esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The deep learning method offers the advantage of automatically extracting image features, which eliminates the influence of subjective factors on experimental results and greatly reduces the workload of rock classification. In addition, deep learning models can extract more abstract and complex image features to classify a wider range of rock types. [17] However, the existing deep learning models have typically been built by manually designing a network module and stacking it [18; 19; 20], resulting in an irrational network structure and redundant parameters. Moreover, for objects like rock images with strong interference and cluttered information, the existing models without attention mechanisms suffer from dispersed attention during image processing, which makes it challenging to effectively capture useful features. These factors ultimately contribute to the limited accuracy of current models in rock image classification. Current methods for rock image classification each have their limitations. Traditional methods can achieve acceptable accuracy under specific conditions but are heavily dependent on manual effort and lack stability in their accuracy. Although machine learning methods have made initial strides towards intelligent rock image classification, they still require manual feature extraction, limiting the degree of automation in image recognition. Existing deep learning methods have realized end-to-end automatic image recognition but still suffer from issues related to redundant network parameters and scattered attention, resulting in limited accuracy in rock image classification. To overcome the limitations of current methods and achieve accurate end-to-end classification of rock images, a rock image classification model based on EfficientNet and a triplet attention mechanism is proposed. The contributions of this study can be summarized as follows:  
* Deep learning methods were introduced to eliminate the dependence of traditional methods and machine learning methods on human intervention, so as to realize end-to-end automatic identification of rock images without requiring additional manual operations. * A rock image classification model was constructed based on EfficientNet, which overcomes the issue of parameter redundancy and scattered attention in previous deep learning models, as well as achieving an efficient and attention-focused network structure through Neural Architecture Search, resulting in higher model accuracy compared to its predecessors. * In view of the problem that EfficientNet neglects spatial attention information of rock images, the triplet attention mechanism [21] was introduced to improve EfficientNet and enhance its ability to extract effective rock features, further improving the accuracy of the rock classification model. * The transfer learning method was utilized in the training process to accelerate the model convergence and significantly enhance its training performance, so as to obtain a classification model with higher accuracy using fewer rock images and less training time.
## 2 Materials and Methods  
### Intensive Observation for Aerosol Phenomena  
To monitor aerosol number concentration by particle size during spring, intensive observations were performed every 10 min at a flow rate of 28.3 L-min\({}^{-1}\) using a laser particle counter (LPC; KANOMAX 3900; Osaka, Japan) from the Pukyong National University (PKNU, 35\({}^{\circ}\)08\({}^{\prime}\) N, 129\({}^{\circ}\)06\({}^{\prime}\) E, 8 m.a.s.l.) in Busan, South Korea (Figure 1). An LPC measures concentrations by particle diameter using the size of the pulse shape (proportional to the amount of light scattered by the photomultiplier tube) concerning the particle size, whereas the number of pulses correlates to the particle number. The specifications of the LPC are listed in Table 1 for the measurable aerosol ranges from 0.3 to 10.0 \(\upmu\)m. The intensive observations were performed in the spring season (March to May) from 2009 to 2011, and the periods that were not observed due to the inspection of equipment were excluded from the analysis. The LPC was located inside the instrument shelter, and since the tubular inlet faces the ground, observation was possible even during precipitation.  
The aerodynamic particle size (APS) observation data was acquired from the World Meteorological Organization/Global Atmosphere Watch regional station located in Anmyeondo (36\({}^{\circ}\)32\({}^{\prime}\) N, 126\({}^{\circ}\)19\({}^{\prime}\) E, 45.7 m.a.s.l.) on the west coast of the central Korean Peninsula, which is relatively unaffected by pollutants. This data was compared with the aerosol size distribution data derived from the LPC. Moreover, classified daily phenomena were recorded via a combination of visual and instrumental observation at a manned observatory of the KMA (Busan, 35\({}^{\circ}\)06\({}^{\prime}\) N, 129\({}^{\circ}\)01\({}^{\prime}\) E, 56 m.a.s.l.).  
The APS monitors velocity according to the size of the particles flowing into the measuring module. This module is composed of the particle acceleration nozzle and light scattering device capable of detecting avalanche photodetector scatter to measure the concentration of each particle size. The APS observes the aerosol number concentration every 3 min for 52 size channels, for particle diameters ranging from 0.5 to 20.0 \(\upmu\)m. Sample and external flow rates were 1 L-min\({}^{-1}\) and 4 L-min\({}^{-1}\), respectively, with a measurement uncertainty of approximately \(\pm\) 10% [29].  
