esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Cybersecurity-Based MLP Trainer
### Computational Complexity and Deployment Potential of Anomaly Detection Algorithm
#### 4.3.1 Computational Complexity  
The computational complexity of the algorithm can be evaluated by analyzing its key components. The initial phase involves population initialization, during which a set of candidate solutions is generated. Each solution represents a collection of weights and biases for the MLP model. The complexity of this step is determined by the population size \(P\) and the total number of parameters to be optimized, represented as \(n_{\text{params}}\), which corresponds to the total number of weights and biases in the MLP. Thus, the computational complexity of the population initialization is expressed by Equation (12):  
\[O(P\cdot n_{\text{params}}) \tag{12}\]  
After the population has been initialized, each candidate solution must be evaluated using the objective function. In the context of training an MLP, this objective function is typically the Mean Squared Error (MSE) calculated over the training dataset. The computational complexity of evaluating the MSE for each individual in the population can be represented as \(O(P\cdot f)\), where \(f\) denotes the complexity of computing the MSE across the dataset. Since the MSE is dependent on the number of training samples \(s\), the number of inputs \(n\), and the number of outputs \(m\), the overall complexity of evaluating the objective function is outlined in Equation (13)  
\[f=O(s\cdot n\cdot m) \tag{13}\]  
Thus, the total computational complexity of the algorithm over all iterations is expressed as shown in Equation (14):  
\[O(G\cdot P\cdot s\cdot n\cdot m) \tag{14}\]  
where \(G\) is the number of generations, \(P\) is the population size, \(s\) is the number of training samples, \(n\) is the number of inputs, and \(m\) is the number of outputs.  
Beyond the evaluation of the objective function, the algorithm also performs operations such as mutation, crossover, and selection, which are applied to every individual within the population. These operations exhibit a linear relationship with both the population size and the dimensionality of the solution space, denoted by the number of parameters \(n_{\text{params}}\). The computational complexity of these operations is captured in Equation (15):  
\[O(G\cdot P\cdot n_{\text{params}}) \tag{15}\]  
Combining the above terms, the total computational complexity of the anomaly detection algorithm is summarized in Equation (16):  
\[O(G\cdot P\cdot s\cdot n\cdot m) \tag{16}\]  
This complexity demonstrates that the computational load increases with the number of generations \(G\), the population size \(P\), as well as the intricacy of the MLP model and the dataset.
# Shuffle Model of Differential Privacy: Numerical Composition for Federated Learning
## 9 Conclusions  
We have presented a numerical composition method for the shuffle model of differential privacy, covering both single-message shuffle protocols and multi-message shuffle protocols, having broad applications to federated analytics and federated learning (e.g., decision tree/forest and deep learning). Specifically, for single-message shuffle protocols that are based on vector randomized response, we provide privacy parameter characterization. For unified dominating pair reduction of single-message/multi-message shuffle protocols, we present an efficient and tight approach for \(K\)-wise sequential composition within \(\tilde{O}(n\cdot K)\) computational complexity. Through simulations of federated learning in the shuffle model, we show that our proposals save 20% budget for single-message shuffle protocols, and 80% budget for multi-message shuffle protocols.  
Nevertheless, several limitations are present in our study. The communication overhead inherent in the shuffling process, particularly in systems with a large number of participants, presents considerable challenges related to scalability and operational efficiency. In the context of a joint learning framework, the additional computational burden imposed by the execution of the shuffling protocol may negatively impact the overall system performance, especially in scenarios characterized by resource limitations or uneven data distribution.  
**Future Directions:** (i) Future research could explore the integration of the shuffle model with complementary privacy-preserving techniques such as homomorphic encryption or secure multi-party computation (SMPC). Such hybrid approaches would enable stronger privacy guarantees for tasks that involve highly sensitive data, such as financial or healthcare data analysis. (ii) Another direction is to investigate the privacy-utility trade-offs in non-numerical tasks, such as text, image, or multimedia analysis, which could expand the shuffle model's applicability to fields like natural language processing and computer vision.  
