esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design of a Self-Adaptive Evolutionary Algorithm
### Generation of the Initial Chromosomes and Population
```
1:\(I=\{1,\dots,m\}\)--set of vessels; \(J=\{1,\dots,n\}\)--set of servies; \(K=\{1,\dots,s\}\)--set of service orders; \(T^{B}\)--beth availability; \(T^{A}\)--vessel arrival times; \(T^{HT}\)--vessel handling times; \(L^{V}\)--length of vessels; \(L^{B}\)--length of berths; \(D^{H}\)--horizontal clearance requirements; \(H^{V}\)--draft of vessels; \(H^{B}\)--depth of berths; \(D^{V}\)--vertical clearance requirements out:\(x\)--initial vessel to berth to service order assignment
2:\(|\widetilde{I}|\gets m\); \(|x|\gets m\cdot n\cdot s\); \(|\widetilde{T}^{B}|\gets n\); \(|\mathbf{T}^{ST}|\gets m\); \(|\mathbf{T}^{FT}|\gets m\) Initialization
3:\(\widetilde{I}\leftarrow\boldsymbol{Sort}(I,T^{A})\)\(\triangleleft\) Sort the vessels based on their arrival times at the MCT
4:\(\widetilde{T}^{B}\gets T^{B}\)\(\triangleleft\) Set the initial availability of berthing positions
5:\(i\gets 1\)
6:for all\(i\in\widetilde{I}\)do
7:\(b\leftarrow\boldsymbol{find}(L^{B}\geq L^{V}_{i}+D^{H}_{i}\)and\(H^{B}\geq H^{V}_{i}+D^{V}_{i})\)\(\triangleleft\) Identify potential berthing positions for a vessel
8:\(j\gets argmin_{b}(\widetilde{T}^{B}_{b})\)\(\triangleleft\) Select the first available berthing position
9:\(k\gets argmin_{k}(x_{ijk})\)\(\triangleleft\) Determine the earliest service order
10:\(x_{ijk}\gets 1\)\(\triangleleft\) Assign a vessel to the first available berthing position in the earliest service order
11:\(T^{ST}_{i}\gets max(T^{A}_{i},\widetilde{T}^{B}_{i})\)\(\triangleleft\) Calculate the start vessel service time
12:\(T^{FT}_{i}\gets T^{ST}_{i}+T^{HT}_{ij}\)\(\triangleleft\) Calculate the finish vessel service time
13:\(\widetilde{T}^{B}_{i}\gets T^{FT}_{i}\)\(\triangleleft\) Update the availability of a berthing position
14:\(i\gets i+1\)
15:endfor
16:return\(x\)
```  
**Algorithm 1** First Come First Served with Spatial Requirements (FCFS-SR) Heuristic.
## 2 Materials and Methods
### Satellite Data Pre-Processing
#### 2.3.1 MOD13Q1/MYD13Q1 Processing  
The MOD13Q1 and MYD13Q1 product's Quality Assessment (QA) reliability was used to identify and remove noisy pixels. To accomplish this, a mask was applied to each image, selecting data only with QA = 0 (good quality) and QA = 1 (marginal data). We also selected pixels where the blue reflectance band value was less than 10%. To reduce the potential effect of anisotropic reflectance, we only used pixels with a view zenith angle of less than 30\({}^{\circ}\). After the corrections, the MOD13Q1 and MYD13Q1 products were combined to generate an 8-day product, referred to as MCD13.
# A Mamdani Type Fuzzy Inference System to Calculate Employee Susceptibility to Phishing Attacks
## 1 Introduction  
It is well established that humans are the weakest link in any secure system and where there is human/computer interaction it can undoubtedly lead to security breaches [1, 2, 3]. There are several factors that contribute to weak cyber security awareness in employees [4, 5]. Businesses can find themselves vulnerable to attacks which result in security or system breaches, this often leads to the release of personal and sensitive data that ultimately results in damage to reputation and reduction in consumer confidence as well as monetary fines [6, 7]. Moreover, during the COVID-19 crisis, employees are encouraged to increase teleworking while at the same time most products and services have become available over the web. The COVID-19 epidemic has resulted in a significant increase of fraudulent mails. Phishing emails, social engineering attacks, malware, ransomware, spyware, are only a few examples of the cyber crime incidents reported during the COVID-19 crisis period. Phishing emails take advantage of misinformation, isolation, and a lack of awareness to make people a vulnerable target for those types of attacks and persuade victims to hand over money, personal information, and even authentication credentials.  
