esa-sceva/satcom-chunk-collection huggingface dataset:
# Fabrication of an Anti-Icing Aluminum Alloy Surface by Combining Wet Etching and Laser Machining
## 3 Results and Discussion  
### Icing Delay Experiment  
By comparing the freezing times of the surfaces at \(-\)10 \({}^{\circ}\)C, it was found that untreated surfaces froze in a very short time, while treated surfaces increased this time by approximately 10 times. The delay in the crystallization of droplets when in contact the superhydrophobic surface is due to the elevated barrier of the supercooled water/ice phase transition [15]. Figure 5 shows the frozen state of four samples placed on the center of the temperature control platform recorded by a CCD camera. Figure 5a shows the average freezing time of different samples at \(-\)10 \({}^{\circ}\)C, and the standard deviation of HS, NS, MS, and SS freezing times are 59.44, 58.11, 56.07, and 11.57, respectively. It can be seen from Figure 5b,c that no small droplets appeared on the surface of MS and SS during the freezing process. The droplet added to the SS surface froze almost completely at about 175 s, except for the central region, and froze completely at 245 s. In addition, it can be seen from real shot pictures that many ice crystals appeared around the droplets on SS and MS when frozen. The droplet added to the MS surface froze completely at 2375 s. At the same time, the HS surface was slightly better than the MS in terms of time, with a delay of approximately 400 s. Preliminary analysis suggests that the microstructure had larger voids than the nanostructure. When the small droplets infiltrated the void, the actual contact area between the droplets and the low-temperature substrate increased, increasing the heat transfer rate and resulting in a faster heat conversion. The comparison of nanostructure and the hierarchical structure demonstrated little difference in terms of freezing time, but there was a big difference in the ice crystal spreading. As shown from Figure 5d,e, both N.S. and H.S. surfaces were covered with small water droplets at approximately 1200 s. There are parts of the NS surface that froze ahead of the water droplet. The reason may be because the NS sample was not etched with a consistent velocity in the hydrochloric  
Figure 4: Schematic diagram of experimental device. (**a**) Icing delay test device; (**b**) icing adhesion test device.  
acid for the whole surface, due to the uneven grooves caused by sandpaper grinding. This phenomenon would result in an ununiform morphology or even an unetched consequence of some areas, which would have had further effects on the overall anti-ice performance of the sample. However, it can be observed that the surface ice of HS was gradually covered from the periphery to the center, with no area frozen in advance.
# Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
## 5 Results
### Theoretical, Practical, Societal, and Methodological Implications  
Our study contributes to the theoretical understanding of botnet detection by applying an HMM and a PHMM. These probabilistic models effectively capture intricate patterns within network traffic data, shedding light on the dynamics of botnet behavior. The comparative analysis between the HMM and PHMM provides valuable insights into their relative complexities and capabilities. Moreover, the successful implementation of these models demonstrates their practical viability for botnet detection. Notably, the achieved accuracy for specific botnet types underscores their practical utility in identifying distinct threats. However, challenges persist in classifying normal traffic, emphasizing the need for ongoing refinement and adaptation to diverse network scenarios.  
From a societal perspective, effective botnet detection directly impacts cybersecurity, safeguarding individuals, organizations, and society as a whole from malicious activities. By accurately identifying botnets, we contribute to a safer digital environment. Methodologically, our approach--encompassing data handling, pre-processing, and model selection--provides a blueprint for future research in this field. Additionally, the F1 score comparison between the HMM and PHMM serves as a valuable guide for model selection based on specific requirements. The PHMM's ability to discern subtle details in sequence alignment further enhances its performance compared to the HMM.  
In summary, our study advances theoretical knowledge, offers practical solutions, impacts society's security, and contributes methodological insights to the critical domain of botnet detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **F1-Score** & **Accuracy** \\ \hline Decision Trees & 0.79 & 0.92 \\ SVM & 0.14 & 0.68 \\ kNN & 0.86 & 0.94 \\ Naive Bayes & 0.53 & 0.78 \\ MLP & 0.99 & 0.99 \\ \hline HMM & 0.78 & 0.94 \\ PHMM & 0.89 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison with other models.
