esa-sceva/satcom-chunk-collection huggingface dataset:
# Diversity Maintenance for Efficient Robot Path Planning
## 2 Materials and Methods
### Population  
The matrix in Equation (1) represents the whole population. Each row describes one individual member, i.e., one potential solution, in this case, it is one trajectory. The initial population consists of \(m\) random individuals with \(n\) real numbers in the range \([-5,\,5]\) on the position of each allele in the gene. So, the population consists of \(m\) genes of length \(n\).  
\[\textit{population}=\left[\begin{array}{cccc}a_{11}=R_{11}&a_{12}=R_{12}& \cdots&a_{1n}=R_{1n}\\ a_{21}=R_{21}&a_{22}=R_{21}&\cdots&a_{2n}=R_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ a_{m1}=R_{m1}&a_{m2}=R_{m2}&\cdots&a_{mn}=R_{mn}\end{array}\right] \tag{1}\]  
The trajectory is, in theory, a polynomial function of arbitrary order, and every member of the population represents a polynomial function, while each allele matches one of its coefficients. The length of the gene represents the order of the polynomial function. For the three-dimensional environment, the trajectory is composed of two different polynomial functions, in x-y, and x-z planes, respectively.  
\[P(x)=a_{n}x^{n}+a_{n-1}x^{n-1}+\ldots+a_{2}x^{2}+a_{1}x^{1},\;a_{i}\in\mathbb{ R},\;i=1\ldots n \tag{2}\]  
Equation (2) defines one row of the matrix, this is one individual or candidate solution to the problem. As defined with Equation (1) there are \(m\) potential solutions that form the population. As for the population size, defined by \(m\), in our simulation, the population size is set to 50 individuals. The dimension \(n\) defines the order of the polynomial used to describe a trajectory.  
Its size is experimentally determined and set to \(n=7\). If the order of the polynomial is set too low, there is not enough flexibility, or inflection points available to evolve a trajectory that avoids obstacles. Too high an order increases processing time significantly, but without any positive impact on the quality of the end solution. The order used in our simulations is determined through trial and error, but might potentially also be a part of the optimization or evolutionary process.  
Figure 2: Testing environments. **Left**: 2D maze for a mobile robot. **Right**: 3D environment for a robotic arm. Blue line—the best solution found by the algorithm.
###### Abstract  
This paper presents a method of high-capacity and transparent watermarking based on the usage of deep neural networks with the adjustable subsquares properties algorithm to encode the data of a watermark in high-quality video using the H.265/HEVC (High-Efficiency Video Coding) codec. The aim of the article is to present a method of embedding a watermark in a video with HEVC codec compression by making changes in a video in a way that is not noticeable to the naked eye. The method presented here is characterised by focusing on ensuring the accuracy of the original image in relation to the watermarked image, providing the transparency of the embedded watermark, while ensuring its survival after compression by the HEVC codec. The article includes a presentation of the practical results of watermark embedding with a built-in variation mechanism of its capacity and resistance, thanks to the adjustable subsquares properties algorithm. The obtained PSNR (peak signal-to-noise ratio) results are at the level of 40 dB or better. There is the possibility of the complete recovery of a watermark from a single frame compressed in the CRF (constant rate factor) range of up to 16, resulting in a BER (bit error rate) equal to 0 for the received watermark.  
neural network; watermark; deep learning; property verification; copyright protection; video; HEVC; H.265
## 3 Results
### Monitoring  
The JAVIS chat analytics dashboard successfully tracks user activity and system performance over time. Figure 5 illustrates the aggregated chat usage metrics for JAVIS on a monthly basis. The blue bar plot highlights the total number of messages sent per day, showcasing peaks of high activity, particularly around 18 July, with over 90 messages. In contrast, subsequent days demonstrate reduced usage, punctuated by smaller activity spikes on 25 July, 1 August, and 7 August.  
Figure 5: The figure illustrates JAVIS’ modular deployment capabilities across three levels of operational requirements. At **Level 1 (Tailoring JAVIS)**, development JAVIS is combined with the versatile mode to enable initial development and experimentation on individual workstations. **Level 2 (Personal/Team Serving)** demonstrates the combination of development/production JAVIS with the versatile mode, supporting small teams with flexible resource adjustments and efficient manual tuning. Finally, **Level 3 (Hospital/Institutional Serving)** combines production JAVIS with the scaling Mode, allowing dynamic resource scaling to meet institutional demands seamlessly. These three levels highlight JAVIS’ ability to transition smoothly from individual development environments to team collaboration and large-scale institutional deployment while maintaining adaptability and performance efficiency.  
