esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Natural Antioxidants and Their Role in Cosmetics
### Minerals
#### 4.4.2 Zinc (Zn)  
Zinc is a trace element that is a component of over three hundred enzymes, including DNA and RNA polymerases and superoxide dismutase. It participates in the production of testosterone and insulin, stabilizes cell membranes, and affects the proper functioning of the nervous system. This element also plays a significant antioxidant role. Deficiency is often caused by malnutrition, diseases, or eating a poorly diversified diet [124]. Vitamin A helps in the absorption of zinc in the body. Animal products show greater absorption than plant products, due to the presence of phytates. High zinc content is found in oysters, fish, meat, nuts, legumes, eggs, and whole grain cereal products [125].  
Topical zinc, in the form of divalent zinc ions, has been reported to provide antioxidant photoproduction for skin. Two antioxidant mechanisms have been proposed for zinc: zinc ions may replace redox active molecules, such as iron and copper, at critical sites in cell membranes and proteins; alternatively, zinc ions may induce the synthesis of metallothioneein, sulfhydryl-rich proteins that protect against free radicals [126; 127].  
Numerous studies have shown the benefits of either topical or oral zinc in the treatment of acne. The topical application of zinc ions has been shown to induce metallothioneein, which may account for its photoprotective effect [31; 126]. Zinc oxide is a very effective sunscreen as well as regulating sebum secretion. Zinc salts are present in many cosmetic products--mouthwashes, toothpaste (antiseptic activity), after-shaving products, deodorants (anti-odor), and face powders. Zink pyrithione is used against dermatitis as well as dandruff. Zinc lauryl ether sulfate can be used in skin cleansing products [128; 129].
# Digital-Twin-Based System for Foam Cleaning Robots in Spent Fuel Pools
## 5 Obstacle Detection and Localization
### Object Detection Based on YOLOv5  
YOLOv5 (You Only Look Once version 5) is a real-time object detection algorithm renowned for its efficient, rapid, and accurate performance. The network architecture of this algorithm can be divided into four main components: the input layer, backbone network, neck, and predictions. Through the collaborative interaction of these four crucial components, YOLOv5 achieves rapid object detection, while maintaining a high accuracy, showcasing its excellence in real-time scenarios and complex environments. The YOLOv5 network architecture diagram is shown in Figure 14.  
(1) Input Layer: Images are initially fed into the YOLOv5 network. This algorithm supports various input sizes, allowing users to balance speed and precision. Users can adjust the algorithm's performance according to specific requirements by choosing different input sizes, better adapting it to various application scenarios.  
(2) Backbone Network: The backbone network extracts features from the input images. YOLOv5 adopts CSPNet (cross-stage hierarchical network) as its backbone, enhancing its ability to capture hierarchical information. This improves its discriminatory power for detecting targets, extracting local and global features from the images. This ensures that YOLOv5 can more accurately represent the content of images in object detection tasks, thereby enhancing overall performance.  
(3) Neck: The neck network fuses multi-scale features from the backbone network. YOLOv5 employs PANet (path aggregation network) as its neck network, primarily designed to connect feature maps of different scales. This enhances the model's understanding and discriminatory ability towards targets, improving object detection accuracy.  
(4) Prediction: The prediction component is responsible for predicting targets based on the output feature maps, including the bounding boxes, categories, and confidence scores of the targets. In this stage, YOLOv5 employs the mechanism of anchor boxes and conducts target predictions based on the scale of the feature maps. This effectively identifies and locates targets within the images.
