esa-sceva/satcom-chunk-collection huggingface dataset:
## 10 Conclusions  
Underwater visible light communication (UVLC) has demonstrated remarkable potential to overcome the limitations of acoustic and RF systems, particularly for high-speed, low-latency applications. Experimental results validate its superiority, with modulation schemes like 32-QAM achieving 3.31 Gbps over 56 m [33] and PAM4 enabling 500 Mbps at 150 m [35], far exceeding the capabilities of traditional acoustic links (<100 kbps). Environmental factors such as turbidity and salinity degrade performance (60% signal loss in harbor water [24]), but adaptive techniques, such as multi-hop relays and hybrid QAM-OFDM, mitigate these effects, enabling reliable communication in diverse conditions.  
Future advancements will focus on integrating UVLC with emerging technologies. Quantum key distribution (QKD) could enhance security in deep-sea deployments, while Reconfigurable Intelligent Surfaces (RISs) may improve spectral efficiency by 30-40% in turbulent channels [84]. AI-driven modulation and hybrid VLC-RF architectures further promise to extend operational ranges beyond 200 m. These innovations position UVLC as a cornerstone for next-generation underwater networks, supporting applications from real-time AUV control to ecological monitoring.  
Conceptualization, methodology, formal analysis, resources, data curation, writing--original draft preparation were performed by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and U.W. Writing--review and editing was performed by [PERSON], [PERSON], [PERSON] and [PERSON]. Supervision and project administration were performed by [PERSON] and U.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Science and Technology Human Resource Development Project, Ministry of Higher Education, Sri Lanka, funded by the Asian Development Bank (Grant No. STHRD/CRG/R3/S/11).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results  
### Strength of Knee Extensors  
The PTE of the injured legs in the ACL group was 3.04 \(\pm\) 0.45 N\(\cdot\)m\(\cdot\)kg\({}^{-1}\), and it was 3.08 \(\pm\) 0.22 N\(\cdot\)m\(\cdot\)kg\({}^{-1}\) in the non-injured group. This difference of 1.5% was not significant (\(p\) = 0.806). The HAM group reached a strength of 3.12 \(\pm\) 0.29 N\(\cdot\)m\(\cdot\)kg\({}^{-1}\) and 3.03 \(\pm\) 0.30 N\(\cdot\)m\(\cdot\)kg\({}^{-1}\). This 3% difference was also insignificant (\(p\) = 0.510) (Figure 1). The statistical comparison between groups (Table 1) showed a non-significant effect by the injury type (GROUP) on the PTE performance level (F\({}_{2,80}\) = 1.581, \(p\) = 0.211) but with a small-to-medium effect size (\(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.041). There was also an insignificant difference between the injured and non-injured legs (LEG) (F\({}_{1,80}\) = 0.403, \(p\) = 0.782) and between the interaction of the main factors (GROUP \(\times\) LEG) (F\({}_{1,80}\) = 0.403, \(p\) = 0.528).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{2}{c}{**Type III**} & \multirow{2}{*}{**df**} & \multicolumn{2}{c}{**Mean**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{**Sig.**} & \multicolumn{2}{c}{**Partial**} \\  & **Sum of Squares** & & & & & **Eta-Squared** \\ \hline GROUP (Types of & & & & & & \\ Injuries) & 0.32 & 2 & 0.16 & 1.587 & 0.211 & 0.041 \\ LEG (Injured Leg, & & & & & & \\ Yes or No) & 0.01 & 1 & 0.01 & 0.077 & 0.782 & 0.001 \\ GROUP \(\times\) LEG & 0.04 & 1 & 0.04 & 0.403 & 0.528 & 0.005 \\ Error & 7.64 & 75 & 0.10 & & & \\ Total & 717.15 & 80 & & & & \\ \hline \hline \end{tabular} Legend: Y = yes, N = no.  
\end{table}
Table 1: Effect of main factors on the level of knee extensor strength.
## 5 Experiment Procedure  
The objective of this experiment was to detect agent data confidentiality violations in the CNS such that its effectiveness can be evaluated. The confidentiality requirements and the possible violations defined in a previous work [51] were utilized. Next, the RVQA implementation within the CNS agent model was executed in order to monitor each agent's activities. Upon execution, the monitoring process to check the reachability of critical states at the individual, social, and content levels was performed. When the critical states were reached, the confidentiality statuses were assessed based on the defined agent confidentiality metrics and checking rules. The detected data confidentiality violations were classified and the total number of detected data confidentiality violations was reported. Finally, the effectiveness of the confidentiality violation detection was evaluated using the evaluation metrics. The RVQA procedures in the CNS and CNS Searching were implemented within the executed CNS for comparison. The RVQA data confidentiality violation detection results were compared to the confidentiality violations that were found using CNS Searching. The following subsections explain the RVQA and CNS Searching procedures in detail.
