esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 3 Data Description: UWF-ZeekDataFall22
### Features in UWF-ZeekDataFall22  
This dataset includes multiple files containing nominal, numeric, and object variables that provide a rich description of network events. Key fields in the dataset are as follows:  
* Connection logs (conn): This provides detailed information about network connections, including source and destination IPs, ports, protocol type, connection state, and transferring data (bytes and packets);
* DNS logs: This provides information on DNS queries, including query types, response times, and whether requests were resolved;
* DHCP logs: This does data linking, for example, linking IP addresses with MAC addresses, assisting in identifying hostnames, and tracking device connections;
* Mission logs: This is used to map and label network events with MITRE ATT&CK tactics and techniques based on simulated attack scenarios.
* Each of these files plays a crucial role in identifying various phases of network interactions, ranging from routine traffic to suspicious or malicious behaviors.
###### Abstract  
In this paper, a new beamforming technique for planar two-dimensional arrays is proposed for optimizing the sidelobe levels (SLLs) by using recursive multibeam pattern subtraction (MPS) technique. The proposed MPS beamformer is demonstrated and its convergence to lower SLL values is investigated and controlled. The performance analysis has shown that the proposed MPS beamformer can effectively reduce the SLL down to less than \(-50\) dB relative to the mainlobe level utilizing the major sidelobes information in the radiation pattern. In addition, the proposed MPS beamformer can be applied to any planar array geometry such as rounded corners rectangular arrays provided that the original array pattern contains sidelobe peaks. The comparison with recent related techniques has shown that the proposed beamformer provides faster convergence time. On the other hand, the proposed technique provides lower sidelobe levels which cannot be achieved by efficient tapering windows for planar two-dimensional arrays. Finally, the scanning performance of the proposed MPS beamformer is demonstrated and the simulation results show solid and consistent SLL levels over the whole angular range from the broadside to endfire directions of the array.  
antenna arrays; sidelobe reduction; optimization techniques; tapered windows Article  
An Efficient Recursive Multibeam Pattern Subtraction (MPS)  
P.O. Box 11099, Taif 21944, Saudi Arabia; [EMAIL_ADDRESS]
## 5 Flying Base Station Capacity Gain Benefit
### Capacity Gain in Full-Duplex Case  
If the capacity gains \(\mathcal{G}>0\), i.e., \(C_{FU}-C_{SU}>0\), then the flying base station is beneficial to use in channel capacity.  
Theorem 4.4: _If the flying base station has full-duplex capability, it can be beneficial and provide greater capacity if_  
\[P_{\mathrm{F}}>P_{\mathrm{S}}\cdot 2^{-\gamma} \tag{8}\]  
Proof: \(C_{FU}>C_{SU}\) using Equations (4) and (6) and \(d_{FU}=0.5d_{SU}\) yields \(\eta>2^{-\gamma}\), which is equivalent to \(P_{\mathrm{F}}>P_{\mathrm{S}}\cdot 2^{-\gamma}\). Appendix A shows the full proof including the algebraic steps and derivations.  
Theorem 4.4 states that if the flying base station's transmission power is greater than \(2^{-\gamma}\) times the stationary base station's transmission power, then the flying base station is beneficial and can increase the capacity and the reliable communication rate in bits-per-second (bps). Otherwise, if the stationary base station transmission power is greater than \(2^{-\gamma}\) times the flying base station's transmission power, then utilizing the flying base station does not provide a capacity benefit. In communications practice, \(\gamma\) is typically 2 to 4, so that the factor \(2^{-\gamma}\) ranges from \(\frac{1}{4}\) to \(\frac{1}{16}\), as discussed in Section 3.3.
## 5 Conclusions  
A multiuser MISO SWIPT system using RSMA was evaluated with the assistance of a PB. The objective was to minimize the combined transmission power from the BS and PB while optimizing the beamforming vectors, common rate variables, and PS ratios. The proposed optimization problem was carried out under constraints that included EH requirements for both EH and PS users and data rate requirements for PS users. The proposed non-convex problem was divided into two parts. The first part was solved using the PCA method to reduce dimensionality and a DNN to predict the common rate variables. The second part used the SDR technique to optimize the PS factors and beamforming vectors. Comparative schemes were developed based on the PSO algorithm and SDR method for RSMA, along with a baseline scheme using NOMA.  
Numerical simulations showed that RSMA significantly reduced the transmission power compared to conventional methods such as NOMA and SDMA. Moreover, the proposed DNN-based method achieved high performance, closely matching the results of the near-optimal PSO-based scheme while considerably reducing computational complexity.  
Figure 10: Total transmission power of the PB and BS versus the initial position range of PS users on the \(x\)-axis.  
