esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving the Strategy of Maintaining Offshore Wind Turbines through Petri Net Modelling
## 5 Simulation Results and Discussions
### System and Subsystem Analysis  
When the wind turbine is equipped with a basic CMS and only \"advanced service\" is assumed to be conducted every six months, the average number of failures and the average number of faults occurring in each subsystem in the lifetime of the turbine can be obtained by calculating the average value of the times that each subsystem fails and the average value of the times that each subsystem develops a fault in the lifetime of the turbine in 100,000 simulations. The calculation results are listed in Table 3.  
From Table 3, it is found that despite different subsystems showing different reliability features, the total number of subsystem failures causing the shutdown of the turbine within the whole design life of the turbine is only 0.68. The fact that there are so few failures is attributed to the use of the CMS and frequent inspections as well as the immediate action to repair the faults as soon as they are detected.  
From the simulation results, it is also found that the total downtime of the turbine is 162.23 days, which means the availability of 98.52% in its lifetime if neglecting the influence of extreme events. Such high availability is very satisfactory for an OWT, but it could be further improved by some approaches; for example, optimising the time interval of periodic maintenance, using a higher quality CMS, etc. However, it is well known that frequently carrying out inspection and using a higher quality CMS will increase costs. How to make a trade-off between the availability and the O&M costs becomes a difficult question that operators of all wind farms must consider. To answer this question, further research has been conducted and described below.
# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
## 4 Methodology
### RL Model
#### 4.2.1 Agent-Environment Interaction  
In the RL framework, the phishing detection model operates as an agent interacting with an environment (the dataset). The environment presents emails with various features, and the agent's task is to classify each as either phishing or benign. The interaction is formalized as a Markov Decision Process (MDP), defined by the tuple (S, A, R, P, \(\gamma\)), where the following variables are used:  
S = Set of states (email features);  
A = Set of actions (phishing, benign);  
R = Reward function;P = State transition probability;  
\(\gamma\) = Discount factor for future rewards.
## 2 Materials and Methods
### Participants  
The sample size was calculated using the web-based software GRANMO version 7.12 (Barcelona, Spain). For this calculation, it was considered 41,400 track and field for female athletes licenses were certified at the moment of the study, estimating 29,000 track and field licenses for females from 18 to 45 years old. Because the estimation of female athletes with PFD is about 50%, a finite population was considered. With a 95% confidence level and 80% statistical power, at least 13 participants were needed for every group to observe an improvement of 2.56 points in the ICIQ-SF scale, with 3.0 points as standard deviation and 15% of dropouts, based on previous studies [18]. As inclusion criteria, participants should train and compete for any track and field modality, at least at regional competition level, be between 18 and 45 years old, and have some of the main symptoms related to PFD (urine leakage, anal leakage, or dyspareunia). To participate, athletes should be capable of performing a correct PF contraction, graded \(\geq\) 2 according to the Modified Oxford scale (MOS). As exclusion criteria, female athletes should not have chronic pelvic pain, a history of pelvic surgery, neurogenic lower urinary tract dysfunctions, or pelvic organ prolapse (POP) in stage higher than 1 (according to the [PERSON] and Walker's classification system) [19], or physiotherapy treatment focused on PFD in the previous 6 months. None of the participants had major medical comorbidities (diabetes, smoking, hypertension, or chronic cough) or were pregnant. All female athletes with a track and field license in Spain received an invitation to participate in the study through e-mail from their regional federations, clubs, and/or training groups. Although 43 athletes were randomly assigned to interventions, 39 female athletes (33.5 \(\pm\) 8.1 years old; mass: 56.1 \(\pm\) 8.3 kg; height: 1.66 \(\pm\) 0.6 m) completed the study and 4 decided not to continue due to personal issues (Figure 1). All the participants were informed about the study and signed the written consent before starting the study. Ethical Committee of the University of the Balearic Islands approved this investigation (ref: 124 CER19) that was prospectively registered on clinicaltrial.org (ref: NCT06014021).  
Figure 1: Flow diagram.
