esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability Study on FRP Composites Exposed to Wet-Dry Cycles
## 4 Discussion
### The Effects of Mean Values of the Load  
According to the above results, five representative mean values, 1660 MPa (\(f_{\rm{fu}}\)), 2010 MPa (\(f_{\rm{fd}}\)), 2300 MPa (\(f_{\rm{13}}\)), 2470 MPa (\(f_{\rm{fu}}\) or \(f_{\rm{fd,fib}}\)), 2609 MPa (\(X_{\rm{d}}\)) and 3947 MPa (\(\bar{f}_{\rm{u}}\)), are selected for the reliability analysis of CFRP, whereas 700 MPa (\(f_{\rm{13}}\)), 800 MPa (\(f_{\rm{fu}}\),\(f_{\rm{d}}\) or \(X_{\rm{d}}\)), 850 MPa (\(f_{\rm{fd}}\)), 1189 MPa (\(f_{\rm{fd,fib}}\)), and 2003 MPa (\(\bar{f}_{\rm{u}}\)) were chosen for GFRP. The upper limit of the range is set as the test ultimate tensile strength (\(\bar{f}_{\rm{u}}\)). The standard deviations of load for CFRP and GFRP were taken as the experimental values, which were 346.88 MPa and 134.48 MPa, respectively. The values of resistance were the same as those in the previous section; the reliability index can be calculated by the previous method as shown in Figures 7 and 8.  
According to Figure 7, when the mean of the load is taken as a different value, the reliability indices curves do not change significantly with the number of wet-dry cycles and can still be divided into two stages. However, the mean of the load has a great influence on the initial value of reliability indices (\(\beta_{0}\)). The \(\beta_{0}\) is less than zero under \(\bar{f}_{\text{u}}\), and the corresponding \(P\) has exceeded 50%. In contrast, under the design tensile strength of the four representative codes, the \(\beta_{0}\) are greater than 3.0, and they provide a safety probability of over 99.83%. In the subsequent wet-dry cycles exposure, the \(\beta\) decreases rapidly due to the continuous erosion of wet-dry cycles and tends to stabilize after 4000 cycles. In the four representative codes, only the \(\beta\) under \(f_{\text{fd}}\) can always stay above 3.2, whereas the \(\beta\) under \(f_{\text{fd}}\) will be less than 2.0. In the guideline of Unified standard for reliability design of building structures (GB 50068) [35], the minimum reliability index of brittle fracture structures is 3.2. According to this standard of the minimum reliability index 3.2, only the design value of the TR-55 code can guarantee sufficient long-term safety. As shown in Figure 8, the decline law in reliability of GFRP with increasing wet-dry cycles is basically consistent with CFRP. Similarly, under the design tensile strength of the four representative codes, the \(\beta_{0}\) are obviously greater than 3.2. However, after 4000 times of wet-dry cycle exposure, the \(\beta\) are less than 3.0 under the design values of all the four codes. This shows that the four codes cannot guarantee the long-term safety and the partial safety factors in these codes are still not conservative. After continuous trials, it was found that when \(\gamma_{\text{mm}}\) was changed from 1.05 to 1.5, the design strength (\(f_{\text{fd}}\)) of TR-55 decreases to 592 MPa and the corresponding \(\beta\) values can always stay above 3.2. Therefore, the \(\gamma_{\text{mm}}\) was suggested to modify.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 5 Application of Infrared and Visible Image Fusion
### Applications in the Field of Biometrics  
The topic of facial recognition research has advanced quickly. The face recognition technology for visual images has advanced to a very advanced stage and has had great success [95]. The rate of face recognition using the visual technique will decrease in low-light situations; however, thermal infrared face recognition technology can perform well. Figure 18 shows a face image captured in both infrared and visible light.  
Figure 17: Example of infrared and visible image fusion for color vision at night. (**a**) visible light image; (**b**) infrared image; (**c**) Reference image; (**d**) Fused image.  
Although visible image-based face recognition technology has been thoroughly researched, there are still significant issues with its practical implementation. For instance, the recognition effect will be significantly impacted by the illumination in the actual scenario, changes in facial expression, background, etc. For the purpose of recognizing faces, infrared photographs can supplement the information that is hidden in visible photos. Recent years have seen a rise in the application of infrared and visible light picture fusion based on biometric optimization algorithms. By increasing the quantity of computing, this kind of approach can increase recognition accuracy and give biometrics more supplementary data. The use of infrared and visible light fusion technologies in the field of biometrics will also become more widespread in the future.  
