esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design
### Synthetic Data Generation for Users and Resources  
The smart contract encapsulates three primary functions essential to the simulated access control environment: user registration, policy creation, and access evaluation. These operations are engineered to map directly to real-world interactions within resource access management systems, ensuring contextual relevance and system fidelity.  
1. User Registration Function (RegisterUser)  
Users are registered on the blockchain ledger with distinct identities and attribute sets. Each user's attributes--such as designation, organization, and roles--are captured in a JSON object. Upon invocation, the smart contract securely stores these attributes on-chain, linking them immutably to the user identifier [19; 31].
2. Policy Creation Function (CreatePolicy)  
Access policies are formulated for each mining license resource, based on ABAC principles. Policies specify permissible conditions (e.g., roles allowed to access specific licenses). The smart contract maintains these policies on-chain as key-value mappings between resource IDs and their access conditions, allowing efficient retrieval and enforcement during queries [32].
3. Access Evaluation Function (EvaluateAccess)  
During access requests, the smart contract is invoked to dynamically evaluate user attributes against the corresponding resource's policy rules. Access decisions are returned as Granted or Denied, with each interaction immutably logged on-chain. This mechanism ensures consistency in policy enforcement and provides a reliable basis for post-simulation analysis.
## 2 Experimental Details  
The binary mixtures were prepared from Analar-grade ethanol (E-Merck chemicals, Darmstadt, Germany), 1-hexanol, and 1-octanol (S.D Fine Chemicals Ltd., India) (27). Ethanol, 1-hexanol, and 1-octanol were purified by the methods described in the literature [40; 41]. The binary mixtures of ethanol with 1-hexanol and 1-octanol were prepared by weighing an appropriate volume of each liquid component and were kept in special airtight bottles. All solutions were prepared in a dry bog. Viscosities were determined using a Cannon-Ubbelhode viscometer [27; 42] calibrated with triple-distilled water. The viscometer containing test liquids was kept for about 20 min in a thermostatic water bath and the temperature fluctuation in the viscometer measurement was minimized. The overall experimental uncertainty was estimated to be \(\pm\)1.5 \(\times\) 10\({}^{-3}\). The densities of pure liquids and binary mixtures were measured using a single-stem pycnometer (made of Pyrex glass) with a bulb capacity of 8 \(\times\) 10\({}^{-3}\) dm\({}^{3}\) and with a graduated stem of 5.0 \(\times\) 10\({}^{-7}\) dm\({}^{-3}\) divisions. The ultrasonic velocities of pure liquids and their mixtures were measured using a single-frequency ultrasonic interferometer operating at 3 MHz with an uncertainty of +0.05% and a temperature (\(\pm\)0.02 K) maintained in a thermostatic water bath [27]. The values of densities and viscosities at 303.15 K were determined precisely up to \(\pm\)0.01 kgm\({}^{-3}\) and \(\pm\)3 \(\times\) 10\({}^{-6}\) Nsm\({}^{-2}\), respectively. The Cannon-Ubbelhode viscometer is conceptually simple: the time it takes a volume of solute to flow through a thin capillary is compared to the time for a solvent flow. It turns out that the flow time for either is proportional to the viscosity, and inversely to the density.  
The relative viscosity is determined using the ratio \(\eta_{rel}=\eta_{soln}/\eta_{solvent}\) (1)
## 3 Tests and Results
### Kitsuine Backplane Verification  
During the satellite's development, board- and system-level verifications were performed. In addition, satellite on-orbit data were available. The subsections that follow discuss both ground and on-orbit results.