# COVID-19 Lung Injury: Unique and Familiar Aspects of Pathophysiology
## 5 Therapeutic and Supportive Strategies for COVID-19
### Ventilatory Support  
Since hypoxemic respiratory failure is the primary cause of death in COVID-19, ventilatory support is potentially the most vital aspect of patient care. Numerous factors such as timing, method, positioning, and respiratory mechanics may all play a role in determining outcomes, highlighting the need for high quality evidence to inform such decisions.  
The goal of oxygen supplementation is to correct hypoxemia and consequently prevent hypoxia and damage to vital organs [123]. For patients with severe COVID-19, the WHO recommends maintaining SpO2 > 90%, which may be achieved through the use of a high-flow nasal cannula (HFNC) or non-invasive ventilation (NIV) [175]. HFNCs and NIV are important intermediate measures as they reduce requirement of invasive mechanical ventilation (IMV), which is associated with risk of VLI [1]. Notably, NIV and HFNO were trialed in 1273 patients, and only the former reduced occurrence of tracheal intubation or mortality compared to conventional oxygen therapy (36% vs. 44%) [176]. Additionally, an important hazard to avoid when using a HFNC or NIV is hyperoxygenation, which is associated with toxic effects in the lungs and other tissues [123].  
If non-invasive oxygen supplementation fails, IMV must be used to oxygenate the patient, which prompts consideration of a range of parameters. Research has identified the importance of lung-protective ventilation strategies characterized by low tidal volumes (4-8 mL/kg predicted body weight), low plateau pressures (<30 cmH2O), and adequate positive end-expiratory pressure (PEEP; 5-20 cmH2O). These factors help to limit VLI by reducing regional overdistension of alveoli and atelectasis [11]. Prone positioning is a simple but highly effective tool for correcting regional V/Q mismatch deficits and has been recommended for 12-16 h per day in patients with severe ARDS [177]. Additionally, conservative fluid management, neuromuscular blockade, and pulmonary vasodilators can be useful in managing ventilated patients [178]. Finally, if lung-protective IMV and associated measures are insufficient to maintain oxygenation, extracorporeal membrane oxygenation (ECMO) can be a life-saving therapy.
## 3 Results and Discussion
### Optimal Performance Range  
Determining the optimal operational window for guide vane inclination is essential for balancing throughput, grain quality, separation efficiency, and fuel consumption in axial-flow combine harvesters. Based on the experimental results, both Combine A and Combine B demonstrated performance trade-offs across the tested guide vane range (10-35\({}^{\circ}\)), but the optimal settings differed due to their structural configurations.  
For Combine A, the best overall performance was achieved in the 25-30\({}^{\circ}\) guide vane angle range. Within this interval, the machine maintained high throughput (8.42-8.9 t ha\({}^{-1}\)), low separation losses (0.96-1.62%), and exceptionally low broken grain values (0.71-0.25%). Specific fuel consumption also reached its minimum at 25\({}^{\circ}\), at only 0.86 L t\({}^{-1}\), confirming this angle as the most fuel-efficient setting. These favorable results are attributed to the fully adjustable guide vane system, which enabled uniform axial flow, minimized frictional stress, and optimized the exposure time for effective separation.  
In contrast, Combine B reached its peak performance slightly earlier, around 25\({}^{\circ}\), with throughput at 8.52 t ha\({}^{-1}\) and separation loss at 0.91%. However, its broken grain percentage remained higher (1.12%), and fuel efficiency was lower at 1.06 L t\({}^{-1}\). Beyond 25\({}^{\circ}\), Combine B exhibited signs of diminishing returns: while feed rate increased marginally, separation and grain losses also rose, and the machine fuel efficiency gains began to plateau. This suggests that the segmented vane configuration limits effective adaptation to higher axial acceleration, particularly in the threshing zone.  
The data indicate that Combine A offers a wider and more stable optimal range, with better performance sustainability as the guide vane angle increases. In contrast, Combine B's optimal range is narrower, and further increases in vane angle lead to less predictable trade-offs between throughput and grain preservation.  