The growing use of facial recognition technology, however, also brings up several ethical and privacy concerns. For instance, while surveillance systems in public spaces contribute to society's safety, they also raise questions about whether people's facial information might be stolen and used inappropriately by outside parties. Concerns regarding abuses and violations of human rights have been raised in various nations where governments are utilizing facial recognition technology to monitor citizens' activities. As a result, we must develop laws, rules, and moral standards to control the use of facial recognition technology and stop abuse. To increase the accuracy and fairness of technology, we also need to put in place an appropriate regulatory framework. Only in doing so will we be able to more fairly weigh the benefits and drawbacks of facial recognition technology and, in the end, establish the peaceful coexistence of science, technology, and human values [96].
# Optimization of Pile Driver Frame Based on Sensitivity Analysis
## 2 Body Model Structure and Analysis
### Static Analysis
#### 2.2.2 Analysis of Stress Results in Typical Working Conditions  
The maximum pile-up force of the pile-up driver is 400 t. The combined effect of the maximum pile-up reaction force, and the gravity of the pile-up table itself, has an upward effect on the pile-up driver's bulk structure. The longitudinal meander mechanism supports the entire structure, and the combined force acts on the connecting surfaces of the cylinder and column in the form of a uniform load q. The calculated load q is 3.061 Mpa, and a diagram of the bulk structure is shown in Figure 4.  
In the finite element, the transmission of force is carried out with the help of nodes, so the load can be applied either centrally on the nodes or uniformly on the surface. This calculation applies the load q on the connection surface of the column with the uniform load applied. The large body structure cross-sectional dimension parameters are shown in Figure 5.  
Figure 4: Large structure component drawing.  
In the above figure, R1 is the thickness of the crane support plate. R2 is the thickness of the side seal cover plate. R3 is the thickness of the inner longitudinal plate. R4 is the thickness of the side sealing plate. R5 and R6 are the thickness of the pile mouth sealing plate and the middle longitudinal plate, respectively.  
In actual operation, the big body structure is lifted on the ground by four cylinders, and the pile is pulled or pressed along the cylinder legs in an up and down direction to complete the working condition. The degrees of freedom of the cylinder legs in other directions are limited, so the big body structure has only one degree of freedom. When the constraints are set for the large body structure model, the degrees of freedom in all directions of the large body structure are restricted, except for the release of the Z-axis degrees of freedom. Therefore, the pile driver is restrained at the connection between the large body structure and the legs. There are 2 degrees of freedom in the translation direction and 3 degrees of freedom in the rotation direction (ROTX, ROTY, ROTZ), as shown in Figure 6.  
The structure was analyzed in ANSYS APDL, and the stress cloud and displacement cloud of the large body skeleton under the compression pile condition were obtained, as shown in Figure 7.  
Figure 5: Section size of large structure.  
Figure 6: Large body structure constraint diagram.  
From Figure 7a, it can be seen that the maximum stress of the big body skeleton is 110 MPa. The maximum stress appears at the connection of the legs of the big body structure, which is consistent with the actual working condition. According to the crane Chinese standard GBT3811-2008, taking 1.34 as the safety factor, the allowable stress of the material is 175 MPa, which meets the design requirements.  
As can be seen from Figure 7b, the maximum displacement is 2.01 mm under the pile pressing condition, which meets the requirements. The maximum deformation near the hydraulic cylinder cover plate is also in line with the actual situation.
# The Effect of Laser Pulse Widths on Laser--Ag Nanoparticle Interaction: Femto- to Nanosecond Lasers
## 2 Background
### Simulation of Laser Fragmentation in the Nanosecond Region Using the Wave-Optic Theory  
The appropriate method for a numerical analysis of laser fragmentation differs depending on the pulse width. At femtosecond pulse widths, the mechanism of laser fragmentation can be indirectly explained by the temperature distribution of electrons and phonons [30; 50]. At nanosecond pulse widths, photothermal ablation occurs. The interaction between the laser and the nanoparticles can be evaluated using wave optics.  
Upon contacting a particle, the laser beam is scattered. The resulting energy waves are absorbed by the particles in a nonuniform manner because of their dispersion and interference effects. By numerically confirming the energy absorption by the particle, laser fragmentation due to nanosecond laser pulse widths can be indirectly analyzed. In wave optics, the energy absorption due to intra-particle dispersion of the laser beam is interpreted as an optical interaction. For this purpose, optical properties, such as the complex refractive index of nanoparticles whose diameter is less than the wavelength of the laser beam, should be evaluated. Unlike the surface of bulk Ag or Au, the absorption of laser energy by submicron Ag or Au particles is high, which can be explained by Mie theory [20; 51].  
Figure 1: Mechanisms causing laser fragmentation for different laser pulse widths.  
Figure 2: Effect of shockwave propagation on laser fragmentation.
# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
## 2 Theory and Method
### Improved YOLOv8 for Target Detection
#### 2.2.4 Loss Function of YOLOv8  
The loss function of target detection models generally consists of two parts: the object classification and the bounding box regression loss function. Additionally, the YOLO series introduces object confidence loss. YOLOv8 uses CIoU loss to consider fully three important geometric factors affecting the performance of the bounding box regression loss function: overlapping area, center point distance, and aspect ratio. These greatly improve the speed and accuracy of the predicted box regression. However, if boxes B and GT have the same aspect ratio but different width and height values, and the penalty term of the aspect ratio always retains zero, the effective optimization of the model is hindered.  
In recent years, the bounding box regression loss functions continuously evolved from smooth L1 loss, IoU loss, GIoU loss, DioU loss, CioU loss, and EIoU loss to alpha IoU [19; 22; 25; 26; 27]. To address this issue, EIoU loss separates the influence factors of the aspect ratio between the predicted box and the ground truth box based on the penalty term of CioU loss, computes the length and width of the predicted box and the ground truth box, respectively, and adds Focal focusing high-quality anchor boxes. Its definition formula is as follows:  
\[Loss_{EIoU}=1-IoU+\frac{\rho^{2}(b,b^{gt})}{c^{2}}+\frac{\rho^{2}(\omega, \omega s^{t})}{c_{\omega}^{2}}+\frac{\rho^{2}(h,h^{gt})}{c_{h}^{2}} \tag{3}\]  
where \(\rho(0)\) represents the Euclidean distance; IoU means the basic intersection over union; \(b\) and \(b^{gt}\) represent the center points of the predicted box B and the ground truth box GT, respectively; \(\omega\) and \(\omega s^{gt}\), as well as \(h\) and \(h^{gt}\), represent the width and height of the predicted box B and the ground truth box GT, respectively; \(c\) is the diagonal distance of the minimum bounding rectangle of the predicted box B and the ground truth box GT; \(c_{\omega}\) and \(c_{h}\) are the width and height of the minimum bounding rectangle of the prediction box B and the ground truth box GT, respectively.
# Forecast Analysis of Pollutant Emissions of Cruise Ship Routes in Western Mediterranean
## 5 Discussion
Considering the best scenario for 2025, for regular routes, the gas emission indicator will reach 25.7 kg/nm of NOx\({}_{\rm x}\) and 1457.4 kg/nm of CO\({}_{2}\), whereas the SO\({}_{2}\) emissions will decline to 3.6 kg/nm after the SECA enters into force. This research is subject to some limitations. First, the most conservative scenario in terms of NOx emissions was applied due to the lack of data to apply the corresponding Tier (I, II or III) to each vessel analysed. Second, cruise traffic figures of some key cruise ports of the Western Mediterranean were not able to be retrieved. Third, projections of the implementation of the SECA were made considering the consumption of liquid fuel of 0.1% sulfur content for the entire analysed fleet. Additionally, the present research focuses on one of the three phases of cruise ship operations with gas emissions. Future research should address these limitations. First, if the missing data to apply the proper Tier to each vessel may be retrieved, future stages of the research could update the NOx emissions by applying the Tier correction factor proposed by European Commission [51] and the equations included in the amendments to MARPOL Annex VI regarding Tier I-III NOx emission limits [7]. Second, it would be interesting to tackle new scenarios of emission inventory, taking into account that part of the fleet could be equipped with engines burning LNG, biofuel, or methanol. Third, other aspects of cruise navigation can be tackled, such as manoeuvring and mooring operations, together with associated activities to set the overall emissions of the cruise fleet. Fourth, it would also be of interest to calculate new scenarios considering how different percentages of the cruise fleet have Selective Catalytic Reduction (SCR) technology implemented to reduce NOx emissions. Fifth, new scenarios should consider the effects of the inclusion of the maritime shipping sector in the EU's Emissions Trading System (ETS), as the European Parliament (EP), the Council of the European Union, and the European Commission have reached an agreement on including shipping in the EU's Emission Trading System (EU ETS) from 2024 [52]. The proposal is to extend the scope of the EU's Emissions Trading System to cover CO\({}_{2}\) emissions from large ships (above 5000 gross tonnage), regardless of the flag they fly. The extension will include all emissions from ships calling at an EU port for voyages within the EU (intra-EU), as well as 50% of the emissions from voyages starting or ending outside of the EU (extra-EU voyages) and all emissions that occur when ships are at berth in EU ports [53]. Finally, forecasting analysis could consider variations in engine regime due to environmental conditions by introducing stochastic functions during the fuel consumption and gas emissions estimation.
