esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Dual-Link Data Transmission Based on Internet of Vessels
## 1 Introduction
To address these issues, this paper designs the HDTA algorithm transmission model and proposes a spatial crowdsourcing task allocation algorithm based on Bayesian reputation. By treating underwater nodes as task publishers and vessels as task executors, vessels participating in data transmission are selected through  
Figure 1: (**a**) Ship distribution. (**b**) Dual-head node. spatial crowdsourcing. The algorithm combines a comprehensive scoring function and a solution to the cold start problem to ensure the stability and efficiency of the data link. The subsequent sections of this paper are organized as follows. In Section 2, a review of the existing literature related to this study is provided, focusing on the main research progress and methods in the field. The strengths and weaknesses of various research methods are compared and analyzed. In Section 3, the research methodology is described, and a crowdsourcing allocation algorithm based on Bayesian reputation for selecting high-quality vessels is proposed. This section also addresses relay vessel selection and transmission path selection issues, combining a comprehensive scoring function and solutions to the cold start problem. Incentive schemes are introduced to ensure the stability of data transmission and encourage more vessels to participate. In Section 4, the research results are presented in detail through relevant experiments, followed by an in-depth discussion. These results are compared with the existing literature to demonstrate the feasibility and advantages of the proposed solution. Finally, in Section 5, a comprehensive summary of the research is provided, along with suggestions for future research directions.
# Organizational Culture: The Key to Improving Service Management in Industry 4.0
## 4 Results
### Data Analysis
#### 4.2.1 Evaluation of Measurement Models  
The reliability analysis of the constructs in this study was based on the recommendation of [PERSON] and [PERSON] [104] to use at least three of the most commonly used indicators. We selected [PERSON]'s alpha, composite reliability (CR), average variance extracted (AVE), cross-loading, heterotrait-monotrait ratio (HTMT), and the Cornell-Larcker criterion to evaluate the measurement model. In our measurement model, the factor loading of organizational culture on service management (Hypothesis H3) was greater than 0.50, a statistically significant level, but the factor loading of the other constructs was less than 0.50, which is generally not considered statistically significant [105]. Additionally, the factor loadings between the constructs were all greater than 0.50, which was statistically significant (see Figure 4). To verify the significance of the above assumptions, bootstrapping (5000 samples) was conducted. Analysis of individual item reliability was conducted as follows. The statistical significance of the loading of each variable was measured by assessing the loading of potential variables (outer loading). The loadings of all variables were higher than 0.6 and significant, and the sample loadings' coefficients ranged from 0.611 to 0.897. The variance inflation factor (VIF) for each indicator was less than five, indicating that there is no collinearity in the variables, following [PERSON], [PERSON], and [PERSON] [106]. In terms of Cronbach's alpha, [PERSON], [PERSON], [PERSON], and [PERSON] [107] stated that a Cronbach's alpha of 0.8 or higher and 0.7 or higher for the total scale and subscales, respectively, indicates good reliability. In this study, the Cronbach's alpha of the questionnaire and the subscales of each component were above 0.8, indicating good reliability (see Table 2). Figure 4: PLS model (outer loadings, path coefficient, and R\({}^{2}\)). In this study, the CR and AVE of the potential variables were used for convergent validity analysis, as suggested by [PERSON] and [PERSON] [108]. Table 2 shows that the CR values ranged from 0.906 to 0.943, higher than the threshold of 0.8 suggested by [PERSON], [PERSON], and [PERSON] [109], indicating good internal consistency of the study's model. The AVE of potential variables was used to calculate the explanatory power of each measured variable on the potential variables. Table 2 shows that the AVE values of the potential variables ranged from 0.555 to 0.702, higher than the standard value of 0.50 suggested
## 4 Qualitative Findings and Discussion
### Theoretical and Practical Implications  
The uniqueness of this study lies in its focus on the user experience of LSAD, aiming to provide universal design improvement guidelines for this specific domain. Firstly, through reflexive thematic analysis and patient-centered design [51], we identified potential usage barriers and perceived pressure of LSAD, a factor overlooked in other relevant studies, thereby further addressing a gap in the literature. The research findings indicated that users of LSAD require scientific guidance to help address uncertainties or errors during the wearing process. These insights provide valuable knowledge for follow-up studies and help provide research directions for researchers from other disciplinary backgrounds. Furthermore, they also provide a solid theoretical foundation for subsequent related research.  
