esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Link Establishment of Mega-Constellations
### Greedy Algorithm for Dynamic Link Establishment
The lack of global optimization in the greedy algorithm exacerbates this problem, causing some of the satellites to struggle to identify suitable counterparts. Additionally, frequent topology changes introduce instability into the communication system. While the greedy algorithm excels in computational efficiency for maximizing link duration times, it results in shorter average minimum link durations and more frequent switching events. This may hinder certain satellites from establishing effective links, impacting the overall system performance. The shorter link durations increase the number of satellites not connected through dynamic links, prolonging their average duration of unconnectedness [15; 16; 17]. This makes forming links more challenging, restricting the network capacity and affecting the system performance and stability.
# Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR
## Appendix B  
According to Table 8, under different orbital altitudes and Walker patten, we make the true anomaly of the seed satellite of the LEO SAR constellation traversing 0-360 degrees with an interval of 10 degrees and estimate the probability. The results are shown in Figures 11 and 12.  
From Figures 11 and 12, it's clear that the probability of GEO SAR receiving specular scattering RFI is much higher than that of LEO SARs and when the number of LEO SARs doubles, \(p_{GEO}\) increases but \(p_{LEO}\) decreases. These conclusions agree with Section 4 and we have illustrated them in detail. Another important conclusion is that different true anomaly of seed satellite of the LEO SAR constellation leads to different probability. In the future, in order to achieve various functions such as InSAR and continuous observation of certain areas, any true anomaly design is possible, thus, the probability is also changeable.
## 4 Strategy Description
### Number-of-Copies Control  
Controlling the number of copies can reduce redundancy and improve content diversity, especially when the content set is huge. From the analysis in Section 3, caching nodes tend to cache popular contents to promote the cache hit ratio. However, from the conclusion of our previous work [14], contents cached downstream will reduce the inflow request rate of the same contents cached upstream in ICN hierarchical topologies. In arbitrary topologies, this problem still exists. That is to say, as the number of copies increases, the marginal benefit of placing new replicas decreases. Thus, controlling the number of copies can help nodes determine whether to cache the transferred contents.  
However, getting the excepted number of copies of contents usually requires global information, such as the cache location of the content copies. When the cache location of the content copy has changed (such as when the content copy is evicted from a caching node), the excepted number of these content copies also requires to be recalculated, which incurs heavy computational overhead. Therefore, we expect to use a lightweight Number-of-Copies Control approach. Through analysis and trade-offs, we considered two easily accessible factors that might affect the excepted number of content copies. The first factor is the number of caching nodes in ICN, which reflects the size of the network and the upper limit of the number of content copies. The distance between users grows with the size of the network, to better serve the users utilizing caching, content copies need to be placed as close to the user as possible. For this reason, we speculate that larger networks will result in larger excepted numbers of content copies. The second factor is the popularity of contents. It is intuitive to think that popular contents will be requested by more users, so having more copies for them will bring more network benefits.  
Based on the above analysis, we specify a base number of copies (denoted as \(n_{base}\)) for the topology and use \(int(n_{base}p_{i,k})\) to represent the expected number of copies of content \(k\) predicted by node \(i\). The base number \(n_{base}\) is positively correlated with the topology size. As the user will get the NA list of content \(k\) when sending a name resolution message to the NRS, we can compare the current number of copies and the expected number of copies of content \(k\). If the current number of copies is smaller, we can perform caching operations while transferring content \(k\). This comparison only needs to be done at the node that serves the request (this node is called a hit node) and will not increase the load of on-path nodes.
# An Improved Pied Kingfisher Optimizer for Maritime UAV Path Planning
## 4 UAVS Path Planning Through IPKO  
In this study, the IPKO algorithm was employed to address the problem of drone path planning, validating its effectiveness in balancing path length, flight time, energy consumption, and obstacle avoidance. Through comprehensive comparisons with seven alternative algorithms that consider various constraints and optimization objectives, the IPKO algorithm demonstrated remarkable superiority in handling complexities of path planning within intricate environments. This not only enhances the efficiency of mission execution efficiency for drones but also establishes a robust foundation for their safety and reliability across various application scenarios.
## 2 Materials and Methods
### Antimicrobial Activity of _G. hederacea Extracts_
#### 2.5.2 Inoculum Preparation and Standardization  
Bacteria and yeasts were cultured for 24 h on agar media (according to Section 2.5.1). The bacteria and yeast inocula were prepared in Mueller-Hinton broth (MHB) for bacteria and SAB for yeasts, with optical density adjusted to 0.5 McFarland standard. In the case of filamentous fungi, hyphae and conidia suspensions were prepared in sterile PDB by mixing harvested mycelium from mature cultures with medium to achieve a final cell concentration of 10\({}^{6}\) cells/mL, determined with a hemocytometer.  