Figure 1: Location of the intensive observation site. PKNU, Pukyong National University.
## 3 Results and Discussion  
### Analysis of the Septum Morphology  
Both PU and PU/PAN nanofiber diaphragms were prepared by a centrifugal spinning device that was constructed in-house. To observe the effect of PAN addition on the microscopic morphology of the PU/PAN composite diaphragm, the PU and PU/PAN membranes were characterized using SEM. As shown in Figure 2, the PU nanofiber membrane prepared based on the centrifugal spinning method, with a mass fraction of 18%, had a smooth surface with uneven fiber thickness and appeared as a series of spindles. Additionally, the PU nanofibers displayed arbitrary bending. As shown in Figure 3, with the addition of PAN, when 18% PU/PAN = 8:2, the fiber surface mesh structure and fiber diameter were more uniform, but the phenomenon of fiber entanglement aggregation was still apparent. As shown in Figure 4, when 18% PU/PAN = 7:3, the fiber surface was smooth and had a more uniform diameter. The fiber morphology was very good, and there were large, interconnected voids between the fibers, forming a 3D porous structure, while PAN had good spinnability. The use of a PU-based nanofiber diaphragm in the lithium-ion battery charging and discharging process could prevent a battery internal short circuit.  
Figure 2: SEM images of an 18%PU fiber membrane.
## 4 Experimental Results
### Comparing GNSS and Acceleration Measurements  
As there are no structural response data available for this bridge, we placed an accelerometer at Position E4, to measure its movements in the \(x\)-, \(y\)- and \(z\)-directions, to verify the GNSS measurement data. The following Figures 26a-c show the frequency responses over time from 0 to 5 Hz as measured by the accelerometer. In the \(x\)-direction (Figure 26a), the sensor clearly shows the 1.3-Hz response that was also detected by the GNSS sensor, as can be seen for example in Figure 24a. Furthermore, the \(x\)-direction acceleration data show a significant frequency at 0.33 Hz, as well as several frequencies above 2 Hz. The 0.33-Hz resonance is also visible in the GNSS dataset as for example visualized in Figure 25a,b, but the frequencies above 2 Hz could not be detected by the GNSS sensors.  
In the \(y\)-direction, the accelerometer measurements indicate a resonance frequency around 2 Hz, and in the \(z\)-direction, there appears to be several frequencies in the range from 1 to 5 Hz. Although the acceleration measurements in the \(z\)-direction are generally noisier, four frequencies can be seen, the first at around 1.3 Hz, the second around 2.5 Hz, the third at 3.7 Hz and the fourth around 5 Hz. The GNSS data show in the \(z\)-direction some response in the area between 2.7 Hz and 4 Hz (Figure 24c), but clearly not as significant as the measurements taken with the accelerometer.  
In summary, the data presented here clearly demonstrate the feasibility of the GNSS measurements to detect low frequencies and underpins the assumption that the detected frequencies are real structural resonance frequencies of the bridge. The comparison of the accelerometer and GNSS sensor measurements shows that the GNSS sensor has a better sensitivity for lower frequencies than the acceleration sensor, but is limited in its response to higher frequency signals probably due to their lower amplitudes.
