaptive Coding
#### 3.3.2 Secure Transmission  
The encoded bits \(X_{A}\) are encrypted by keys \(K_{A}\), and we can obtain \(E_{A}=X_{A}\oplus K_{A}\). Then, the ciphertexts \(E_{A}\) are transmitted through the transmission channel, and [PERSON] receives the ciphertexts \(E_{B}\). The source bits recovered by [PERSON] can be expressed as  
\[\begin{split} M_{B}&=\text{Decode}(E_{B}\oplus K_ {B})\\ &=\text{Decode}(\text{Encode}(M_{A})\oplus K_{A}\oplus K_{B})\\ &=\text{Decode}(\text{Encode}(M_{A})\oplus\epsilon)\end{split} \tag{11}\]  
where \(\epsilon=K_{A}\oplus K_{B}\) represents the difference between \(K_{\text{A}}\) and \(K_{\text{B}}\). Note that there is a difference between \(E_{A}\) and \(E_{B}\) due to channel fading and noise, which can be corrected by adaptive coding.
# Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL
## 3 Methodology
### Experiments Design
Larger values allow for more complex encryption computations, making it more difficult for potential attackers to derive useful information from encrypted data and hence providing better security, but also results in larger ciphertext sizes and increased computational overhead because ciphertext will contain more coefficients. The execution time and CPU usage were measured across the different data sizes and _poly_modulus_degree_ values, aiming for a deep understanding of performance under various configurations. The _chrono_ library was included in the program to measure the execution time. Time measurements started just before the cipher and stopped just after the execution of the cipher. The confidence interval of the results was 95%, which was calculated with a normal distribution using 5 samples for each combination of the values for _poly_modulus_degree_ [1024, 2048, 4098, 8192, 16,384, and 32,768] and data samples {1, 10, 100, 1000, 10,000}; hence, averages will be shown in the results. Regarding the CPU measurements, for the first batch of experiments with the PC, we used the Windows resource monitoring tool. For the second batch of experiments with the Jetson, we used _htop_ as the system monitoring tool. It runs on the terminal and visually provides system resource utilization. In both cases, instead of carrying out several consecutive runs as we did to measure the execution time, we kept the _poly_modulus_degree_ as a fixed value and carried out one run for each dataset to observe the CPU behavior. Then, the same process was repeated for all values of the _poly_modulus_degree_. ```
Initialize vector plain_numbers to store integers Generate and store x random integers in plain_numbers Repeat the following steps 5 times: Start timing the encryption process Set up encryption parameters: Define \(\text{poly\_modulus\_degree}\) as y Set coefficient modulus based on BFV default for the defined degree Set plain modulus as 1024 Initialize the SEAL encryption context with these parameters Generate cryptographic keys: Create a key generator using the SEAL context Generate a public key and a secret key Initialize encryptor and decryptor: Create an encryptor with the context and public key Create a decryptor with the context and secret key Initialize an empty vector cipher_numbers to store encrypted data For each number in plain_numbers: Convert the number to a plaintext object Encrypt the plaintext to ciphertext using the encryptor Store the ciphertext in cipher_numbers Stop timing the encryption process Calculate and print the duration of the encryption process in seconds Print the total number of samples in plain_numbers
```  
**Algorithm 1**Pseudocode to show the process of encrypting a set of integers using the Microsoft SEAL library's BFV homomorphic encryption scheme and measure the execution time.