5.3 Minimal Inhibitory Concentration (MIC), Minimal Bactericidal Concentration (MBC), Minimal Fungicidal Concentration (MFC) Determination  
The MIC, as well as the MBC/MFC, of the tested _G. hederacea_ extracts were determined using the microdilution method according to [PERSON] et al. (2018) [47] and [PERSON] et al. (2019) [48] with some modifications. Twofold dilutions of the extracts were prepared in 96-well microtiter plates in MHB for bacteria, SAB for yeast and PDB for filamentous fungi. The final concentration of tested extracts was established in the range of 0.04-5 mg/mL; in the case of _Fusarium_ species the range was 0.08-10 mg/mL. The final concentration of methanol in control samples was established in the range of 0.2-25% in proportion to its content in the samples. Next, 100 uL of the microorganism solutions were added to each well. The plates inoculated with bacteria and yeasts were covered and incubated for 24 h at 30 \({}^{\circ}\)C or 37 \({}^{\circ}\)C, depending on the microorganism. In the case of filamentous fungi, microtiter plates were sealed with parafilm (to minimize the risk of extracts evaporation) and incubated at 25 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C for 5-10 days under aerobic conditions. Culture media containing _G. hederacea_ extracts without microbial inoculum were used as negative controls, whereas bacterial or fungal cultures without extracts were used as positive controls. After incubation, the optical density of the bacterial and yeasts samples was determined at a 600 nm wavelength using the BioTek Epoch 2 microplate reader. The MIC value was defined as the lowest concentration of extract that exhibited at least 90% growth inhibition. The MBC/MFC value was determined via spot inoculation of 10 uL of microbial culture with the addition of an extract at a concentration equal to or higher than the MIC value (100% inhibition based on spectrophotometric measurements using BioTek Epoch 2). MIC/MFC values for filamentous fungi were determined through a visual assessment of the fungal growth on the plate. All tests were performed in triplicate.
# Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems
## 1 Introduction
In order to comprehensively analyse the received information, the target data need to be transmitted to a data fusion center. Therefore, secondary forwarding devices such as relays may be applied during long-range communications. The networked UAV radar system should control complexity of information transmission process, and control the risks of information interception and jamming. In order to address the aforementioned design concerns, this paper proposes the use of reinforcement learning for training the behavior of the networked UAV radar systems. Although reinforcement learning is being adopted as an effective method in radar anti-jamming, there is a lack of research on multi-parameter programming in the literature. Meanwhile, most researchers only regard signal-to-interference ratio (SIR) as the criterion of the reward, which may not fully reflect the ability of the radar system to acquire target information. Therefore, this paper applies information representation theory to evaluate the capability of radar information acquisition. Specifically, this paper investigates the radar countermeasures under the conditions of malicious jamming, constrained UAV motion, fewer airborne radar elements, and complex topology of UAV networking. We construct two radar networking methods, introduce the theory of radar information representation, and use modified Q-Learning based on a double greedy algorithm to generate anti-jamming policies. The simulation results will prove the effectiveness of the algorithm. The contributions of this paper are as follows:  
1. A radar countermeasure system based on intelligent networked UAV swarms is established. Compared with the existing radar countermeasure models, our model introduces joint programming of UAV-borne radar to make full use of various degrees of freedom, where the policies can be generated from three dimensions of antenna, frequency and position. 2 Two UAV radar networking modes are proposed and implemented. The simulation results show that the proposed algorithm can realize the effective programming of radar system parameters in both modes. 3. This paper introduces a novel reward design by the information representation method to quantify the information received by the networked UAV radar systems. The structure of this paper is organized as follows: Section 2 introduces the related works of UAV networking and anti-jamming based on reinforcement learning. Section 3 is the system modeling. We will introduce the intelligent UAV swarm networking model and the phased array beam synthesis method with limited phase coding bits. Section 4 introduces DGQL (Double-Greedy Reinforcement Learning) algorithm based on information representation. Section 5 gives the simulation results and analysis, which proves the effectiveness of the DGQL. Section 6 summarizes the contribution of this paper.