## Methods and Materials
### Optimization of Waveguide Geometry of PhC NB Cavity Structure with a Horizontal Air Gap
Figure 4a shows Q-factors of the air gap cavity mode as functions of the thickness of the air gap with various thicknesses of the Si waveguide. According to these results, the Q-factor increases when the horizontal air gap becomes thin and the Si waveguide becomes thick, which is caused by a strong PBG effect from the high index contrast. However, the value of \(S\) of the air gap cavity mode shows exactly the opposite behavior compared with the Q-factor owing to the increase in the air portion (See Figure 4b). In particular, \(S\) can exceed 1200 nm/RIU when the air portion increases even though the Q-factor becomes low. For the evaluation of the optical sensing performance, the figure of merit (FOM) was defined as follows [26]:  
\[\mathrm{FOM}=S\cdot\mathrm{Q}/\lambda_{\mathrm{res}} \tag{5}\]  
where \(\lambda_{\mathrm{res}}\) is the resonant wavelength. Figure 4c shows the FOM of the air gap cavity mode as a function of thickness of the air gap with various thicknesses of the Si waveguide. Because the Q-factor increases exponentially with decreasing \(t_{\mathrm{g}}\), the FOM shows a similar behavior to that of the Q-factor. For the best performance of the optical sensor, the Q-factor should be balanced with \(S\). Therefore, the PhC NB cavity structures with different thicknesses of the Si waveguide have different optimal thicknesses of the air gap, as marked with circles in Figure 4c, which have similar a Q-factor (~1000) and \(S\) (~1000 \(nm\)/RIU), simultaneously. It is worth noting that by simply increasing the number of mirror holes, the Q-factor could be exponentially increased; therefore, the optimal thickness of the air gap is also changed. Figure 3: Characteristics of the air gap cavity mode in a PhC NB cavity with a horizontal air gap. (**a**) Q-factor and \(S\) as functions of the variation in the size of air holes with different modulation functions to reduce the air holes from the center to the mirror. In the simulation, \(w=0.95a\), \(t=1.2a\), \(t_{\mathrm{g}}=0.15a\), and \(r_{0}=0.45a\). (**b**) Fine optimization of variations in the size of air holes. The inset indicates the mirror strength as a function of variations in the size of air holes. (**c**) Q-factor and \(S\) as functions of the number of mirror holes. In the simulation, all other structural parameters are the same as those in (**b**) except \(\Delta r/r_{0}=0.12\).
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 4 Testbed and Evaluations
### Performance of Central Control Platform
#### 4.4.2 Dissemination Delay and Throughput  
In order to evaluate the dissemination delays of attack definitions, we generated 100 different payloads with attack definitions and forwarded each payload to 100 connected controllers in three different modes. i.e., Burst mode, with 100 ms delay (the delay added between two different attack definitions), and with 500 ms delay. Figure 11 shows the results. In burst mode i.e., with 0 delay between attack definitions, it took approx. 28 s to dissipate all the flow policy to 100 connected controllers. With a delay of 100 ms, it took approx. 34 s and with a delay of 500 ms it took approximately 50 s.  
According to the CIDR report [36], the number of ASes in the routing system today are approx. 58,000. Figure 12 shows the growth of ASes count over the years. We measure how the number of ASes effects the time required to process the attack definitions at the central control and the throughput in terms of AS count. For this, we varied the AS count between 5000 to 60,000 (emulating realistic global AS count) and the central control platform forwarded the attack definition with a payload of 100,000 malicious IP addresses, in burst mode, to all the connected ASes. Figure 13 shows the average processing delays for different AS counts. On average it took 21.74 s to process and forward the attack definition to 5000 ASes. An increase in AS count adds a linear increase in the delays and with 60,000 ASes the overall delay was around 252.71 s. These numbers represent delays as per current global AS numbers on average commodity hardware. The performance can significantly improve with high-end computational devices.  
Figure 14 shows the throughput in terms of number of ASes handled per second. The average throughput remained fairly constant throughout the experiment and we were able to forward the attack definitions to 230-237 ASes per second. This is equivalent to disseminating 23 million malicious IP addresses per sec.  
Figure 11: Central Platform: Load testing at varying delays.  