###### Abstract
Several standards, such as R6 [16], BS 7910-2019 [17], CSA Z662-2015 [18], and API 579-1-2016 [19], recommend the use of the FAD method to evaluate the applicability of pipeline girth welds. Among them, after more than 40 years of research, BS 7910 has made significant developments in both breadth and depth. In particular, BS 7910 is a safe and reliable defect assessment standard which can provide assurance for new structures in the design phase, for structural integrity in the manufacturing phase, and for the entire life of the structure in the operational phase [20; 21; 22]. Meanwhile, the calculation of the ultimate axial load of pipelines with crack defects is challenging and time consuming, since numerous influencing factors affect the failure behavior of pipes. To this end, the prediction of the ultimate axial load based on data driven models is necessary to assess the safety of pipelines with cracks. The machine learning technology has been recently utilized for prediction. Support vector regression (SVR) is a machine learning algorithm which is highly applicable for nonlinear, and high-dimensional pattern recognition problems. The algorithm can also achieve a balance between accuracy and complexity of regression models by simultaneously minimizing the empirical errors and maximizing the geometric marginal zones. [PERSON] et al. [23] used SVR models to perform prediction analysis on the maximum pitting corrosion depth in oil and gas pipelines. Their research results demonstrated that SVR models are capable of providing a more precise prediction. [PERSON] et al. [24] performed prediction research on the crack propagation in aviation aluminum alloy. The results suggest that their prediction model was effective, since the crack growth was predicted accurately. [PERSON] et al. [25] performed discrete sizing optimization of a stepped cylindrical silo using the PSO method and implicit dynamic finite element analysis. In this paper, a BS 7910 stress-based FAD method is used for the X65 pipeline with a diameter of 813 mm, i.e., the Mo-da crude oil pipeline in China. Considering crack parameters (crack depth ratio; crack length-to-cycle ratio), pipeline geometric parameters (wall thickness; misalignment), and material parameters (tensile strength; yield ratio), the effect of various influencing factors on the serviceable evaluation results is systematically analyzed under axial loads equal to 0.5-0.9 times the minimum yield strength. Moreover, the effect of various influencing factors on the ultimate axial load is clarified as well. To this end, an ultimate axial load database of the X65 pipeline with cracks is constructed. With the help of particle swarm optimization (PSO) and SVR, a prediction model of the ultimate axial load of the X65 pipeline based on stress failure assessment criteria is developed, providing reference and guidance for ensuring the intrinsic safety of the X65 pipeline with cracks.
# Recycled Carbon Fibers with Improved Physical Properties Recovered from CFRP by Nitric Acid
## 3 Results
### Tensile Strength of Carbon Fiber  
A single fiber tensile test was performed for vCF and rCF. The results of the tensile strength \(\sigma\) and tensile modulus \(E\) are shown in Figure 5. A Weibull plot of the tensile strength only for the representative data is shown in Figure A4. Since Figure A4 shows linear plots, tensile strength could be approximated using a Weibull distribution. The Weibull modulus of \(m\) was calculated from the slope of the Weibull plot and the scale factor \(\sigma_{0}\) was calculated from the intercept, as shown in Table A1. The value of the Weibull modulus \(m\) indicates the magnitude of the variation in the data, in which a larger value of \(m\) indicates a smaller variation. It shows approximately \(m\) = 3 to 7 for the PAN carbon fibers in a previous report [17]. Therefore, the shape factor of \(m\) calculated in this research as shown in Table A1 is considered to be a reasonable value. This indicates that the tensile strength values acquired by measurement are highly reliable results.  
According to the tensile strength results shown in Figure 5, vCF 8 h exhibited a 1.3 times higher value than that of vCF. However, we can see that the value subsequently declines in vCF 12 h and vCF 24 h to or below that of vCF. On the contrary, in rCF 12 h, the tensile strength increased 1.4 times than vCF, and a high value was maintained up to rCF 72 h. However, excessive immersion in nitric acid (rCF 120 h) resulted in decreased tensile strength to or below that of vCF. The tensile modulus shown in Figure 5, demonstrates that there were no significant differences for the samples of vCF 0 h to 24 h and rCF 8 h to 48 h. This suggested that there was no large structural change in the materials [18]. However,  
Figure 4: Interfacial shear strength between carbon fiber and resin at resin droplet size \(L\) = 70 \(\upmu\)m for vCF, vCF 8 h–24 h, and rCF 8 h–120 h.  
Figure 5: Tensile strength and elastic modulus obtained by tensile test for vCF, vCF 8 h–24 h, and rCF 8 h–120 h.  
rCF 72 h and rCF 120 h showed 0.8 to 0.9 times lower values than vCF. Since it is known that a highly crystalline graphite structure demonstrates a high modulus [18], this result suggested that the crystallinity of the graphite structure might decrease after excessive immersion in nitric acid.
