esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Analysis of Temperature after Laser Treatments  
The detection of any surface temperature increase was carried out during irradiation (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline  & **T0** & **T1** & **\(\Delta\)T** \\ \hline
980 nm Diode & 31.2 \({}^{\circ}\)C & 57 \({}^{\circ}\)C & 25.8 \({}^{\circ}\)C \\ \hline
1064 nm Nd-YAG & 31.2 \({}^{\circ}\)C & 55.4 \({}^{\circ}\)C & 24.2 \({}^{\circ}\)C \\ \hline
1064 nm Q-switch & 31.2 \({}^{\circ}\)C & 34.1 \({}^{\circ}\)C & 2.9 \({}^{\circ}\)C \\ Nd-YAG nano & & & \\ \hline \end{tabular}
\end{table}
Table 2: Increase in temperature during laser-titanium interactions.  
From a basic starting point of 31.2 \({}^{\circ}\)C, the treatment with the 980 nm diode laser reached 57 \({}^{\circ}\)C in two seconds of interaction, thus raising the temperature by 25.8 \({}^{\circ}\)C.  
Using the 1064-nm Nd-YAG laser, starting from 31.2 \({}^{\circ}\)C, it reached 55.4 \({}^{\circ}\)C within two seconds of interaction, raising the temperature by 24.2 \({}^{\circ}\)C.  
Finally, in the two-second interaction with the 1064-nm Q-switch Nd:YAG nanosecond pulse laser, starting from 31.2 \({}^{\circ}\)C, we arrived at 34.1 \({}^{\circ}\)C with a temperature increase of 2.9 \({}^{\circ}\)C.
# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
## 1 Introduction
### Aim and Objectives  
The aim of this research is to propose a unified vendor-agnostic stream processing solution for big data analytics in a multi-cloud environment. It is supported by the following research objectives: RO1--examine several existing architectures for big data processing and systematically evaluate the architectures of three well-known real-world companies (i.e., Facebook, Twitter, and Netflix) with the purpose of understanding non-functionalrequirements for real-world big data systems. The selection criteria for these companies, the methodology, and the results of this evaluation are explained in a separate publication [31]; RO2--based on the findings in RO1, propose a new reference architecture based on industry's best practices and focused on vendor lock-in mitigation; RO3--develop a prototype based on a case study to enable the empirical evaluation of the proposed reference architecture; RO4: identify and implement a set of relevant performance metrics for the prototype in RO3; and RO5--design and execute distinct sets of experiments to evaluate the proposed reference architecture in terms of the non-functional requirements implemented.
# AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks
###### Abstract  
Underwater acoustic sensor networks, crucial for marine monitoring, face significant challenges, including limited bandwidth, high delay, and severe energy constraints. Addressing these limitations requires an energy-efficient design to ensure network survivability, reliability, and reduced operational costs. This paper proposes an artificial intelligence-driven framework aimed at enhancing energy efficiency and sustainability in applications of marine wildlife monitoring in underwater sensor networks, according to the vision of implementing an underwater acoustic sensor network. The framework integrates intelligent computing directly into underwater sensor nodes, employing lightweight AI models to locally classify marine species. Transmitting only classification results, instead of raw data, significantly reduces data volume, thus conserving energy. Additionally, a software-defined radio methodology dynamically adapts transmission parameters such as modulation schemes, packet length, and transmission power to further minimize energy consumption and environmental disruption. GNU Radio simulations evaluate the framework effectiveness using metrics like energy consumption, bit error rate, throughput, and delay. Adaptive transmission strategies implicitly ensure reduced energy usage as compared to non-adaptive transmission solutions employing fixed communication parameters. The results illustrate the framework ability to effectively balance energy efficiency, performance, and ecological impact. This research contributes directly to ongoing development in sustainable and energy-efficient underwater wireless sensor network design and deployment.  
Keywords:underwater communications; energy efficiency; CNN; software-defined radio systems; environmental sustainability +
Footnote †: journal:
# Connectivity Analysis in VANETs with Dynamic Ranges
## 3 Analytical Procedure
### Random Communications Ranges  
For a general channel effect on the transmitted signal, \(G\) would be considered random due to effects of fading (fast and slow), shadowing and therefore used in deriving the random transmission ranges, \(R\). Rayleigh, Nakagami-\(m\) fading channels and Lognormal shadowing effects coupled with bus shadowing effects are used in the derivations.
# The Impact of Gamifications and Serious Games on Driving under Unfamiliar Traffic Regulations
## 2 Background  
### Gamification  
Gamification refers to the \"use of game techniques in non-game contexts to motivate user engagement\" [10]. In terms of software design, gamification attempts a process of \"incorporating game elements into non-gaming software applications to increase user experience and engagement\" [10]. Gamification provides an interactive learning experience which can motivate the users to achieve optimal outcomes (e.g., [11; 12]). Gamifications are also used for better user engagement and satisfaction with a new environment or changes of surroundings in different contexts such as tourism [13] and organizational environments [14; 15].  
