esa-sceva/satcom-chunk-collection huggingface dataset:
# The Risk of Using Cosmetics and Cosmetic Procedures During Pregnancy
## 4 The Risks of Cosmetics and Cosmetic Procedures
### Aesthetics and Biomedical Technology
Only long-term epidemiological studies that analyze IPL exposure as a specific variable could confirm or refute the associated risks [37]. There is a potential risk of developing basal cell and squamous cell carcinomas in keratinocytes and melanomas in melanocytes. However, this risk is primarily linked to severe damage to the cut-off filter within the IPL device. No publications have yet identified cancers caused by the thermal effects associated with IPL. The limited follow-upof IPL use, the short duration of follow-up in existing studies, and the small number of subjects included do not provide sufficient information on these potential long-term effects (which may be observable 5 to 15 years later). The safety of using laser and IPL for hair removal depends on the energy absorbed by the epidermis, hair follicle, and adjacent tissues, which varies with the emission characteristics of the devices and the individual characteristics of the users. Key factors for safe exposure include:  
* The nominal operating characteristics of the device defined during its design. * The stability of the device's operating characteristics, which depend on design and maintenance. * Correct device usage, which is linked to the training and skill of operators, proper user information for household devices, and adherence to best practices. * The characteristics of the pregnant woman at the time of hair removal, including phototype, skin color, hair color, and individual response. * Possible contraindications, such as pre-existing diseases, photosensitive treatments, etc. [38]. Despite concerns, lasers have been used safely to treat medical conditions in pregnant women. Laser and IPL are generally considered safe for patients with granulomatous conditions and warts. Limited studies indicate that Neodymium-doped Yttrium Aluminum Garnet (Nd:YAG) laser can be safely used to treat skin lesions during pregnancy. Genital warts have been successfully treated with the Nd:YAG laser without complications for the mother or fetus [39]. Other studies report the safe use of the Nd: YAG for severe inflammatory acne and pyogenic granulomas during pregnancy, with no observed side effects. In a recent study, a pulse dye laser (PDL) (585 nm) was used to treat pyogenic granulomas and warts during pregnancy, demonstrating it to be a safe treatment. Available studies indicate a good safety profile for PDL use in pregnancy, with minimal side effects such as mild erythema and pain. In conclusion, laser treatments appear relatively safe for medical conditions in pregnant women. However, laser and IPL are not recommended for cosmetic procedures during pregnancy due to a lack of safety studies [40].
## 5 Open Challenges and Research Perspectives
### Security and Privacy  
Coupling INC with ICN introduces intricate security and privacy challenges that demand innovative solutions. Research should focus on developing end-to-end trust frameworks that enable the verification of computation correctness and data authenticity. As discussed in [69], provenance tracking systems can play a pivotal role by documenting the lifecycle of computational tasks, allowing consumers to validate results without requiring task re-execution. Preserving privacy during computation is equally critical [82], particularly for sensitive data in applications like healthcare and manufacturing. Approaches such as homomorphic encryption allow computations on encrypted data, safeguarding privacy without compromising functionality. Complementary to this, trusted execution environments provide a secure enclave for isolated computations, ensuring that even resource-constrained nodes can execute sensitive tasks safely.  
In addition to trust and privacy, resilience to adversarial environments is paramount. Distributed networks face threats such as tampered computations and DoS attacks, which can undermine system integrity and availability. Effective solutions should include mechanisms for real-time anomaly detection and fault tolerance, supported by redundancy and request authentication to maintain reliability. Furthermore, ensuring the integrity of cached computation results is vital. Cryptographic signatures can authenticate outputs, while strategies for maintaining cache freshness and consistency will prevent the reuse of outdated or corrupted results.  
Lastly, as INC deployments grow in scale, efficient key management will become critical to ensure seamless and secure operation. Scalable methods for key distribution and rotation must be devised to support large-scale networks with minimal overhead. Application-specific security protocols, tailored to the unique demands of fields like healthcare and autonomous systems, will also be necessary to mitigate risks where errors or breaches could have significant consequences.
