esa-sceva/satcom-chunk-collection huggingface dataset:
### Research Contributions  
The principal research contributions of this proposed work are outlined as follows:  
* An innovative, secure routing protocol was introduced to combat black hole attacks within VANETs. This protocol is designed to optimize the allocation of tasks by selecting the most secure and efficient path for data transmission.
* Integrating a cluster-based routing technique facilitates the streamlined delivery of content to the target vehicle following a meticulous verification process based on the vehicle's past performance, utilizing ANNs.
* Establishing clusters through a meticulously designed algorithm incorporating security considerations further bolsters the dependability of the hop count in the network.  
Figure 5: Problem diagram under black hole attack scenario in network for packet loss.  
Figure 6: Routing attack scenario of proposed approach.  
* The deployment of ANN techniques to identify and combat black hole attacks and secure routing within VANETs. This approach leverages knowledge and related skills to pinpoint routing paths between source and destination nodes accurately.
* Implementing an authentication-based method for efficient content delivery ensures secure analysis and path prediction. This is achieved by utilizing a modified AODV algorithm, which effectively predicts the route after securing the network using an ANN-based machine learning (ML) algorithm.  
Thorough performance analysis of the proposed technique, featuring a comparative evaluation against AODV-L, AODV-R, and T-AODV. This assessment encompasses critical metrics such as end-to-end delay, average hop count, network throughput, and other relevant performance indicators expounded upon in this paper.  
The subsequent sections of this paper adhere to the following organizational structure.  
* Section 2 explores related work focused on secure routing within VANETs, shedding light on previously adopted methodologies. Various established secure routing solutions are presented alongside examining multiple clustering techniques. Towards the conclusion of this section, diverse machine-learning methods for detecting malicious nodes are scrutinized.
* Section 3 intricately expounds upon the proposed solution framework and model diagram. Secure routing is realized through a cluster-based machine learning model and a modified AODV algorithm. This section elucidates the proposed solution by incorporating diverse flowcharts and model diagrams, ensuring a lucid comprehension of the methodology.
* Section 4 involves simulating our techniques within a suitable simulation environment, utilizing the NS-2 simulator and Python for machine learning. Existing datasets are harnessed for model training, and this section offers a comprehensive description of the experimental setup and various components of the experiments.
* Lastly, Section 5 concludes the paper, and potential avenues for future research are delineated.
## 4 Inorganic Fertilizers
### Nitrogen Fertilizers  
Nitrogen, as a single nutrient, is needed by the soil to enhance crop productivity. It is commercially sold in solid or liquid forms as anhydrous ammonia, urea, and urea-ammonium nitrates [53]. It is applied into the soil and reacts with water to release the fertilizer as ammonium ions, which are further nitrified by bacteria to nitrate ions needed by the plants. Farmers sometimes apply fertilizers in excess quantity so that they can get bountiful harvest, but such practices may be of disadvantage to the soil, as it will change the structure and concentration of the soil. It may also lead to eutrophication when the excess nitrogen compounds are leached into water bodies.  
There could be addition of nitrification inhibitors, stabilizers or additives when the nitrogen fertilizers are applied to reduce the loss of NH\({}_{3}\) formed during the course of reaction of the fertilizers with the soil and water molecules. Such compounds may include NBPT [N-(n-butyl) thiophosphoric triamide], boric acid, which can be used with urea or urea-ammonium nitrate fertilizers and they function to slow the action of urease bacteria that transforms the fertilizers to NH\({}_{3}\)[54]. Such action allows the fertilizers to be well absorbed by the soil, giving room for reaction with enough water molecules. US environmental protection agency approved dicyandiamide (DCD) and 3, 4-dimethylpyrazole phosphate (DMPP), in the late twentieth century, as a nitrification inhibitor [55]. The best form of the nitrogen fertilizers is to be in ammonium ions, which are easily absorbed by the soil. In the form of nitrates, they are difficult to be absorbed by clay soil or organic matter, while, in the form of ammonia, they tend to escape into the atmosphere.  
Special farm practices are also important in the application of these fertilizers. When they are applied on the surface, they tend to be converted to NH\({}_{3}\) and escape very easily. The best practice, therefore, requires the tilling of the soil and applying them in the inner part with adequate water. The water molecules will enable them to be converted to ammonium ions but not too much water that may eventually lead to leaching.  