In practical terms, these results suggest that for crops with similar density and moisture content as tested winter wheat, operators should prioritize 25-30\({}^{\circ}\) for Combine A and limit adjustments to \(\leq\)25\({}^{\circ}\) for Combine B to maintain optimal balance between capacity, fuel use, and grain quality.
## 4 Experimental Results and Discussions
### Mechanical Tests
#### 4.1.2 Results of Bending Strength Tests  
Bending strength tests were conducted to evaluate the effect of wood chip and stabilizer content on bending strength. Figure 7 illustrates that an increase in wood chip content resulted in a decrease in bending strength, with a maximum reduction of 24%. Conversely, the addition of more stabilizer content led to an improvement in bending strength, with values increasing up to 2.9 times between 5% and 15% stabilizer content.
## 4 Wind Retrieval Testing
### Buoy Comparisons  
Applied using the alignment criteria of 1\({}^{\circ}\) longitude/latitude and 10 min, the total number of matchups was 16,552 after the simple antenna 3-dB beam-width quality control. This quantity is insufficient for generating an FDS GMF with multiple elevation bins. Therefore, the NDBC TAO buoy measurements were introduced to validate the robustness of the model functions.  
The sea surface wind speeds derived by the proposed empirical FDS GMFs were compared with the NDBC TAO buoy measurements. After a reference transition from 4 m to 10 m, the buoy wind speed measurements were 0.2-11.6 m/s. As shown in Figure 8, compared with the buoy measurements, the reanalysis GMF-derived unbiased RMSE is 1.77 m/s and the coefficient of determination is 0.45. Meanwhile, the ASCAT-derived GMF has an unbiased RMSE of 1.82 m/s and a coefficient of determination of 0.41.
## 3 Result and Analysis
### Damage Diagnosis  
For the purpose of obtaining the damage indicators of fatigue damage model, the parameters is screening use the DFS (Digital Feature Screening) method. The DFS method establishes the selection criteria of the damage characteristic index: obtaining the crack image in a fixed area under the step of increasing the loading level, and sequentially performing image numbering. Finally, damage characteristic index of the crack image is obtained. Further, a digital feature screening analysis chart is established, as shown in Figure 9.  
Figure 9a shows the variation of the characteristic parameters as the damage grows in 3D space. After analysis, it is known that as the fatigue damage increases, the ASM characteristic parameters will continue to increase according to certain rules. The remaining characteristic parameters change irregularly with the development of the fracture, so the remaining characteristic parameters are excluded. In order to monitor the development of fractures in 3D space, ASM characteristic parameters with good positive correlation with fracture development trends were selected.  
Figure 8: Transformation algorithm.  
Figure 9(b) shows a tendency of characteristic parameters to change as the damage advances in the 2D plane. Analysis of the data changes in the graph shows that as the fatigue damage increases, the CLS characteristic parameters continue to decrease. As the crack develops, the other characteristic parameters show different trends. Therefore, it is excluded. In order to monitor the development of two-dimensional spatial crack damage, the CLS characteristic parameters which are negatively correlated with the crack development trend are selected.  
Figure 9(a),b respectively count the changes of the crack development of the six characteristic parameters, and obtain the monitoring basis of the 2D plane and 3D space cracks. The characteristic parameters ASM and CLS can quantify the development of cracks in different spaces and fully describe  
Figure 9: Screening result.  
the development of cracks. Therefore, CLS and ASM characteristic parameters can be used as damage indicators for fatigue damage in RPC bending loading test to detect the development of damage.