# Multi-Strategy Hybrid Whale Optimization Algorithm Improvement
## 5 Practical Application
### Weight Minimization of a Speed Reducer  
The speed reducer design problem is an engineering design problem. The specific variable description is shown in Figure 14.  
The problem can be formulated as follows:  
\[f(\overline{x})=0.7854x_{2}^{2}x_{1}\left(14.9334x_{3}-43.0934+3.3333x_{3}^{ 2}\right)+0.7854\left(x_{5}x_{7}^{2}+x_{4}x_{6}^{2}\right)-1.508x_{1}(x_{7}^{2 }+x_{6}^{2})+7.477(x_{7}^{3}+x_{6}^{3}) \tag{34}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Best** & **Mean** & **Worst** & **Comparison to** \\  & & & & **the Best Results** \\ \hline MHWOA & 1.614 & 1.615 & 1.617 & 0\% \\ GWO & 1.683 & 1.684 & 1.685 & 4.10\% \\ AVOA & 1.843 & 1.844 & 1.844 & 12.42\% \\ MFO & 1.852 & 1.859 & 1.864 & 13.13\% \\ AO & 2.402 & 2.477 & 2.695 & 34.80\% \\ SO & 2.288 & 2.315 & 2.402 & 30.24\% \\ BWO & 1.984 & 2.107 & 2.351 & 23.35\% \\ WOA & 3.342 & 3.403 & 3.480 & 52.54\% \\ GTO & 1.908 & 1.969 & 23 & 17.98\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Welded beam design cost comparison.  
Figure 14: A schematic diagram of weight minimization of a speed reducer problem.  
Subject to:  
\[\left\{\begin{aligned} g_{1}(\overline{x})=-x_{1}x_{2}^{2}x_{3}+27 \leq 0\\ g_{2}(\overline{x})=-x_{1}x_{2}^{2}x_{3}^{2}+397.5\leq 0\\ g_{3}(\overline{x})=-x_{2}x_{4}^{2}x_{3}x_{3}^{-3}+1.93\leq 0\\ g_{4}(\overline{x})=-x_{2}x_{3}^{2}x_{3}x_{3}^{-5}+1.93\leq 0\\ g_{5}(\overline{x})=10x_{6}^{-3}\sqrt{16.91\times 10^{6}+(745x_{4}x_{ 2}^{-1}x_{3}^{-1})^{2}}-1100\leq 0\\ g_{6}(\overline{x})=10x_{7}^{-3}\sqrt{157.5\times 10^{6}+(745x_{ 5}x_{2}^{-1}x_{3}^{-1})^{2}}-850\leq 0\\ g_{7}(\overline{x})=x_{2}x_{3}-40\leq 0\\ g_{8}(\overline{x})=-x_{1}x_{2}^{-1}+5\leq 0\\ g_{9}(\overline{x})=x_{1}x_{2}^{-1}-12\leq 0\\ g_{10}(\overline{x})=1.5x_{6}-x_{4}+1.9\leq 0\\ g_{11}(\overline{x})=1.1x_{7}-x_{5}+1.9\leq 0\\ \end{aligned}\right. \tag{35}\]  
With bounds:  
\[\left\{\begin{aligned} 2.6\leq x_{1}\leq 3.6\\ 0.7\leq x_{2}\leq 0.8\\ 17\leq x_{3}\leq 28\\ x_{4}\leq 8.3\\ x_{5}\geq 7.3\\ 2.9\leq x_{6}\leq 3.9\\ 5\leq x_{7}\leq 5.5\\ \end{aligned}\right. \tag{36}\]  
By applying MHWOA to the weight minimization of a speed reducer problem and comparing it with standard swarm intelligence algorithms, Figure 15 shows that MHWOA achieves the highest computational accuracy in solving this problem compared with other baseline algorithms.  
Based on the comparison of the best cost, mean cost, and worst cost obtained from 30 runs, the cost savings rate of each algorithm can be statistically determined. Taking  
Figure 15: Convergence visibility of weight minimization of a speed reducer design.  
MHWOA as the benchmark, the cost savings rates compared with other algorithms are shown in Table 12.  
MHWOA has a cost savings rate of 1.75% over GWO; 0.6% over AVOA; 0.83% over AVO; 39.69% over AO; 0.86% over GTO; 0.9% over SO; 9.42% over BWO; 38.6% over WOA.
# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
## 3 Application of the Methodology
### Updating Probabilities and Impact Contributions of REs  
According to the proposed methodology, the initial occurrence probabilities of REs as well as UEs, including CEs and OEs, need to be updated upon the acquisition of new plant-specific failure data pertaining to specific REs. The updated probabilities, as calculated and tabulated in Table 4, reveal a notable variation from their initial estimates, albeit remaining within the same order of magnitude. This discrepancy in probabilities underscores the dynamic nature of PRA, reflecting real-time shifts in risk profiles. In instances where the probability of occurrence of a CE or OE exceeds the acceptable threshold set forth by regulatory bodies or governing authorities, the implementation of integrity maintenance measures becomes imperative and must be undertaken without delay.  
The contribution of each RE to the occurrence of UEs exhibits slight variations, as clearly demonstrated in Figures 6 and 7 (refer to the red bars, signifying the updated contribution weight values assigned to the REs). To ensure that the updated results remain timely and can effectively guide the allocation of maintenance resources for the subsequent year, it is advisable to restrict the maximum time interval for probability updating to one year. The optimal application of this \"dynamic\" approach lies in its integration with the station's integrity data management platform, thereby facilitating the dynamic updating of probabilities based on real-time failure sample data. The reduction in uncertainty in prior probabilities, which is a notable advantage of the Bayesian update estimation method, is expected to intensify as the amount of plant-specific data from GDS continues to accumulate. This trend, as thoroughly explained in previous studies [7], highlights the significance of continuous updating. Consequently, the dynamic updating of probabilities using real-time data enables a more precise pinpointing of critical monitoring points within the station.
## 2 Materials and Methods  
### Neural Network  
#### 2.1.1 Neural Network Model on Impedance Dataset  
An NN architecture was developed and trained using the Levenberg-Marquardt backpropagation algorithm, with Mean Squared Error (MSE) as the performance metric [39]. A key design principle was minimizing the NN's complexity to mitigate overfitting and facilitate future implementation on a microcontroller. Consequently, a relatively small NN, comprising a single hidden layer of 10 neurons, was employed. This contrasts with the more complex architecture reported in [40], which utilized five hidden layers, each containing 15 neurons. For direct comparison with the results presented in [40], all code development for the present study was conducted within the MATLAB (R2024b) environment. However, the architecture's simplicity is intended to enable future translation to C for embedded deployment. The data were normalized to the range [0, 1] through uniform scaling. The conversion factor was retained to denormalize predictions, facilitating comparison with the original data and enabling accurate graphical representation. Consistent with the methodology in [40], the dataset was partitioned into training and testing subsets, with a 70%-30% split. Two distinct input configurations were explored: the S-parameters or the Z-parameters. The NN's output targets included the inter-coil distance, configuration parameters (describing coil alignment and rotation), and capacitance values (\(C_{1}\), \(C_{2}\), and \(C_{3}\)) employed by the Adaptive Impedance Matching Network (AIMN) for impedance matching. This approach allows for the NN to predict both geometric and electrical characteristics of the WPT system. For the prediction of the capacitance values, a bigger NN was used, with two layers of 10 neurons each, instead of only one layer. This improved the accuracy without overfitting. All the NN parameters are reported for clarity in Table 1.
## 5 Experiments
### Limitation  
Although this study successfully incorporates the saturation line prior into sand-dust image restoration and effectively mitigates the color distortion caused by its failure, the robustness of dust removal heavily depends on the accurate construction of the saturation line. If the saturation line prior fails across most regions, achieving satisfactory restoration results becomes challenging, as shown in Figure 10. In the distant regions near the sky, the boundary constraint is employed to compute the transmission map due to the failure of the saturation line prior. However, as the transmission map is set to 0 in these areas for artifact removal through illumination adjustment, the distant buildings and bridges are not effectively restored, remaining blurred.  