# Study on the Fire Resistance of Axially Restrained H-Shaped Steel Beams Under Real Fire
## 3 Analysis of Fire Resistance of Steel Columns Based on Quasi-Real Fire Heating Curve
### Establishment of Finite Element Model of H-Section Steel Beam
#### 3.1.3 Steel Beam Model Establishment  
(1) Model Overview  
The thermal-mechanical coupling method is used to simulate the heating process and stress change process of the steel beam in the quasi-real fire. To verify the accuracy of the model, the steel beam model builds an H-shaped cross-section steel beam according to the specimen used in the literature [30]. The cross-section specification is H150 \(\times\) 100, the length is 3000 mm, the element type is a four-node thermal-mechanical coupling shell element of S4 RT, and the mesh size is taken as 1/10 of the width of the steel beam model. The material properties refer to the measured data obtained by [PERSON] et al. [29], and the specific data refer to 3.1.2.  
(2) Boundary conditions  
To simulate the restraining effect of the steel column on the steel beam, axial and rotational restraints are set, respectively, at both ends of the beam (as shown in Figure 10), with magnitudes taking the test data values of 1956 N/mm and 6.8 \(\times\) 10\({}^{8}\) N\(\cdot\)mm/rad in the verification literature. The area within 200 mm to the left and right of the mid-span of the steel beam is coupled to the point RP-7.  
Figure 10: Model validation. (**a**) Comparison of temperature–mid-span deflection curves; (**b**) comparison of failure modes.  
Figure 9: Stress–strain model of Q690 high-strength steel at high temperature.  
(3) Initial defects  
In Abaqus, the eigenvalue buckling analysis of the steel beam is first performed through the buckle analysis step to identify its potential buckling mode. Next, a buckling mode that is closest to the initial deformation of the steel beam in the experiment is selected. This buckling mode is imported into the finite element model for high-temperature analysis to make the steel beam model more refined.
# 2D Ultrasonic Antenna System for Imaging in Liquid Sodium
## 4 Experimental FMC/TFM Images Performed in Water and Associated Simulations with CIVA
Discussion for Future Work: Improvement of the Images' Signal-to-Noise Ratio and their Computation Time  
It has been experimentally shown that the TFM applied to an FMC acquisition performed with the two-antenna system allows to image spherical targets at large distances in water. However, two main drawbacks have been identified. The first one is the low Signal-to-Noise Ratio (SNR), especially for far-reaching targets. The second one is the high computation time required to obtain the images, which is due to the large size of the field of vision (several hundred centimetres) and the large number of elements of the antennas. In order to optimize the SNR and the computation time, several leads are being investigated.
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
## 3 Customer Need to Use Flying Taxis
### Flying Taxis Benefit People Who Need to Travel for Medical Reasons  
Flying taxis could offer several benefits for people who need to travel for medical reasons. For example, flying taxis could provide fast and efficient transportation to medical facilities, especially in cases of emergency where time is of the essence. They could also improve accessibility to medical facilities in rural areas or areas with limited ground transportation options. Additionally, flying taxis could offer a comfortable and convenient way for patients to travel to and from medical appointments, which could be especially important for individuals with mobility limitations or medical conditions that make ground transportation difficult [163].
# Correlation Between Co-Design and Psychological Need Satisfaction Based on fNIRs Hyperscanning
## 3 Results
### IBS Is Related to Psychological Need Satisfaction Rate  
Further analysis were conducted on the correlation between brain synchronicity IBS in both brains and the users' psychological need satisfaction rate, exploring the significant relationship between brain synchronicity and psychological need satisfaction rate discovered in traditional and co-designs. Firstly, the total score in the questionnaire data above was divided by the total score to obtain the psychological need satisfaction rate. Secondly, a correlation analysis was conducted between the IBS and the psychological need satisfaction  
Figure 8: Topographic map of t-values representing the differences in IBS between draw and Com blocks; color indicates the t-value: (**a**) traditional; (**b**) co-design.  
Figure 9: Line graph of IBS across significant channels under Com and draw block conditions: (**a**) traditional; (**b**) co-design.  
rate of Com and draw blocks in traditional and co-designs for CH2 and CH3. Finally, a scatter diagram was constructed, highlighting the linear trend to visualize the association.  