In driving, gamification approaches, especially feedback, are the preferences to encourage young drivers for safer driving behavior [16]. These approaches are various. Some use smartphones to receive feedback on driving scores (e.g., [12; 17; 18], while others use physical platforms for training in a driving simulator (e.g., [19]). Additionally, some studies use visual feedback [12], while others use auditory feedback [20]. While providing ambiguous feedback may distract drivers [10], useful feedback may improve their performance. For instance, [PERSON] et al. [21] proposed a list of preferred feedback modality and presentation mechanisms to enhance drivers' awareness of their surroundings.  
[PERSON] et al. [22] suggested that the extrinsic rewards (i.e., entry to lottery for cash prize) can motivate people to perform better. However, some others believe that gamification can motivate the players to improve their behavior by virtue of the rewards related to gameplay (e.g., attaining a target score) instead of extrinsic rewards [23].  
Gamification is a good evidence-based technique for applying real-life situations to training in the field of driving vehicles. Approaches such as [19] use robotic platform simulators to capture lane-following data from artificial intelligence (AI)-driven and human-operated agents, training convolutional neural networks to transfer work on the physical driving platform. Some of these approaches (e.g., [12; 23]) defend green behavior as a justification for choosing the simulators for training.  
In the context of driving, gamification may generate \"flow\" [24] and, consequently, intrinsic motivation and enjoyment by increasing the level of challenge of the otherwise mundane task of driving. Repeated practice offered by the game elements is seen as the major factor of the success of its efficiency [25]. As a result, some studies (e.g., [9]) tend to assess the gamification effectiveness at several points of time, asking the player to repeatedly drive the same track and assessing the driver's performance and behavior each time. Serious games are seen as \"as step beyond gamification\" [26].
## Materials and Methods
### Characterization Methods
The PB's intensity changes resulting from the TL effect were detected by a Si-amplified detector (PDA 36A-EC,  
Figure 1: Schematic of the BDS experimental setup. L1, L2: lenses; M: turning mirror; IF: interference filter; QP: quadrant photodiode. Thorlabs) equipped with a 0.5 mm diameter pinhole. The TL signal was recorded directly using a digital oscilloscope (RIGOL DS1102E, RIGOL Technologies, Inc., USA). The degradation of RB19 by unmodified and Cu- or/and Zr-modified TiO\({}_{2}\) nanoparticles was investigated under simulated irradiation with a 300-watt UV lamp (Osram Ultra Vitalux 300W E27, UK) with a radiated power of 13.6 W in near-UV (315-400 nm) and 3.0 W at shorter wavelengths (280-315 nm). The lamp was placed in a vertical position, 5 cm away from a 1 \(\times\) 1 \(\times\) 5 cm quartz cuvette to irradiate the sample from the top through the 1 \(\times\) 1 cm top opening/1 \(\times\) 5 cm window. A sample (3 mL) containing 6 \(\upmu\)g/L of photocatalyst suspended in aqueous solution with an initial concentration of 5 mg/L RB19 was pipetted into the cuvette and illuminated by the lamp for 50 min. After each 5 min of irradiation, the suspension was left for about 20 min to relax and remove any residual thermal energy induced by illumination. The TLS measurements were performed immediately after each 5-minute interval of irradiation.
## 4 Feasibility of Agro-Waste Utilization in Reinforced Concrete (RC) Elements
### Future Research Directions  
Understanding the behavior of CS-based concrete beams in real structural environments requires field studies and applied research. Developing new reinforcement techniques is critical to enhancing the durability of these types of concrete. Such advancements can promote the broader adoption of CS-based concrete in the construction industry and help maximize its potential benefits. Additionally, investigating the effects of different components and mix ratios of CS-based concrete on the behavior of RC elements is essential. Exploring specific structural properties, such as fire resistance, could reveal the potential of this material to improve fire safety. The findings from these studies can encourage the widespread use of CS-based concrete and contribute to the sustainability of construction projects.  
Similarly, the positive effects of banana fiber reinforcement on concrete structural elements indicate its potential as a performance-enhancing material in the construction sector. Further research is required to explore the economic benefits and structural performance of banana fiber-reinforced RC elements.  
Future studies on the use of AWs in RC elements should focus on the following areas:  
* Fire Resistance: The fire behavior of AW-based concretes, particularly CS-based concrete, should be examined to evaluate its potential for improving fire safety in structural applications.  