Most importantly, we proposed a set of universal recommendations for LSAD design improvements. These hold potential practical implications for LSAD manufacturers, healthcare professionals, designers, and individuals suffering from lower-back pain. They contribute to enhancing the quality and applicability of such assistive devices, thereby improving rehabilitation outcomes for individuals with lower-back pain.
## 5 Discussion
### Opportunities and Future Challenges
#### 5.1.2 Future Challenges  
A major challenge faced using IoT technologies is related with data privacy and security, as no entity would like to have IAQ sensitive data of its buildings available to the public. In the surveyed articles, only a few authors included messaging security protocols. From an ethical point of view, third parties can develop ways to earn money from these data, creating a market for the sale of information.  
Another challenge is the connectivity between IoT devices, and with back-end platforms, which may open gaps for attacks and intrusions into systems by malicious hackers. It is safe to say that the risk of an attack increases exponentially the more the system is connected to the Internet, and it is evident that most of the surveyed systems go with Wi-Fi communication technology, instead of looking for autonomous and independent solutions. At the forefront of this challenge are manufacturers (in commercial devices) and re-searchers (in prototype systems), with the task to include security keys in IoT devices.  
Regarding the communication infrastructure, there is also the challenge to avoid possible constraints in the use of bandwidth, as frequency spectrum is a finite resource. The more devices transmitting at the same time, the worse the transmission quality becomes. This happens either in free use bandwidth or made available by Internet Service Providers, and in the latter, it depends on approval by government authorities.  
Due to the massive collection of data, the servers must have an increased storage capacity. This capacity can be allocated on local servers or in a public/private cloud, however, it must be considered that invariably there will be an increase in data storage costs.  
A final challenge concerns the creation of standards among market providers, as de-vices need to communicate with each other, and work integrated. Whenever a device fails and is discontinued, the substitute device must be compatible to ensure the system's sustainability. In addition, as they are wireless communication devices, the hardware must have the certifications required by the regulatory bodies of the various countries.
# A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
## 2 Automatic Feature Extraction  
[PERSON] and [PERSON] [16] state that to develop a structural health monitoring system based on classical machine learning algorithms, four steps are required to be taken including (i) operational evaluation, (ii) data acquisition, (iii) feature extraction, and (iv) statistical modelling. Operational evaluation attempts to provide answers to questions related to the life-safety and economical justification of developing such systems. In addition, it addresses what types of damage are of concern and in case of multiple damage scenarios, which types are of most concern. Financial limitations play a strong role at the second step, which involves choosing the data acquisition hardware. Feature extraction is defined as deriving informative and non-redundant values from the raw data acquired in the previous step to facilitate the subsequent training and generalisation step. Lastly, the training and evaluation of the statistical machine learning algorithm is known as the statistical modelling step. Given the difference between data modelling using classical machine learning and deep learning algorithms, the aforementioned framework can be modified for the task of deep learning-based crack detection.  
Deep architectures are called the new generation of neural networks in which multiple deep layers are used to gradually extract high-level features from raw inputs [12]. Deep learning models have been utilised in different areas such as speech and audio processing [17], language processing [18], information retrieval [19], and computer vision [20]. The most important characteristic of deep architectures can be implied from the aforementioned definition, which is the fact that the process of feature extraction is carried out by the architecture itself, without necessitating the step of engineering handcrafted features. A transition from manually extracting handcrafted features to the application of deep learning architecture for automatic feature extraction has occurred in different areas, such as medical imaging [21], and video analysis [22], as well as crack detection [13; 14; 15]. Giventhe provided definition for deep architectures and the proposed framework by [PERSON] and [PERSON] [16], the procedure of developing a deep learning-based crack detection system can be summarised in three steps where feature extraction and statistical modelling are performed in one step. A summarised diagram is shown in Figure 2.  