## 3 Results and Discussion
### Characteristics at Low Latitudes
Finally, the main peak occurred around 20\({}^{\circ}\)-70\({}^{\circ}\)W longitudes during the December solstice. The overall occurrence rate increased under high solar activity (Figure 9c,d, with a larger color-bar range). In addition, it is interesting to note that the main peak after the September equinox occurred around Doy 290, about 2 months earlier, compared with the periods under low solar activity (Doy 350). Figure 9: Variations of the plasma irregularity occurrence rate as a function of Doy and longitudes. The equinoxes and solstices are indicated by the arrows on the top of each panel. The panels from top to bottom show results for RODI occurrence (F107 \(<\) 100, (**a-b**)), RODI occurrence (F107 \(>\) 100, (**c–d**)), ROTI occurrence (F107 \(<\) 100, (**e–f**)), and ROTI occurrence (F107 \(>\) 100, (**g–h**)), from left to right show results for Swarm A and Swarm B. The same distributions of topside ionospheric phase scintillation are shown in Figure 9e-h. The overall occurrence rate in Swarm A was significantly higher. Under low solar activity, the scintillation distribution was quite similar to the result of RODI, though with a lower occurrence rate, especially around the June solstice. Under high solar activity, the main peak near the September equinox appeared earlier than that during periods of low solar activity, similar to the results of RODI. Additionally, the RODI and ROTI distributions showed some discrepancy, as the longitudinal distribution of the peak occurrence rate of ROTI before the March equinox was wider. The greater ROTI occurrence might be related to the small-scale plasma perturbations, and this needs further confirmation. During the June solstice, the phase scintillation did not show a corresponding occurrence compared with the results of RODI, and it may be caused by ROTI's insensitivity to new irregularities generated after midnight.
# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 3 Review Findings
### Analysis Domain
#### 3.2.1 Image  
An image is a matrix of pixels organized in columns and rows. The grayscale image represents a one-dimensional matrix and the RGB image represents a three-dimensional matrix. Based on the characteristics of the image, a digital machine or processor can analyze the medical image to detect the abnormality. Over eight hours, a CE captured approximately 55,000-57,000 images throughout the GI tract in one experiment. In [21], the authors proposed a CE image dataset obtained from 10 patients at the University of Malay Medical Center (UMMC). The dataset consisted of 100 bleeding and 300 normal images, which had a resolution of 288 \(\times\) 288 pixels. A total of 1131 bleeding lesions and 2164 normal non-sequential images were utilized by an algorithm to detect bleeding. The size of the images was 320 \(\times\) 320 or 512 \(\times\) 512 pixels [22]. In [23], the authors suggested the KID Dataset 2, which contained a total of 2352 CE images (bleeding: 303) with a resolution of 360 \(\times\) 360 pixels, and MICCAI 2017, which contained a total of 3895 CE images (bleeding: 1570) with 320 \(\times\) 320 or 512 \(\times\) 512 resolution. A total of 1200 CE images with a resolution of 576 \(\times\) 576 pixels were utilized in the study [24]. According to [25], the model was trained using 2000 images that were extracted from 20 different videos. The image dataset was available on capsuleendoscopy.com. An OMOM capsule produced 3596 CE images with 256 \(\times\) 240 pixels size, which were extracted from five subjects [26]. A dataset of CE images with a size of 240 \(\times\) 240 \(\times\) 3 pixels, which contained 148 bleeding and 152 inactive images from 60 CE video snippets of 12 subjects, was utilized in a study [27].
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion
### Datasets  
To comprehensively evaluate the performance of the proposed ProSAF-Net, we conduct experiments on three widely used remote sensing object detection datasets: RSOD [45], NWPU VHR-10 [46], and DIOR [47]. These datasets contain images with complex backgrounds, covering diverse object categories and varying scales, making them highly challenging. The specific information for each dataset is outlined below.
## 3 Classification of Optical Waveguide Sensors
### MZI-Structured Optical Waveguide Sensor
#### 3.2.5 Gallium Arsenide-Based MZI Optical Waveguide Sensor  
Gallium arsenide (GaAs) has a wide transparent window (0.9-17 \(\upmu\)m) compared to Si, SiO\({}_{2}\), polymers, and Ge, making it suitable for various electronic and optoelectronic devices. GaAs is heat-resistant, radiation-resistant, and sensitive to magnetic fields. In particular, GaAs has been considered a promising material for MIR optical devices because of its high nonlinear sensitivity, low absorption, high laser damage threshold, high thermal conductivity, and developed material technology. [PERSON] et al. [115] proposed a mid-infrared biochemical sensor based on an asymmetric MZI with double-suspended GaAs waveguides. The design uses a suspended GaAs waveguide to enhance the interaction between the evanescent field and measured material. The asymmetric MZI structure improved the RI sensitivity of the sensor. After optimization, the NaCl solution value with different concentrations could reach 854.5 nm/RIU, whereas the value of \(Q\) obtained was 208.2.  