In contrast, the line plot provides insights into each user's trends, allowing them to hide or show any specific user for better visibility, thus revealing consistent monitoring or activity during the same intervals. Weekly, daily, and hourly reports are also available.  
Additionally, the Ray performance dashboard ensures real-time performance monitoring, such as resource utilization, replica health, and model-serving loads, which aid in maintaining optimal system performance.  
These results demonstrate the system's ability to capture user interaction patterns while enabling proactive monitoring of infrastructure health and resource allocation.
## 3 Results and Discussion
### Optimization of the Working pH  
The influence of pH in the detection of cysteine was investigated by varying the pH from 2 to 11 using dilute HCl and NaOH solutions. According to the results in Figure 3c, the  
Figure 3: Optimization of the experimental conditions: (**a**) AuCl\({}_{4}\)\({}^{-}\) concentration (other conditions: 75 mM CTAB, 50 uM Cys, no buffer, room temperature, reaction time 30 min); (**b**) CTAB concentration (other conditions: 0.25 mM AuCl\({}_{4}\)\({}^{-}\), 50 uM Cys, no buffer, room temperature, reaction time 30 min); (**c**) pH (other conditions: 0.25 mM AuCl\({}_{4}\)\({}^{-}\), 10 mM CTAB, 50 uM Cys, no buffer, room temperature, reaction time 30 min); (**d**) reaction time and temperature (other conditions: 0.25 mM AuCl\({}_{4}\)\({}^{-}\), 10 mM CTAB, 50 uM Cys, sodium acetate/acetic acid buffer pH 6).  
highest signals were obtained at acidic pH values and specifically at pH 6, while at alkaline conditions the signal decreased. Since CTAB is a non-pH responsive surfactant [21], we reasoned that the pH-dependent response may be related to the hydrolysis of Au salts as well as to the ionization state of cysteine and its interaction with CTAB. Au forms anionic complexes with halogens and hydroxyl anions over a wide pH range [22]; therefore, the formation of Au-CTAB complexes is feasible over a wide pH range. In addition, the formation of ion-pair AuCl\({}_{4}\)\({}^{-}\)CTAB complexes precedes the addition of cysteine. Therefore, hydrolysis of Au species at different pH values should not play any role since Au is not free in the solution but as a complex with CTAB. Based on these phenomena, we argue that the effect of pH is mainly related to the ionization state of cysteine.  
The maximum signal obtained at a pH of 6 is close to the isoelectric point of cysteine, suggesting that electrically neutral biothiols favor the formation of the S-Au bond. At pH \(\geq\) 7, the signal decreases and completely diminishes at pH \(>\) 10. At these conditions, cysteine is negatively charged since the amine group is protonated at pKa\({}_{2}\) = 8.3 while the thiol group is deprotonated at the pKa\({}_{3}\) = 10.8 [23]. The reason for the reduction in the analytical signal may lie in the electrostatic attraction of cysteine to CTAB micelles. CTAB is present in a very large excess compared to Cys (CTAB:Cys \(>\) 200); therefore, the unprotonated COO- groups may interact with the positively charged amine headgroup of the CTAB micelles, and possibly reduce the mobility and reactivity of Cys and thus its ability to form Au-S complexes. The inhibitory effect of electrostatic interactions between the free thiols and charged molecules on the surface of the AuNP assemblies has been reported to affect the formation of the S-Au bonds, and is more favorable when the thiol groups were electrically neutral [24]. Based on these observations, the pH of the solution was regulated at pH = 6, where the predominant Au anions are (AuCl\({}_{3}\)(OH))\({}^{-}\) and (AuCl\({}_{2}\)(OH)\({}_{2}\))\({}^{-}\), using sodium acetate/acetic acid buffer.