## 2 Experimental Procedure
### Non-Contact Laser Imaging System for Ultrasonic Wave Propagation  
Figure 2 displays a photograph of the non-contact laser imaging system for visualizing ultrasonic wave propagation. This system consists of a rapid pulsed laser scanning unit for ultrasonic generation and a LDV unit for ultrasonic reception. Pulsed thermoelastic ultrasonic waves are generated by illuminating the specimen surface with a Q-switched Nd: YAG laser (Wedge-HB-1064-DB, Bright Solutions, Pavia, Italy) with a wavelength of 1064 nm, pulse width of 1.5 ns, maximum pulse energy of 2 mJ, and maximum repetition frequency of 2 kHz. The diameter of the laser beam is reduced by using a varifocal lens (APL-1050, Holochip, Hawthorne, CA, USA). The laser beam is scanned on the specimen surface using a computer-controlled galvanometer mirror (VM500+, Novanta, Bedford, MA, USA). A green laser beam with a wavelength of 532 nm is also illuminated for convenience, since the pulsed laser beam is invisible. The LDV system is used to receive the ultrasonic wave signals. It consists of a modular vibrometer (OFV-5000, Polytec, Waldbronn, Germany) and a sensor head (OFV-505-KA, Polytec, Waldbronn, Germany). A He-Ne continuous wave (CW) laser with a wavelength of 633 nm and energy of 2 mW is illuminated at a fixed position on the specimen surface, and the out-of-plane displacement at that position is measured based on the Doppler effect. The received signals are bandpass-filtered from 50 to 400 kHz using a variable-frequency filter (3628 Dual Channel Programmable Filter, NF, Yokohama, Japan) and stored in the computer through a high-speed digitizer (NI PCI-5124, National Instruments, Austin, TX, USA). A snapshot of the propagating waves at any given time is obtained by plotting the amplitude of each waveform at that time on a contour map. The snapshots can be continuously displayed in a time series to form a video of the waves propagating beneath the CW laser.  
Figure 1: C-scan images depicting impact-induced delamination for carbon fiber reinforced polymer (CFRP) laminates subjected to impact loading with an energy of 6 J. (**a**) \([0/90]_{\text{ZS}}\), (**b**) \([0/45/90/-45]_{\text{S}}\).  
Figure 2: Photograph of the non-contact laser imaging system for visualizing ultrasonic wave propagation.
# A BIM-Enabled Workflow for the Rehabilitation of Heritage Steel Bridges
## 2 Current State of the Art
### BIM for Infrastructure Management  
The application of the BIM framework to infrastructure projects has increased rapidly due to its capacity to streamline the management processes. During the planning stage, BIM enables rapid solution exploration with the fast identification of the best solution. For large-scale projects, BIM streamlines communication, enhances collaboration among stakeholders, reduces coordination errors, augments quality checks and fast clash detection, etc. A well-developed BIM model reduces errors during the construction phase, limiting the number of missing details and on-site clashes, while streamlining construction processes and increasing overall construction quality [18]. A specific BIM project for road infrastructure is generally characterized by a topographic nature, with point clouds linked to laser scans used for the acquisition of refitting data and meshes with precise geo-referencing [19]. The usual first step is to create a Digital Terrain Model (DTM) from the available cartographic maps, composed of nodes with associated elevations. The horizontal alignment and vertical profile are generally implemented in BIM with the cross section to realize the 3D parametric model. Next, the geometry of various assemblies within the structure is defined, along with the characteristics of the materials and the necessary details for the work breakdown structure. This information is then used to calculate quantities for various processes, which in turn are utilized to estimate costs.  
Further BIM uses for infrastructure projects may include the development of models that leverage combined BIM-GIS models [20]. This approach can be successfully employed to enhance construction efficiency and construction safety. [PERSON] et al. [21] proposed a tunnel information modelling framework for safe tunnel construction. For the sequential construction and reconstruction of highways, [PERSON] et al. [22] proposed 4D BIM to prevent the probability of potential clashes. [PERSON] et al. [23] developed an advanced multidimensional modelling technique that integrates a work breakdown structure and other construction code structures for rail transit construction.  
By leveraging the complementary strengths of BIM and GISs within a holistic system, authorities can achieve multiple objectives of modern urban development, foster innovation, optimize resource allocation, and improve the efficiency and safety of urban systems for real smart cities [2]. Nevertheless, BIM-GIS integration presents a series of challenges related to spatial scale, granularity level, geometric representation methods, archiving and access methods, and semantic discrepancies. A key issue lies in the divergent purposes of BIM and GISs as BIM focuses on object relationships, while GISs emphasize geospatial context and georeferencing.
# Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
## 2 Space Exploration through Different Methods/Vehicles
### Space Exploration through UAVs
#### 2.1.1 Mars UAVs
In article [23], as the design project priority, the MARV team chose to deploy the wings of the MAP. The project for MARV was split into four stages: initial design, deployment system, machining and fabrication of components, and step of integration and checking. The final aim of the project was to plan for the MAP with the wing packaging and wing launch. The outcome was a fully deployable wing with the associated actuator, microprocessor, and supporting applications. The secondary purpose of the deployable wing was to perform wind tunnel testing of the durability of its pitch. For the MAP, a full software architecture design was also built along with all the related electrical components required to incorporate the aerospace. In [24], the research explains the design and development of different autopilot device architectures for unmanned aerial mini/micro rotary-wing vehicles via the model-based design approach. Various flight control methods are discussed with theoretical history under the different chapters and evaluated through simulations and experimental campaigns. Table 1 shows the comparison between the parameters of various Mars UAVs discussed above.
