esa-sceva/satcom-chunk-collection huggingface dataset:
# Forgery Detection for Anti-Counterfeiting Patterns Using Deep Single Classifier
As the update iteration of electronic equipment becomes faster and faster and the equipment used for forgery becomes more and more sophisticated, it is difficult to collect all types of forgery during design, which makes the differentiation of traditional features easy to decline. 2. At present, most of the identification methods rely on special equipment, and the identification methods on mobile devices are not mature. There is a lack of accurate and efficient anti-counterfeiting pattern authentication methods for mobile devices. 3. The blur degradation of the printed anti-counterfeiting patterns is often produced in the process of using mobile devices to capture [31; 32]. Due to the relative motion of the camera and the pattern in the process of photo acquisition, as well as the error of the focusing operation and other factors, the acquired image often degrades, which has an impact on the authenticity identification of the anti-counterfeiting pattern. Therefore, it is necessary to consider the blurry situation in the design of the identification algorithm for mobile devices so as to reduce the impact of blur on the identification results. Targeting the problems above, based on the fact that there are far more authentic products than fakes, a forgery detection method for anti-counterfeiting patterns using U-Net and a single classifier is proposed. A two-stage framework for deep one-class classification [33] is applied. First, an auto-encoder (AE) based on U-Net architecture [34] is trained to extract features of the anti-counterfeiting pattern images, and blurry images are used to train the AE to enhance the robustness of extracted features of input blurry images. Then, we build a one-class classifier on the extracted features, the feature vectors are input into one-class support vector machine (OCSVM) for classification, and the decision boundary of OCSVM is modified according to certain strategies to improve the robustness of the input blurry images. The experimental results show that the method proposed in this paper is suitable for practical applications. On the basis of being able to better distinguish the authentic and counterfeit anti-counterfeit pattern images, the recognition rate of the blurry image of the authentic anti-counterfeit pattern is improved.
# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 2 Related Work
### Deep Learning-Based Time Series Prediction Methods  
Deep learning has undergone rapid development and achieved significant progress in predicting time series data. CNN [PERSON] (2016), RNN [PERSON] (2016), [PERSON] _et al._ (2017), Transformer Wang _et al._ (2017), and GNN Wang _et al._ (2017) are extensively employed across a range of time series prediction tasks. Numerous enhanced methodologies have been proposed based on these models. TCN [PERSON] _et al._ (2017) treats the time series as a one-dimensional object, capturing long-term relationships through iterative multilayer convolution. SCINet [15] employs a hierarchical convolutional network structure to extract and aggregate features at different temporal resolutions. To predict future diseases, RETAIN [16], Dipole [17], and Timeline [18] integrate RNN with attention mechanisms [12] to model and analyze patients' historical disease diagnostic data. Deep State Space [19] models the relationship between consecutive hidden states via an RNN, enabling predictions from the current hidden state to the desired outcome. Log Sparse Transformer [20] employs causal convolution to generate Queries and Keys in the self-attention layer, thus introducing log sparse sparsity into the model. [PERSON] [21] integrates GNN with the attention mechanism to accurately predict diseases for patients. In order to capture latent spatial dependencies in the data, Graph Wave Net [22] introduces an adaptive graph modeling technique. CoDMO [23] utilizes correlation-enhanced hierarchical propagation models and prior interactions in historical records to learn dual medical ontology representations for predicting a patient's future conditions and procedures during the next admission. HGV4 Risk [24] proposes the Global Graph Embedding module and \(\beta\)-attention mechanism, thereby enabling risk prediction based on temporal sequential data.  
Although the aforementioned methods exhibit robust performance in time series prediction, their applicability to directly predict vehicle maintenance projects is limited. This limitation arises from their inability to accommodate the unique characteristics inherent to these tasks. Unlike these methods, our study comprehensively integrates the correlations between maintenance projects and the effects of vehicle mileage and foundational data to ensure precise predictions of vehicle maintenance projects. With the continuous development of the vehicle industry, methods for forecasting vehicle maintenance demand are evolving and can be categorized based on the data source into single data-based and combined data-based vehicle maintenance demand predictions.