Urea is the most widely used nitrogen fertilizer due to its high nitrogen content, compatibility with other nutrients, easy handling and application.
# Threat Modelling and Beyond-Novel Approaches to Cyber Secure the Smart Energy System
## 6 Attack Scenarios and Exploitation Probability Assessment
### Smart Home and HVAC Hijacking Attack Scenarios
#### 6.1.2 Attack Scenario 1.2--Hijack HVAC  
In addition to manipulating the heating, a remote attacker can affect ventilation and air conditioning in smart homes as well. By assuming control over the HVAC system, she is able to cause a temperature overload in the system. In this way, the system can be damaged and increases the power consumption. A known intrusion against the HVAC system, called HVACKer [60], relies on an already installed malware in order to manipulate temperature settings. This attack exploits a vulnerability that is caused by the lack of insights about thermal communication protocols.
# Evaluation of the Distal Periodontal Bone Defect Associated with Lower Third Molar Extraction
[21] demonstrated that orthodontic extraction of mandibular premolars might slightly accelerate root development but does not significantly influence the angulation of lower third molars. Similarly, [PERSON] et al. [22] found that Class I and II malocclusion extraction treatments exhibited more favorable angulations and a greater number of erupted third molars compared to non-extraction treatments. Non-extraction groups exhibited a higher percentage of unerupted third molars. [PERSON] et al. [23] leveraged artificial intelligence (AI) to predict lower third molar eruption, finding a low incidence of fully erupted molars with hygienic cleanability, supporting prophylactic extraction in certain cases. Furthermore, [PERSON] et al. [24] identified impaction, prevention of pericoronitis, and stability post-treatment as key factors favoring prophylactic extraction in orthodontic practice. Finally, [PERSON] et al. [25] highlighted the correlation between lack of retromolar space, increased arch length discrepancy, and mandibular incisor angulation with higher chances of third molar impaction in Class II malocclusion patients. The choice of surgical technique and pre-extraction periodontal preparation must consider the patient's clinical condition. Advanced periodontal surgical methods, such as guided tissue regeneration or use of biomaterials, may improve healing outcomes and reduce the risk of long-term defects. CBCT analysis plays a pivotal role in planning these interventions, offering precise evaluations of bone and soft tissue conditions. These findings underscore the need for a tailored approach to managing third molar impactions, considering both clinical and patient-specific factors.
# MODIS Retrieval of Aerosol Optical Depth over Turboid Coastal Water
## 4 Results
The number of collocated pairs (N), root mean square error (RMSE), normalized mean bias (NMB), and linear correlation coefficients are also shown. error. Figure 7d-f show RMSE as a function of MODIS; RMSE goes up as MODIS AOD increases and combined retrievals (Figure 7f) are comparable to MxD04 (Figure 7d).
## 2 Principle
### Optical Path Structure and Design Parameters of an Unobscured Off-Axis Three-Mirror Telescope
\(\overset{\rightarrow}{H}_{Aj}\) and \(\overset{\rightarrow}{H}\) represent the normalized effective field vectors before and after the aberration field center shift, respectively; \(\overset{\rightarrow}{\rho}^{\ast}\) and \(\overset{\rightarrow}{\rho}\) represent the normalized pupil vectors of the off-axis system and its on-axis parent system, respectively; \(\overset{\rightarrow}{h}\) represents the normalized position change vector of the pupil center of the off-axis system relative its on-axis parent system; \(r\) and \(R\) denote the pupil radius of the off-axis system and its on-axis parent system, respectively. In our previous work [11], based on Equation (1), we derived the functions of third-order net astigmatic fringe Zernike coefficients (C5/C6), third-order net coma fringe Zernike coefficients (C7/C8), and third-order net spherical aberration fringe Zernike coefficient (C9) induced by axial and lateral misalignments. To facilitate the description of the proposed compensation correction principle, their field dependence is further clearly expressed in this section, as shown in the following equations. \[\begin{array}{c}C_{5}\\ \\ +\left[\begin{array}{c}M^{2}\overset{\rightarrow}{h}_{y}\left(W_{131}+ \Delta W_{131}\right)\overset{\rightarrow}{\sigma}_{j}^{\prime}+\frac{M^{2}} {2}\left(W_{222}+\Delta W_{222}\right)\left(\overset{\rightarrow}{\sigma}_{ j}^{\prime}\overset{\rightarrow}{\sigma}_{j}^{\prime}+2\overset{ \rightarrow}{\sigma}_{j}^{\prime\prime}\right)_{x}\\ \\ +\frac{M^{2}}{2}\Delta W_{222}\left(\overset{\rightarrow}{\sigma}_{j}^{\prime }\overset{\rightarrow}{\sigma}_\[C_{9}=\left[\frac{M^{4}}{6}\Delta W_{040}\right]_{C}. \tag{4}\]  