## 5 Existing Security Mechanisms
### Key Management  
Key management protocols are fundamental requirements to develop a secure application. These protocols are used to set up and distribute various kinds of cryptographic keys to nodes in the network. Generally, there are three types of key management protocols, namely, trusted server, key pre-distribution and self enforcing [40,80]. (i) _Trusted server_ protocols rely on a trusted base station responsible for establishing the key agreement in the network. It is considered that the trusted server protocols are well suited to hierarchical networks in the presence of unlimited resource gateways. Although, trusted server based schemes provide stronger security to hierarchical networks, in a real-time environment, a trusted server could become a single point for the entire network failure; hence, they are not suitable for critical applications (e.g., healthcare) [40]. (ii) _Key pre-distribution_ protocols are based on symmetric key cryptography, where secret keys are stored in the network before the network deployment. The key pre-distribution protocols are easy to implement, and offer relatively less computational complexity, making them more suitable for resource constrained sensor networks. (iii) _Self enforcing_ protocols using a public-key infrastructure provide many advantages, such as, strong security, scalability, and memory efficiency. Earlier public key based solutions were thought to be too computationally expensive (_i.e._, RSA [81] and Diffie-Hellman key exchange [82]) for wireless sensor networks. However, some researchers [83-85] have shown that Elliptic curve cryptographic based schemes are viable on resource constrained networks. In fact, in real-time implementation, the ECC based necessary cryptographic primitives (e.g., signature generation and verification) are still expensive in term of the time complexity.
# Early Ransomware Detection with Deep Learning Models
## 5 Experimental Evaluation
### Error Analysis  
In the error-analysis phase, we utilized t-Distributed Stochastic Neighbor Embedding (t-SNE) [66], a powerful algorithm for dimensionality reduction, which is well suited to the visualization of high-dimensional datasets. Our primary goal with this analysis was to identify patterns and clusters in models' predictions, specifically focusing on distinguishing between correctly classified instances and errors.  
We transformed the test data into a two-dimensional space with t-SNE and plotted the two-dimensional features with plot points color-coded to distinguish between correctly classified instances (in light gray) and errors (in red). This visualization reveals areas where the model performs well and highlights regions where errors are concentrated.  
In both t-SNE visualizations (shown in Figures 9 and 10), errors, represented by red dots, are interspersed among correctly classified instances, rather than clustering in isolated areas. This pattern suggests that the errors do not stem from distinct, well-defined regions of the feature space. Instead, they appear to be spread throughout, indicating that these misclassifications are not readily separable based on the model's current understanding of the features. This dispersion of errors points to the intrinsic difficulty of the classification task, where simple linear separability is not achievable, and more complex decision boundaries are necessary. Furthermore, we observe substantial regions within the t-SNE plots where correctly classified samples are dominant, with no errors nearby. This implies that, for a significant portion of the dataset, the model can classify instances with high confidence and accuracy. Such regions are indicative of samples that are likely easier to classify, either because they have more distinct feature representations or they fall far from the decision boundary within the feature space.  
Overall, while the model showed competence in accurately classifying a large fraction of the data, the scattered errors highlight the challenges present in the more ambiguous regions of the feature space.