# Home-Based Lower Limb Exercises for Individuals with Diabetes: A Scoping Review
## 3 Results
### Additional Details of Retrieved Articles

Table 3: _Cont._After 4 dropouts, 76 participants completed the study. The study did not report any side effects or complications. [PERSON] et al. [16] (Taiwan) utilized secondary data analysis from a larger RCT. Their findings revealed that adherence was highest before hospital discharge (31.7%) but gradually declined to 17.7% after 12 months. By the one-year follow-up, 62 participants remained, while 8 had passed away and 18 were lost to follow-up. However, no side effects or complications were reported. [PERSON] et al. [17] (Brazil) conducted a single-blind RCT with high adherence (92.5%), though dropout rates were not specified. There were also no reports of side effects or complications. [PERSON] et al. [18] (Brazil) conducted an RCT as part of the FOCA II program, showing an adherence rate of 77.7%, with dropout rates of 9% to 11% in both the intervention and control groups. Again, no side effects or complications were mentioned. [PERSON] et al. [19] (Indonesia) performed a double-blind RCT where patients in the intervention group demonstrated excellent compliance. The dropout rate was low, with only four participants dropping out from the intervention group and two from the control group. While no side effects were mentioned, the study noted recurrence rates of plantar foot diabetic ulceration. At 12 weeks, 4% of patients in the intervention group had recurrence, compared to 68% in the control group. At 24 weeks, the recurrence rate was 16% in the intervention group and 72% in the control group. [PERSON] et al. [20] (Brazil) also conducted an RCT as part of the FOCA II program. The adherence rate was 72%, though dropout rates were not specified, and there were no mentions of side effects. Lastly, [PERSON] et al. [21] (Brazil) performed an RCT focusing on a 12-week web-based foot-ankle exercise program. The study showed a compliance rate of 41%, with 53% completion based on the user bank, and 84% adherence at 12 weeks. Dropout rates were 19% in the intervention group and 16% in the control group. The program was deemed feasible and well-accepted, with minimal adverse events, including delayed onset muscle soreness and foot muscle cramping. In summary, the studies indicate varying adherence rates and dropout percentages, with some reporting excellent compliance, particularly in the Brazilian studies. While most studies did not report side effects or complications, a few, such as [PERSON] et al. [19] (Indonesia), noted the recurrence of foot ulceration as a complication; however, the recurrence rate of foot ulceration was reduced in the intervention group in comparison with the control group.
# Passive Power Line Communication Filter Design and Benchmarking Using Scattering Parameters
## 3 Filter Performance Measurement  
This section presents the performance tests that should be performed as part of the benchmarking: insertion loss, PLC signal filtering, and impedance measurement.
# An Overview of Cooperative Driving in the European Union: Policies and Practices
## 4 Current State of the Art of C-ITS Systems
### C-Roads Pilot Sites
#### 4.1.8 Portugal  
C-Roads Portugal involves the Atlantic Corridor in Portugal and covers major sections of the core and comprehensive networks, together with Lisbon and Porto urban nodes. In particular, the programme is developed through five pilot sites whose activities are specified below.  
* Pilot 1 for designing a National Single Point of Access (SPA) prototype able to cover information for around 3390 km (20%) of the network and developing an SPA mobile application (SPApp) covering Day-1 services
* Pilot 2 for testing Day-1 and Day-1.5 on different kinds of roads (metropolitan areas, interurban roads, streets, and highways) using a hybrid G5/cellular communication system. Pilot activities cover over 460 km of the core and comprehensive network, including cross-border sections in Valencia and Caia and roads giving access to urban nodes of Lisbon and Porto
* Pilot 3 for providing connected and autonomous vehicles with automation on levels two and three of the Trans-European Networks-Transport (TEN-T) network, also using a hybrid G5/cellular communication framework
* Pilot 4 consisting of the following subactivities focused on the Lisbon node: testing traffic monitoring and travel time prediction tasks by means of cellular technology; investigating infotainment services on parking availability with, in addition, the development of an in-vehicle app based on a hybrid communication framework; analysing bus corridor prioritisation services supported by cellular technologies; and evaluating potential benefits of the integration of private car usage with other transport modes in the last mile of interurban motorway corridors in a hybrid communication architecture
* Pilot 5 for investigating applications on traffic prediction services, by means of cellular and Wi-fi technologies, and testing the incorporation in the Porto network of a smart bus covering a corridor of around 1.4 km, using the DATEXII communications protocol and cellular communication technologies  
More than 30 partners are involved, including: Instituto da Mobilidade e dos Transportes, Universidade do Porto, Ascendi Grande Lisboa, Ascendi do Grande Porto, IP Telecom SA, Siemens SA, Vialivre SA, and GMVIS Skysoft SA. According to the planned timeline, by the end of 2020, C-ITS services will be implemented along 964 km, ensuring a continuity of service in urban nodes and the core network. In all, 212 RSUs will be installed with 180 OBUs and 162 vehicles in operation.