# A Fast Algorithm for Three-Way Object-Oriented Concept Acquisition
## 5 Experimental Evaluation
### Performance Evaluation  
To evaluate the efficiency of Algorithm 1, we measured its run time and memory usage when constructing three-way object-oriented concepts from scratch in comparison with those of the QM algorithm. We used both the UCI datasets and randomly generated datasets for these experiments.  
Table 7 lists the run times and memory usage of both algorithms on the UCI datasets (Balloons, Lenses, COVID-19 surveillance). It shows that Algorithm 1 was significantly more efficient than the QM algorithm on each dataset. However, Algorithm 1 used more memory than the QM algorithm on each dataset. This is because Algorithm 1 not only stored the hash key of each OEO-concept but also was implemented with the dictionary and list data structure in Python 3.8, whereas the QM algorithm was implemented with the list data structure in Python 3.8. On the other hand, Table 7 shows that Algorithm 1 used the same size of memory on both datasets, Balloons and COVID-19 surveillance, due to the memory preallocation mechanism of Python 3.8.  
The QM algorithm requires computing the power set of attribute sets and obtaining the corresponding extent of object-oriented concepts based on the size of the power set. The more attributes there are, the greater the number of power sets of attributes that needs to be calculated. By contrast, Algorithm 1 derives OEO-concepts directly from single-attribute concepts. By iteratively merging these concepts, all OEO-concepts are obtained while avoiding the exhaustive calculation of the power sets of the attribute sets. Furthermore, it uses bitwise operations with the hash values, in place of union operations on extents of concepts, to improve its efficiency.  
Figures 4 and 5 show the run times of Algorithm 1 on datasets Data_A5 and Data_A10, respectively. These results reflect the inherent property of the proposed algorithm for constructing formal concepts: its time complexity increased exponentially with the number of objects and attributes, and the density of the datasets. Figures 6 and 7 show the run times of the QM algorithm on Data_A5 and Data_A10, respectively. It is clear that Algorithm 1 delivered better performance than the QM algorithm on each dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & \(|\mathbf{U}|\) & \(|\mathbf{V}|\) & **Num. of Concepts** & **Run Time of 3 WOC** & **Mem. Usage of 3 WOC** & **Run Time of QM** & **Mem. Usage of QM** \\ \hline Balloons & 20 & 10 & 91 & 0.0108 s & 4696 bytes & 5.5065 s & 904 bytes \\ Lenses & 24 & 12 & 229 & 0.0964 s & 9312 bytes & 64.3453 s & 1920 bytes \\ COVID-19 Surveillance & 14 & 17 & 139 & 0.0178 s & 4696 bytes & 86.4654 s & 1240 bytes \\ \hline \hline \end{tabular}
\end{table}
Table 7: Number of concepts, run times and memory usage on the UCI datasets.  
Figure 3: Number of concepts in Data_A10.
# Crossover Rate Sorting in Adaptive Differential Evolution
## 2 Materials and Methods
### Proposed Approach
```
1:Input: \(D\), \(NFE_{max}\), \(N_{max}\), objective function \(f(x)\)
2:Output: \(x_{best}^{top}\), \(f(x_{best}^{top})\)
3:Set \(N_{curr}^{0}=N_{max}\), \(N_{min}=4\), \(H=5\), \(M_{F,r}=0.3\), \(M_{Cr,r}=1\)
4:Set \(pb=0.3\), \(k=1\), \(g=0\), \(nc=1\), \(kp=0\), \(pm=2\)
5:Initialize population \((x_{1,j}^{near},...,x_{N_{max},j}^{new})\) randomly, calculate \(f(x^{new})\)
6:Copy \(x^{new}\) to \(x^{top}\), \(f(x^{top})\) to \(f(x^{top})\)
7:while\(NFE<NFE_{max}\)do
8:\(S_{F}=\emptyset\), \(S_{Cr}=\emptyset\), \(S_{\Delta f}=\emptyset\)
9:Rank either \(x^{new}\) by \(f(x^{new})\)
10:for\(i=1\) to \(N_{curr}^{S}\)do
11:Current memory index \(r=randInt[1,H+1]\)
12:Crossover rates \(Cr_{i}=randn(M_{Cr,r},0.1)\)
13:\(Cr_{i}=min(1,max(0,Cr))\)
14:endfor
15:Sort crossover rates according to \(f(x^{new})\)