Figure 10: Effect of payload size on processing delays.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 2 Principles
### Multi-Slit Diffraction Model  
As illustrated in Figure 2b, uniform 1D EF OPAs can be described by the multi-slit diffraction model. The EF OPA is regarded as a rectangular aperture array in the diffraction model. Then the far-field angular intensity distribution [13, 59, 60] is in the form of:  
\[I=I_{0}[\frac{\sin[\frac{1}{2}(k_{0}\delta_{x}\sin\theta_{x})]}{\frac{1}{2}(k_ {0}\delta_{x}\sin\theta_{x})}]\frac{\sin[\frac{N}{2}(k_{0}\Lambda_{x}\sin \theta_{x}-\Delta\varphi_{x})]}{\sin[\frac{1}{2}(k_{0}\Lambda_{x}\sin\theta_ {x}-\Delta\varphi_{x})]}]^{2} \tag{5}\]  
where \(\delta_{x}\) is the element size of the \(N\)-element OPA, \(I_{0}\) stands for the unitary peak intensity in the far field of a single element, and \(\theta_{x}\) indicates the angle of \(x\)-direction. The identical phase difference of \(\Delta\varphi_{x}\) is applied between adjacent channels placed at a pitch of \(\Lambda_{x}\). The EF OPAs are commonly used to achieve 1D beam forming and steering by phase-shifting between waveguides. As shown in Figure 3a, the first fabricated OPA with a half-wavelength element spacing is used to demonstrate beam steering over a range of 64\({}^{\circ}\) by integrated thermo-optic (TO) heaters. The measured intensity of the horizontal cross sections and stitched-together captured images are presented at the top and the bottom [61].
## 3 Experimental Observation of Various Soliton Molecules in Different Fibre Lasers  
### Anomalous Dispersion Regime  
Fibre laser cavities operating in anomalous dispersion regime allow solutions of conventional sech-profiled solitons and have been widely investigated due to the great potential in abundant applications such as fibre optical communications and military security. The typical spectral working bands lie in the range of 1.5 and 2 microns. In this section, main experimental findings on soliton molecules at this spectral window are given, which simultaneously indicate what we have studied in the past several years.
# \(K\)-Means Cloning: Adaptive Spherical \(K\)-Means Clustering
## 2 K-Means Cluster Cloning
### K-Means Colony Growth  
A major challenge in partitional clustering lies in determining the number of clusters that best characterize a set of observations. The question is: In starting with an initial number of clusters, what mechanism through which the search strategy can find the best solution, i.e., actual number clusters? To answer this question we define the cluster merging and splitting procedures which are discussed in this section.  
For further explanation, we used two synthetic datasets described as follows:  
1. Dataset 3-2: This dataset used in [74], consists of 76 data points distributed over three clusters. Some points of one cluster are symmetrical with respect to the other cluster center, see Figure 1a.
2. Dataset 10-2: This dataset used in [69], consists of 500 two dimensional data points distributed over 10 different clusters. Some clusters are overlapping in nature. Each cluster consists of 50 data points, see Figure 1b.  
Figure 1: Two synthetic datasets, (**a**) Dataset 3-2 with three clusters and (**b**) Dataset 10-2 with ten clusters.  
The adaption process to fix the number of clusters depends on three sub-processes; how to evaluate the current number of cluster, which cluster(s) should be split or merged, and how we perform the cluster splitting or merging. These sub-processes are discussed in the following.
# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## 4 Simulation Results and Discussion
### Results Discussion
#### 4.4.2 Remaining Energy (RE)  
In MWSN, energy dissipation rate is higher compared to the static sensor network. In this study, the CL selection was carried out based on connectivity and RE. This criterion prohibits nodes to become CL having maximum remaining energy, but they are far from their member nodes and are not connected, resulting in a long network lifetime. Therefore, RE is an important performance parametric to evaluate the energy efficiency of routing protocols. Our results show that the proposed LEACH-MEEC outperforms LEACH-MAE, Mobile-LEACH and LEACHDistance-M regarding RE. Figures 6a-d and 7a-d refer to RPGM, RVP, GM and MH datasets with two different speeds, respectively. The graphs further explain that RPGM mobility model has maximum RE, since the mobile nodes that are a part of that RPGM have uniform and reflective velocities to the velocity of the group leader similar such as CL. Inter node distance among nodes remain uniform, hence nodes are well connected. It is also pertinent to mention that increases in speed have reduced the energy efficiency, which can be seen in Figure 7a-d.  
Figure 6: Remaining energy versus time for 100 nodes, maximum speed 1.5 m/s, (**a**) RPGM Dataset; (**b**) RVP Dataset; (**c**) GM Dataset; (**d**) MH Dataset.