The correlation between IBS and the psychological need satisfaction rate of CH2 and CH3 under traditional and co-design tasks in Com block is shown in Figure 10. The results showed that there was neither a significant correlation between IBS and psychological need satisfaction rate in CH2 nor CH3 under traditional, with negative values for r (T2C: r = \(-\)0.248, \(p\) = 0.194, T3C: r = \(-\)0.153, \(p\) = 0.429). There was also no significant correlation between IBS and psychological need satisfaction rate in CH2 and CH3 under co-design, with positive values for r (C2C: r = 0.257, \(p\) = 0.179, C3C: r = 0.113, \(p\) = 0.558).  
The correlation between IBS and the psychological need satisfaction rate of CH2 and CH3 under traditional and co-design tasks in draw block is shown as shown in Figure 11. The results showed that there was neither a significant correlation between IBS and psychological need satisfaction rate in CH2 nor CH3 under traditional, with negative values for r (T2D: r = \(-\)0.087, \(p\) = 0.653, T3D: r = \(-\)0.015, \(p\) = 0.939). However, under co-design, there was a significant positive correlation between IBS and psychological need satisfaction rate in CH2 and CH3, with positive values for r (C2D: r = 0.393, \(p\) = 0.035, C3D: r = 0.435, \(p\) = 0.018).  
Figure 10: Scatter plot of IBS versus psychological need satisfaction rate in CH2 and CH3 under the Com block condition: (**a**) Traditional CH2 Com (T2C); (**b**) Traditional CH3 Com (T3C); (**c**) Co-design CH2 Com (C2C); (**d**) Co-design CH3 Com (C3C).
# Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach
## 1 Introduction
By combining reasonable data acquisition rules, the level of precision when addressing collision problems can rival that of tactile-sensor-based collision processing methods. Regarding the extremely open issue of collision classification, it is often necessary to integrate the practical application scenarios of robots, potential contact forms, and other factors to design a rational collision classification strategy. This is crucial for significantly enhancing the applicability of the proposed classification methods in their corresponding domains. In the context of robot-based automatic electric vehicle charging, when there is minimal deviation in visual positioning, a robot can achieve effective insertion through impedance control. When there is a significant deviation in visual positioning, it may result in the end effector of the robot not making contact with the charging port but instead coming into contact with other parts of the electric vehicle. Due to the evident disparity, deploying a global visual system in the world coordinate system can facilitate collision contact discrimination, thereby preventing potential damage. When there is a moderate deviation in visual positioning and the end effector of the robot, carrying the charger is still able to make contact with the charging port but encounters difficulty in insertion, so the aforementioned methods are unable to address this situation. Given the limited size of the collision area and the frequent insertions, often involving significant insertion force, the system faces potential safety risks when lacking the capability to classify collisions in such scenarios. In order to enhance the system's safety, this paper proposes a novel data-driven collision classification method. Utilizing the vibration signals generated during collisions, the objective is to effectively discern whether the collision contact is acceptable in scenarios involving moderate deviations. Additionally, given the presence of passing vehicles introducing environmental noise in the automatic charging scenario, this paper also specifically introduces a noise suppression module into the proposed collision classification method to enhance the robustness of the proposed method in noisy scenarios. The rest of this paper is organized as follows: Section 2 reviews related work and clarifies the main contributions of this article. Section 3 describes the details of the datasets and the framework of the proposed method. Section 4 presents and discusses the experimental results, and Section 5 concludes this paper.
