esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 State of the Art
### Other Applications  
The application domains of multi-UAV are varied and almost ubiquitous. Apart from the mentioned works, several use cases remain to be explored. [PERSON] et al. [48] present an application of multi-UAV systems for road traffic monitoring. There, they present a swarm coordinated with Vehicular Ad-Hoc Networks (VANETs) for vehicle tracking and driver behavior analysis. This approach reduces the number of accidents due to unsafe drivers and regulates traffic better. More applications and challenges of multi-UAV systems related to intelligent transportation systems for smart cities are highlighted in [16; 71].  
Another research work [72] in the same scope presents a cooperative traffic monitoring system based on a two-layer network (UAV aerial layer and vehicle ground layer). The UAVs in the FANET layer work together to control vehicles from the ground in the VANET layer. The link between UAVs is performed through air-to-air (A2A) communication, while the one inside the ground vehicular sub-network relies on air-to-ground (A2G) communication.  
Another relevant use of multi-UAV is search and rescue [7; 28]. The authors in [7] implemented a rescue mission using a set of stream videos from a swarm to a distributed ground control station. A similar work [28] introduces a machine learning approach to predict the best UAV swarm formation, allowing them to find the optimal path loss for the rescue.
## 2 Materials and Methods
### Trueness and Precision  
To assess the distortion of the scan dataset, a three-dimensional analysis software (Geomagic Control X 64; 3D systems, Rock Hill, SC, USA) was used. All STL datasets from the four experimental models were analyzed using a three-dimensional (3D) and two-dimensional (2D) analysis tool. To assess trueness, one scan data was randomly selected as reference data among the datasets of the laboratory scanner. By using the best-fit algorithm, the other datasets of each group from each intraoral scanner were superimposed on the reference data. Using 3D analysis, the root mean square (RMS) value between the reference data and the datasets from three IOSs was calculated for trueness. To analyze precision, a pairwise comparison was conducted between the datasets from the same scanner and the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Type** & **Producer** & **Acquisition Method** & **Powder** & **Color** \\ \hline T500\({}^{\circledR}\) & Laboratory & Medit Corp, & Phase-shifting optical & & \\  & scanner & Seoul, Korea & triangulation with Blue light & No & Yes \\  & & & scanning technology\({}^{\text{TM}}\) & & \\ \hline Trios3 & Intraoral & 3 shape A/S, & Structured light-Confocal & & \\ wireless\({}^{\circledR}\) & scanner & Copenhagen, & microscopy and Ultrafast & & \\  & & & Denmark & optical scanning\({}^{\text{TM}}\) & & \\ \hline CS3600\({}^{\circledR}\) & Intraoral & Carestream & & & \\  & scanner & Dental LLC, & Structured light-Active & & \\  & scanner & Atlanta, GA, & speed 3d video\({}^{\text{TM}}\) & & \\  & & USA & & & \\ \hline I500\({}^{\circledR}\) & Intraoral & Medit Corp, & & 3D-in-motion video & & \\  & scanner & Seoul, Korea & technology\({}^{\text{TM}}\) and rapid & & \\  & & & video-based scan & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Various scanners used in this study.  
Figure 2: Experimental model. (**A**) FPS: Full Posterior Support, (**B**) PPS: Partial Posterior Support, (**C**) SSDEB: Short Span Distal Extension Base, (**D**) LSDEB: Long Span Distal Extension Base.  
same experimental model. The RMS value between datasets belonging to the same group of each scanner was calculated for precision.
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 2 Materials and Methods
### Data
#### 2.2.3 Data Used for Calibration  
For the calibration, in addition to Sentinel-1 images, optical images from Sentinel-2 satellites are used. Part of the ESA Copernicus program, Sentinel-2 comprises twin satellites: Sentinel-2A and Sentinel-2B. The combination of the two satellites provides a 5-day revisit period. Sentinel-2 images have a native spatial resolution of 10 m, 20 m or 60 m depending on the spectral band. The bands used--visible and near-infrared--offer a resolution of 10 m. Cloudless optical images are often used to validate flood detection from radar image processing. Optical images allow one to obtain very high accuracy due to their multispectral bands [13].  