Employers mitigate potential cyber risks through various system protections, for example, automated software that detects potential phishing attacks and automatically resolves issues. In terms of employees, i.e., the human element; they are put through training courses which can be ineffective and \"boring\" [8]. Training courses are often  
habit and proactive measures while on the same time showcased that only procedural [[https://www.overleaf.com/project/61407](https://www.overleaf.com/project/61407) bb8 ef6f86 cc9a3484 ef]([https://www.overleaf.com/project/61407](https://www.overleaf.com/project/61407) bb8 ef6f86 cc9a3484 ef) countermeasures were not affecting the users' behavior.  
In an interesting work [13] that focused on phishing mails, it was proved that by clearly defining the main differences between legitimate and phishing emails, the risk of a phishing attacks reduces. By applying fuzzy logic the authors developed a novel AI detection system for classifying legitimate against malicious emails. Following a similar approach the authors in [14] developed a phishing detection mechanism using fuzzy logic.  
This proposed method builds upon the previous mentioned work. This research investigates a framework where specific rules have been designed that include a number of aspects relating to particular employee characteristics, security policies and phishing sophistication levels. To fully understand an individuals susceptibility to a potential phishing attack, employee characteristics include individual experience and security level as well as personal behaviours: job satisfaction and procrastination. These employee traits are fed into a Mamdani FIS that outputs a overall phishing susceptibility rating for that particular employee.
## 2 Materials and Methods
### Input Data Preparation and Elaboration  
The Heim Energy Line method [27] is limited to a local scale application; in order to apply it to a regional scale, and thus to all the fast-moving landslides in Valle d'Aosta, its spatialisation is necessary. To apply this model, the same model for shallow landslides and rockfalls, a procedure in the GIS environment was implemented for which the landslide perimeters and a digital elevation model (DEM) were requested. The landslide polygons were obtained from the IFFI Project, the Inventory of Landslide Phenomena in Italy compiled by ISPRA [30; 31]. A DEM with a 10 m resolution [38] was cropped on each landslide polygon, hence obtaining the base file from which the length (L) and the maximum and minimum height values were extracted for each landslide, taken as the top and tip of the landslide, respectively. Afterwards, due to the large amount of data to be analysed and the absence of a specific tool in the GIS environment, all the data were processed using Matlab software (MathWorks, version R2020a). For the application of the Matlab code, provided in the Supplementary Materials, (Supplementary File S1), the required data were prepared; after converting the DEM into points (so that a regular grid of points was obtained), each point was associated with its spatial coordinates. Then, two tables were created: one showing the maximum height value of each polygon with its coordinates and the landslide identification code, and the other with the height of each point belonging to the polygons, its spatial coordinates, the length of the landslide (L), the difference in height between the top and the tip (\(\Delta\)H), and the landslide identification code. Therefore,  
Figure 3: Slide model of a landslide; CM = centre of mass of the displaced body; u = potential energy; k = kinetic load; w = work of the friction forces; \(\varphi_{a}\)= apparent angle of friction (tan \(\varphi_{a}\) = (1 - \(r_{u}\))tan \(\varphi^{\prime}\), where \(r_{u}\) represents the ratio of water pressure to total vertical lithostatic pressure) (modified from [PERSON] and [PERSON] [37]).  
a specific calculation routine was performed in Matlab, which allowed for obtaining the following parameters for each point within all the landslides (Figure 4):  
1. L'--horizontal distance of each point within the landslide from the top;
2. Tan\(\beta\)--tangent of the propagation angle (constant for each landslide);
3. G--vertical distance between the top and the energy line, G = L' \(\times\) tan\(\beta\);
4. Q2--height of the energy line, Q2 = Qmax-G;
5. k--kinetic load, k = Q2--landslide point elevation (DEM).  