16:for\(i=1\) to \(N_{curr}^{S}\)do
17:\(r1=randInt(N_{curr}^{S})\)
18:Current memory index \(r=randInt[1,H+1]\)
19:repeat
20:\(F_{i}=randc(M_{F,r},0.1)\)
21:until\(F_{i}\geq 0\)
22:\(F_{i}=min(1,F_{i})\)
23:repeat
24:\(pbest=randInt(1,N_{curr}^{S}*pb)\)
25:\(r2=randInt(1,N_{curr}^{S})\) or with rank-based selection
26:\(r3=randInt(1,N_{curr}^{S})\)
27:until indexes \(r1\), \(r2\), \(r3\) and \(pbest\) are different
28:Apply mutation to produce \(v_{i}\) with \(F_{i}\)
29:Apply binomial crossover to produce \(u_{i}\) with \(Cr_{i}\)
30:Apply bound constraint handling method
31:Calculate \(f(u_{i})<f(x_{r1}^{new})\)then
32:\(u_{i}\to x_{temp}\)
33:\(F\to S_{r}\), \(Cr\to S_{Cr}\)
34:\(\Delta f=f(x_{r1}^{new})-f(u_{i})\)
35:\(\Delta f\to S_{\Delta f}\)
36:\(x_{nc}^{new}=u_{i}\)
37:\(nc=mod(nc+1,N_{curr}^{S})\)
39:endif
40:endfor
41:Get \(N_{curr}^{S+1}\) with LPSR
42:Join together \(x^{top}\) and \(x^{temp}\), sort and copy best \(N_{curr}^{S+1}\) to \(x^{top}\)
43:if\(N_{curr}^{S}>N_{curr}^{S+1}\)then
44:Remove worst individuals from \(x^{new}\)
45:endif
46:Update \(M_{F,k}\), \(M_{Cr,k}\)
47:\(k=mod(k+1,H)\)
48:\(g=g+1\)
49:endwhile
50:Return \(x_{best}^{top}\), \(f(x_{best}^{top})\)
```  
**Algorithm 1** L-NTADE_s  
The block-scheme of the algorithm is shown in Figure 1.
## 4 Experimental Validation
### Analysis of Wavelet Cyclic Spectrum  
The most effective standard for distinguishing fault signals induced by different positions on the same equipment is the characteristic frequency components generated by the rolling elements passing the fault position in each fault condition. For early fault signals or weak fault signals with large external interference, the fault characteristic frequency is often overwhelmed by the rotation frequency or noise components. In order to strengthen the target information representation and expression ability of each signal in the data set, this paper proposed a pre-processing method of wavelet cycle frequency feature extraction to enhance the fault feature frequency. The wavelet cycle spectrums for different conditions are shown in Figure 5. It can be observed that fault-related information is mainly concentrated in the low frequency region. In order to clearly illustrate the target information enhancement ability of the proposed algorithm, the low frequency region spectrum is amplified locally, shown in Figure 6. In Figure 6a, there are only rotation frequency and harmonic components of the planet carrier and sun gear, corresponding to the normal condition. In Figure 6b-d, the components related to the fault of the inner race outer race and rolling elements of the planetary bearing are highlighted.  
It can be concluded that the wavelet cycle spectrum is very effective in enhancing fault characteristic information. The cyclic characteristic information spectrum is input into a CNN model as an analysis data set, which can eliminate the interference of irrelevant information to a certain extent and improve the representation ability of the model for target information.  
Figure 4: Picture of the test rig.  
Figure 5: Wavelet cycle frequency spectrum of each operating condition (**a**) Normal condition; (**b**) Inner-race Fault; (**c**) Outer-race Fault; (**d**) Ball Fault.  
Figure 6: Partial enlarged view of wavelet cycle frequency spectrogram; (**a**) Normal condition;\(f_{r}\) is the rotation frequency of planet carrier,\(f_{s}\) is the rotation frequency of sun gear; (**b**) Inner-race Fault;\(f_{\text{fib}}\) is the planetary bearing inner race fault characteristic frequency; \(f_{p}^{(o)}\) is the absolute rotation frequency of planetary gear; (**c**) Outer-race Fault; \(f_{\text{fib}}\) is the planetary bearing outer race fault characteristic frequency; (**d**) Ball Fault;\(f_{\text{fib}}\) is the planetary bearing rolling element fault characteristic frequency.