## 3 End-to-End SNR Statistics
### Variable-Gain Relaying  
For variable-gain relay, the equivalent instantaneous SNR can be calculated by using the upper bound [8]:  
\[\gamma_{eq}^{V}=\frac{\gamma_{RF}\gamma_{FSO}}{\gamma_{RF}+\gamma_{FSO}+1} \cong\min(\gamma_{RF},\gamma_{FSO}) \tag{21}\]  
Based on (21), the CDF for variable-gain relay is expressed as:  
\[\begin{split} F_{\gamma_{eq}^{V}}(\gamma)&=\Pr( \min(\gamma_{RF},\gamma_{FSO})<\gamma)\\ &=F_{\gamma_{RF}}(\gamma)+F_{\gamma_{FSO}}(\gamma)-F_{\gamma_{RF} }(\gamma)F_{\gamma_{FSO}}(\gamma)\end{split} \tag{22}\]  
Substituting (3) and (17) into (22), the final expression of the CDF for variable-gain relay is derived as:  
Therefore, the integral in (29) can be solved by employing the exponential function expressed above and [34] (Equation (07.34.21.0013.01)). The CDF of fixed-gain relay can be derived as:  
\[F_{\gamma\bar{\gamma}}(\gamma)=1-A_{1}\text{exp}(-\gamma/\gamma_{RF})G_{2t,2t+1} ^{2t+1,t}\left[\frac{C\gamma\varphi_{m}^{t}}{\zeta_{RF}\zeta_{FSO}}\begin{matrix} B_{1}\\ B_{2}\end{matrix}\right] \tag{31}\]  
where \(A_{1}=\frac{m^{2}}{\Gamma(\alpha)\Gamma(b)}\times\frac{t^{a+b-2}}{(2\pi)^{t-1}}\) and \(B_{1}=\left[\Delta(t,1-b),\Delta(t,1+m^{2})\right]\), \(B_{2}=\left[\Delta(t,a),\Delta(t,m^{2}),\right.\)  
Substituting (31) into (24), and employing [33] (Equation (11)) and [34] (Equation (07.34.21.0011.01)), the MGF can be derived as:  
\[\begin{split}\mathcal{M}_{\gamma}(s)&=1-\frac{sm^{2 }}{\Gamma(a)\Gamma(b)}\times\frac{t^{a+b-2}}{(2\pi)^{t-1}(s+1/\gamma_{RF})}\\ &\times G_{2t+1,2t+1}^{2t+1,t+1}\left[\frac{C\varphi_{m}^{t}}{(s+ 1/\gamma_{RF})\gamma_{RF}\zeta_{FSO}}\begin{matrix} B_{1}, \Delta(1,0)\\ B_{2}\end{matrix}\right]\end{split} \tag{32}\]  
Substituting (31) into (26), and employing [33] (Equation (11)) and [34] (Equation (07.34.21.0011.01)), the moments can be derived as:  
\[\begin{split} E[\gamma^{n}]&=\frac{nm^{2}\zeta_{ RF}^{n}}{\Gamma(a)\Gamma(b)}\times\frac{t^{a+b-2}}{(2\pi)^{t-1}}\\ &\times G_{2t+1,2t+1}^{2t+1,t+1}\left[\frac{C\varphi_{m}^{t}}{ \zeta_{FSO}}\begin{matrix} B_{1},\Delta(1,1-n)\\ B_{2}\end{matrix}\right]\end{split} \tag{33}\]
# Evaluating the Efficiency of Physical and Cryptographic Security Solutions for Quantum Immune IoT
## 5 Evaluation Approach for Quantum Immune Security
### Simulation Approach  
The ABSOLUT approach [19] is intended for the evaluation of computer system performance and power consumption in the early phases of design. ABSOLUT uses virtual system modeling, where abstract workload models of applications are simulated on top of performance capacity models of the computing platform (Figure 6). The workload models have basic block, function, process and application layers [19] and are implemented in SystemC [58]. The basic block layer of the workload models contains the abstract instructions read, write, and execute as well as requests for higher-level services, such as video decoding or direct memory access (DMA) transfer. Tools exist to enable the automatic generation of workload models from traces, measurements, source code or application binaries.  
Figure 6: The ABSOLUT performance evaluation approach for quantum immune key exchange approaches for Internet of Things platforms.  
The platform models are also layered and semi-automatically generated from library components and text-based platform description and configuration files. The platform description defines which components are instantiated from the library and their connections, whereas the configuration file sets up parameters like the clock frequency. The model library includes capacity models of the processing unit, interconnect, memory, and hardware accelerator components implemented in transaction-level SystemC.  
The processing unit models consume the abstract instructions from the workload models. They estimate the time needed to execute the instructions and calculate the resulting utilization and power consumption. They utilize other components of the system through transactions. High-level services are requested from an OS model and processed by the service provider, which typically is a HW accelerator or a workload model incorporated in the platform.  
The modeling approach enables early evaluation, since mature hardware or software is not required for modeling and simulation. ABSOLUT is able to estimate the execution time of an application or a part of an application, the utilization of the modeled components in the execution platform, and the system-level power/energy consumption of the use case.