Furthermore, this study tested the proposed DNN-based scheme across challenging scenarios that were significantly different from those used for training during data collection. Across these scenarios, the DNN-based scheme exhibited high generalization performance. Furthermore, strategically placing the PB close to EH users substantially decreased the overall transmit power, effectively meeting their EH requirements.  
Conceptualization, M.R.C. and C.E.G.; methodology, M.R.C.; software, M.R.C. and C.E.G.; validation, C.E.G. and I.K.; formal analysis, M.R.C.; investigation, M.R.C. and C.E.G.; resources, I.K.; data curation, M.R.C.; writing--original draft preparation, M.R.C.; writing--review and editing, C.E.G. and I.K.; visualization, M.R.C.; supervision, I.K.; project administration, I.K.; funding acquisition, I.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Research Foundation of Korea (NRF) through the Korean Government's Ministry of Science and ICT (MSIT) under Grant NRF-2021R1A2B5 B01001721, and in part by the Regional Innovation Strategy (RIS) through the NRF funded by the Ministry of Education (MOE) under Grant 2021 RIS-003.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
## 3 Proposed Method
### Clustering Algorithm for Applying the Path-based Clustering Model
#### 3.3.3 Message Format for a Clustering Process  
The proposed PCDP has the following general message protocol. Figure 7 illustrates the general message protocol of the PCDP. All protocols of the PCDP are derived from the PCDP general message format. PCDP is a protocol based on WAVE Short Message and is basically an implemented protocol for the transmission method, such as Transmission Control Protocol (TCP) and User Datagram Protocol (UDP).  
The discovery message is shown in Figure 8. This is a message used in the discovery process described above, in which a neighbor is identified, or a new neighbor cluster is identified in a situation where a cluster is not assigned. In particular, since members become cluster members, ordinary members do not use discovery messages. This is because the cluster head is connected to 1-hop and the dissemination message is unconditionally forwarded to the head.  
The discovery answer message in Figure 9 is the reply to the node when it receives the discovery message. This is used to accumulate the connection probability in the case of the existing CPB, but it is used only in the PCDP to identify the neighboring node.  
Figure 8: PCDP Discovery Message.  
Figure 6: Example of a Neighbor Table.  
Figure 7: PCDP General Message Architecture.  
The head announcement message in Figure 10 is used to notify the neighboring node that it is the head. Members use the discovery answer message to report to the head that they are connected.  
If the unclustered node receives it, it asks the subscription according to the priority following the path information, as described above.  
Figure 11 illustrates the cluster assignment request message format. For a node receiving an announcement from a neighboring cluster head, generally, an unclustered node determines whether to join by considering the path distance. If the node wants to join, the ID of the cluster head in the cluster ID field of the cluster assignment request message is unicast.  
The format of the cluster confirmation message is shown in Figure 12. The cluster head that received the cluster assignment request from the neighbor node normally receives it and informs it through the cluster confirmation message that it will participate as a cluster member.  
Figure 13 shows the path announcement message format. When a node encounters a source ID for the first time, it automatically broadcasts a path announcement message, as shown in Figure 13. Each path is an integer of four bytes, which causes a large overhead when the first neighbor is encountered.  
Figure 11: DCDP Cluster Assignment Request Message.  
Figure 12: PCDP Cluster Confirmation Message.  
Figure 10: PCDP Cluster Head Announcement Message.  
Figure 9: PCDP Discovery Answer Message.  
However, this is a one-time message because the path has not changed. Therefore, it has a small effect in the long term.
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 5 Conclusions  
This study successfully validates the efficacy of a network-driven fog generation approach for improving dehazing performance in surveillance systems. By leveraging the GridNet architecture as the backbone for both fog synthesis and removal, the proposed Fog Generator Model produces realistic synthetic fog that enhances the robustness of dehazing networks. Comparative experiments reveal that dehazing models trained on GridNet-generated fog outperform those trained on traditional methods, as evidenced by superior PSNR and SSIM scores on the O-HAZY dataset. These findings confirm that integrating high-performance dehazing networks into fog generation not only bridges the gap in paired dataset availability but also elevates the quality of visibility restoration under adverse weather conditions. The proposed solution offers a practical and effective advancement for real-world surveillance, ensuring reliable operation in foggy environments. Moreover, the proposed fog generation model can be effectively applied in various practical domains such as traffic surveillance, autonomous driving, smart city security cameras, and drone-based environmental monitoring, where robust vision systems are required under adverse weather conditions. Future research could explore real-time implementation, broader dataset applications, and further optimization of the fog generation-dehazing pipeline to enhance its applicability across diverse scenarios.  
Conceptualization, S.Y.; methodology, S.Y.; software, S.Y.; validation, H.L.; formal analysis, S.Y.; investigation, S.Y. and B.P.; writing--original draft preparation, B.P.; writing--review and editing, S.Y.; visualization, Y.-K.K.; supervision, S.Y.; project administration, S.Y.; funding acquisition, S.Y.; resources, B.P.; data curation, B.P. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by Wonkwang University in 2025.  