# Automated Fillet Weld Inspection Based on Deep Learning from 2D Images
### Contribution  
Thus, to our knowledge, there is no study for the detection of fillet weld defects using convolutional neural networks trained with high-quality 2D images. Therefore, the main contributions of the work are the following:  
* A set of images depicting weld seams has been developed and made available to the scientific community. This set includes images of seams that have been deemed acceptable as well as seams that exhibit defects, such as a lack of penetration or undercuts. These images have been taken with FCAW and GMAW welding differently. The images are captured with high-precision and high-quality camera instead of X-ray images, as has traditionally been done in other works.
* The development of a methodology with a series of steps that can be used in other research dealing with the detection of objects through 2D images.
* The study and results of three experiments based on the application of neural networks through the analysis of 2D images: the detection of the type of FCAW/GMAW weld, the verification of the goodness of a weld, and the detection of certain defects in a weld seam.
## 3 Results and Discussion
### Effect of Gold Ion Concentration  
According to previous reports, AuCl\({}_{4}\)\({}^{-}\) reacts quantitatively with CTAB at a 1:1 ratio to form water-insoluble precipitates [20]. As the concentration of CTAB increases above the critical micellar concentration (cmc) (i.e., the Au/CTAB ratio decreases), the Au-CTAB complex solubilizes in the CTAB micelles, reaching its maximum solubility at Au/CTAB = 1/60 [20]. Therefore, the effect of the Au ion concentration was investigated at an excess amount of CTAB (i.e., 75 mM) to ensure that the Au/CTAB ratio is lower than 1/60. This value also prevents the formation of insoluble precipitates of the Au-CTAB complex. According to the results depicted in Figure 3a, the maximum analytical signal was obtained at AuCl\({}_{4}\)\({}^{-}\) concentration of 0.25 mM, which was used as the optimum value. Based on the retail prices of gold chloride salts (which is the most expensive reagent used in this method), this concentration corresponds to a cost of <1.2 cent per sample (i.e., less than 1.2 \(\upxi\) per 100 samples), which is affordable even in resource-limited settings. Lower Au concentrations produce solutions with faint colors and as a result the colorimetric changes induced by the addition of Cys are minor. On the contrary, when the Au ions are in excess, the added Cys (50 uM) cannot induce any significant color changes because there is an excess amount of Au ions complexed with the CTAB micelles. On both occasions, the net analytical signal, which is defined as the difference in the color intensity of the blank and the sample solutions, decreases.  
Figure 2: Graphical representation of the potential mechanism of biothiol sensing using gold-coated CTAB micellar assemblies.
# Performance Analysis of Two-Way Satellite-Terrestrial Relay Network with SWIPT
## 2 System Model and Selective PNC
The received signal at \(S_{2}\) for decoding information and the harvested energy during the second time slot \((1-\beta)T\) can be respectively written as:  
\[y_{2,ID}=\sqrt{\rho}\Big{(}g\sqrt{P_{R}}s_{R}+n_{3}\Big{)}+n_{3,z} \tag{3}\]  
\[E_{S_{2},EH}=\eta(1-\beta)T(1-\rho)P_{R}|g|^{2} \tag{4}\]  
where \(\eta\) is the energy conversion efficiency, the additive noise \(n_{3,z}\) indicates the complex Gaussian noise with zero mean and variance \(W\sigma^{2}\). After employing the minimum Euclidean distance rule, \(S_{1}\) and \(S_{2}\) can detect \(\widetilde{m}_{i}\) as \(\widetilde{m}_{2}=\underset{\hat{m}\in(0,1)}{\text{argmin}}|y_{1}-h\sqrt{P_{ R}}s_{R}|^{2}\) and \(\widetilde{m}_{1}=\underset{\hat{m}\in(0,1)}{\text{argmin}}|y_{2,ID}-\sqrt{ \rho}g\sqrt{P_{R}}s_{R}|^{2}\), respectively. Then, the satellite source node and the user source node can detect the message by applying XOR operation on \(\widetilde{m}_{i}\) with its own message \(m_{i}\).