The studies focusing on the application of deep architectures to perform the task of crack detection can be categorised in different ways. In [13], they are categorised based on the final output of the approach. According to this categorization, by and large, they are classified in three groups of (i) IC, (ii) OR, and (iii) SS studies. To summarise, the categorisation of image-based crack detection approaches is shown in Figure 3.
## 3 Design of Segmented Mirror with a GRoC Actuation System
### Actuation Point's Position Design of Segmented Mirror  
#### 3.3.1 Problem Description  
After the segmented mirror support design was completed, we optimized the actuation point's position of the GRoC actuation system on the segmented mirror's back with the help of proposed multiple surrogates. Due to the surface shape error RMS of residual, the stress and the amount of GRoC actuation are proportional to the actuator's output. The influence of the actuator on the optimization object can be eliminated by division. In other words, the surface shape error RMS per millimeter of GRoC actuation has nothing to do with the actuator's output, and the same is true of the maximum local stress per millimeter of GRoC actuation. In this optimization, the output of the actuator was set as 1 N. The design variables of the actuation point's position are shown in Figure 7, where \(h\) is the depth of the actuation point, \(l_{2}\) is the distance between the actuation point of the strut and the actuation point of the actuator, and \(d_{1}\) and \(d_{2}\) are the diameters of the actuation point of the actuator and the strut, respectively. Due to the fact that the range of GRoC actuation is required to be greater than 2 mm, in order to ensure the normal work of the mirror, the maximum local stress should be checked for strength. The objective functions of the activation point's position are constructed as follows:  
\[\text{min}Y_{\text{RMS}}=f(d_{1},d_{2},l_{2},h), \tag{16}\]  
\[s.t.\left\{\begin{array}{c}0<h<10\text{ mm}\\ 5\text{ mm}\leq d_{1}\leq 20\text{ mm}\\ 5\text{ mm}\leq d_{2}\leq 20\text{ mm}\\ 20\text{ mm}\leq l\leq 90\text{ mm}\\ 2\delta_{\text{MAX}}\leq\frac{\delta_{s}}{n_{s}}\end{array}\right., \tag{17}\]  
where \(Y_{\text{RMS}}\) and \(\delta_{\text{MAX}}\) are the surface shape error RMS of residual and the maximum local stress per millimeter of GRoC actuation, the tensile yield strength of Zerodur, \(\delta_{s}\), is 57 MPa, and the safety factor, \(n_{s}\), is 2.  
Figure 7: The optimization variables of the actuation point’s position design.
# Impulse Controllability for Singular Hybrid Coupled Systems
## 1 Introduction
Specifically, they analysed these aspects in detail for large-scale interconnected descriptor systems with two subsystems operating under derivative feedback. Reference [20] explored how a delay can have a stabilising effect on complex neural networks made up of uniform systems using pinning impulsive control. Reference [21] examined the development of non-fragile decentralised \(H_{\infty}\) controllers for interconnected systems of integer order based on the existing literature. This paper delves into the issue of controlling impulses in singular systems. A decentralised P-D output feedback controller is employed in hybrid coupled systems. In order to effectively handle the system's nonlinearity, a specific technique is employed. According to the Differential Mean Value Theorem (DMVT), the term that is removed necessitates the implementation of a universal Lipschitz constraint and circumvents the use of high-gain control mechanisms. The process of transformation causes a change in the nonlinear singular large-scale system, transforming it into a linear one. This is a singular large-scale system with varying parameters, designed for the analysis of nonlinear systems. The analysis of behaviour is conducted using the tenets of linear system theory. Firstly, to preserve the impulse controllability in singular hybrid coupled, the primary outcome is the existence of a decentralised output feedback controller, which depends on the necessary and sufficient algebraic conditions we derived. In addition, we provide a novel sufficient condition for the admissibility of an impulse-controllable singular hybrid coupled system. Finally, we present illustrative examples to demonstrate the effectiveness of this method. This paper proceeds as follows: in Section 2, we introduce the notations, definitions, and lemmas required for the subsequent sections. Section 3 presents a necessary and sufficient algebraic condition for impulsive controllability using a decentralised derivative output feedback controller. Section 4 provides illustrative examples demonstrating the feasibility of the proposed theorems. Finally, Section 5 offers a summary of the paper.