Knowing the kinetic load k, by using the inverse formula, it is possible to get the velocity at each point belonging to the landslide polygons, as shown in Equations (2) and (3).
## 4 Conclusions  
Thermo-responsive SiO\({}_{2}\)-g-POEGMA, pH-responsive SiO\({}_{2}\)-g-PAA and thermo- and pH-responsive SiO\({}_{2}\)-g-P(OEGMA-_stat_-AA) polymer-grafted nanoparticles were successfully prepared by SI-ATRP. The LCST of SiO\({}_{2}\)-g-POEGMA nanoparticles was found to be 64 \({}^{\circ}\)C. The hydrodynamic diameter of the SiO\({}_{2}\)-g-PAA nanoparticles increases with increasing pH, owing to an increased electrostatic repulsive force between the polymer chains. At pH 11, the LCST of SiO\({}_{2}\)-g-P(OEGMA-_stat_-AA) nanoparticles increases with the increase of POEGMA content, which can be influenced by the intramolecular hydrogen bonding and the intermolecular electrostatic repulsive force. These results imply that the LCST of SiO\({}_{2}\)-g-P(OEGMA-_stat_-AA) nanoparticles can be altered by changing the ratio between the monomers. Our study provides a new approach to designing dual-responsive nanoparticles and expands the range of responsive temperatures for the nanoparticles. The dual-responsive nanoparticles have potential applications in biology and chemistry fields, such as controlled drug delivery [9; 42], ultra-stable Pickering emulsion [43] and bio- and chemosensors.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12083799/s1](https://www.mdpi.com/article/10.3390/app12083799/s1)]([https://www.mdpi.com/article/10.3390/app12083799/s1](https://www.mdpi.com/article/10.3390/app12083799/s1)), Methods for characterization of the nanoparticles.  
Conceptualization, [PERSON] ([PERSON]) and [PERSON]; methodology, [PERSON] ([PERSON]), [PERSON] and [PERSON]. ([PERSON]); investigation, [PERSON]; writing--original draft preparation, [PERSON] ([PERSON]), [PERSON] and [PERSON]. ([PERSON]); writing--review and editing, [PERSON] ([PERSON]), [PERSON], [PERSON], [PERSON] ([PERSON]), [PERSON] and [PERSON]; supervision, [PERSON] ([PERSON]) and [PERSON] All authors have read and agreed to the published version of the manuscript.  
The research was funded by the National Natural Science Foundation of China (NSFC) (21902098, 21808249).  
Not applicable.  
The data presented in this study are available in the submitted article.  
The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (NSFC) (21902098, 21808249).  
The authors declare no conflict of interest.  
Figure 8: Hydrodynamic diameter of SiO\({}_{2}\)-g-P(OEGMA\({}_{30}\)-_stat_-AA\({}_{70}\)) (**a**), SiO\({}_{2}\)-g-P(OEGMA\({}_{50}\)-_stat_-AA\({}_{50}\)) (**b**), SiO\({}_{2}\)-g-P(OEGMA\({}_{70}\)-_stat_-AA\({}_{30}\)) (**c**) nanoparticles as a function of temperature, these measurements were performed at pH = 11. The error bars correspond to standard deviations from three measurements.