###### Abstract  
Tracking of hypersonic glide vehicles (HGVs) by a constellation tracking and observation system is an important part of the space-based early warning system. The uncertainty in the maneuver intentions of HGVs has a non-negligible impact on the tracking and observation process. The cooperative scheduling of multiple satellites in an environment of uncertainty in the maneuver intentions of HGVs is the main problem researched in this paper. For this problem, a satellite constellation tracking decision method that considers the HGVs' maneuver intentions is proposed. This method is based on building an HGV maneuver intention model, developing a maneuver intention recognition and prediction algorithm, and designing a sensor-switching strategy to improve the local consensus-based bundle algorithm (LCBBA). Firstly, a recognizable maneuver intention model that can describe the maneuver types and directions of the HGVs in both the longitudinal and lateral directions was designed. Secondly, a maneuver intention recognition and prediction algorithm based on parallel, stacked long short-term memory neural networks (PSLSTM) was developed to obtain maneuver directions of the HGV. On the basis of that, a satellite constellation tracking decision method (referred to as SS-LCBBA in the following) considering the HGVs' maneuver intentions was designed. Finally, the maneuver intention prediction capability of the PSLSTM network and two currently popular network structures: the multilayer LSTM (M-LSTM) and the dual-channel and bidirectional neural network (DCBNN) were tested for comparison. The simulation results show that the PSLSTM can recognize and predict the maneuver directions of HGVs with high accuracy. In the simulation of a satellite constellation tracking HGVs, the SS-LCBBA improved the cumulative tracking score compared to the LCBBA, the blackboard algorithm (BM), and the variable-center contract network algorithm (ICNP). Thus, it is concluded that SS-LCBBA has better adaptability to environments with uncertain intentions in solving multi-satellite collaborative scheduling problems.  
satellite constellation tracking system; hypersonic glide vehicles; maneuver intention prediction; sensor-switching strategy; parallel stacking neural network [1][  
issue of current research. The continuous tracking of moving targets in space by multiple satellites is influenced by the target's maneuverability. The HGVs have gained considerable interest in recent years because of their high speed and high maneuverability. The coherence of satellite tracking can be greatly affected by their maneuver uncertainty. Therefore, this paper addresses the problem of cooperative tracking of HGVs by constellations.
## 3 Related Work
#### Overall Patterns  
* Growing empirical rigor (more testbed deployments, clearer performance/security metrics).
* Transition to decentralized trust (Blockchain as standard underpinning).
* Emergence of architectural integration [45]--but full, real-world ZT + FL O-RAN validation is not yet realized.  
Table 4 serves as a benchmark summarizing key research papers that focus on security and privacy approaches in the O-RAN ecosystem. This table provides an overview of various security and privacy techniques, including Zero Trust (ZT), FL, and Blockchain-based solutions, which have been applied to different O-RAN components such as xApps and RIC. Each paper is categorized by the security or privacy approach used, the target system, empirical validation methods, and the outcomes or metrics achieved. The papers presented span across several years, from 2022 to 2025, showcasing the progression of research in this field. Notable approaches include Federated Deep Reinforcement Learning for jamming mitigation [39], secure aggregation in FL [44], and decentralized zero-trust frameworks for O-RAN [37]. These papers highlight the diverse methodologies and innovative solutions being explored to secure and optimize O-RAN systems. The summary in Table 4 encapsulates the state-of-the-art advancements in the area, offering a comprehensive overview of current research efforts in securing O-RAN infrastructures.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Year** & **Milestone/Paper** & **Focus Area(s)** & **Method/Validation** \\ \hline
2022 & Federated DRL for O-RAN Slicing [40] & FL for optimization & Empirical/simulation \\ \hline
2023 & ZTRAN [36], OZTrust [38] & Zero-Trust for O-RAN & Testbed/prototype \\  & OpenAI dApp [41] & FL RL for O-RAN & Testbed \\ \hline
2024 & FL for Jamming [39], FL Anomaly [42] & FL for O-RAN security & Simulation/testbed \\  & ZTORAN [45] & ZT + FL Integration & Simulation \\  & TrustORAN [37] & Decentralized ZT & Blockchain deployment \\  & HSADR [44] & Secure FL aggregation & Security proof/experiments \\ \hline
2025 & Blockchain FL Reputation [43] & FL trust/reputation & Blockchain testnet (conceptual) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Timeline of milestones/papers and their focus areas and methods/Vilidation.