## 3 Simulation Experiments and Discussion
The blue line indicates the mean and standard deviation of the residuals of the detection results with the method of retaining the aliased data. The vertical axis indicates the distance between the actual position of the detected target and the detection results in pixels. As shown in Figure 4, the residuals of both methods decrease as the detection time increases. The residuals of the retention method are always smaller than those of the removal method. The range of the residuals is also smaller than that of the removal method. For different detection times, the addition of the aliased data decreases both the mean and  
Figure 4: Mean (markers) and standard deviation (error bars) of the distribution of residuals between generated and reconstructed target positions versus detection time T at s = 10 MHz. range of the residuals. The retention method can be accomplished in a much faster time for the same accuracy requirements for the same detections. Different rates \(s\) are chosen for the simulation experiments shown in Figures 5 and 6. As shown in Figure 5, the residuals of the retention method decrease as the rate \(s\) increases. But the residuals of the removal method are not reduced. This is due to variations in the number of data and the percentage of aliased data. When the rate is small, the time interval between the photons being received is large. Therefore, photon superposition is less likely. As the rate increases, although the detected data increase, the data obtained are more of the photon superposition data. If the superposition data are removed, the number of data is too small. This leads to inaccurate detection results. As shown in Figure 6, the residuals and residual range of the retention method tend to stabilize with an increase in the rate \(s\), while the residuals of the removal method increase significantly. Too few data make it difficult to improve the detection of the removal method, even if the detection time is extended to 10 microseconds. In order to minimize aliasing of the photons, an XS detector generally needs to limit the rate. However, in center of mass detection, for a larger rate, the resulting aliasing data are still valid for center of mass calculations. As the distance increases between the detected object and the detector, the diameter of the diffuse spots produced using the detector increases. The simulation experimental results for different dispersion spot diameters are shown in Figure 7.  
For different diffuse spot diameters, the mean value of the residuals of the retention method is smaller than that of the removal method. The range of residuals is also smaller than that of the removal method. As the diameter of the diffuse spot increases, the difference between the two methods becomes more and more obvious. Figure 5: Mean (markers) and standard deviation (error bars) of the distribution of residuals between generated and reconstructed target positions versus rate s for detection time T = 1 \(\upmu\)s.
# Evolution towards Smart and Software-Defined Internet of Things
## 2 IoT Classification and Applications
### Environmental IoT  
The application of IoT can be extended to monitor and impact the environment as well. Environmental IoT can be applied to both indoor and outdoor environments. The first can fall into smoke detection, fire alarms, light detection, movement detection, indoor heat, and moisture monitoring, etc. [29], whereas the latter lies in the large outdoor deployments such as traffic monitoring; lightening and cloud detection; rain monitoring; pollution monitoring; and earthquake, chemical hazards, and flood detection; etc. [30]. Outdoor environmental IoT reliability and maintenance are difficult due to harsh climate conditions and high maintenance costs, whereas indoor environmental IoT reliability and maintenance are less challenging due to low cost and ease of being used [31].  
It is observed that the open nature deployment of the IoT network and communication protocols for reliable operation is time-consuming and costly. There are energy management and environmental maintenance concerns for the smooth operation of open nature outdoor IoT deployments. However, modern techniques, such as machine learning, blockchain, and industrial automation, can be applied for the enhancement of reliability and cost reduction in environmental IoT. Such applications are studied in [32; 33; 34; 35], where the authors propose a real-time, fine-grained, and power-efficient air quality monitor system based on aerial and ground sensing to keep a check on air pollution which is becoming the largest environmental health risk.
## 5 Current Cosmetic Applications of Cork  
Cork and its by-products have been increasingly sought as a source of new ingredients for pharmaceutical and cosmetic use (Figure 7). In terms of commercial applications, cork is the main ingredient in a brand of anti-aging cosmetics which claims that the suberin present in the cork extract has a lifting effect on the skin [4; 96]. There is only one cork cosmetic ingredient with established in vitro [79] and in vivo [36] activity on human skin described in the \"CosIng\" database, namely _Quercus suber_ bark extract. This ingredient may be used alone as a unique ingredient or combined with other ingredients, such as plant extracts. The \"CosIng\" database is a cosmetic ingredients database from the European Commission, and it provides information on cosmetic ingredients, including their regulatory status according to the Regulation (EC) No 1223/2009 [97]. Several cork-based ingredients are available from different suppliers. The ACTISCRUB(tm) Cork by Lipotec is a cosmetic raw material constituted by a _Q. suber_ bark extract and has exfoliant and peeling activity [98]. The Suberlift(tm) by Ashland Specialty Chemical is another cork raw material containing _Q. suber_ bark extract with tensor and firming effects on human skin [99]. One last example, the DIAM Oleoactif(tm) by Hallstar, is a raw material constituted by a mixture of Cocos nucifera oil, oak root extract, and _Q. suber_ bark extract with soothing, antiaging, anti-redness, and anti-inflammatory effects [100].  
Furthermore, cork was also studied to be used in skin exfoliants. A granulated cork was studied and considered suitable as a mild exfoliant due to its morphology and properties. Cork particles were also tested as stabilizers of a Pickering emulsion for topical application with antioxidant and anti-elastase activity [4; 34].