# Target Image Mask Correction Based on Skeleton Divergence
## 2 Materials and Methods  
We propose a simultaneous source and mask-images optimization model to model and process discrete pixels, which is based on skeleton divergence, as shown in Figure 1. Firstly, the boundary curve of the discrete pixel region is represented by scale-invariant intrinsic variables, and then the boundary of the discrete pixel region is smoothed by the angle bilateral filtering based on the directional corner. After a new set of directional corner parameters are obtained and utilized, the new vertex coordinate equation of the smoothed boundary curve is established to reconstruct the smoothed boundary curve. Secondly, based on the Delaunay triangulation [16], the boundary discrete curve is utilized to extract the skeleton of the global discrete region, and partition discrete pixel regions using a single connected region surrounded by skeletons and boundaries. Then, taking the skeleton as the divergent center of the internal pixel combination, the discrete pixel edge similarity of the local region is used to guide the internal discrete pixel merging. Thirdly, in order to reduce the error accumulation and improve the region merge accuracy, the technique for order of preference by similarity to ideal solution (TOPSIS) [15] is employed to make a strategic decision in the screening stage of the candidate merge strategies. According to the value of the minimum merge cost of each candidate merge strategy, it is judged whether it is \"hard\" or \"easy\" to choose the optimal from many candidate merge strategies. When it is easy to make a choice, the calculated three single-channel statistical costs are used. The three values of single-channel statistical cost and the minimum merge cost are considered as evaluation indicators as well as strategies to sort and then get the optimal strategy. When it is hard to make a choice, the point cloud regularization constraint results corresponding to the candidate merger strategies are added into the original evaluation indicator set for an optimal merge strategy. Fourth, considering the huge computational complexity of dealing with dense point clouds, the acquisition of the matching degree of the point cloud regularization constraint is based on the sparse point cloud. In the same way as the method in [17], with the help of incremental and hierarchical clustering, and iterative simplification, these point clouds are simplified in advance so that they are concentrated in the high-curvature region, a surface deformation model is established [18], and then the system accuracy is guaranteed while reducing the amount of calculation in the process. Finally, with the regional constraints of the global skeleton and boundary, combined with the morphological features and structural similarity, the hole filling of the merged discrete pixel regions is performed to improve the accuracy of the binary mask correction and efficiency.
# Static and Dynamic Response of Micropiles Used for Reinforcing Slopes
## 3 Results
### The Internal Force Response Analysis of the Pile
#### 3.4.2 The Axial Force Response of Pile  
Figure 16 shows the axial force distribution curve of each row of piles in the static model test. As it can be seen from the figure, the axial force distribution of each row of piles was distributed triangularly and was principally due to compressive stress (positive). At about 0.036 m (here: about one time the pile diameter) below, the sliding surface reached the maximum. Pile #2 was compressed under all levels of the load. When the load increased to 26 kPa, the pile #6, located near the top of the pile, occurred locally tensioned. Pile #11, also located near the top of the pile, had a local tension zone and the maximum axial force also decreased as the load increased. This was due to the circular sliding surface, and the  
Figure 15: Dynamic bending moment distribution of each row of pile (shaking table test).  
landslide thrust acting on the pile tilted with the sliding surface. Hence, the micropiles were not completely horizontally loaded. Therefore, there was a landslide thrust component in the vertical direction that compressed each row of piles. Each row of piles could not achieve complete coordinated deformation during the deformation of the landslide. The rear-row piles had excessive deformation and tended to be tensioned, and to a certain extent, tended to offset the compressive stress of the pile. Therefore, the axial force decreased as the load increased. On the other hand, due to the connecting beam, the top of each pile was linked together, so the upper part of the pile was under tension. This also reflects that when the micropiles are used for slope stabilization, the micropiles do not completely fail after bending and shear failure, but continue to block the continuous deformation of the slope in the form of tensile and compression resistance.  
Figure 17 shows the axial force distribution curve of each row of piles in the shaking table test. As it can be seen from the figure, similar to the case of the static model test, the axial force distribution of each row of piles is distributed triangularly, and it is mainly caused by compressive stress. Additionally, the maximum axial force appeared near the sliding surface, but the regularity is much more complicated than the static model test situation. It is worth noting that the maximum axial force of the front row and the middle row of piles are both obtained when the peak acceleration is 0.8 g. However, for the rear row of piles, the axial force under 0.8 g was smaller than it was under 0.6 g. As illustrated in the comparison of the results, the dynamic results are similar to the static case. After the damage of the rear piles exceeded a certain limit, the piles gradually transformed from compression to tension, and the axial force was somewhat reduced.  
Figure 16: Axial force distribution for each row of piles (static model test).