## 5 Conclusions  
In MANETs, various attacks disturb the network operation during communication, which is a major concern. The rapid detection of malicious attacks is an essential task to prolong the lifetime of the network. In order to maintain the accuracy of the detection rate of malicious nodes, in this research we have proposed a technique which provides a detection mechanism for black and gray hole nodes in MANETs. The CDS approach and IDS nodes are used to detect malicious nodes. Using the CDS approach, small-sized groups of nodes are created, which are called IDS sets. In the IDS set, a trusted node with the highest energy will be selected as the IDS node for broadcasting the status packet. After the selection of the broadcaster node, it sends the status packets periodically in the IDS set to check that every node is forwarding the data packets properly or to identify any malicious nodes that are present. All of the legitimate nodes provide the correct routing information to the IDS node. Malicious nodes send false information to drop the data packets. After receiving all of the replies from all of the nodes, the IDS node compares the answers, as the malicious node is a liar and can easily be detected. The simulation results prove that our proposed technique is successful in detecting the malicious nodes, as it receives many replies from the status packet. The proposed technique provides a high packet delivery ratio and less delay because only trusted nodes communicate in the network after the detection of malicious nodes. One drawback of the proposed GGBG is the limited battery power of the nodes, as it cannot continuously monitor the nodes for a long period of time. Additionally, the proposed work detects well-known attacks but not all attacks. Our experimental outcomes indicated that the proposed DDBG technique is an effective and prominent approach for the detection of black and gray hole attacks. In future work, this technique can be extended into wireless sensor networks for any environmental fix to sense the data for a particular application.  
**Author Contributions:** Conceptualization, J.H. and N.Z.; software, M.I.H.; validation., M.S.P., K.H.M. and M.Q.M.; formal analysis, J.H.; writing--original draft preparation, methodology, Z.A.Z.; writing--review and editing, supervision, J.H.; funding acquisition, N.Z.  
**Funding:** This research has been funded by National Natural Science Foundation of China, grant number 61602456.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Synthesis and Porous Structure Characteristics of Allyl Methacrylate/Divinylbenzene Polymers
## 1 Introduction
CMSt polymer has a completely amorphous structure because CMSt and DVB have chemical similarities in terms of reactivity ratios, and their copolymerization resulted in an even distribution of polar nitrile groups throughout the polymeric network. However, those CMSt/DVB sorbents displayed a high dependence on the specific surface area and the number of crosslinkers. A surface area of 560 m\({}^{2}\)/g was obtained when the resin was crosslinked with 70 wt% of DVB. For lower crosslinking level--50 wt%--the surface area dropped sharply to 308 m\({}^{2}\)/g [4]. That meant that it was not possible to have both a high specific area and a high density of polar groups on the sorbent at the same time. Some investigations were devoted to the use of crosslinkers other than DVB in the synthesis of polar porous polymers. It is yet another way to increase the surface polarity and hydrophilicity of porous polymers. For example, in [22], ethylene glycol dimethacrylate (EGDMA), glycerol dimethacrylate(GDMA), and trimethylolpropane trimethacrylate (TMPTMA) were copolymerized with DVB, and the resulting porous polymers showed better recoveries of polar compounds. In [23], the results of the application of a