Conceptualization, S.W. and J.L.; methodology, S.W. and S.Z.; software, S.W.; validation, J.L., S.H. and Y.C.; formal analysis, S.W.; investigation, S.W. and S.Z.; resources, S.H.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, S.Z., J.L., S.H. and Y.C.; visualization, S.W. and S.Z.; supervision, S.W.; project administration, S.W. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Guangzhou Basic and Applied Basic Research Foundation (No. 2025A03J3182, No. 202201010194, No. 202201020139).  
Not applicable.  
Figure 4: Privacy accounting comparison in federated decision tree(s) scenarios with tree height \(H=8\).  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 3 Results and Discussions
### Assessing the Healing Effect of AALipo by \"Scratch\" Method  
The collective migration of human dermal fibroblasts and the \"wound closure\" phenomenon under the effect of liposomal formulations with and without AA was monitored for 36 h, as shown in Figure 6. At the end of the interval, it can be noticed that there was a complete coverage of the gap created in the fibroblasts layer when AALipo20% was applied, while the control sample (no treatment) indicated a very poor healing effect. The quantitative results are presented comparatively in Figure 7, assessing the wound healing process in the presence of liposomal formulations compared to control, according to formula (2). The statistical interpretation revealed a significant difference between the samples even after the first 12 h. Increasing the concentration of AA in the liposomes from 20% to 25% does not have an increased effect in terms of fibroblast migration and gap coverage.  
Figure 6: Evolution of wound healing by “scratch” method and fibroblast migration until complete coverage of the gap, influenced by different treatments: CTRL—no treatment; Lipo—blank liposomes; AALipo15%, AALipo20% and AALipo25%—liposomes loaded with AA in concentrations 15%, 20%, and 25% respectively.  
When comparing with similar works reported in literature, we noticed a study conducted in 2019 by [PERSON] et al. [19] using an ethosomal formulation to deliver azaelaic acid. Ethosomes are deformable and elastic carriers and the authors highlighted that ethanol contained in vesicles is responsible for the temporary disorganization of the lipid structure of the stratum corneum, increasing the permeability of AA in the cytoplasm of _P. acne_. In this study, azaelaic-acid-based ethosomes were made up of 35% (_w/w_) ethanol and included in a cream formulation, which showed better antibacterial activity when compared to commercial Zelface(r) cream.  
[PERSON] et al. [48] elaborated a method to prepare an azaelaic acid liposomal gel formulation based on phospholipid 50 IP, methyl parahydroxybenzoate, prophyl parahydroybenzoate, prophylene glycol, and 10%AA as organic phase. They found that the new formulation presented a high accumulation of an active substance in the stratum corneum in comparison to a reference formula represented by commercially available cream with 20% of azaelaic acid. However, this study was limited only to physico-chemical and antibacterial characterization, with a focus on the trans-dermal permeation of azaelaic acid and in vitro release, while the biocompatibility and cytotoxicity assays were completely missing.
## 5 Research Findings
### RQ1: What Techniques Are Utilized to Abuse the CPLS as C&C Communication Channels?
#### 5.2.3 Correlation between Platform User Base and Abuse Occurrence  
A positive correlation is observed between the number of users on a platform and the number of malware instances targeting that platform. Platforms with a larger user base, such as Google Docs, Dropbox, Twitter, Google Docs, Google Drive, YouTube, and Facebook, tend to report more abuse instances. The logic behind this correlation is intuitive; a larger user base provides a wider pool of potential victims for cyber attacks. Based on the conducted statistical analysis, there appears to be a positive association between the number of users on a platform and the number of abuse occurrences. Specifically, the slope of the regression line was calculated to be approximately 1.647--see Figure 6. This suggests that for each increase of one unit (one billion users) in a platform's user count, the occurrence of abuse tends to increase by 1.647 units on average.
## 4 Experiments and Evaluation
### Results and Analysis  
The purpose of this experiment is to explore the effects of noise introduction on payroll data analysis in an FL context with payroll data. It evaluates the relationship between privacy and performance, as well as the effectiveness of applying DP to prevent MIAs.  