To study a simple, low-cost, and mass-production-compatible biochemical sensor, the MZI structure was used because of its advantage of a single-signal output. Owing to the small step in the refractive index of the polymer system between the waveguide core and cladding, the mode confinement in the polymer waveguide is small; hence, the bending radius must be large to reduce the loss and the distance between the two interference arms. Therefore, an asymmetric design of the MZI was chosen, using waveguides of different widths in the straight sections of the two interferometer arms.  
Compared with inorganic material systems, polymer sensors are less sensitive owing to their low refractive index difference. [PERSON] et al. [116] proposed an MZI optical waveguide refractive index sensor integrated with polymer foil. In this study, deionized water and glucose solutions were used as analytes for sensing and detecting, and the _LOD_ was determined to be \(3\times 10^{-3}\).  
Table 3 shows the performance comparison of MZI optical waveguide sensors.
# Enhanced Bit Repair IP Fast Reroute Mechanism for Rapid Network Recovery
## 2 Related Works
### Cost-Based Calculations  
The majority of existing IP Fast Reroute mechanisms, such as the LFA [59] and R-LFA [29], calculate the backup path based on the cost of link metrics. There are several publications related to routing algebra [67, 68, 69]. However, in some circumstances, these FRR solutions are unable to calculate alternative next-hop routers. Figure 4 shows an example of a topology where the LFA mechanism can select the loop-free next-hop router N1.  
However, if the link metric between routers R2 and R3 changes from 1 to 10, the conditions used in the LFA mechanism leads to a situation where the LFA will not be able to calculate the correct loop-free next-hop. Therefore, it is demanding to design such  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **B-REP** & **EM-REP** & **MRC** & **MRT** &
\begin{tabular}{c} **Not-Via** \\ **Addresses** \\ \end{tabular} & **LFA** & **R-LFA** & **D-LFA** & **TI-LFA** \\ \hline
100\% Repair Coverage & Yes & Yes & Yes & Yes & Yes & No & No & Yes & Yes \\ Custom Alternative Path & Yes & No & Yes & No & No & No & No & Yes \\ Precomputing & Yes & No & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ Packet Modification & Yes & Yes & Yes & Yes & Yes & No & Yes & Yes & Yes \\ Link-State dependency & Yes & No & Yes & Yes & Yes & No & Yes & Yes & Yes \\ \hline \hline \end{tabular}  
* B-REP—Bit Repair. EM-REP—Enhanced Multicast Repair. MRC—Multiple Routing Configurations. MRT—Maximally Redundant Trees. LFA—Loop-Free Alternate. R-LFA—Remote Loop-Free Alternate. D-LFA—Directed Loop-Free Alternate. TI-LFA—Topology Independent Loop-Free Alternate.  
\end{table}
Table 1: Comparison of existing FRR solutions.  
Figure 4: The LFA mechanism with correct metrics.  
FRR mechanisms that are capable of selecting a backup path even in situations that have incorrect or unsuitable metrics [58].
# Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection
## 3 Experimental Results
### Online Learning Curve  
We record the learning curve of pedestrian detection on the CAVIAR database [42], as shown in Figure 27. We analyzed the learning effect of our system according to this online learning curve. After training in the initial model, the positive patterns that the system has seen are not diverse enough, and thus the learned weak classifiers suffer from overfitting to the positive patterns. Therefore, in the beginning of the learning curve, the precision was high, but the recall was relatively low. With the input of images, the observed positive patterns became more diverse, and the weak classifiers were replaced and updated. This can increase the description ability of positive patterns. Finally, although some false positives were generated, causing precision to decrease, recall was increased on a large scale. At around 260 frames, recall and precision crossed; the learning of the system converged, but new positive patterns and negative patterns are updated to the system and combined.  