# NiO-Based Electronic Flexible Devices
## 3 NiO-Based Flexible Devices
### Supercapacitors and Electrodes
Mounted on a watch, the supercapacitor could guarantee 45 min autonomy while being subjected to different deformations. NiO-Plastisol Electrodes  
Cheap, practical, flexible free standing electrodes were achieved by mixing synthetic graphite, with plasticizer agents (i.e., high molecular weight polyvinylchloride powder (PVC), bis(2-ethylhexyl)adipate (BEA) and THF [102]), followed by stirring, sonication, and solvent evaporation at room temperature overnight. NiO nanoparticles could be incorporated into the free-standing electrode by mixing them to the slurry, prior to the evaporation phase [103]. The resulting electrodes displayed a quite homogenous distribution of nanoparticles. Furthermore, NiO of different shapes and morphologies and at variable concentrations could be included. In Figure 3A through c, the flexible NiO-decorated free standing electrode is shown. It can be cut with scissors (Figure 3A) in any kind of shape and size (Figure 3B), the original dimension being solely dependent on the size and shape of the glass tray hosting the slurry during the evaporation phase. Different morphologies of NiO were achieved by varying the synthesis condition, in particular the precipitating agent and the calcination  
Figure 2: Scheme of the synthesis of sandwich-structured NiO-based flexible electrode and subsequently assembling of CAFS device (Reproduced from [98]. Copyright 2020, Elsevier: (**a**) Ni-based MG ribbon, (**b**) sandwich-like flexible electrode, (**c**) flexible electrodes after coating with PVA-KOH electrolytes, (**d**) flexible cable-like structure. (**e**) flexible all-solid-state symmetric supercapacitor. temperature. More in detail, triethylamine, NaOH and urea reacted with Ni(NO\({}_{3}\))\({}_{2}\) to yield Ni(OH)\({}_{2}\), converted into NiO by calcination at either 400 \({}^{\circ}\)C or 600 \({}^{\circ}\)C. The corresponding samples were labelled A (triethylamine), S (NaOH) or U (urea), followed by a 4 (400 \({}^{\circ}\)C) or 6 (600 \({}^{\circ}\)C). Overall, A4, A6, S6 and U6 NiO-decorated plastic electrodes were implemented for non-enzymatic amperometric detection of H\({}_{2}\)O\({}_{2}\) in milk. The A4-NiO/plastisol displays the highest current density of all probed electrodes in response to H\({}_{2}\)O\({}_{2}\) detection. This also corresponded to the best NiO dispersion within the plastisol matrix, though still retaining its original flower shape, as it can be appreciated at the backside of the electrode (Figure 3C--A4). A selection of SEM images of the flexible NiO-doped electrodes (front- and backside) is reported in Figure 3C. When probed on commercial milk, the A4-NiO/plastisol electrode reached a LOD of 5 \(\upmu\)M and a linearity range up to 4 mM.
# Broadband Microstrip Antenna for 5G Wireless Systems Operating at 28 GHz
## 6 Optimization Process and Discussion of Simulation Results
### Antenna Gain  
Usually, antenna gain is given in relation to an isotropic antenna and is expressed in dBi. In some cases, antenna gain is also given in relation to the dipole antenna and expressed in dBd. The energy gain of an antenna is dependent on its directivity and the energy loss of the antenna, which results from the material it is made of [35; 37]. The proposed antenna has a gain of 5.06 dBi at a resonance frequency of 28.00 GHz, which is considered to be high for compact microstrip antennas. Values of antenna gain as a function of frequency are shown in Figure 8.