new crosslinker--2,3-(2-hydroxy-3-methacryloyloxypropoxy) naphthalene in copolymerization with DVB were presented. The new monomer possessed ester and secondary hydroxyl groups, which gave the desired hydrophilicity to the porous polymers. This way was continued in [24], where trimethylolpropane trimethacrylate and another derivative of naphthalene diol:di(methacryloxymethyl) naphthalene were applied as polar crosslinkers in the copolymerization with 1-vinyl-2-pyrrolidone. We decided to look for other monomers which could be an alternative source of polarity and hydrophilicity. In the search for the appropriate monomer, we turned our attention to the allyl methacrylate, the so-called dual crosslinker monomer, which is not so frequently described in the literature. Soluble and non-crosslinked copolymers of styrene with allyl methacrylate were prepared via atom transfer radical polymerization [25]. To our best knowledge, allyl methacrylate has not been used to obtain porous, highly crosslinked polymers. There is only one study about the synthesis of the terpolymer styrene/divinyl benzene/allyl methacrylate, which was evaluated for Fe(II/III) removal [26]. In this study, a set of allyl methacrylate/divinylbenzene copolymers was prepared as a novel, permanently porous material. An added value of polymerization of allyl methacrylate would also be the possibility of using allyl groups remaining after polymerization as anchor points in chemical modifications.
# Efficient Multifocal Structured Illumination Microscopy Utilizing a Spatial Light Modulator
## 2 Methods
### Illumination Modality of MSIM  
The SLM is programmed with phase profiles of 2D periodic checkerboard phase plates and illuminated by a horizontally polarized laser beam, resulting in two mutually perpendicular sets of diffraction laser beams. Figure 2 illustrates the schematic of the generation of the multifocal arrays. Focused by the MO2, the four remaining laser beams interfere and overlap to form the multifocal illumination patterns on the sample plane as shown in Figure 2b.  
A multifocal array is generated by the interference and overlap of two sets of \(\pm 1\) order diffraction laser beams. The spatial frequency of the multifocal array is confined by the fringes generated by every single set of \(\pm 1\) order diffraction laser beams. The spatial frequency of the fringe \(k_{s}\), is determined by the intersection angle of the two wave vectors of the laser beams [29]:  
\[k_{s}=2n\times sin(\theta)/\lambda, \tag{1}\]  
where \(n\) is the refractive index of surrounding medium, \(\theta\) is the half intersection angle and \(\lambda\) is the wavelength. These laser beams are interfered by a microscope, so Equation (1) can be turned into  
\[k_{s}=2\text{NA}/\lambda, \tag{2}\]  
where NA is the numerical aperture of the microscope. It means that the spatial frequency of multifocal arrays is determined by the diffraction limitation composed by the microscope. The above equations  
Figure 1: Schematic diagram of the multifocal structured illumination microscope. Abbreviations: MO1-MO2: microscope objective lens; L1-L3, lens from Union Optic with focal lengths of 125, 150 and 150 mm; RM, reflective mirror; HPW, half wave plate; LP: linear polarizer; SLM: spatial light modulator; SF: spatial filter; DM, dichroic mirror; LPF, long-pass filter.  
Figure 2: Schematic of the generation of the multifocal arrays. (**a**) The four remaining laser beams are focused by the MO2. (**b**) The multifocal array generated by the interference and overlap of the four laser beams.  
show that, to obtain multifocal arrays with high spatial frequency for achieving resolution enhancement as much as possible after reconstruction, the four diffraction incident laser beams should be close enough to the edge of the rear pupil aperture of the microscope.