Figure 26: ORF 1 approaches the same error as RF.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Aircrafts** & **Motorcycles** & **Vehicles** & **Faces** & **Average** \\ \hline \multirow{2}{*}{Recall} & RF [13] & 82.2\% & 94.8\% & 89.9\% & 96.2\% & 90.8\% \\ \cline{2-6}  & Ours & 96.7\% & 98.0\% & 99.3\% & 99.4\% & 98.4\% \\ \hline \multirow{2}{*}{Precision} & RF [13] & 96.4\% & 97.0\% & 99.0\% & 96.8\% & 97.3\% \\ \cline{2-6}  & Ours & 87.6\% & 96.9\% & 98.5\% & 98.2\% & 95.3\% \\ \hline \multirow{2}{*}{F-measure} & RF [13] & 88.7\% & 95.9\% & 94.2\% & 96.5\% & 93.8\% \\ \cline{2-6}  & Ours & 91.9\% & 97.4\% & 98.9\% & 98.8\% & 96.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison result of our research with RF 1 in Caltech 1.  
Here, we analyzed only the F-measure, as shown in Figure 28. We found that our approach starts to converge in terms of the F-measure after around 260 frames. The F-measure after that is basically oscillating between 80% and 85%. Sometimes, recall and precision decrease somewhat, and thus the F-measure decreases as well. After observing the pattern pool, we find that by this point, the pattern pool is performing merging, and this makes the positive and negative patterns that the system can refer to decrease temporarily. However, after updating is performed a few more times, the F-measure increases again. From the whole learning curve, we can see that there is an obvious upward trend in the F-measure.
## 3 Results
### Quantitative Evaluation of Fog Detection
#### 3.1.2 Fog Detection Performance by Algorithm
Consequently, when fog occurrence substantially decreases in coastal areas in August, POD values are the lowest, while FAR and bias values are the highest (Figure 4h), a tendency that is also observed in inland regions. In contrast, January exhibits high POD values. Nonetheless, FAR and bias also remain high, likely due to the influence of low stratiform clouds forming under stable atmospheric conditions [44]. Table 7 summarizes seasonal detection performance. The seasonal POD reaches its maximum in winter, remains moderate in spring and autumn, and declines substantially in summer (0.47-0.65). Although FAR remains generally high across all seasons (0.75-0.93), it is lowest in autumn and highest in summer. Bias shows a marked tendency toward over-detection, with values of 6.44 in summer and 6.43 in winter, compared to a significantly lower value of 2.48 in autumn.
## 4 Systematic Review of the Research Pillars
### Sustainability in Urban Development
#### 4.3.5 Data-Driven Approaches to Build Environment Flood Resilience  
The importance of data-driven methods in improving flood resilience in urban design is becoming more widely acknowledged. By utilizing cutting-edge technologies and approaches, cities may more accurately forecast, control, and lessen the impact of flooding. The best practices found in current research are described in the sections that follow.  
The first usage is Big Data Analytics for traffic Flow Analysis; this is by analyzing human travel patterns using mobile phone signals, exposure hazards in flood-prone areas can be predicted with a 98.975% forecasting accuracy [93]. The second is Real-time monitoring during flood occurrences, timely notifications, and better decision-making, which is made possible by combining deep learning models with large data analytics [93]. The third technique is Model Predictive Control (MPC) system optimization, by improving system design, MPC improves urban stormwater management while lowering maximum flood depths and raising resilience indices [94]. The fourth is cost effectiveness, according to [PERSON] et al. [94], the implementation of MPC delivers significant cost reductions in urban drainage systems in addition to improving flood prevention. The fifth is Geo-Computation Approaches via GIS and remote sensing. These technologies improve situational awareness during catastrophes by enabling efficient evacuation planning and real-time flood tracking [95]. The sixth is the integration of machine learning, adaptive response plans, and early warning systems, which can be developed by combining machine learning with geospatial data analytics [95]. The seventh is multidisciplinary frameworks, collaboration between different governing agencies is necessary for effective urban flood control, combining water resource management and land-use planning to build resilient urban settings [96; 97]. The eighth is geographical factor analysis; this is by comprehending how urban subsystems interact, important drivers of flood resilience, like technology advancement and governmental authority, can be found [97].