# Key Technologies for 6G-Enabled Smart Sustainable City
## 4 Key Candidate Technologies for Future SSC
### Prime Technologies for SSC
#### 4.2.4 Vision-Aided Wireless Communication
Moreover, with the advancement of sensing technologies, positioning errors are expected to be further reduced in higher frequency bands. * _Latency reduction_: Leveraging location information extracted from collected RGB images enables the replacement of transmission latency with the processing latency of DL techniques. This is because the handshake operation for conventional beam refinement is no longer needed. Considering that the minimum latency of 5G beam management is around 20 ms, VAWC-based beam management achieves approximately 4 ms faster speeds [49]. Therefore, we conjecture that VAWC emerges as a promising technology to support HRLLC. * _Environment-aware operation_: VAWC can utilize acquired visual information to identify components of the radio environment, such as the exact location of users and obstacles. Visual data from NTN devices can be used to analyze user distribution, which facilitates the planning of the power budget for optimal energy efficiency [103]. Further, visualization information can be used to perform intelligent functions such as predicting beam blockages and managing mobility. Figure 5: Illustration of vision-aided wireless communication. * _Improve service availability:_ The VAWC systems enhance the availability of mobile services by efficiently optimizing the utilization of wireless resources because they can collect a substantial amount of visual data required for real-time monitoring of surrounding environments [107]. For example, power-limited IoT devices can deliver seamless mobile services by optimizing energy consumption via the transmission power prediction process. In addition, deep learning techniques that use visual information can be employed to predict LOS blockage situations to secure uninterrupted radio connections in fast handover scenarios. VAWC can offer a diverse range of services for SSC. For example, visual data obtained from cameras can establish connections with nearby terminals and vehicles by effectively understanding traffic conditions [51]. Assuming an LOS channel scenario, BS can facilitate fast and reliable transmission and reception for mobile users by predicting vehicles and pedestrians that may interfere with wireless connections. Furthermore, real-time visual information can be leveraged to predict future traffic conditions or detect accidents in changing road conditions. This information can be incorporated into future traffic schedules to mitigate traffic congestion. In UAV communication systems, VAWC technology pursues the optimal wireless solutions by enabling accurate positioning between the UAV and the receiver. For instance, flight path planning and power allocation utilizing visual information, collected via cameras mounted on UAVs, enable the optimal design of UAV communication systems [50]. Moreover, when multiple UAVs are employed, visual information from ground base stations and UAVs can be leveraged to establish wider service coverage and reliable wireless connections.
## 3 Results
### Characterization of Protonated Glass Surface--Results of Zeta Potential  
The surface charging of the glass surface by protonation can be described by zeta potential measurements. Zeta potential was calculated using the Smoluchowski approximation. The measured pH and calculated zeta potential data are shown in Table 2.  
The most influential factors of zeta potential measuring are ionic strength and pH. In our measurements the ionic strength was constant. The pH of ground silica powder (d\({}_{\text{mean}}=1183\pm 230\) nm) in the background electrolyte was 10.14. The alkaline pH was explained by the ion exchange between the protons/hydronium ions in the liquid layer outside the glass particles and sodium and calcium ions in the glass [15; 16]. The glass surface is highly negatively charged in the alkaline region with zeta potential values down to -57.22 mV. The pH of the suspension of protonated glass particles was 1.82. The zeta potential of protonated ground glass particles shifted to smaller mV values, numerically +4.42 mV compared to unprotonated glass suspension. The acid etching with nitric acid provides a positively charged glass surface that can influence the adsorption of the next layer (10-MDP containing bond) to the silica.  
Characterization of Protonated Glass Surface--Results of X-ray Photoelectron Spectroscopy (XPS) Measurements  
The surface concentration of calcium and sodium are shown to decrease (Figure 4 and Table 3), while the Na 1s and Si 2p peaks undergo a slight shift towards higher binding energies (BE). This is most likely caused by dealkalization, a type of glass corrosion caused by aqueous solutions wherein alkaline ions are leached into solution from a thin surface layer of the glass and replaced by other  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Groups** & **pH** & **Zeta Potential mV** & **Mobility \(\mu\)mcm/Vs** \\ \hline Background electrolyte & 6.73 & \(-9.72\pm 1.13\) & \(-0.76\pm 0.09\) \\ Unprotonated ground glass suspension & 10.14 & \(-57.22\pm 0.45\) & \(-4.49\pm 0.03\) \\ Protonated ground glass suspension & 1.82 & \(4.42\pm 0.67\) & \(0.35\pm 0.05\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Zeta potential and pH values of background electrolyte (ultra-pure water and 0.001M NaCl), bare and protonated ground glass suspension in the background electrolyte (n = 5).  
Figure 3: Mean values of shear bond strength measurements of epoxy on glass soaked in nitric acid for various hours with MDP containing bond and photopolymerizable resin materials (n = 7 per hour). * \(p<0.05\).  
cations from the solution, most often H\({}^{+}\) and H\({}_{3}\)O\({}^{+}\). This exchange is energetically favored and accelerated in highly acidic solutions (such as cc. HNO\({}_{3}\)) owing to the large excess of H\({}^{+}\) and H\({}_{3}\)O\({}^{+}\). Binding energies usually increase with the number of bound oxygens, thus, the BE shift shows that the leached alkaline ions are replaced with an oxygen-containing species, which in our case is H\({}_{3}\)O\({}^{+}\).