In Equations (2)-(4), the subscripts C, L, and Q represent the field-constant dependence, field-linear dependence, and field-quadratic dependence of different aberration types, respectively; the subscripts \(x\) and \(y\) denote the x-component and y-components of a vector, respectively, \(W_{klm}=\sum\limits_{j}(W_{klm})_{j}\), \(\Delta W_{klm}=\sum\limits_{j}\left(\Delta W_{klm}\right)_{j}\), and \(M=\frac{r}{R}\). In addition, because the aperture of PM in astronomical telescopes is usually relatively large, it is prone to figure errors due to factors such as gravity, support stress, and thermal stress. In this paper, third-order astigmatic figure errors, third-order coma figure errors, and third-order spherical aberration figure errors of PM are specifically considered. When PM is used as the stop surface, the beams emitted from different FOVs entering the optical system have the same footprint on PM and completely cover PM. At that time, it can be considered that the aberration contribution of PM to different FOVs is the same, that is, the figure errors of PM will only introduce field-constant aberrations [22; 26]. In consequence, we can add the field-constant aberrations induced by PM figure errors to the field-constant aberrations induced by misalignments.
# Algorithm for Determining the Position of a Ship Hull-Cleaning Robot
## 2 Research Context  
The development of SR Robotics' magnetic adhesion robot marks a significant advancement in the domain of autonomous maintenance systems for marine and industrial infrastructure. Designed to operate on ferromagnetic surfaces both above and below the waterline, the robot offers a comprehensive solution for cleaning ship hulls and, additionally, surfaces such as petrochemical tanks, without requiring the vessel or structure to be decommissioned or dry-docked. Its ability to navigate and perform cleaning operations throughout the full surface of the hull, while continuously extracting contaminants and documenting the process via video cameras onboard, represents a unique integration of mechanical, environmental, and digital functionalities.  
As part of a recent R&D initiative, SR Robotics developed this innovative robotic system (Figure 1) to address critical challenges related to biofouling and corrosion, significantly improving the efficiency, quality, and safety of surface maintenance operations. Operating with a water pressure of up to 2500 bar and sufficient magnetic adhesion forces to move reliably on vertical metallic structures, the robot is equipped with a specialized cleaning module, a high-efficiency waste extraction system (approximately 95%), advanced localization sensors, optical inspection cameras, and data acquisition tools compliant with Industry 4.0 standards. It is remotely controlled via an umbilical cable or a radio link, enabling safe and flexible deployment in various working environments.  
A key challenge in the development of this system was the need for a universal positioning algorithm capable of supporting navigation in both submerged and above-water environments. Conventional navigation methods such as GPS are limited to surface operations, while hydroacoustic positioning systems or Doppler Velocity Logs (DVL) function only underwater. SR Robotics and its research partners addressed this challenge by developing a positioning system based on odometry and inertial measurement units (IMUs), which operate effectively in both domains. This foundational approach provides reliable relative positioning while allowing for modular enhancements. In underwater scenarios, additional sensors, such as DVLs and depth sensors, can be integrated; conversely, GPS receivers can be utilized for surface-level operations. This hybrid architecture ensures flexibility, scalability, and robustness of the navigation solution.  
The requirement for a new positioning algorithm was driven by the robot's unique mission profile--full-hull coverage across variable environments--where existing solutions lacked the adaptability and precision required for seamless transitions between above-water and submerged navigation. As a result, the algorithm described in this article forms a critical component of a system that not only automates hull maintenance but also sets new standards for operational autonomy and environmental compliance in the maritime and industrial sectors.  