# Effect of Wind Direction and Velocity on PV Panels Cooling with Perforated Heat Sinks
## 3 Materials and Methods
### Sensitivity Analysis of Grid Spacing
However, according to literature, the variation of the relative humidity could affect the final results by 4-5% [37]. The thermophysical properties of the materials defined for the simulations are presented in \[GCI_{2-3}=\frac{F_{s}|\varepsilon_{2-3}|}{\left(r_{2-3}^{p}-1\right)} \tag{2}\]1 The geometry and set-up of the simulation are shown in \[GCI_{2-3}=\frac{F_{s}|\varepsilon_{2-3}|}{\left(r_{2-3}^{p}-1\right)} \tag{2}\]2  
where:  
\(g\)--thickness of the layers composing the PV panel [m]; \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & \(g\) **[mm]** & \(\lambda\) **[W/m**\(\cdot\)**K]** & \(\rho\) **[kg/m\({}^{3}\)]** & \(c_{p}\) **[J/kg**\(\cdot\)**K]** \\ \hline Glass & 3.00 & 1.00 & 2300 & 0.50 \\ PV cells & 0.35 & 168 & 2330 & 0.757 \\ EVA & 0.50 & 0.35 & 960 & 2.09 \\ Tedlar & 0.20 & 0.20 & 1500 & 1.20 \\ Copper & 1.00 & 386 & 8960 & 0.376 \\ Air & - & 0.025 & 1.20 & 1.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Thermophysical properties of the materials defined. The steady-state conditions were assumed for the numerical modeling. During the simulations, the following inputs were established:  
* **constant**: ambient temperature, \(t_{air}=35\)\({}^{\circ}\)C--worst scenario in terms of heating [6];
* **constant**: solar radiation, \(G_{mnd}=1000\) W/m\({}^{2}\)--worst scenario in terms of heating [6];
* **variable**: velocity of the air, \(v_{air}=\) 1-5 m/s;
* **variable**: wind direction: towards the back, front and side of the PV panel;  
The main results obtained from the simulations were:  
* operating temperature of the photovoltaic panel;
* normalized power production. In terms of wind direction, the simulations were made in 3 Scenarios--for each case, different faces of the volume represent the inlet and outlet of the flow--Figure 3. The blue arrows are used for the back wind. The red arrows are used for the front wind. The green arrows are used for the side wind. The inlet face of the domain varies as the position but has the same boundary conditions for each case: constant air temperature of 35 \({}^{\circ}\)C, velocity vector normal to the inlet surface and variable wind velocity: from 1 m/s to 5 m/s. The boundary conditions regarding the flow were the turbulent intensity of 5% and turbulent viscosity ratio of 10.