## 3 An overview of AVs
### Sensor Technologies
#### 3.1.2 Radar  
The fundamental components of ADS and self-driving automobiles are automotive radars and a variety of additional sensors. The successful use of these technical advances depends on complex systems with long signal-processing chains that link the radars and sensors to the controller. The vital tasks of identifying objects and obstructions, pinpointing their exact location, and evaluating their relative speed in relation to the vehicle fall to automotive radar equipment. Advances in millimeter-wave (mm-wave) semiconductor technology, along with the development of signal processing techniques, form the basis for the progress of car radar systems. A variety of signal processing methods have been developed to improve resolution and reliability in a range of measuring parameters, including target velocities in the vicinity, azimuth elevation perspectives, and the radius of the vehicle [40]. Frequency Modulated Continuous Wave radar, or FMCW radar, modifies the broadcast signal's frequency continually at a predetermined pace. By measuring the gap between the sent and reflecting signals, which is inversely related to the amount of time required for the electrical signal to make its way to and from the object being targeted, we can determine the range due to this modulation of frequency. FMCW radar has excellent accuracy and range resolution, as well as speed measurement features, which make it especially useful for autonomous driving applications [41].
## 3 Methodology  
The present study performs a scientometric review of the current scholarly literature, investigating the application of AI, ML, and DL in cybersecurity. Scientometrics is a research methodology to study the scientific literature through publication patterns, citation trends and research development [46]. It enables the quantification and visualisation of research evolution and deeper insights, for instance, on emerging trends, methodologies, and knowledge networks in different domains. The main goal of this scientometric study is to discover the global research trends concerning the implementation of ML and DLtechnologies in the cybersecurity domain and to identify the influential contributions of researchers, institutions, organisations, and countries.  
The Scopus database was chosen for this analysis due to the vast coverage of high-impact articles in all aspects of scientific studies, high-quality indexed journals and its reliability for scientometrics studies. Scopus is deeply trusted to provide comprehensive citation data and to conduct bibliometric research [47]. Moreover, the potential reason for choosing the current time frame, January 2016 to January 2025, was to focus on the latest and most advanced research and developments in the field. The data were gathered from 2016 to 2026 due to the significant growth of ML and cybersecurity research and ongoing trends. Hence, the chosen time frame confirms that scientometric analysis is related to more recent research and discussions.  
With extensive metadata, the database is suitable for scientometric tools, such as VOSviewer, which facilitates data export in formats supporting bibliometric analysis. The relevant publications were retrieved on 24 December 2024, employing a Boolean search strategy. The Boolean search string is given below:  
(TITLE-ABS-KEY (machine AND learning OR machine-learning) OR TITLE-ABS-KEY (deep AND learning OR deep-learning) AND TITLE-ABS-KEY (cybersecurity)) AND PUBYEAR > 2014 AND PUBYEAR < 2026 AND (LIMIT-TO (DOCTYPE, \"ar\") OR LIMIT-TO (DOCTYPE, \"re\")) AND (LIMIT-TO (PUBYEAGE, \"final\")) AND (LIMIT-TO (LANGUAGE, \"English\")).  
Boolean search was used because it filters large datasets with the Boolean operators and keywords used in the research [48]. The search minimises irrelevant studies and ensures the query retrieves more relevant results. Hence, it improves the efficiency and transparency of the search in the data collection process.  
The search produced 9813 publications, including journal articles, conference papers, review articles, and book chapters. These publications were exported to CSV format to be compatible with any bibliometric analysis software. The data were thoroughly screened for completeness, eliminating records with incomplete bibliographic information, duplicate entries, or documents that did not directly address using ML and DL technologies in cybersecurity. Figure 1 shows a search framework for retrieving relevant documents.
# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 3 Materials and Methods
### YOLO Family  
The YOLO family of architectures [33; 34] has revolutionized object detection by introducing a unified and efficient approach. Unlike previous methods such as R-CNN (Region-Based Convolutional Neural Networks) and its variants, which divided the problem into several stages, YOLO models process the image in a single way, allowing for real-time detection with high precision. The architecture is made up of three main components: the backbone, the neck, and the head. Based on [35], the general flow of the YOLO architecture is illustrated in Figure 4.  