Figure 1: The cleaning robot.
## 4 Experiment and Discussion  
In this section, we conduct digital simulations and the real flight experiment to verify the effectiveness of the controller. The purpose of the simulation is twofold: (1) to evaluate the control effect of the proposed controller when the time-varying control command is input; (2) to compare the control effect of the proposed adaptive controller and the PID controller. In addition, a real flight test is implemented on the real FMAV with thrust attenuation on one side. The magnitude of thrust attenuation is unknown before the experiment, and this flight experiment is used to evaluate the control effect of the proposed controller in the presence of uncertain model parameters. The tracking performance in simulation and flight is evaluated based on the Root-Mean-Square (RMS) errors of tracked state, system overshoot, and decoupling performance.
## 4 Results and Analysis
### Grafana Dashboard Implementation with InfluxDB  
We selected the InfluxDB database, operating on the AWS cloud server, to store the water parameter data. Initially, it is necessary to establish a bucket that serves as a repository for the sensors' data. The process of creating a bucket is straightforward; users can utilize either the user interface or the command shell. Connecting Grafana to InfluxDB necessitates only a few simple steps. Grafana can be installed on a local computer and connected to the AWS server where InfluxDB is operating. First, you must log in with your credentials and navigate to the settings after installation. The subsequent step is to select InfluxDB as the data source by clicking on it. You then define the URL that will serve as the connection to the cloud and configure the InfluxDB data source. The database name is set to the bucket name that was established on InfluxDB and InfluxDB authentication granted. Configuration of the connection is then complete. Subsequently, we can generate a new dashboard that presents our data and classifications in a more user-friendly manner. The Grafana dashboard is illustrated in Figure 7.  
The proposed system is established in such a way that it can classify the water samples into multiple classes. In this study, dataset 1 contains six distinct classes, whereas dataset 2 contains five. Based on parameter values such as pH, DO, COD, and NH3-N, the proposed model determines the class number. The class number also represents the quality of any particular water sample. In setting the standard values for the water parameters in this study, the Environment Conservation Rules (1997) were followed [33]. The Grafana module was employed to present the results in a clear and user-friendly manner. The Grafana monitoring system utilizes a color scheme to distinguish between standard and non-standard parameter values, where green represents the standard parameter values and other colors, such as blue and red, stand for non-standard parameter values. For instance, when all the concerned parameter values fall within the accepted range, the Grafana dashboard shows the water sample as excellent-quality, as illustrated in Figure 7a. Similarly, when all the concerned parameter values are too high and fall outside of the accepted range, the Grafana dashboard marks those values as red in color. The proposed  
Figure 6: Robustness testing of proposed model using confusion matrix, ROC, and AUC on dataset 2.  
model classifies this sample as class 5, and the Grafana dashboard shows the predicted result as bad-quality, as illustrated in Figure 7b.
## 3 Results and Discussion
### Investigation of Peptide Coupling Using UV-Visible Spectroscopy
#### 3.1.3 Investigation of Zeta Potential and Hydrodynamic Radius  
To further assess the stability of the modified Au NP formulations, the zeta potential and average hydrodynamic radius were measured to give further insight into the understanding of the state of the NP surface and to predict the long-term stability of the NP solution. Table 3 summarizes the zeta potential values and hydrodynamic radii of the different functionalized Au NP preparations. The higher negative charge on the surface imparts higher stability owing to the electrostatic repulsion between neighboring particles. The zeta potential values further confirm that the highest stability is achieved with the bifunctionalized Au NP preparation.  
The stability of C/G-capped Au NPs is improved by increasing the molar ratio of CALNN to GPDC in the peptide mixture, as can be seen in Table 3. The data are in good agreement with the stability noticed in high-ionic-strength solutions. However, it should be noted that the PEGylated ligand displays the lowest zeta potential, which does not coincide with the stability it demonstrates in saline solutions, and this can be interpreted as steric repulsion imparted by coiled PEG, while the zeta potential measures the electrostatic potential that exists between the shear plane of the NPs and the solvent [28].