## 5 Discussion  
### Key Benefit of Bike-Sharing System  
The findings of this study provide valuable insights into the relationship between bike-sharing services and the perceived benefits for users in the Silesian region of Poland. They contribute to the expanding body of research on smart cities and shared mobility, aligning with ongoing discourse on sustainable urban development and the application of technology in transport systems [31; 32; 33; 34; 35; 36; 37; 38]. This research presents a comprehensive, multifaceted analysis of bike-sharing systems in Silesia, employing descriptive statistics, factor analyses, and regression models to examine both user-perceived benefits and infrastructural-related challenges. The results provide a detailed understanding of how technological performance, operational reliability, and service integration collectively influence user satisfaction and broader urban mobility outcomes. By situating these findings within the context of active transportation studies [44; 63] and sustainable urban development, this study underscores the transformative potential of bike-sharing systems when supported advanced technological and infrastructural solutions.  
This study shows that health-related benefits are among the most valued features of bike-sharing systems. Users report notable improvements in physical fitness (mean = 3.86) and overall health promotion (mean = 3.84), supporting the view that active transportation helps mitigate the negative effects of sedentary lifestyles--an area of growing concern in public health research [63]. Eco-friendliness stands out as an important advantage, with an average score of 3.82, showing that users acknowledge bike-sharing's role in decreasing urban carbon emissions and easing traffic congestion. These findings are consistent with previous studies on the environmental benefits of shared mobility [52; 73] and highlight the contribution of bike-sharing systems to broader climate action goals.  
Simultaneously, the assessment of infrastructural aspects shows that numerous essential elements--spanning from the usability of mobile applications (mean = 3.31) to the bicycles' technical and visual states (both approximately averaging 3.00)--are evaluated at moderate levels. While these ratings indicate that the bike-sharing system fulfils basic functional needs, they also reveal significant potential for innovation. For instance, although the performance of elements like bike availability and bike path networks is satisfactory  
Figure 4: Hierarchical model of bike-sharing system in Poland.  
(averages close to 3.00), the considerable variability in responses (indicated by standard deviations frequently over 1.00) implies that user experiences differ significantly. This diversity could be linked to variations in user demographics, the geographic spread of services, or differing anticipations about the quality and dependability of shared mobility services.
## 6 The Digital Frontier: AR/VR Applications in Dental Surgery and Education  
AR and VR are becoming valuable tools in dental education and surgical training. VR, in particular, offers a fully realistic experience where students and professionals can practice complex dental procedures in a risk-free environment [43, 44]. These simulations allow for repeated practice, real-time feedback, and skill development without involving real patients. While VR-trained students show improved procedural accuracy, the strength of evidence varies. For instance, a randomized study with 42 dental students reported significant skill improvement using a haptic VR simulator (\(p<0.05\)), but larger trials with long-term follow-up are scarce [45]. Similarly, AR studies often rely on observational data, limiting the ability to draw causal inferences.  
AR enhances real-world environments by overlaying digital elements, such as anatomical markers or surgical guides, directly onto the operating field. This technology helps users understand space better and navigate in real time, which is especially useful in precise procedures like dental implants, jaw surgery, and complex orthodontic interventions [46, 47]. Early studies show that AR may enhance learning outcomes in anatomy teaching and procedural planning, but stronger evidence is still needed [48].  
Beyond individual learning, AR and VR are also enhancing teamwork in dental care through a broader approach known as Extended Reality (XR) [49]. XR encompasses AR, VR, and MR. These technologies enable remote consultations, real-time mentoring, and treatment planning with input from multiple specialists, even from different locations [50]. Integrating biosensors and eye-tracking systems further boosts interactivity by providing real-time feedback and tracking users' physical responses, which supports both learning simulations and clinical applications [51, 52, 53].  
However, several challenges make it difficult to widely use immersive technologies in dentistry. These include high costs, a lack of clear training standards, technical issues such as calibration, and concerns about data security. There are also not enough long-term studies comparing AR/VR with traditional training. Even with these challenges, the future of immersive technology in dentistry looks promising. As AR/VR systems continue to improve, they could transform the way dentists learn, diagnose, and treat patients, making procedures more accurate and more personalized. New tools like AI-powered simulations, real-time virtual guidance, and intelligent feedback systems could help make dental care faster, more accurate, and more accessible.