Adding noise to payroll data for privacy protection has an influence on the model's ability to accurately interpret payroll features Table 3). This underscores the need to carefully balance privacy and utility according to the specific objectives of the analysis. However, unlike public opinion data, payroll data analysis frequently includes the detection of anomalies and outliers in addition to uncovering general insights. Table 3 also shows how adding noise affects the F1 score, which combines precision and recall. In the context of payroll data, eliminating false negatives (missing anomalies) can be crucial, even if it means sacrificing privacy protections. This is because unnoticed deviations can result in financial loss or security vulnerabilities.  
Furthermore, Figure 5 explores the relationship between noise levels, training rounds, and the privacy parameter epsilon (\(\epsilon\)). It reveals a stronger association between increased noise and lower levels of epsilon (\(\epsilon\)), indicating stronger privacy guarantees. This empowers organizations to make informed decisions about their privacy goals when analyzing sensitive payroll data. The figure also introduces RDP, another crucial metric for quantifying privacy protection. Similarly to opinion data, increasing noise levels correspond to a lower epsilon (\(\epsilon\)) and a higher RDP, signifying improved privacy protection. Organizations prioritizing privacy could choose a scenario with a lower epsilon (\(\epsilon\)) and a higher RDP, but this could require accepting a compromise in the clarity or granularity of the insights obtained. This underscores the critical need to navigate the privacy-performance trade-off when working with sensitive data sets, such as payroll data.  
The accuracy of the attack on Client 1 drops from 0.94 to 0.33 as the noise multiplier increases, and this applies to the other clients as well. This shows that the addition of noise restricts attacker attempts to identify participants by effectively reducing the model's ability to consistently determine whether a given data point belonged to the training set Table 5.  
A consistent correlation can be observed in Figure 6 for every client, where the F1 score decreases as the noise multiplier increases. This implies that introducing noise effectively prevents adversaries from recognizing training data points, even while doing so lowers the model's overall performance, as measured by the F1 score, which strikes a compromise between precision and recall. This experiment also highlights the trade-off between privacy and performance that comes with employing noise to mitigate MIAs.
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 5 Security Analysis and Comparison
### Informal Security Analysis
#### 5.3.2 Anonymity and Un-Traceability  
In the proposed scheme, if the adversary obtains \(\{PID_{i},M_{1},M_{3},M_{4}\}\) and \(\{M_{5},M_{7},M_{8}\}\) in the interaction process of \(DC_{j}\) and \(SM_{i}\) by means of eavesdropping, the \(PID_{i}\) is generated by a one-way hash function and updated periodically. \(M_{2}\) is generated by random numbers, \(M_{3}=(ID_{i}||E_{i})\bigoplus H_{0}(M_{2})\) is constantly changing with \(M_{2}\), and \(M_{4}\)= \(H(ID_{i}||PID_{i}||E_{i}||M_{2}||M_{3})\) is also variable. The adversary cannot derive or track the real \(ID_{i}\), so the scheme provides anonymity and non-traceability for users.
# Measurement of Absolute Acoustic Nonlinearity Parameter Using Laser-Ultrasonic Detection
## 2 Brief Description of Theoretical Background  
### Absolute Acoustic Nonlinearity Parameter (\(\beta\))  
Physically, the \(\beta\) parameter is directly related to the second-order nonlinear elastic constants of the material [9]. Because these nonlinear elastic constants are closely related to the microstructural features and micro-damage of the components, the \(\beta\) parameter measured using the NUT has been used as an indicator for quantitative NDE [9]. Assuming that the planar longitudinal wave propagates in lossless isotropic media, \(\beta\) can be defined as follows based on the nonlinear wave equation solution [28]:  
\[\beta=\frac{8A_{2}}{k^{2}x_{u}A_{1}^{2}}, \tag{1}\]  
where \(A_{1}\) and \(A_{2}\) are the displacement amplitudes of the fundamental frequency component for the propagating ultrasonic wave and that of the harmonic component produced by the wave distortion depending on the different nonlinearity sources existing in the material, respectively, \(k\) is the wavenumber, and \(x_{u}\) is the ultrasonic wave propagation distance. When an ultrasonic wave with a monochromatic fundamental frequency generated by an ultrasonic transmitter propagates through the material, the nonlinearity sources frequently interact with the propagating ultrasonic wave, leading to wave distortion. Consequently, the second-order harmonic component for the initial fundamental wave is generated, resulting in a varying \(\beta\). The relative \(\beta^{\prime}\) parameter, which has been conventionally used for relative comparison, is defined by  
\[\beta^{\prime}=\frac{8{A_{2}}^{\prime}}{k^{2}{x_{u}}{A_{1}^{\prime}}^{2}}, \tag{2}\]  
where the superscript \({}^{\prime}\) indicates the amplitude of the electrical signal measured by the receiver.