Not applicable.  
The data presented in this study are available upon reasonable request from the corresponding author.  
The authors thank the anonymous reviewers and editors for their insightful comments and suggestions.
# Distribution and Deposition of Cylindrical Nanoparticles in a Turbulent Pipe Flow
## 3 Numerical Method and Main Steps of Simulation  
1. Equations (1), (2), (4)-(6) with \(\Phi=\mu_{a}=S_{k}=S_{\varepsilon}=0\) are solved to get \(\overline{u}_{j}\), \(k\), \(\varepsilon\) and \(\overline{u^{\prime}_{i}u^{\prime}_{j}}\).
2. Equations (10)-(12) are solved to get \(\overline{n}(v)\) and \(\Phi\).
3. Substitute \(\Phi\) into Equation (2) to get \(\rho_{m}\) and \(\mu_{a}\).
4. Substitute \(\overline{u}_{j}\), \(k\), \(\varepsilon\) and Equation (8) into Equation (9) and solve it to get \(\overline{\psi}\).
5. Substitute \(\overline{\psi}\) into Equation (3) to get \(\overline{a}_{ij}\) and \(\overline{a}_{ijkl}\).
6. Substitute \(\rho_{m}\), \(\mu_{a}\), \(\overline{a}_{ij}\) and \(\overline{a}_{ijkl}\) into Equations (1), (4)-(6) to get \(\overline{u}_{j}\), \(k\), \(\varepsilon\) and \(\overline{u^{\prime}_{i}u^{\prime}_{j}}\).
7. Turn to step (2) based on the new values of \(\overline{u}_{j}\), \(k\), \(\varepsilon\) and \(\overline{u^{\prime}_{i}u^{\prime}_{j}}\) if necessary.
8. Calculate the particle volume \(V\) with Equation (12).
9. Calculate the deposited particle volume \(V_{de}\) with Equation (14) and penetration efficiency PE with Equation (13).  
The equations in this paper are all partial differential equations and must be solved numerically. Equations (1), (4)-(6) and (10) are solved using the finite difference method. Equation (3) is integrated with the Simpson formula. The grid system consists of 70(\(r\)) \(\times\) 30(\(\theta\)) \(\times\) 240(\(z\)) = 504,000 grid points. A grid independence test is performed by changing grid points from 60 to 80 in radial direction, 25 to 35 in circumferential and 230 to 250 in axial direction, respectively. The parameters used in the computation are: \(\rho\) = 1.205 kg/m\({}^{3}\), \(\mu\) = 1.808 \(\times\) 10\({}^{-5}\) Pa-s, \(T\) = 293 K and \(k_{b}\) = 1.38 \(\times\) 10\({}^{-23}\) J/K.
# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 4 Results
### Technological Innovations
#### 4.3.2 Time-Based Low-Emission Zones Preserving Drivers' Privacy [22]  
This study presents a novel electronic road pricing (ERP) system designed explicitly for LEZs that addresses two significant challenges: preserving the privacy of honest drivers and improving fraud detection. Unlike traditional ERP systems, which compromise privacy by tracking vehicle paths and recording data at checkpoints, this proposal uses a user-centric approach. It ensures that only dishonest drivers are identified, maintaining the anonymity of compliant users. Its key contributions are as follows:  
* User-Centric Design: The system protects the privacy of honest drivers while maintaining a robust mechanism for detecting fraud. Only vehicles suspected of fraudulent behavior are photographed at checkpoints, preserving privacy for compliant users.
* Anonymity Revocation for Fraud Detection: Dishonest users lose their anonymity when fraudulent activity is detected. The system utilizes cryptographic techniques to revoke anonymity while safeguarding honest users' data.
* Advanced Cryptographic Protocols: The ERP system incorporates secure proof generation and verification processes. Vehicles interact with checkpoints using encrypted proofs, ensuring data integrity, non-repudiation, and limited traceability.
* Feasibility and Efficiency: Empirical evaluations demonstrate the system's practicality, highlighting its ability to operate seamlessly in real-world scenarios with mobile checkpoints and dynamic traffic conditions.  
This work highlights a critical trade-off between privacy and fraud detection in ERP systems and provides a balanced solution. Its contributions align with the need for privacy-preserving technologies in LEZs, ensuring public acceptance while enhancing system reliability.  
The integration of privacy-preserving technologies into dynamic LEZ systems is essential for fostering user trust and compliance. This study showcases how such technologies can be implemented without compromising fraud detection or system efficiency, offering valuable insights for next-generation LEZ strategies.