## 4 Case Study
### Weather Information Analysis  
The weather information taken into account encompassed various factors, such as temperature, humidity, wind speed, wind direction, rainfall, tide, and wave data. These data were periodically extracted from Chilean weather forecast websites, namely [94,95], each of which updated their information at varying time intervals. Specific details regarding the frequency of updates for each of these weather parameters can be found in Table 1.  
The selected weather websites employ various table formats to display their data. Tables presenting the environmental parameters like temperature, humidity, and wind speed, as well as tide and wave information, are depicted in Figure 6a, Figure 6b, and Figure 6c, respectively.  
The weather alerts under consideration adhere to Chilean regulations and encompass the following: (1) alerts for fire hazards, determined by factors such as temperature, wind  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Weather Parameter** & **Updating Frequency** & **Website** \\ \hline Temperature [\({}^{\circ}\)C] & \\ Humidity [\%] & \\ Wind speed [km/h] & 1 h & [94] \\ Wind direction [direction] & \\ Rain [mm] & \\ \hline Tide [m] & 8 h & [95] \\ \hline Wave [m] & 2 h & [50] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about the monitored weather parameters.  
Figure 5: Case study: (**a**) location; (**b**) BIM model; (**c**) mass/facade representation.  
Figure 6: Weather information: (**a**) temperature, humidity, wind speed, wind direction, rain [94]; (**b**) tide [95]; (**c**) waves [50].  
and humidity [96], (2) alerts for wind hazards, based on wind speed [97] and (3) alerts for swell conditions, dependent on wave direction, wave height, and wave period [98]. Table 2 [96; 97] and Table 3 [98] provide an overview of these weather hazard alerts, offering insights into the three condition levels (normal, warning, and danger) addressed by these alert systems.  
The Risk Index presented in Table 2 is calculated based on the Wildfire Index Risk provided in [96]. This approach for the project location (Vifa del Mar) is based on the following equation:  
\[\text{Risk Index}=39.9622-0.6125\text{T}-0.7252\text{H}+0.7459\text{W}+25.7198\text {S}, \tag{1}\]  
where T is the current temperature (\({}^{\circ}\)C), H is the relative humidity (%), W is the wind speed (km/h), and S is a dimensionless seasonal factor which increases in summer and decreases in winter [96].
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 2 Methodology of Literature Review
### Data Extraction and Analysis  
For each included study, a structured data extraction framework was employed to ensure consistency and comprehensiveness. The extracted information covered key research components, including research objectives, methodologies, applications, findings, and identified gaps. A standardized coding scheme was developed to categorize data, ensuring that relevant aspects, such as study design, Large Language Model (LLM) architectures, AI techniques, and experimental setups, were systematically analyzed. The extracted data were reviewed by multiple researchers to mitigate bias and enhance reliability.  
Thematic analysis was conducted to identify recurring patterns and significant trends across the studies. The synthesized findings were grouped into well-defined thematic categories, including technical mechanisms of LLMs, addressing algorithmic advancements, model training processes, and optimization techniques; ethical and regulatory challenges, exploring concerns related to data privacy, bias, transparency, and compliance with international maritime regulations; and real-world applications in maritime safety, analyzing case studies where LLMs have been successfully implemented for incident detection, risk assessment, and decision support.  
To enhance the robustness of the analysis, a mixed-methods approach was applied. Quantitative metrics, such as citation frequency and performance benchmarks of AI models, were combined with qualitative insights from expert evaluations and policy discussions. Additionally, a comparative assessment was conducted to highlight methodological similarities and differences among the reviewed studies.  
The data extraction process adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, ensuring transparency and reproducibility in study selection and analysis. By integrating these structured approaches, this review provides a detailed and objective synthesis of current advancements in LLM applications for maritime safety, identifying both their potential and the challenges that need to be addressed in future research.