## 3 Results
### Health Risk
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Oil Samples**} & \multicolumn{6}{c}{**Concentrations 1 (mg/kg) \(\pm\) sd**} \\ \cline{2-7}  & **Mn** & **Cr** & **Cu** & **Cd** & **Pb** & **Co** & **Ni** \\ \hline Sunflower & 0.023 \(\pm\) 0.005 & 0.010 \(\pm\) 0.001 & 0.098 \(\pm\) 0.010 & 0.010 \(\pm\) 0.001 & 0.075 \(\pm\) 0.014 & 0.010 \(\pm\) 0.000 & 0.006 \(\pm\) 0.001 \\ Grapeseed & 0.035 \(\pm\) 0.008 & \(\sim\)1.0 & 0.062 \(\pm\) 0.006 & 0.004 \(\pm\) 0.000 & 0.011 \(\pm\) 0.003 & 0.018 \(\pm\) 0.005 & 0.013 \(\pm\) 0.002 \\ Extra virgin olive & 0.010 \(\pm\) 0.002 & 0.033 \(\pm\) 0.007 & 0.010 \(\pm\) 0.001 & 0.001 \(\pm\) 0.000 & 0.026 \(\pm\) 0.004 & 0.005 \(\pm\) 0.001 & 0.012 \(\pm\) 0.001 \\ Organic extra virgin olive & 0.027 \(\pm\) 0.006 & 0.011 \(\pm\) 0.001 & 0.312 \(\pm\) 0.023 & 0.007 \(\pm\) 0.001 & 0.101 \(\pm\) 0.034 & 0.015 \(\pm\) 0.002 & 0.015 \(\pm\) 0.002 \\ Rapeseed & 0.083 \(\pm\) 0.009 & 0.018 \(\pm\) 0.002 & 0.099 \(\pm\) 0.026 & 0.005 \(\pm\) 0.001 & 0.061 \(\pm\) 0.009 & 0.031 \(\pm\) 0.005 & 0.007 \(\pm\) 0.001 \\ Blend & 0.023 \(\pm\) 0.004 & 0.062 \(\pm\) 0.006 & 0.672 \(\pm\) 0.045 & 0.004 \(\pm\) 0.000 & 0.099 \(\pm\) 0.011 & 0.013 \(\pm\) 0.001 & 0.006 \(\pm\) 0.001 \\ Walnut & 0.017 \(\pm\) 0.004 & 0.006 \(\pm\) 0.000 & 0.085 \(\pm\) 0.008 & 0.005 \(\pm\) 0.001 & 0.059 \(\pm\) 0.006 & 0.009 \(\pm\) 0.002 & 0.003 \(\pm\) 0.000 \\ Rice & 0.007 \(\pm\) 0.002 & 0.020 \(\pm\) 0.002 & 0.029 \(\pm\) 0.004 & 0.005 \(\pm\) 0.001 & 0.217 \(\pm\) 0.051 & 0.003 \(\pm\) 0.000 & 0.006 \(\pm\) 0.001 \\ \hline \hline \end{tabular}  
* Average of three samples. sd—standard deviation.  
\end{table} 
Table 2: Potential toxic element concentrations in the blend oil samples. The highest value of \(\sum\)THQ was that for the rice oil sample (0.02672 mg/kg/day), and the lowest value was that for the grapeseed oil sample (0.00369 mg/kg/day); Table 3.  
The sums of the ILCRs for the heavy metals (the sum of Cr, Cd, Pb, and Ni) for adults were 7.19 \(\times\) 10\({}^{-6}\) for sunflower oil, 4.75 \(\times\) 10\({}^{-6}\) for grapeseed oil, 10.38 \(\times\) 10\({}^{-6}\) for extra virgin olive oil, 10.18 \(\times\) 10\({}^{-6}\) for organic extra virgin oil, 7.83 \(\times\) 10\({}^{-6}\) for rapeseed oil, 16.39 \(\times\) 10\({}^{-6}\) for blend oil, 4.41 \(\times\) 10\({}^{-6}\) for walnut oil, and 12.61 \(\times\) 10\({}^{-6}\) for rice oil, respectively.
## 6 Protocol Comparison and Security Analysis
### Man-in-the-Middle Attacks  
Man-in-the-middle attack is a common means of network intrusion. Man-in-the-middle attack means that the attacker obtains the communication information of both sides through illegal eavesdropping, and then intercepts and tampers with the message to control the whole session. The fundamental reason that man-in-the-middle attack may succeed is that both parties cannot prove their identity through identity authentication. In BAPC, when an attacker intercepts a user's message and tampers with the attacker's public key, the satellite can easily identify the illegal public key. Because the attacker's address cannot be found on the blockchain as legal users save the address on the blockchain through the registration stage. If the attacker uses his registered legal address, the satellite will know that it is communicating with the attacker, not a normal user.
# Computational Load Analysis of a Galileo OSNMA-Read Receiver for ARM-Based Embedded Platforms +
## 2 The OSNMA in Brief  
To ease the reading of the paper, this section briefly recalls the main concepts behind the OSNMA scheme; refer to [21; 22; 23; 24] for more complete and detailed descriptions.  
The OSNMA, as sketched in Figure 1, is based on the Timed Efficient Stream Loss-tolerant Authentication (TESLA) protocol [25], which is structured into two elements: (i) the transmission of a message authentication code (MAC), used to authenticate the plaintext navigation message, and (ii) the delayed transmission of the key used to compute the MAC.  