# Research on Gangue Detection Algorithm Based on Cross-Scale Feature Fusion and Dynamic Pruning
## 4 Experimental Validation and Analysis
### Model Pruning Experiments  
Based on the proposed improved pruning algorithm in the above-mentioned study, pruning experiments were conducted on the detection model, and the results of channel comparison before and after pruning are shown in Figure 8. The model pruned channels are mainly concentrated in the feature fusion part, and the number of feature extraction channels is pruned less, so that the redundant channels with low impact on model performance are eliminated, and the number of parameters is reduced from 7.02 M to 4.24 M on the basis of maintaining the model performance, reducing the complexity of the model. It makes the model detection more efficient.
## 1 Introduction  
Liquid crystal polymers (LCPs) are polymers that exhibit the properties of liquid crystals. Due to their dense crystal structure, LCPs are self-reinforced polymers that exhibit good mechanical characteristics. LCPs have fluidity, which makes them suitable for the production of thin-walled components. Additionally, LCPs exhibit a high dimensional stability, high gas barrier properties, stiffness, and a low thermal expansion. By taking advantage of these characteristics, LCPs are expected to be applied to fuel cell components and aircraft parts. Moreover, in next-generation wireless communication systems, LCPs are expected to be used as electronic materials with a low permittivity and low loss factor (tan \(\delta\)) [1, 2]. LCPs can form flexible, printed, circuit-like, thin-film substrates and are attractive for use in high-frequency, flexible antennas due to their low dielectric loss, low moisture absorption, and resistance to chemicals. The liquid crystal orientation affects the various characteristics of LCPs, such as their mechanical properties. Therefore, nondestructive testing techniques for measuring the liquid crystal orientation of LCPs are becoming increasingly important.  
Although LCPs are opaque to light in the UV, VIS, and IR regions, they are transparent to terahertz (THz) waves (0.3-10 THz). THz wave applications in fields including medicine [3], pharmaceutical science [4, 5, 6], security screening [7] and testing for industrial materials [8, 9] have been reported. LCPs exhibit birefringence in the THz range; therefore, the measurement of LCPs based on THz polarized waves was investigated. So far, the polarization imaging of birefringent materials including LCPs has been performed by using THz time-domain spectroscopy (THz-TDS) [10, 11, 12, 13]. However, THz-TDS faces some hurdles in being adopted for practical applications, such as the space required for the bulky optical setup, including a femtosecond laser; the stability of the measurement environment (temperature stability and low vibrations); and the need for skilled operators. Therefore, the development of a compact, stable, and easy-to-use THz light source is expected for industrial applications.  
Recently developed compact THz sources, based on intra-cavity difference-frequency generation (DFG) in a dual-color, mid-infrared quantum cascade laser (QCL) integrated with two different laser active regions, use mid-infrared active regions engineered to exhibit a large optical nonlinearity \(\chi^{(2)}\) for an efficient THz DFG process [14]. Currently, these are the only electrically pumped, monolithic semiconductor and room-temperature-operable sources used at frequencies between 0.6 THz and 6 THz [15; 16; 17; 18].  
In this paper, we demonstrate the polarization imaging of an LCP with a compact THz DFG source and show the feasibility of nondestructive testing for LCPs by using this DFG source. This work is the first demonstration of THz polarization imaging for LCP using a very compact, monolithic semiconductor THz DFG source.
## 4 Discussion
### Beach Ridge Morphodynamics and Radar Facies  
There are a relatively small number of GPR studies of mixed sand and shingle beaches, and the results tend to show two contrasting reflection patterns. GPR surveys of beach ridges in British Colombia show beach face progradational facies with limited 'beach cap' facies [17]. Seaward inclined beach face progrades, from beach ridges on the Island of Anholt, are reported by [30], and this reflection pattern was confirmed by [31]. In contrast, GPR profiles across mixed sand and gravel beach ridges in Canada described by [32] show lens-like geometries and convex reflections with onlapping landward terminations attributed to vertical accretion and overwash on the beach ridges. Lens-like geometries and low-angle landward dipping reflection patterns with seaward dipping bounding surfaces within sand and gravel beach ridges on the Suffolk coast, UK are also reported by [29]. These are attributed to landward migration of multiple berm ridges with overtop and overwash deposits during storm events to form beach ridges [29].  
The two reflection patterns that appear to correspond with the two types of beach ridges, Mode I and Mode II, described by [33]. Mode I forms comprise seaward dipping swash units and are associated with shore-normal sediment movement ([PERSON] 1986). Mode II beach ridges are dominated by landward dipping units associated with spillover and washover. Mode II is associated with longshore drift [33], which presents a conundrum, because Dungeness is dominated by longshore drift but shows the seaward inclined beachface reflection pattern expected for shore normal, rather than longshore, sediment transport.