## 5 Numerical Experiments
### A Real-Life Production Line  
The previous two examples involve homogeneous production lines with exponential machines. In this section, we consider an inhomogeneous line with non-exponential machines, which was originally proposed by [PERSON] et al. [50]. It is an assembly line of electronic components in Guangdong, China. Table 7 presents the machine data, where CVF and CVR denote the coefficients of variation of machine failure and repair times, respectively. Unlike the previous examples, the machine processing rates are very fast. Accordingly, the total buffer capacity is set to a large value: 22,750. In our simulation, the failure and repair times are modeled by Weibull and log-normal distributions, respectively. Each simulation was run for a total of 100,000 time units and the first 10,000 time units were used as a warm-up period. We solved this problem using the FG algorithm and the AG algorithm, with the results shown in Table 8. Although the two algorithms yield slightly different solutions, they obtained the same throughput. Compared to the FG algorithm, the AG algorithm has a remarkably shorter computation time, less than 1/300 of the FG algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Solution** & \multicolumn{3}{c}{**Throughput**} & \multirow{2}{*}{\(\epsilon\)} & \multirow{2}{*}{\#Eval.} \\ \cline{3-3} \cline{5-6}  & & **DDX** & & & & \\ \hline \multirow{4}{*}{Li et al. [31]} & 111, 68, 62, 51, 35, 24, 21, 23, 29, 29, & & & & \\  & 26, 18, 16, 16, 15, 20, 37, 79, 74, 103, & & & & \\  & 155, 62, 62, 50, 35, 24, 21, 23, 29, 30, & & & & \\  & 25, 18, 16, 16, 15, 20, 35, 66, 41 & & & & \\ \hline \multirow{4}{*}{FG} & 88, 82, 48, 53, 35, 27, 20, 33, 38, 35, & & & & \\  & 35, 21, 16, 22, 26, 24, 49, 72, 50, 78, & & & & \\  & 131, 85, 46, 51, 35, 27, 19, 34, 38, 32, & & & & \\  & 33, 19, 17, 19, 25, 19, 39, 52, 27 & & & & \\ \hline \multirow{4}{*}{AG} & 87, 80, 49, 52, 36, 28, 22, 30, 37, 38, & & & & \\  & 35, 22, 19, 25, 26, 23, 46, 69, 53, 75, & & & & \\  & 129, 83, 48, 49, 37, 27, 24, 30, 40, 31, & & & & \\  & 32, 18, 21, 20, 24, 17, 39, 50, 29 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of solutions obtained by different algorithms for the forty-machine production line.  
Figure 4: Comparison with the buffer capacity distribution in [PERSON] et al. [31].
## 4 SDR Implementation of FM-ACSK System Prototype
### FM-ACSK Transmitter  
As shown in Figure 3, the transmitter of the prototype system consists of ACSK modulator with master chaos generators. This modulator's output is connected to upsampling block that converts ACSK modulator's sample rate into SDR's baseband sample rate. This upsampling makes it possible to use large FM deviations in the baseband FM modulator. Finally, the output of FM modulator is sent to Adalm PlutoTM SDR driver. The antipodal shift keying is performed by switching between inverted or direct master chaos generator's signal \(R_{out}\) depending on the transmitted data bits. Transmitted data in the system with a Hamming encoder is modified by adding parity bits for single error correction capability.
### Problem Statement
* We implement a **zero-trust enforcement architecture** with behavioral authentication, real-time anomaly detection, and micro-segmentation of xApps, reducing unauthorized access attempts by 89% (Section 5.1). * We develop an **FL framework** with \(\epsilon\)-differential privacy to defend against poisoning and inference attacks, achieving 32% energy savings with <5% latency impact even under 15% data corruption (Section 5). * We validate our framework using the **DAWN Dataset**[11] and **OpenRAN Gym**[19] to simulate realistic 5G/6G traffic patterns and RIC behavior. Our study uses the following **methodological components:** a formal threat modeling process to identify vulnerabilities in AI-based RIC components and communication pathways; a practical ZTA deployment with behavioral analysis using anomaly detection for access control in the RIC; an FL setup deployed across simulated RIC instances using OpenRAN Gym, enhanced with DP-based model aggregation; and experimental validation using 6G slicing traffic from the DAWN Dataset to benchmark performance, energy consumption, and latency under adversarial conditions. The rest of the paper is organized as follows: Section 2 provides a technical background on RIC architecture and ML threats in O-RAN. Section 3 reviews related work in O-RAN security and privacy-preserving FL. Section 4 formalizes our threat model, addressing both infrastructure and AI vulnerabilities. Section 5 presents our integrated Zero Trust and FL framework for secure O-RAN optimization. Section 6 details the implementation and a case study on dynamic DU sleep scheduling. Section 7 validates the framework through comprehensive experiments using the DAWN and OpenRAN Gym datasets. Section 8 concludes with key findings and future research directions, including implications for 6G standardization.