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADS & Accelerator Driven System \\ AGL & Above Ground Level \\ ANSI & American National Standards Institute \\ APD & Avalanche PhotoDiode \\ APS & Advanced Processor for Scintillators \\ BGO & Bismuth Germanate \\ Ce:GAGG & Ce-doped Gd-Al-Ga-garnet \\ CIP & Critical Infrastructure Protection \\ CMOS & Complementary Metal-Oxide Semiconductor \\ CLLB & Cs\({}_{2}\)LiLiBr\({}_{6}\)(Ce) \\ CLLC & Cs\({}_{2}\)LiACl\({}_{6}\)(Ce) \\ CLYC & Cs\({}_{2}\)LiYCl\({}_{6}\)-Ce\({}^{3+}\) \\ COTS & Commercial Off-the-shelf \\ CZT & Cadmium Zinc Telluride \\ DPA & Displacements Per Atom \\ FDNPP & Fukushima Daiichi Nuclear Power Plant \\ FLO & Front-Line Officer \\ FND & Fast Neutron Detector \\ FOV & Field Of View \\ FWHM & Full width at half-maximum \\ GCS & Ground Control Station \\ GM & Geiger-Muller \\ GNSS & Global Navigation Satellite System \\ GPR & Ground-Penetrating Radar \\ GPS & Global Positioning System \\ HDPE & High Dense PolyEthylene \\ HPGe & High purity Germanium \\ IAEA & International Atomic Energy Agency \\ IEC & International Electrotechnical Commission \\ INS & Inertial Navigation System \\ LiDAR & Light Detection And Ranging \\ LYSO(Ce) & Cerium-doped Lutetium-yttrium oxyorthosilicate \\ MAV & Micro Air Vehicle \\ MARIA & Mobile Application for Radiation Intensity Assessment \\ MDA & Minimum Detectable Activity \\ MCDM & Multi-Criteria Decision Making \\ MCSR & Mobile Cloud System for Rad Monitoring \\ ML-EM & Maximum Likelihood Expectation Maximization \\ MPPC & MultiPixel Photon Counter \\ MRD & Mobile Radiation Detection \\ MRI & Magnetic Resonance Imaging \\ MTOW & Maximum Take-Off Weight \\ MURS & Mobile Urban Radiation Search \\ \end{tabular}
# A Review on Distribution System State Estimation Algorithms
## 6 Future Trends and Challenges  
Taking the presented analysis into account, it is estimated that in the near future, DSSE will be implemented at a higher scale (instead of being mostly implemented in pilots) including large MV and LV distribution systems of all configurations, with a variety of DER, supporting advanced optimization algorithms that are nowadays utilized by the DSOs. In this sense, the DSOs are also expected to exploit the possible contribution of DSSE in the field of ancillary services which are derived from efficient monitoring, such as congestion management, black start, etc., thus further enhancing the cooperation between the DSOs and Transmission System Operators (TSOs) [147]. In addition, stability assessment-which is an important issue for power systems-could be related to DSSE algorithms for either stand-alone or connected systems, taking into account various aspects such as reduced order transfer function models of droop-controlled inverters via Jordan continued-fraction expansion [148; 149], etc.  
Furthermore, regarding the design of the DSSE, a future trend will probably be the more extended use of neural networks, which constitute a modern approach with an increasingly strong presence in the energy sector, either for state estimation per se or for pseudo-measurement generation and bad data detection [82].  
However, all these benefits come with a few challenges. For example, a more standardized approach regarding the selection of state vectors, main and auxiliary algorithms, meter placement strategies, etc., needs to be established, promoting the easier implementation to future adopters, which is a major requirement for market penetration. Furthermore, it should be noted that the renovation of the distribution system, the installation of PMUs and SMs, etc., comes to a certain cost. Further, for the implementation of data-driven algorithms, large databases and advanced engineering expertise are required.  
In the end, it can be concluded that, given the necessary requirements, DSSE may transform the passive, insufficiently monitored distribution system into an active, key part of the ongoing energy transition.
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 3 Problem Analysis
### Time Slice Division Principle
Therefore, as long as the initial satellite in any orbit is calculated in the first time slice from the nearest satellite node in the whole network, the link connectivity of the remaining \(65\) satellites can be directly derived, thus reducing computational effort and improving computing performance.