The backbone is responsible for extracting visual characteristics from the image, such as textures and patterns. It acts as the basis of the network, processing the raw image and generating spatial representations that will be refined in the following stages. In versions of YOLO, the backbone is often implemented with architectures such as Darknet or CSPDarknet, which are efficient at extracting features. The neck acts as a bridge between the backbone and the head, and is crucial for combining information at different scales. Techniques such as the Feature Pyramid Network (FPN) or Path Aggregation Network (PANet) are often used to integrate features from multiple depth levels, allowing the YOLO architecture to have an enhanced ability to detect objects of varying sizes and resolutions. This ensures that both large and small objects are dealt with effectively [33].  
Next, head generates multiple bounding boxes, assigns confidence scores and class probabilities for each detection. After generating these predictions, non-maximum suppression (NMS) is applied as a post-processing step to compare the boxes based on their overlap (using the Intersection over Union index - IoU) and keep only those with the highest confidence scores, discarding the rest. This results in a cleaner and more accurate final detection, avoiding duplications that could harm the system's efficiency. The integration of the NMS into YOLO's processing flow allows the model to maintain its accuracy even in  
Figure 4: YOLO architecture, highlighting the main components: backbone, neck, and head.  
complex scenarios with multiple objects and overlapping [33]. The functioning between the head and the NMS is illustrated in Figure 5.  
The operation of head is based on dividing the image into a grid, where each grid cell is responsible for predicting the presence of objects and their characteristics, such as position, size and class. This mechanism is represented in Figure 5, which highlights the process of subdividing the image and assigning detection responsibilities to the cells. After the bounding boxes, confidence scores and class probabilities have been generated by the head, the NMS is applied as a post-processing step to eliminate redundant or overlapping detections [35].
## 7 Conclusions  
The purpose of this research is to provide a framework for the analysis of the Juno spacecraft's trajectory attributes. Within this framework, the attributes' temporal and spatial changes are described and evaluated as trajectories. In this model, the trajectory coordinate characteristics, together with the upper order derivatives (velocity, acceleration, jerk, snap) until order four, help determine the human-controlled modification of the Juno spacecraft. It is proved that usage of the Hilbert transform on the instantaneous average acceleration, jerk, and snap makes visible the time-frequency dependence of the object moving in 3D and being influenced by gravity and other artificial activated forces. The Hilbert spectrum of this non-linear and non-stationary 3D movement process helps to detect extreme-event-type situations of the Juno spacecraft. It was found that the Juno satellite executes chaotic movement in the state space of velocity, acceleration and jerk.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Accuracy of EE Detection/Operation** & **OP1** & **OP2** & **OP3** \\ \hline DBSCAN & 98.8\% & 98.2\% & 97.4\% \\ \hline OPTICS & 99.3\% & 99.1\% & 98.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy of the outlier identification with the DBSCAN and OPTICS algorithms.  
When applied to spacecraft trajectory, unsupervised machine learning offers a potent tool for research and improvement and improved skills to choose from various possibilities for trajectories to fulfil strategic objectives. The use of visual analytics paves the way for the successful completion of mission design procedures and gives access to hitherto unexplored possibilities. Although this framework was created by analyzing the trajectory of moving objects over time, it applies to the study of objects with additional qualities. The paradigm developed is helpful in terms of characteristic changes that may be regarded as time series data. In remote sensing analysis, the idea of trajectories has been employed; however, such trajectories have not been examined using approaches specific to moving object trajectories. Because of this, extending this concept to remote sensing trajectories might also be intriguing. In conclusion, the presented paradigm has the potential to serve as a springboard for using attribute trajectory examination in several other domains.  
In conclusion, our findings demonstrate the superiority of the OPTICS algorithm in outlier detection compared to DBSCAN across all the investigated orbital phases (OP1, OP2, OP3). OPTICS achieved accuracy levels of 99.3%, 99.1%, and 98.9% for OP1, OP2, and OP3, respectively, while DBSCAN obtained 98.8%, 98.2%, and 97.4% for the corresponding phases. This suggests that OPTICS may be a more dependable method for identifying outliers within elliptical orbit data analysis.