* Innovative Reinforcement Techniques: Novel reinforcement methods should be developed to enhance the durability of RC elements. These innovations can support the sustainable use of materials in both existing RC elements and new constructions.
* Economic and Environmental Impacts: The economic advantages and contributions of AWs to environmental sustainability as construction materials should be analyzed in detail. Life cycle assessments can provide valuable insights into these impacts.
* Applied Research: Field studies and practical experiments in real structural environments are essential. Such research would provide a deeper understanding of the long-term durability, load-bearing capacity, and other structural properties of AW-based concretes.  
These research directions aim to encourage the use of AWs in RC elements, thereby supporting environmental and economic sustainability goals within the construction industry.
## 2 Efficiency Estimation Methods  
### Air Gap Torque Method  
Among the low-invasiveness efficiency estimation techniques for induction motors, the air gap torque method (AGT) can be considered the most accurate [10].  
The air gap is the gap between the stator and the rotor. The electromechanical energy conversion process occurs when a time-varying magnetic flux produced in the stator crosses the air gap and induces an electromotive force in the rotor winding in short circuit, the magnetic moment (air gap torque) is produced by the tendency of existing fields in the stator and rotor to align its magnetic axes (Answer 3). The air gap torque can be calculated according to  
\[T_{ag}=\frac{p\sqrt{3}}{6}\Bigg{\{}\begin{array}{l}(i_{a}-i_{b})\int\left[v _{\alpha t}+r(2i_{a}+i_{b})\right]dt+(2i_{a}+i_{b})\\ \int\left[v_{ab}-r(i_{a}-i_{b})\right]dt\end{array}\Bigg{\}}, \tag{1}\]  
where \(v_{\alpha t}\) and \(v_{ab}\) are the voltages between phases, \(i_{a}\) and \(i_{b}\) are the two-line currents, r is the resistance of the stator winding and p is the number of poles of the motor. This equation is valid for a three-phase IM connected in a Y configuration and not connected to the neutral, or in a delta configuration using three wires.  
During the conversion of electrical power into mechanical power, losses occur. Some losses can have tabulated values, for instance, the additional losses (stray load losses) \(\mathrm{L_{ar}}\), which are losses associated with the construction flaws of the machine, and the mechanical losses \(\mathrm{L_{mec}}\), which are caused by mechanical contact opposed to the movement of the rotor, as shown in Table 1[5; 7].  
Therefore, it is possible to obtain the torque in the motor shaft given as  
\[T=T_{ag}-\frac{L_{mec}}{\omega}-\frac{L_{ar}}{\omega} \tag{2}\]  
where \(\omega\) is the angular speed (in rad/s). The value of \(\omega\) can be estimated by processing the current signal in the frequency domain [11] or in the time domain [12].  
The output power can be obtained from the speed and torque in the motor shaft. In turn, the input power is obtained from the real-time values of two current sensors and two voltage sensors. With the input and output power, it is possible to obtain the motor efficiency using  
\[\eta=\frac{P_{out}}{P_{in}}=\frac{T\omega}{-v_{\alpha}(i_{a}+i_{b})-v_{abi}i_{ b}}\mathrm{100\%} \tag{3}\]
# Internet of Things in Marine Environment Monitoring: A Review
## 5 Major Challenges and Opportunities
### Standardization  
With the wider scope of marine environment monitoring and protection, international cooperation projects are emerging one after another. According to the interests of the projects, various application platforms and methods have their own characteristics and cannot be compatible with each other. Even though a number of networking protocols and standards for Internet of Things have been proposed and developed [94; 95], they are not sufficient for applications in marine environment monitoring. In addition to the standards of IoT networking, it is also important to provide the industry with standardsfor IoT devices, equipment, and platforms for marine environment monitoring and protection applications, and providing different levels of governments and marine environment management agencies with standards for marine environment data management, analysis, and reporting. As a result, the standardization of platform development for marine environment monitoring and protection systems brings challenges, including:  
1. Standardization of IoT devices specifically for marine environment monitoring and protection, including sensor and actuator nodes, routers and gateways.
2. Standardization of IoT platforms and system technologies for marine environment monitoring and protection, including communication network structures, protocols, and algorithms.
3. Standardization of computing and data storage technologies for marine environment monitoring and protection, including cloud, fog, and edge computing mechanisms, data archiving and warehousing techniques.
4. Standardization of data analysis outputs and reporting formats for exchange among different organizations and governments.