## 2 Sites, Instruments, and Data
### Coherent Doppler Wind Lidar  
An all-fiber micro-pulse CDWL operating at a wavelength of 1548 nm is used [9]. The pulse energy and repetition frequency are 100 \(\upmu\)J and 10 kHz, respectively. The CDWL is operated in a velocity azimuth display (VAD) scanning mode to measure the radial wind velocity along laser beam. It is a conical scanning by a laser beam around the vertical axis with a fixed elevation angle. The elevation angle and azimuth angle resolution are set to 60\({}^{\circ}\) and 5\({}^{\circ}\), respectively. The radial range resolution is set to 30 m/60 m in the range of 0-2.5 km/2.5-5.5 km. The period of one scan is about 144 s.  
After one scan, the carrier-to-noise ratio (CNR), wind vector, and TKEDR profiles are measured simultaneously. In detail, for every radial measurement, the aerosol backscatter signal is mixed with a local oscillator, obtaining a Doppler signal. The ratio of the Doppler signal power to noise power over the entire spectral bandwidth is the CNR [9]. Then, the wind vector is determined from the sine dependence of radial wind velocity versus azimuth angle [21]. Finally, the TKEDR is estimated by fitting the azimuth structure function of radial wind velocity to a model prediction [22].  
Figure 1: (**a**) Map of the ground-based experiment sites (Inner Mongolia, Hefei, and Sansha) and the ship-borne experiment site (sea). (**b**) Picture of ship-borne CDWL.
## 4 Blast Analysis Results
### Cable  
Table 8 presents the maximum displacement and maximum effective stress of each scenario when a blast load was applied to each cable selected in this study (see Figure 2). The displacements and effective stresses on the cables increased with the blast load. In scenario D of Cable no. 53, a momentary stress was found to exceed the static tensile strength (f\({}_{\text{pu}}\)) of the cable (1765 MPa); however, no damage or breakage was observed, owing to the strain rate effect of the material. Furthermore, when the initial stress was excluded, the stresses resulting from the blast load only were found to be approximately 531-1292 MPa for Cable no. 37, 824-1258 MPa for Cable no. 53 and 646-1095 MPa for Cable no. 68 depending on the scenario. Cable no. 37 is observed to have the largest stress variation compared to the initial stress of the design. The reason for this is that Cable no. 37 has a maximum diameter, or maximum blast load reception area. In addition, the maximum displacement and the maximum effective stress occurred on Cable no. 53, which has a minimum cable diameter. These results suggest that the diameter of the cable has a greater influence on the behavior of stay-cables under blast load, or a cable with a small diameter is more vulnerable to blast load. Figures 13 and 14 show the displacement and effective stress contours resulting from the blast load. The fringe range of the displacement contour was set as 0-400 mm, and that of the effective stress contour was set to 0-1765 MPa, considering the tensile strength of the cable. The result output time was 0.18 s and 0.05 s, at which point the maximum displacement and effective stress have occurred.  
Figure 13: Displacement contours of cables: (**a**) Cable no. 37, (**b**) Cable no. 53, and (**c**) Cable no. 68 (fringe level unit: mm).
# Fast Point Cloud Registration Method with Incorporation of RGB Image Information
## 2 Methods
### Initial Alignment Based on RGB Images
#### 2.1.2 Feature Point Matching  
Upon extraction of feature points from two RGB images, the commonly used method for matching is brute force matching. However, when the number of feature points is large, this approach can lead to a lengthy matching time and a significant number of false matches. To tackle the aforementioned issues, this paper proposes a bidirectional K-nearest neighbor (KNN) algorithm for feature point matching. Specifically, a k-d tree is constructed for both the source and target images, and KNN matching is performed independently on each of them. The common matching point pairs resulting from both matches are then  
Figure 1: The overall methodological flow of this work. RGB image input source I\({}_{\text{s}}\) and target I\({}_{\text{T}}\) are applied to initial alignment, P\({}_{\text{s}}\) and P\({}_{\text{T}}\) are represented as the source and target point clouds.  