## 3 Experimental Set-Up
### Experimental Matrix and Pre-Test Analysis  
At the time when the present study was carried out, the thermal hydraulics of the FWMU had already been simulated for TBM operating conditions, namely 90 g/s per channel with a helium inlet temperature of 300 \({}^{\circ}\)C and a pressure level of 8 MPa. While the simulation in [13] assumes that the helium enters and exits the U-shaped channel through horizontal nozzles as well as a uniform heating on the whole mock-up surface, the results indicate that the existence of the 180\({}^{\circ}\) bend that leads to a locally increased heat transfer due to the strong mixing associated with the sharp flow turn. The subsequent stress analysis indicates that, given the assumed mock-up fixation, a testing of the mock-up at 0.35 MW/m\({}^{2}\) should be possible, while at higher loads, the ratcheting criteria are not met. In these studies, the back side of the mock-up was not allowed to deform in the vertical (Z) direction, and four points, close to the center of the mock-up, were assumed as fixed points. In [7] these studies were continued, examining the possible attachment of the mock-up in more detail. Restricting the length of the heated surface to the area between the insert points of the vertical nozzles and using a specially designed attachment system (attachment F1 in [7]), the study showed that, by leaving the sides of the mock-up free to move, the mock-up would survive even the high heat loads for the assumed lifetime of a TBM set in ITER (850 h operating time and 3000 cycles/year, assuming a total operation of 2.5 years). If the movement on the border of the mock-up is restricted (attachment F2 in [7]), the maximum heat flux, for which the design criteria are met, is reduced.  
These previous studies were mainly carried out for TBM-specific conditions and considered higher flow rates and quasi-stationary conditions. To assess if the mock-up can be used to simulate a LOFA event for a DEMO blanket as modeled in [7], dedicated CFD and system codes calculations were carried out. These simulations were also used to determine the operating regime that would provide temperature gradients in the FW cross-section similar to a DEMO breeding blanket. These simulations encompass both detailed thermal-hydrodynamic simulations (only FWMU) using Ansys CFX, as well as system-wise simulations (FWMU and complete HELOKA loop) using the RELAP-3D code.
## 2 Rectifier Analysis
### Sensitivity  
In WPT and harvesting energy systems, sensitivity is a key factor in designing rectifiers. It is utilized to define the Minimum Desirable Signal (MDS) that a rectifier can deal with.  
\[\text{Sensitivity}=\ P_{dBm}=10\ log(P_{mW}) \tag{3}\]  
With the aim of capturing low input power levels, the rectifier components should show a low threshold voltage to attain high sensitivity [9; 18].
## 5 Application of IdMSecMan
### Process for Identification
#### 5.1.2 Question Phase  
Vis4 Sec requires controlled process cycles. In order to enable them, we used SABSA with its layers. We started with the first question of the contextual layer and gathered related information. We then proceeded either by refining and improving the question or moving on to the next question. Thereby, we proceeded from the top down until we answered the last question of the management layer.  
**Contextual:**: What is the business risk without the usage of a proper and usable identification proof, such as eID? What are the requirements for eID? How should eID be integrated and for which areas? What are the legal and regulatory restrictions? What are the consequences of eID in relation to the General Data Protection Regulation (GDPR) or other data protection regulations?
**Conceptual:**: Which concepts for identity proofing and verification are possible? What goals and design does the usage of eID follow? How should they be integrated into the current infrastructure?  