## 2 Hybrid Framework Description
### Adaptive Fusion Module
#### 2.2.3 Computing the Heading Error Measurement  
The EC and HDR algorithms output heading error measurements, i.e., \(\Delta\psi_{k}^{m}\) and \(\Delta\psi_{k}^{R}\), respectively. To trade off between the inertial-data-based method (i.e., HDR) and the magnetic-data-based method (i.e., EC), the final heading error measurement can be defined as:  
\[\Delta\psi_{k}=a_{m}\Delta\psi_{k}^{m}+a_{R}\Delta\psi_{k}^{R}, \tag{4}\]  
where \(a_{m}\in[0,1]\) and \(a_{R}\in[0,1]\) represent the weights of the heading error measurements for the EC and HDR algorithms, respectively, and \(a_{m}+a_{R}=1\) or \(a_{m}+a_{R}=0\). The weights are given in Table 1. Compared with inertial data, magnetic data are not absolutely reliable. The reason is that the detected magnetic field may be a magnetic disturbance, even though the norm of magnetic filed vector is near one [51]. Therefore, the EC algorithm is designed to have a lower weight when a magnetic field with low quality is detected, and the HDR algorithm is simultaneously triggered. When a magnetic field with high quality is detected and the HDR algorithm is simultaneously triggered, the EC and HDR algorithms have the same weights. Conversely, the heading error is not updated by the ESKF only when a magnetic field with low quality is detected or no heading error correction algorithms are triggered. Therefore, \(\lambda_{1}\) is recommended to have a lower value than \(\lambda_{2}\), and \(\lambda_{3}\) is recommended to have the same value as that of \(\lambda_{4}\). In our experiment, \(\lambda_{1}=0.2\), \(\lambda_{2}=0.8\), and \(\lambda_{3}=\lambda_{4}=0.5\).
# Can OCT Angiography Be Made a Quantitative Blood Measurement Tool?
## 6 Empirical Validation of OCTA
Moreover, typically, OCTA has a worse volumetric resolution than TPM, and asynchrony in measurements [18] can additionally confound comparisons between modalities unless physiology is carefully maintained. Thus, rigorous verification of OCTA with simultaneous TPM is a promising topic for further investigation. The most appealing and direct validation approach is to use another OCT modality or algorithm to cross-validate OCTA. In 2012, [PERSON] et al. [74] noticed that the passage of a red blood cell through the OCT coherence volume led to phase and intensity transients. Based on this insight, they developed a particle counting method for measuring the flux, speed, and linear density in a capillary. Using particle counting, they developed and validated a phase intensity mapping (PIM) algorithm for measuring quantitative cerebral blood flow (CBF) [75]. It remains unclear whether individual red blood cell passage can be measured at all locations in an image, or whether these results are merely anecdotal. Moreover, the intensity pattern created by decorrelation can create random transients that could be easily mistaken for RBC passage (e.g., Figures 4E and 6B). Still, particle counting remains an attractive approach for validating OCTA in stable preparations.
###### Abstract  
(1) Background: Products, manufactured using additive manufacturing technologies (AM) are increasingly present on the market. The research was undertaken to determine the possibilities of increasing the use of AM technology in Polish manufacturing companies. The aim of the paper is to determinate the level of the AM technology use of Polish Metal and Automotive Manufacturing and the influence of AM technology use on the increase of manufacturing company's competitiveness-in the context of Polish Manufacturing Companies. (2) Methods: This paper uses literature studies to determinate the AM technology used within the production processes in the automotive and metal industry companies (so called dimensions) and a questionnaire survey, which was carried out on a sample of 250 Polish Metal and Automotive Manufacturing Enterprises. (3) Results: The results were verified by a statistical analysis, using correlation coefficients. Based on the data obtained, it was determined that both metal and automotive Polish companies use, or have in their investment plans, the implementation of AM technology, due to the need to reduce production costs and increase speed and flexibility when responding to customer needs. Moreover, the relationship between applied additive manufacturing technologies and the effects of their use, in enterprises, was analysed. The novelty of our work is defining the dimensions of the AM technology use for our empirical research and determining the influence of AM technology use on the increase Polish manufacturing company's competitiveness. (4) Conclusions: The possibilities of using the results of research in economic practice were demonstrated. We also highlighted the impracticality for managers to support the selection and implementation of AM technology in the context of obtaining possible benefits for a manufacturing company.  