Figure 2: The performance of feature point extraction using SIFT and ORB.  
obtained as the initial matching result, effectively reducing the number of false matches in the initial set.  
A k-d tree is constructed from the feature descriptors and the variance of X and Y dimensions is used to dimensionally segment the feature points. Afterwards, the dimension with the largest variance is sorted, and the median is taken as the pivot node. Nodes with values less than this median are assigned to the left sub-tree, while nodes with values greater than this median are assigned to the right sub-tree. This process is repeated until all nodes have been assigned. Figure 3 depicts the process of constructing a k-d tree.  
Bidirectional KNN feature matching refers to the process of building k-d trees (k-d tree 1 and k-d tree 2) for both source and target images, and then searching for the k nearest neighbor feature points of the target image, starting from the root node of k-d tree 1. At the same time, this process is also performed on k-d tree 2. The similarity between each feature point and other nodes is measured by the Euclidean distance between two feature points. The smaller the ratio of the Euclidean distance between two feature points, the higher the match between the two feature points, and vice versa, the lower the match. The matching points for the current feature point are selected as the top k (k = 2) highest similarity points, and the common matching point pairs resulting from both matches are chosen as final matching result.
## 2 Evolution of Maritime Communication Networks
### Current Challenges and Limitations in MCNs  
The deployment of 5G in maritime environments is currently in an exploration phase, with several pilot projects and feasibility studies underway. For example, 5G-VINNI [42] and 5G-ACIA [43] initiatives have proposed maritime testbeds that integrate 5G base stations on coastal infrastructure and seaborne platforms. In East Asia, South Korea has launched projects to provide 5G-based infotainment and operational services on ferries, while European ports are exploring private 5G networks for port automation and vessel tracking [44]. These implementations typically combine terrestrial 5G coverage with satellite backhaul to extend service into nearshore maritime zones. Table 5 presents a mapping between the key limitations identified in current MCNs and the corresponding enabling technologies that 6G is expected to bring. This aims to provide a high-level synthesis of how future solutions can directly target present bottlenecks. The advantages of 5G for MCNs are numerous: enhanced bandwidth, low-latency communication, support for massive IoT, and network slicing for traffic prioritization. Fifth-generation also introduces MEC capabilities that can reduce response times and enable on-site data analytics for maritime use cases such as video surveillance, real-time navigation assistance, and remote diagnostics [2]. However, maritime deployment of 5G faces unique challenges. Coverage is limited by the absence of terrestrial infrastructure in open waters, necessitating hybrid solutions involving UAVs, ships-as-BSs, or satellite connectivity. Channel modeling over the sea is also more complex due to the reflection and scattering effects of water surfaces [32]. Furthermore, handover management for fast-moving vessels and UAVs remains a critical research issue, as standard terrestrial mobility protocols often fall short in maritime conditions. Based on the above, despite the advances brought by 5G and hybrid satellite-terrestrial architectures, current MCN implementations still exhibit significant limitations. One of the primary concerns is coverage, particularly in deep sea and polar regions where terrestrial and even satellite connectivity may be intermittent or highly latency-inefficient. LEO satellites, while promising, still require dense constellations and reliable inter-satellite links, which are costly and technically challenging to maintain. Moreover, network densification and resource sharing introduce interference management problems, especially in nearshore zones with overlapping coverage from different sources (e.g., ports, ships, UAVs). Spectrum scarcity, exacerbated by regulatory fragmentation across maritime jurisdictions, further complicates the efficient use of available communication resources. From an optimization standpoint, existing technologies are ill-suited to handle the highly dynamic and mobile topology of MCNs [2]. Conventional routing, power control, and scheduling algorithms assume relatively stable nodes and known traffic patterns, which are assumptions that do not hold in real-world maritime scenarios [13]. Additionally,