**Logical:**: What level of trust in the identification is required? For which areas is a higher level of trust needed? Which identification methods provide which level of trust? Which logical components are necessary for the eID integration (software, hardware, backup,...)? What should eID protect and how to protect eID and its information? What are the threats?
**Physical:**: Which identification methods can and should be used? What are the specifications and processes for integrating eID? What are the physical components? What is the content of the monitoring and how is it carried out?
**Component:**: Which products, applications, tools, and people will be using eID? Are additional components required?
**Management:**: How is the eID integration maintained, updated, and upgraded? What operational aspects do we have to consider due to the integration of eID (service desk, cross-organizational processes, etc.)? How to ensure the security of the integration?
# Multi-UAV Area Coverage Track Planning Based on the Voronoi Graph and Attention Mechanism
## 3 Method
### Route Planning  
Based on the above regional decomposition results, the drone is initially located in the grid at the upper left corner of the grid map. We used Pygame to build the UAV area coverage simulation platform. The dark grid at the edge in Figure 3 represents the boundary of the task area, and the UAV can only move within this boundary. The light grid within the remote boundary represents the grid to be covered, and the dark grid represents the stationary obstacle. The cartoon quadcopter drone icon represents the drone performing area coverage. Specify that drones and moving obstacles can only move from the current grid to another adjacent grid at each step. Every time the drone covers a dark grid, the grid turns white, indicating that it has been covered. In the process of area coverage, the environment model program uses the current frame of the simulation platform as the current state value at each step and then converts it into the input of the agent network through appropriate preprocessing.  
The simulation platform screen was captured multiple times during the experiment. The simulation platform screen was captured multiple times during the experiment, and the final result of the basic trajectory optimization algorithm is shown in Figure 8. The final result of the trajectory optimization algorithm integrating CSAM is shown in Figure 9.  
Figure 8: Visual results of basic optimization algorithm.  
We can calculate the path lengths with the algorithm in Algorithm 1. Using the basic ResNet50 model and the model with channel spatial attention mechanism, the average path lengths are 40 km and 36 km, respectively. This means that the path length of the improved model is reduced by 10%, greatly improving efficiency.  
This study delves into the problem of drone trajectory optimization, initially employing the basic ResNet50 model. Building on this foundation, we integrated a CSAM and reinforcement learning strategies. The enhanced model demonstrated a substantial performance improvement, achieving a path length reduction of approximately 10%. These results underscore the effectiveness and superiority of the proposed method, highlighting its potential for advancing drone trajectory optimization.
## 3 Results and Discussion  
### Microbial Load Analysis  
As displayed in Table 1, the initial TAM and Enterobacteriaceaeaee counts in untreated donkey colostrum (day 0) were below the detection limit (<1.0 log CFU/mL), and during refrigeration, there was an increase in TAM and Enterobacteriaceaeaee counts in untreated donkey colostrum at day 4 (6.7 log CFU/mL) and day 7 (3.5 log CFU/mL), respectively, surpassing 8.0 log CFU/mL after 18 days for both microorganisms. Coliforms as well as presumptive _E. coli_ (the most common contaminant of untreated and processed milk and dairy products [19]) were also not detected in raw donkey colostrum. The low microbial counts in raw donkey colostrum found in this work indicate good hygienic conditions during the collection of the colostrum and are also possibly due to the presence of natural antimicrobial substances, such as lysozyme and Igs [17], since it is reported in the literature that these substances may work synergistically in order to inhibit bacterial growth. For instance, [PERSON] et al. (2008) [17] did not verify microflora variations in donkey milk after 96 h of storage at 4 \({}^{\circ}\)C except for coliforms, which showed a 1.0 log CFU/mL increase. In the literature, nevertheless, the results from a biennial study on untreated donkey milk showed an occasional isolation of _E. coli_ and a wide variability in coliform contamination (<1.0-6.6 log CFU/mL; [19]), which indicates environmental sources of contamination [20].  
After TP and HPP treatments and subsequent refrigerated storage for 40 days, all microorganisms studied remained below the detection limit (<1.0 log CFU/mL), pointing to both treatment processes assuring an effective pasteurization effect (Table 1).