Keywords:polish manufacturing company; additive manufacturing technology; questionnaire survey; empirical research +
Footnote †: journal: Applied Sciences
# Bioactivity and Delivery Strategies of Phytochemical Compounds in Bone Tissue Regeneration
## 2 Bone Structure and Function  
Bone is a metabolically active tissue with self-healing capability, in constant renewal, adapting its structure to mechanical stimuli, stress, hormonal changes, and repairing structural injuries through a process of remodeling [16]. Bone homeostasis is preserved by the coordinated action between osteoblasts (bone-generating cells derived from mesenchymal stem cells, MSCs) responsible for bone growth and osteoclasts (multinucleated bone-resorbing cells differentiated from the hematopoietic stem) involved in bone resorption [17]. During bone matrix synthesis, first osteoblasts secrete the organic matrix: collagen proteins, mainly collagen type I and non-collagen proteins, such as osteonectin (ON), osteoclast (OCN), bone sialoprotein (BSPI/II), and osteopontin (OPN), and proteoglycan [18; 19; 20]. Secondly, deposition and mineralization of the bone matrix take place through the production of a protein mixing called osteoid that promotes calcium and phosphate adhesion, resulting in the organization and mineralization of new bone [21; 22].  
Mature osteocytes (derived from MSCs through osteoblastic differentiation) are completely trapped inside the mineralized bone matrix. Due to their strategic location, osteocytes maintain the connections to other osteocytes and osteoblasts and react to several biochemical signaling paths and mechanical stimuli contributing to the regulation of calcium and phosphate homeostasis [23]. When bone resorption should not take place, lining cells avoid direct interface among osteoclasts and bone matrix, playing a significant role in calcium homeostasis and osteoclast differentiation [24; 25; 26; 27]. Osteoclasts resorb bone through the release of enzymes and acids capable of dissolving and digesting minerals in the bone, but they also secrete cytokines that affect the activity of surrounding cells inducing mesenchymal stem cells and osteoblasts to initiate osteogenesis in resorption lacuna (remodeling) or another non resorbed site (modeling) [28; 29; 30]. The RANKL/RANK/OPG system is a crucial mediator of osteoclastogenesis. In particular, the interaction of RANK with RANKL is required for osteoclast formation, differentiation, activation, and survival. On the contrary, OPG can block RANK/RANKL interaction, thus preventing osteoclast differentiation and activation [31; 32]. Irregularities in osteoclastic activity lead to disorders such as osteoporosis and osteopterosis [33].
## 4 Self-Healing Approaches
### Machine-Learning Algorithms
#### 4.3.7 Behavioural Modelling Intrusion Detection System  
The behavioural modelling intrusion detection system (BMIDS) algorithm analyses familiar simulations within a friendly environment to detect anomaly activities. It is an intrusion detection system that uses ML techniques to monitor system behaviours and detect anomalous activity. BMIDS works by analysing behaviour patterns in the system logs and network traffic and comparing them to expected behaviour based on a model of normal system behaviour. BMIDS can detect and respond to attacks or intrusions in a system. Once an anomaly is detected, the system can mitigate the threat by blocking the offending traffic or alerting security personnel. The functionality is explained in Arrington in [31], where BMIDS is shown to use a computing process to detect intrusion within a smart house environment. A three-stage method for connecting real-world scripted events schemes called behavioural script event scheme (BSES) was presented. BMIDS is a subset of IDS and AIS, which includes in functionality the mechanisms for a subset of IoT intrusions detection systems. BMIDS executes its processes effectively because it creates behavioural models from the BSES dataset, in which use-case IoT devices capture the behaviour of a given anomaly situation, thereby allowing the system to correctly associate the anomaly event with a scripted behaviour and recreate the instance of BSES. The main advantage of BMIDS is its ability to detect previously unknown attacks, also known as \"zero-day attacks\", that may not be detectable using traditional signature-based approaches. BMIDS can also adapt to changes in system behaviour over time, improving its accuracy and effectiveness in detecting and responding to attacks. However, one limitation of BMDIS is its reliance on accurate models of expected system behaviour. If the model is not comprehensive enoughor the system undergoes significant changes over time, the BMIDS may produce false positives or miss essential threats. Additionally, BMIDS can be computationally expensive to train and maintain and may require significant expertise to configure or optimise. BMIDS can be used with other self-healing techniques, such as automatic system configuration or network rerouting, to help restore the system to a healthy state after an attack.