## 4 Results
### Undisturbed Trajectory Tracking
(**a**) The three-axis spatial coordinates of the UAV in the absence of disturbances. (**b**) The three-axis position information of the UAV with respect to time in the absence of disturbances. Figure 5: Acceleration commands. Blue, traditional backstepping and sliding mode with saturation; Red, traditional backstepping and bioinspired sliding mode; Yellow, bioinspired backstepping and bioinspired sliding mode. Figure 6: Thrust and torque commands. (**a**) Blue, traditional sliding mode; Red, bioinspired sliding mode. (**b**) Blue, sliding mode with saturation; Red, bioinspired sliding mode.
## 3 Results and Discussion
### Effect Analysis of Exogenous Substances on PAHs Degradation
#### 3.3.3 Surfactants  
Many types of surfactants have a high solubilization effect on PAHs [54] and emulsification and dispersion on crude oil [55]. In order to investigate the effect of surfactants on PAHs degradation, four surfactants aqueous solutions (Tween-80, TritonX-100, APG-1214, and Sodium dodecyl sulfate) were added into different media under their critical micelle concentrations (CMC, 100, 50, 500, and 500 mg/L).  
Due to the addition of surfactant, the degradation rates of NAP, PHE, and PYR in the Tween-80 group were decreased to 87.85%, 52.32%, and 40.95%, and those in the APG-1214 group were decreased to 86.24%, 66.07%, and 61.72%. The degradation rates of crude oil in all groups were increased (Figure 9). This indicates that surfactants preferentially  
Figure 8: Resistance of P&A bacterial consortium to different concentrations of different compounds. (**a**) Resistance of P&A bacterial consortium to different concentrations of CuSO\({}_{4}\).5H\({}_{2}\)O. (**b**) Resistance of P&A bacterial consortium to different concentrations of ZnSO\({}_{4}\).7H\({}_{2}\)O. (**c**) Resistance of P&A bacterial consortium to different concentrations of Pb(NO\({}_{3}\))\({}_{2}\).  
increase the solubility of crude oil; thus, the degradation rate of crude oil is increased. As shown in Figures 6 and 9, although the NAP, PHE, and PYR degradation rates in the Triton X-100 group do not decrease significantly, the crude oil degradation rate only increases by 3.85% (from 80.85% to 84.70%). It can be seen that the non-ionic surfactant (Tween-80 and APG-1214) had negative effect on the degradation of NAP, PHE, and PYR in the presence of crude oil, and the negative effects of non-ionic surfactant (Triton X-100) and anionic surfactant (SDS) are not obvious. However, their positive effects are also very limited, which means that no additional surfactants are needed.
## 4 Results
### Third Validation Cycle  
Once the \(C^{*}_{D,\text{OF},a}\) was optimized based on the CFD results, the OF model was updated, and the results of these simulations were plotted together with GPGP data and the 15% increase in the mean load, explained in Section 3.4, to produce Figure 19c. Based on the latter and using the same validation criteria as for the 1 st and 2 nd validation cycles, the following findings can be drawn:  
* The optimized \(C^{*}_{D,\text{OF},a}\) for a narrow span produced more-accurate results than the first cycle. The results were also more conservative than the second cycle ones. Nevertheless, the curve in no waves was above all points. This means that the DT was overestimating, specifically by a mean deviation of 97%. This overestimation might be due to a missing phenomenon or effect related to the flexibility of the net and the shape it adopts under water. In spite of the overestimation and from an engineering perspective, the 3 rd cycle curve was preferred over the 2 nd cycle curve. A better option, again from an engineering perspective and to obtain more-accurate results, is to average both curves. That averaged result is plotted in Figure 19d. In terms of validation, it can be said that the DT had not been validated yet for the narrow span. The validation for narrow span depends on future studies. Nevertheless, the validation effort brought the 276% deviation of the 1 st cycle to about a \(-10\%\) deviation after the 2 nd cycle and to about 97% after the 3 rd cycle. If the averaging option was applied, the combined deviation was about 43%.
* The optimized \(C^{*}_{D,\text{OF},a}\) produced a more-accurate nominal span curve than for the second cycle, since almost all GPGP data points were inside the 15% increase area curve. The mean deviation in this case was just 7%. This also means that the DT was validated for a nominal span.
* The optimized \(C_{D,\text{OF},a}\) led to the majority of the GPGP data points falling inside the 15% increase area, and the mean deviation was calculated to be 8%. Then, the DT at the third cycle can be considered as validated for a wide span. Since the wide span curve was considered as validated already on the second cycle, this result also means that the CFD calculation verified the AS calculation for a wide span.