Furthermore, the saturation line prior assumes equal transmission maps for the RGB channels, which is typically invalid for sand-dust images. To address this issue, we employ a color correction approach. While the color correction process effectively removes the color veil, it tends to result in overcompensation under low-illumination and multi-light-source scenarios, as shown in Figure 11. Since the light sources near the observer are not subject to significant absorption and attenuation, this overcompensation often causes them to appear bluish. Defining a mask for the light sources [48] can alleviate this issue.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Platform** & **Hardware** & **640 x 360** & **1280 x 720** & **1920 x 1080** \\ \hline FBE [3] & Matlab & CPU & 0.1966 & 0.6878 & 1.5065 \\ HRDCP [11] & Matlab & CPU & 1.0338 & 4.0810 & 8.0943 \\ NGT [4] & Matlab & CPU & 0.0659 & 0.2290 & 0.4581 \\ ROP [12] & Matlab & CPU & 0.0631 & 0.2384 & 0.5311 \\ SCB [5] & Python & CPU & 0.1160 & 0.2293 & 0.4052 \\ ROP+ [13] & Matlab & CPU & 0.6070 & 2.5339 & 4.6092 \\ CVC [14] & Python & CPU & 0.0674 & 0.3555 & 0.7745 \\ DedustGAN [10] & Python & GPU & 2.8102 & 7.2574 & 14.4687 \\ Ours & Matlab & CPU & 0.7800 & 2.9054 & 6.3091 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Running times of various sandstorm image enhancement methods (unit: seconds).  
Figure 9: Visual object detection results for YOLOv5: (**a**) The detection results of sandstorm images; (**b**) The detection results of de-dusted images obtained by the proposed method.
## 3 Results
### Evolution of Research Activity  
Figure 4 illustrates the annual distribution of publications in the selected research domain from 2010 to 2025. To obtain a broader perspective on research trends, all filtering criteria from this study were applied except for the year filter. Additionally, the broad query, as outlined in Section 2.2, was used by considering articles that mention the key terms in their title, abstract, or keywords, rather than limiting the search to the title alone. The results reveal a steady rise in research activity, surging after 2017 and peaking in 2024, reflecting growing interest and advancements in the field. The drop in 2025 is due to the year still being in progress. The trend line confirms an overall upward trajectory, underscoring the field's increasing relevance.
## 3 Methodology
### Design of Workspace  
In general, the data service container provides data services for users in the form of a single service. In other words, users can only call one data service (data resource service or data-processing service) in the data service container each time. When users need to handle complex data requirements, they need to manually invoke data services several times to meet their data requirements, which is very inconvenient. Therefore, a place that can provide data access and configuration for users is needed, which is the workspace proposed in this paper. In the workspace, users can integrate required data resources and data-processing methods, and then customize their data requirements based on these data services. Figure 5 shows the design of the workspace.
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 3 System Model  
### Network Module  
The overall objective of the system is to find a given number of paths within a given network topology while also achieving optimal performance in terms of delay, delay variation, security, and other comprehensive factors. Specifically, for a given network, its topology is represented as \(G=<V,E>\), where \(V=\{v_{1},v_{2},\ldots,v_{N},\}\) represents the set of all vertices, and \(E=\{e_{1},e_{2},\ldots,e_{M}\}\) represents the set of all edges. The network topology \(G\) is represented using two adjacency matrices, \(M_{topo}\) and \(M(\tau)\). \(M_{topo}\) is the initial adjacency matrix that includes delay information and is used for preliminary exploration in the depth-first algorithm. \(M(\tau)\) is an adjacency matrix that represents the connectivity of links and their corresponding delay. It serves as the input state for the deep reinforcement learning component. For nodes \(v_{i}\) and \(v_{j}\), if there is a connected edge \(e_{x}\) with a current delay of \(d\), then \(e_{x}=e_{i,j}=d\). Conversely, if there is no connected edge or \(i=j\), then \(e_{i,j}=-1\). In this paper, the policy-based network topology optimization problem is transformed into a maximization problem of the objective function:  
\[\max_{\theta}J(\theta)=\mathbb{E}_{\tau\sim\pi_{\theta}}[R(\tau)] \tag{1}\]  
where \(\theta\) represents the parameters of the deep reinforcement neural network, \(J(\theta)\) represents the objective function of the neural network, and \(\mathbb{E}_{\tau\sim\pi_{\theta}}[R(\tau)]\) is the expected reward \(R\) at time \(\tau\), following the distribution of the neural network policy \(\pi_{\theta}\).