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 6 Results
### Results Obtained in the Signature Generation Phase  
To address the second set of research questions, we implemented both algorithms: SPHINCS-256 using Chacha12 PRNG and SPHINCS-256 using QRNG to generate HORST secret key. We named the two models _SPHINCS-Chacha12_ and _SPHINCS-QRNG_. For comparative analysis, we considered the root of the SPHINCS tree as the public key. And, we considered the private key of the SPHINCS tree at the topmost (d-1) layer. The signature size obtained in both models is 27873 bytes. We generated five random numbers from each algorithm and fed each of them to the NIST statistical tool. We compared both the algorithms by measuring execution time, testing randomness of the generators used in each algorithm, and based on [PERSON] et al.'s [57] research on testing Chacha12 PRNG.
# Angle-Based Parametrization with Evolutionary Optimization for OESCL-Band Y-Junction Splitters
## 3 Optimization Algorithms
### Particle Swarm Optimization  
In [16], the authors proposed PSO to simplify the social behavior of a flock of birds. It has shown success in discontinuous multidimensional problems in electromagnetism applications [18; 20; 27]. The PSO algorithm is similar to GA, but the \(i\)-th individual is related to a number \(v^{(i)}\) and a \(p\)-dimensional vector \(x^{(i)}_{best}\).  
We can think of the \(i\)-th individual as a particle defined by: (i) its position \(x^{(i)}\) (the \(p\)-dimensional vector associated with the individual), (ii) its velocity \(v^{(i)}\), and (iii) the best position the particle has found so far, named \(x^{(i)}_{best}\). Each particle accumulates speed in a favorable direction given by the best position found by the particle and the best position globally found. As a result, particles can move independently of local perturbations. By adding random walks, the particles can move in unpredictable ways that can potentially find better directions.  
Using the framework of Algorithm 1, we can describe PSO methods as follows:  
* \(generate\_population(n,p)\): returns \(n\) particles defined by \(p\) angles (\(\alpha_{i},i\in[1,p]\)) defined in Section 2, where its attributes have random values from a uniform distribution in the range of the constraints of each parameter.
* \(select(population)\): returns the particle with the best FOM.
* \(crossover(population,parents)\): returns the population updated with the use of Equation (4) (this updates the angles \(\alpha_{i},i\in[1,p]\) of each of the \(n\) particles) and Equation (5): \[x^{(i)}\gets x^{(i)}+v^{(i)}\] (4) \[v^{(i)}\leftarrow\omega v^{(i)}+c_{1}r_{1}\Big{(}x^{(i)}_{b}-x^{(i)}\Big{)}+c _{2}r_{2}\Big{(}x_{b}-x^{(i)}\Big{)}\] (5) where \(x_{b}\) is the best position globally found, \(\omega\), known as inertia weight, represents the will of the particle to conserve its current velocity, \(c_{1}\) and \(c_{2}\) quantify the relative attraction of \(x^{(i)}_{b}\) and \(x_{b}\), respectively, and \(r1,r2\in U(0,1)\) represent the unpredictable behavior.
## 2 Theory and Method  
### Overall Architecture  
In this research, the end-to-end reconstruction deep convolutional neural network implemented by deep learning framework Pytorch [22] was utilized to reconstruct the expand process in the S-CO\({}_{2}\) turbine based on main design parameters and then predict the aerodynamic performance of S-CO\({}_{2}\) turbine from reconstructed results.  
As illustrated in Figure 1, the proposed end-to-end framework includes three stages. The stage 0 was applied to obtain real field structures and performance of the designed S-CO\({}_{2}\) turbine from numerical results in the off-design analysis. In the next two stages, a deep convolutional neural network was employed to reconstruct interested physical fields and predict turbine performance based on physical fields. At stage 1, the interested physical fields were reconstructed with design variables including geometric variables and environmental condition variables as input. Subsequently, the performance of the S-CO\({}_{2}\) turbine was predicted at stage 2. It should be noted that the input of performance prediction model can be the reconstructed fields from stage 1 or the real fields from off-design analysis.
# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
## 5 Numerical Results and Discussions
### Secrecy Outage Probability
Specifically, when \(U_{t}\) increases, the proposed scheduling scheme allows maximization of the the SNR of the users selected for transmitting. Therefore, the UT's achievable rate, given by (21), increases, which implies a lower SOP. When comparing Figure 9 and Figure 10, the lowest SOP values appear in the last figure. For instance, for \(c=0.2\) and \(U_{t}=5\), the SOP in Figure 9 is equal to \(3\times 10^{-3}\) and in Figure 10 is equal to \(4.1\times 10^{-5}\). This occurs because the trusted BS employs one more antenna than the eavesdropper's receiver. Despite this, it is observed that when \(U_{t}\) increases, the SOP still decreases. Therefore, these results show that the proposed scheduling algorithm, together with other techniques, allow for an improved PLS of MIMO systems. It is important to indicate that if the eavesdropper is not aware of the channel state information between the UTs and the BS, he cannot know which users are transmitting, because the user selection is made based on this information. Thus, this ensures an additional degree of freedom to protect vulnerable information transmitted by certain users in the MIMO system. Finally, Figure 11 shows the impact of the number of receiving antennas at the eavesdropper receiver, i.e., \(A_{E}\), on the secrecy performance of the MIMO system. Thus, this figure shows the OP as a function of the \(E_{b}/N_{0}\), parameterized by \(A_{E}\) and \(U_{t}\), considering \(M=4\), \(A=3\), \(U=3\), \(e=0.1\), \(c=0.05\), and \(R=1\) bps in a Rician fading channel with \(K=2\). In the results, notice that as \(A_{E}\) increases, the OP also increases. This occurs because the eavesdropper has a greater capacity to decode the information of the users, and this is due to the fact that its diversity increases. In addition, it is observed that there appears a floor in the the OP curves that cannot be reduced as the \(E_{b}/N_{0}\) increases. In fact, an increase in the \(E_{b}/N_{0}\) is associated with an increase in transmission power of the user terminals, which benefits both the trusted BS and the eavesdropper, resulting in no change in the OP curves. However, notice that as the \(U_{t}\) is increased from 3 to 5, the OP is dramatically reduced. In particular, when \(U_{t}=3\), a scheduling process is not carried out since \(U_{t}=U\)On the other hand, when \(U_{t}=5\), the scheduling is performed and, as a consequence, the MIMO system security is improved.
## 5 Conclusions  
In [12], the authors introduced a group key management protocol that allows the participants to exchange a common secret through an insecure channel that could later let them communicate confidentially. We have provided a protocol that allows an active adversary with certain capabilities to become a legal member of the group without the knowledge of the rest of the participants in the communication group of \(n\) members. The adversary can share a common key with the group members, being the real number of participants \(n+1\), whereas the group members think that they are simply \(n\) entities communicating. We have also provided a real case of study using elliptic curves, a widespread setting for these types of communications, especially in the Internet of Things environment, where the light capabilities of the devices make this setting very appropriate, offering a high level of security against passive attacks. We also show how possible strategies for the rekeying protocol not cited in the original work also fail against our proposal, and even an extension of the group key exchange protocol to a more general algebraic setting is also vulnerable to the attack we are introducing. We conclude this paper by pointing out the necessity of continuous research not only to find new mathematical settings that will allow us to adapt to new challenges arising from the advances in quantum computing but also to test existing protocols in different cryptographic models in order to continue using them in mathematical environments that can resist attacks from this new generation of computers.  
All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministerio de Ciencia e Innovacion PID2020-113552 GB-I00, Junta de Andalucia FQM 0211 and MICIU/AEI/10.13039/501100011033 and ERDF/EU PID2022-138906 NB-C21.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.