## 2 Theoretical Analysis and Experimental Details
### Theoretical Model Analysis of Digging
#### 2.1.1 Active Material Pressure and Its Failure Surface  
This can be simplified by studying the interaction force between the grab and the material. The schematic diagram of the active pressure and the failure surface of the grab is shown in Figure 4, and the surface BC represents the active failure surface. Analyzing the size of the friction force R between the damaged failure surfaces of the material, we find the friction force is related to the characteristic parameters of the material [18]. The material gravity is calculated according to the calculation in Formula (1). On the back-of-AB surface of the grab bucket, the material generates active pressure, and its calculation is obtained using Formula (2). To analyze the active pressure of the grab, the depth can be calculated using Formula (3).  
\[W=\rho bS_{ABC} \tag{1}\]  
\[F_{B}=\int_{0}^{H}p_{B}(z)dz \tag{2}\]  
Figure 3: The failure surface of the material excavated by the grab.  
Figure 2: The theoretical and experimental research on the flat digging of grab dredgers.  
\[dF_{B}=\rho_{B}(z)dz \tag{3}\]  
where \(W\) is the weight of the material boundary body ABC (N), \(\rho\) is the material density (N/m\({}^{3}\)), \(b\) is the grab width (m), \(S_{ABC}\) is the area of the material volume ABC (m\({}^{2}\)), \(F_{B}\) is the active pressure resultant value (N), \(H\) is the digging depth (m), \(z\) represents the integral formal parameters (m), \(\rho_{B}\) is the pressure value at depth \(z\) (N/m), and \(dF_{B}\) is the element of active pressure at depth \(z\) of body ABC.  
According to the dynamic balance of the material body ABC, the equilibrium state of the material body ABC is calculated using Formula (4). The angle of the active pressure on the horizontal plane can be determined according to Equation (5):  
\[\overset{\rightarrow}{F}_{B}+\overset{\rightarrow}{W}+\overset{ \rightarrow}{R}=0 \tag{4}\]  
\[\frac{dF_{B}(\beta)}{d\beta}=0 \tag{5}\]  
where \(R\) is the frictional force generated by the soil (N) and \(\beta\) is the angle of the active pressure on the horizontal plane (rad).
## 3 Results
### Physical Modeling Data Example
Additionally, within the red-boxed area in the northern target layer--corresponding to Fracture Set 1 (Figure 5b)--three fault groups with widths of 10 m, 20 m, and 30 m were embedded. The C3 attribute (Figure 7c) only resolves the 30 m fault, while the instantaneous phase (Figure 7b) identifies both 20 m and 30 m faults. In contrast, the OOCA method (Figure 7d) clearly delineates all three faults, including the 10 m feature. These results demonstrate that the OOCA achieves a lateral resolution of approximately 10 m under the current model configuration, significantly outperforming conventional attributes and offering notable advantages in characterizing densely distributed or fine-scale fracture systems in structurally complex settings.
# Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
## 3 Literature Review and Proposed Research
Gaussian Naive Bayes, neural networks, and Decision Trees are employed for botnet detection with the CTU-13 dataset [16]. Ref. [17] focused on detecting social bots using deep learning techniques. [PERSON]. [18] employed an ensemble learning algorithm for botnet detection, combining results from various supervised machine learning algorithms. Smartphone-specific features were considered for botnet detection in internet-connected smartphones, demonstrating a novel approach [19]. Android botnets were targeted using machine learning algorithms like Naive Bayes and Random Forest [20]. Machine learning models were also used to detect botnets in the Internet of Things (IoT), handling data balancing with SMOTE [21]. Thus, using machine learning models to detect and classify botnets using network flow data can achieve good results. Also, several researchers have used the CTU-13 dataset with machine learning approaches with successful outcomes. Some studies also used SMOTE with the CTU-13 dataset for oversampling to balance the data [21]. Notably, some studies also explored the use of HMMs and PHMMs in different areas of cybersecurity [22; 23]. These probabilistic models capture activity patterns that indicate botnet behavior. However, it is worth noting that gaps exist in the literature regarding their specific application in botnet detection. In our proposed work, we aim to bridge those existing gaps by applying an HMM and a PHMM for botnet detection. Our goal is to contribute novel insights to the field of botnet detection, ultimately improving network security and safeguarding against cyber threats.