The delayed release mechanism ensures that the key is not known until after the message and the MAC are received. The protocol also prevents the spoofer from generating messages, keys and MACs, and broadcasting them compliant to the specifications. For this, the key belongs to a chain of keys, referred to as TESLA key chain, in which each key is generated from the previous one with a one-way function. The generation of a chain of length \(N\) starts with a random secret key \(k_{N}\), and ends with a public root key \(k_{0}\), certified as authentic. The disclosure of the chain occurs in the opposite order. The root key \(k_{0}\) is transmitted along with a digital signature generated using a standard asymmetric scheme, based on a pair of private and public keys: the receiver can use the digital signature and the public key to check the authenticity of the received \(k_{0}\).  
The receiver, once certified the root key as authentic, can start the authentication verification process, which is basically performed by two steps:  
1. The current received TESLA key is authenticated with the root key, by performing the one-way function the required number of times. Alternatively, if one or more authentication verifications have been already successfully occurred, the current key can also be authenticated with a previous key from the chain, closer than the root key.
2. The MAC is then regenerated using the current key and the navigation data. If it coincides with the previously received MAC, the navigation data are authenticated.  
The OSNMA information will be transmitted in the E1B Galileo I/NAV message [26], using the 40 bits marked as \"Reserved 1\" in the odd page part, thereby providing an equivalent bandwidth of 20 bps, for a total of 600 bits every I/NAV subframe.  
The authentication information is sent in two sections transmitted in parallel:  
Figure 1: Schematic of the TESLA key chain and its use in the OSNMA scheme.  
* The HKROOT section that contains the global headers and the digital signature message (DSM), usually signing a root key (8 bits per page);
* The MACK section containing the MACs and the associated delayed keys (32 bits per page).  
The OSNMA will be transmitted only from a subset of satellites, able to cross-authenticate also those satellites whose signals do not carry the OSNMA information.
# Multi-Beam Circular Polarized Reflectarray on Parabolic Reflector by Variable Rotation Technique
## 2 Required Phase Shift to Tilt Dual CP Focused Beams in Specular Directions
The remaining geometric parameters are the following: focus in the \(z\)-axis at 2.718 m height, symmetry plane of the parabola at \(y=0\), diameter of the disk of 1.812 m, clearance of 0.35 m (focal length/diameter ratio is 1.5). The \(\mathbf{k}_{\text{ref}}\) direction of the main beam without introducing the opposite additional phases is the \(z\)-axis. The specular desired directions were \(\theta_{\text{beam,1}}=\ +0.28\text{deg}\), \(\phi_{\text{beam,1}}=0\text{deg}\) and \(\theta_{\text{beam,2}}=\ -0.28\text{deg}\), \(\phi_{\text{beam,2}}=\ 0\text{deg}\) of the main beams. The incident angle \(\theta_{\text{inc}}\) which suffers the central element of the parabolic reflectarray is 13.03 deg. Note that for most elements of the parabolic reflectarray, the incident angle \(\theta_{\text{inc}}\) can be approximated by its value at the center of the parabolic reflectarray. This will be exploited in the next section in order to optimize the reflectarray element to provide few calls to the full-wave analysis routine in the design process to reduce the consumption of CPU time in the design process. For this reflector, the maximum and minimum values of the additional phase given by (1) were obtained for the extreme values of \(x\) coordinates, which were 0.35 m and 2.162 m, respectively. The difference of the values that takes in the extreme values for \(\theta_{\text{beam,1}}=\ +0.28\text{deg}\) was 3.66 radians at 19.7 GHz. If we consider the value of the additional phase given by (1) for the center of the disk, we note that the phase change was only 1.83 radians from this center to any extreme point of the disk (i.e., only 105 deg). According to [15; 19] there is a rule stating that the phase shift added by the rotating element is twice the rotation angle. This rule is quite accurate when the elements are much smaller than the operation wavelength. In that case, the coupling between elements should not change much with rotation. However, as shown in the next section, the proposed reflectarray element has a period size roughly half of the operation wavelength and the variation of the coupling with rotation is not negligible. So, using the rule that the phase shift added by the rotating element is twice the rotation angle, we can state that regarding the angular rotation value at the center of the disk, the unit element rotation angle is always in the range from \(-52.5\) to \(+52.5\) degrees.