# Natural Convection of Dusty Hybrid Nanofluids in an Enclosure Including Two Oriented Heated Fins
## 4 Results and Discussion  
This section discusses results of the natural convection for dusty hybrid nanofluids in an enclosure including two inclined heated fins. The streamlines and isotherms are represented in contours forms. The local and average Nusselt numbers are represented in curves profiles under effects of the current physical parameters. The variations of parameters are summarized as: the inclination angle \(\gamma_{1}\big{(}0^{\circ}-180^{\circ}\big{)}\), fins lengths \(L_{1}=L_{2}(0.1-0.9)\), nanoparticles volume fraction \(\phi_{Al_{2}O_{3}}=\phi_{Cu}\) (\(0.01-0.04\)), mixture densities ratio \(D_{s}(1-1000)\) and dusty parameter \(\alpha_{s}(0.001-0.01)\). The fixed parameters are: Rayleigh number \(Ra_{E}=10^{5}\) and ratio of specific heat of the mixture \(\gamma=0.1\).
# AUV-Aided Optical--Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning
## 6 Results and Discussion
### Impact of the AUV Velocity on Performance
Furthermore, with the increasing AUV velocity, the weighted energy consumption of the three schemes will be convergent, since the AUV power increases geometrically with the velocity of travel.
## 3 Method
### Golf Swing Analysis Algorithm
#### 3.2.2 Swing Similarity Calculation  
We detail a method for comparing the actions of professional golfers and learners using golf swing embedding to find the most similar pro golfer. To accurately represent the golf swing, we employ swing embedding, using the swing style and physical features of the golfer. The swing style is inspired by golf styles classified as hitter and swinger and is characterized by elements that can distinguish each style. We utilize four features of golfer and swing motion: gender, body proportion, the ratio of y-values of two top positions (Backswing-top, Finish), and the body angle at the impact position. To separate two genders in a large difference of similarity, we assign gender values of 0 and 10\({}^{3}\) in the embedding. The body proportions are calculated to divide all 15 edges of the joints by the height of the golfer. To acquire the relative speed of swing, we calculate the ratio of five positions to the total frame. Because the Impact and Finish positions are fixed motions, Takeback, Backswing, Backswing-top, Downswing, Impact, and Follow are used in the representation of the swing speed. Furthermore, we represent the swing style by calculating the ratio of the y-values of the two top positions and the right knee and right elbow angle at the impact. The generated 24-dimensional embedding is used to compare the swing actions of professional golfers and a learner by their cosine similarity scores and select the most similar pro golfer. This allows learners to refer to the swing of a pro golfer similar to their own swing and proceed with the necessary joint correction.
## 2 Related Work
### Markers  
Square markers are square shaped tags, as shown in Figure 1. They have a thick black border and the inner region contains images or binary codes represented in the form of grids of black and white regions. The reason for using thick black border is to ensure quick detection on any surface. For example, when considering Aruco markers [25], which have been used in our system, it is a square marker where the detection process is performed in two main steps: Detection of the marker candidates and codification analysis.  
[PERSON] et al. proposed an AR system to be used in kindergarten to learn the alphabet by detecting the markers that may be present in a scene using CNN [35]. Markers have been printed on papers within a rectangular box. Then, children can show them in front of the camera attached to automatically render the virtual object over the marker with appropriate position and orientation. This system achieved high accuracy in marker identification and augmentation of the virtual objects making the system resistant to environmental noises and position variation. However, it may fail to detect markers from a long distance.  
Figure 1: Examples of square markers.  
[PERSON] et al. proposed an approach for indoor localization and navigation using Bluetooth and an embedded camera on the smartphone [46; 47]. It operates in two modes: Background and foreground mode. The background mode gives a low-level accuracy position estimation using Bluetooth. The foreground mode provides high accuracy by using the smartphone's camera to detect visual tags deployed onto the floor at known points. The system can detect tags in real-time to estimate the PVI position with a high level of accuracy and navigate towards the target. The marker's colours are chosen to be different from the colour of the floor to enhance the speed and efficiency. These colours guarantee high contrast between the floor's colour and the tag's border. However, the system failed to detect markers from a long distance and cannot detect more than one tag at the same time. Moreover, adding haptic feedback enables it to work in noisy places.  
[PERSON] et al. presented an autonomous flying quadrocopter using a single onboard camera and augmented reality markers for localization and mapping [48]. The system is capable of estimating the position of the quadrocopter using a coordinate system defined by the first detected marker. To improve the robustness of marker-based navigation, it used a fuzzy control to achieve a fully autonomous flight. However, the precision of the mapping approach and the response time requires improvement. The system also fails to detect markers from a long distance.