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 2 Overview of Smart Grid
### Advanced Metering Infrastructure (AMI)  
SGs are considered as one of the largest potential IoT network implementations with smart meters and wireless smart sensors placed throughout the grid, and smart appliances communicating with each other to ensure reliable and efficient power generation and distribution. The advanced metering infrastructure consists of physical and virtual components, including sensors, monitoring systems, smart meters, software, data management systems, and communication networks. AMI is responsible for collecting, analyzing, and storing metering data sent from sensors and monitoring systems and smart meters at the end-user to the utility companies for billing, grid management, and forecasting. SG interactions based on measured data and communication from sensor networks [29; 30].  
The smart metering devices installed on the customer premises use different technologies for communicating. These vary depending on what manufacturer smart meter the utilities company are installing, and the application. For large apartment buildings, the metering devices can be connected to the master device by RS-485 [31]. As illustrated in Figure 2, the metering devices can also be directly connected to the Head-End System (HES) through 3G/4G/5G or fiber networks, so-called end-to-end connection. The master device uses 3G/4G/5G, Ethernet, fiber optics, or power line communication (PLC) to communicate with the head-end system at the utilities company. Inside the premise area, the smart meter communicates through the HAN-port, the communication is based on IEC 62056-7-8, with RJ45 connector and M-Bus interface. From this port, other third-party equipment can be installed i.e., HEMS or household appliances [32].
## 5 Discussion  
### Biological Applications  
In Section 4, we proposed that Bragg scattering is a special case of tropospheric scattering. This means that clear-air echoes caused by atmospheric factors are consistent with the ITU-R's recommendations. As the ITU-R recommended, the relationship between the reflectivity factor of weather radar and the observation range has been clarified by the equivalent transmission loss, Equation (25). However, we can see that some echoes did not meet the ITU-R recommendations in Figure 19. We assume those echoes were generated by particle scattering (especially biological). Therefore, we plotted the echoes that did not meet the ITU-R recommendations, as shown in Figure 20. In Figure 20, we extracted those echoes for which the radar transmission loss was 3 dB larger than the ITU-R recommendations. For reliability, echoes with an SNR less than 12 dB were discarded. It can be seen that the distribution of the radar transmission loss (Figure 20a) is relatively close to the theoretical value (Figure 20b). Some of the echoes with large differences have large values of differential reflectivity (Figure 20c). There are also parts of the ring-like echoes suggesting that the organisms were congregating, which may be due to the stratification of insect migration.  
These features seem to indicate that these echoes were caused by biological activity. However, no similar biological echoes were observed in the X-band (in Figure 16). There are two completely different interpretations for this phenomenon. One is that the creatures happen to be in the blind spot of the X-band. The other is that the mountains unexpectedly make the tropospheric scattering stronger. Although we recognize the role of terrain, it is difficult to completely deny the role of organisms in echo generation. Thus, we need longer and more sophisticated observations to solve this problem.  
Figure 19: Radar transmission loss with altitude for elevation angles of 0.5\({}^{\circ}\) (**a**) and 1.45\({}^{\circ}\) (**b**) every day from 12:30 to 16:00 UTC in May. The white dotted line is the regression line.
## 8 Implementation Challenges and Solutions
### Technical Constraints and Computational Limitations  
Operating autonomously at sea demands that vessels process massive data streams from radar, LIDAR, AIS, and onboard sensors in real time and with minimal latency.  
However, onboard computational capacity is often limited, especially on smaller vessels or in retrofitted systems [140; 141].  
AI agents must make split-second decisions, e.g., evasive maneuvers or system shutdowns, based on environmental inputs and predictive modeling. Without adequate processing power, delays can occur, undermining safety and response effectiveness.  
The following solutions are proposed:  
1. Edge AI devices equipped with hardware accelerators (e.g., TPUs, FPGAs) enable localized, low-latency data processing without relying on satellite links [142; 143].
2. Portable and modular AI units allow systems to scale with mission needs, supporting computational flexibility without overhauls [144; 145; 146].
3. Lightweight AI models, such as MobileNet or TinyML, reduce the hardware footprint while maintaining decision accuracy [147; 148].
4. Hybrid computing architectures combine onboard, edge, and cloud resources, enabling dynamic load distribution between critical local operations and secondary cloud-based processing [117].  
By embedding scalable, energy-efficient computing platforms, AI agents can maintain high-performance navigation and fault handling under maritime constraints [55; 70].