# Model Test Analysis of Groundwater Level Fluctuations on
## 4 Analysis of Model Test Results  
### Effect of Different Recharge on Cover Deformation  
The tests show that there is no obvious damage and deformation of the cover soil during the initial rainfall and lateral recharge, only a significant rise in the water level of the cover soil, but scattered soil in the karst channels while the rainfall and lateral recharge infiltrate the soil. With the increase of cumulative recharge and continuous infiltration, the soil above the karst channels gradually forms soil cavities in the process of continuous descent. After the formation of the soil cavities, the continuous infiltration of recharge continues to raise the water level of the cover soil. The infiltration of soil water from the karst channel into the underlying karst cavity intensifies, and when there is a sudden change in the infiltration flow, the infiltrated water carries a small amount of soil particles, but when the flow is stable, the water is clearer. However, after the formation of the soil cavity, the continuous rainfall accelerates the destructive deformation of the soil and accelerates the upward development of the cavity. Taking the first recharge at the beginning of the test as an example, the relationship between the magnitude of displacement of each cover layer by extracting displacement meter data at the end of the test is shown in Figure 5: 6 cm (1.44 mm) \(>\) 9 cm (1.13 mm) \(>\) 12 cm (0.69 mm) \(>\) 15 cm (0.58 mm) \(>\) 18 cm (0.45 mm), the data indicates that for this cover type karst collapse, the thicker the cover layer the smaller the deformation displacement of the cover soil. However, the subsequent cumulative recharge will cause the cumulative deformation of the cover to become more and more severe until the collapse eventually occurs.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 2 Satellite Networks Penetration Testing Method
### Segment Identification  
As depicted in Figure 2, satellite networks comprise three interdependent segments: the user, ground, and space segments.  
The user segment encompasses a diverse array of distributed terminal nodes, such as satellite phones for voice and data communications in remote areas, fixed/mobile very-small-aperture terminals (VSATs) enabling broadband internet access, and portable user terminals for emergency response operations. These terminals access the satellites either  
Figure 1: Satellite networks penetration testing method.  
through direct user links or via terrestrial telecommunications infrastructure operated by service providers [20].  
The ground segment usually incorporates ground stations (GS), gateways, and network control center (NCC). Ground stations encompass a variety of types, including traditional ground stations, cloud-based ground stations offering low-latency data collection via cloud platforms, remote ground stations distributed globally to maintain continuous satellite visibility, and custom ground stations tailored for specific mission requirements [21]. Gateways serve as a critical interface for interconnecting satellite networks with terrestrial networks (e.g., the internet and cellular networks), enabling protocol conversion and data routing between these heterogeneous communication systems. The NCC centrally coordinates operations, resource allocation, and integration with terrestrial networks (e.g., 5G/WLAN) [6].  
The space segment of satellite networks is primarily composed of LEO satellites, which are increasingly deployed in large-scale constellations to facilitate global broadband connectivity. Notable examples include Starlink (SpaceX, Hawthorne, CA, USA), OneWeb (London, UK), Kuiper (Amazon, Seattle, WA, USA), and Lightspeed (Telesat, Ottawa, ON, Canada), which utilize commercial off-the-shelf (COTS) hardware and advanced communication technologies to achieve cost-efficient, high-speed data transmission with worldwide coverage. Modern LEO satellites operate within a power range of 2-5 kW, tailored to mission-specific payload demands, and employ sophisticated power management strategies such as deployable solar arrays and lithium-ion battery systems to sustain operations during orbital eclipse phases. While inter-satellite links (ISLs) are critical for inter-satellite communication in many systems, some constellations like OneWeb operate without ISLs, relying instead on dense ground station networks to maintain connectivity [22].  
Figure 2: Overview of satellite network segments.  
To facilitate practical implementation, Table 2 outlines key testing methodologies tailored to each segment alongside their anticipated hazards, bridging the theoretical framework with operational risk assessment.  
By characterizing each segment's architectural components and threat surfaces, testers can prioritize high-impact targets, aligning testing efforts with mission-critical risks.
# Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis
###### Abstract  
Image-based viewpoint estimation is one of the tasks in image analysis, and another is the inverse problem of selecting the best viewpoint for displaying a three-dimensional object. Currently, two issues need further exploration in image-based viewpoint estimation research: insufficient labeled data and a limited number of evaluation methods for estimation results. To address the first issue, this paper proposes a spherical viewpoint sampling method based on a combination of analytical methods and motion adjustment, and designs a viewpoint-based projection image acquisition algorithm. Considering the difference between viewpoint inference and image classification, we propose an accuracy evaluation method with deviation angle tolerance for viewpoint estimation. Based on constructing a new dataset with viewpoint labels, the new accuracy evaluation method has been validated through experiments. The experimental results show that its estimation accuracy can reach 89% according to the new estimation evaluation indicators. Additionally, we applied our method to estimate the viewpoints of images from a furniture website and analyzed the viewpoint preferences in its furniture displays.  
Keywords: viewpoint estimation; viewpoint evaluation; display angle of object; spherical uniform sampling; deep learning; image analysis _Article_