## 2 Problem Formulation
### OFDM-Based Communication Systems
#### 2.1.3 Diffusion Model for Image Reconstruction in OFDM  
As illustrated in Figure 3, generative AI models have attracted considerable attention in the field of communication owing to their capacity to model nonlinear functions and adapt to challenging channel conditions [18; 19]. The increased demand for high-dimensional data transmission necessitates the implementation of high-order modulation  
Figure 2: Comparison of received signal \(y\) under the modulation schemes \(M_{1}\)-PSK and \(M_{2}\)-QAM at different SNR levels (0 dB to 15 dB) for CIFAR-10 images.  
schemes. Nevertheless, in the context of image transmission utilizing OFDM, the presence of noise and channel impairments degrades the fidelity of the transmitted images. As the modulation order increases, distortions become more apparent, thereby impairing the quality of the transmitted images.  
Generative models, including GANs and VAEs, were employed for image restoration. Nevertheless, these models often face difficulties when dealing with the complex distortions introduced by wireless channels. In order to tackle these issues, the CDM has emerged as a viable option for reconstructing distorted images in OFDM-based image communication. In the context of the CDM, the distortions resulting from multipath fading and interference within OFDM channels are addressed as a forward process. The diffusion model is designed to learn the reverse process by systematically eliminating noise [13].  
During CDM training [13], noise is steadily inserted into the data until the signal itself is entirely distorted. Then, the model is trained to systematically eliminate the noise, thereby reconstructing a signal that closely approximates the original data. In contrast to VAEs, which operate under the assumption of solely Gaussian noise, CDMs adeptly manage intricate distortions, including multipath fading. Furthermore, by calibrating the model based on parameters such as SNR and modulation order, CDMs can more reliably adjust to diverse channel distortions, leading to enhanced image reconstruction.
## 5 Discussion
### Open Challenges
#### 5.3.3 Hardware Implementations  
The new generation of multibeam satellites could be designed with complete flexibility. However, design hardware implementation deficiencies may result in the addition of nonlinearities, which could be detrimental to downlink. Methods of predistortion or adjusting the frequency plan to take this effect into account are used to solve this problem. Substantial hardware complexity increases substantially with the first option [59]. On the other hand, additional constraints are introduced to the optimization problem, making it even more challenging to deal online, and this should be studied carefully.  
Additionally, AI is expected to play an essential role in the future automation of SatCom systems, building on the significant advances in ML techniques in recent years. However, computer processors that have not been developed simultaneously as AI algorithms may limit or delay the expected benefits. In that sense, neuromorphic processors are ideally suited for AI tasks that require large parallel operations. Currently, neuromorphic computing systems are still under investigation. So far, there is still little development and a limited amount of prototyping. However, this technology is rapidly gaining momentum, and large companies, such as Intel and IBM, are involved in research projects related to this promising technology. Therefore, we consider that neuromorphic processors may represent the best approach to unlock the potential benefits of AI and ML solutions for SatCom systems [60].
## 2 Materials and Methods  
The medical literature reports several classifications of plateau tibial fractures. Currently the classification of Schatzker [1] is the one most adopted. This classification is based on a two-dimensional representation of the fracture, and divides tibial plateau fractures into six different groups according to pure shearing, pure compression and their combination (Figure 2) [1, 2]. Other aspects considered are the age of the patient, the bone quality, the morphologic architecture of the fracture and the energy  
Figure 1: A common tibial locking compression plate (LCP) [13].  
of the trauma. The first three groups (Types I, II, III) are fractures of the lateral tibial plateau and are generally associated with a low-energy mechanism and no fracture communution. Types IV to VI are high-energy injuries associated with knee joint instability. Ranging from subluxation to dislocation, these types of fracture are therefore more serious and related to considerable damage of the soft tissue.  
In this work, we decided to study the Schatzker type I fracture (Figure 2), which is a cleavage fracture of the lateral column. This kind of fracture is quite common in younger people [5], who have a denser cancellous bone that better withstands impact. In particular, we studied a fractured right tibia equipped with a plate fixed to the bone with locking screws.  
The research activity was articulated into three main steps. The first step consisted of the 3D acquisition and CAD modelling of the tibial bone and the locking plate. In the second step, all the 3D reconstructed models were imported and assembled through a CAD system and a common fracture of the tibia was virtually simulated. Finally, FEM analyses were performed to evaluate the stability of the system at different fixing screw orientations.