During the beginning of 2018, there was abundant rainfall in the area around Antana-narivo up to the beginning of March, flooding low-lying zones around the city. In March, the rainfall was driven by a tropical depression and then Cyclone Dumazile. On 13 March 2018, good weather conditions enabled a Sentinel-2 optical image with only 1% cloud cover to be acquired, while a Sentinel-1 image was available for 12 March 2018. This Sentinel-2 image and another Sentinel-2 image acquired during the dry season were used to delineate the flooded areas and calibrate processing for Sentinel-1 data. In Sentinel-2 images, water surfaces are detected by thresholding the NDWI (normalised difference water index) with Otsu algorithm [42]. The same method is applied to the reference Sentinel-2 image to detect and filter out permanent surface waters. The results thus obtained are compared to the processing of radar images.
# Mitigating Digital Ageism in Skin Lesion Detection with Adversarial Learning
## 2 Related Work  
Several studies have focused on mitigating bias in training their predictive models. From a dataset perspective, a commonly used method is data augmentation. [PERSON] et al. [22] used a generative adversarial network (GAN) that included a style-based generator and discriminator. Images were categorized into four age groups, and the GAN was employed to produce digitally aged images to balance the training dataset among each group. In another study, [PERSON] and [PERSON] [23] utilized two augmentation methods, random Gaussian tinting and random cropping, to expand their training dataset for a deep neural network model. However, the effectiveness of data augmentation in mitigating bias is impacted by the quality and diversity of the original data augmentation techniques employed; it could also lead to overfitting problems.  
From a model learning perspective, bias mitigation methods usually involve modifying the training loss function to regularize the model for fairness. [PERSON] et al. [24] added a task-specific prior to implicitly regularize the model to not pay attention to the sensitive attribute-related information for its prediction. Adversarial training has been applied in several works ([20, 25, 26]) to achieve fairness by removing sensitive information with an adversarial learner or calculating correlations between actual and predicted protected variables. [PERSON] and [PERSON] [27] proposed a method for the mitigation of skin tone bias in skin lesion classification. They first detected the skin tones of the lesion images and then debiased the feature representations to reduce the impact of the skin tone on the final prediction. However, it addressed bias related to skin tone and did not consider age-related bias. In this paper, we present a new adversarial method that accounts for age as a continuous attribute and reduces the bias while maintaining the classification performance.
# The Future of the Human-Machine Interface (HMI) in Society 5.0
## 5 Human-Centric Manufacturing (HCM)
### Parallelism of Biological Vision System with Cyber-Physical Vision System  
The biological vision system and the cyber-physical vision system share some similarities in terms of their parallelism. Here are a few ways in which they are similar [51]:  
**Neural networks:** Both biological and cyber-physical vision systems rely on neural networks to process visual information. In the biological vision system, the brain's neural networks process and interpret visual signals from the eyes. In the cyber-physical vision system, artificial neural networks are used to process and interpret visual data captured by cameras and sensors;  
**Parallel processing:** Both vision systems use parallel processing to process visual information quickly. In the biological vision system, multiple regions of the brain process visual information in parallel. In the cyber-physical vision system, multiple processors or cores can be used to process visual data in parallel;  
**Hierarchical processing:** Both vision systems use hierarchical processing to analyze visual information. In the biological vision system, the brain processes visual information in multiple stages, with each stage processing increasingly complex features. In the cyber-physical vision system, algorithms are used to process visual information in a hierarchical manner, starting with basic features and progressing to more complex features;  
**Adaptability:** Both vision systems are adaptable and can learn from experience. In the biological vision system, the brain can adapt to changes in the environment, such as changes in lighting or the presence of obstacles. In the cyber-physical vision system, Machine Learning algorithms can be used to improve the system's ability to recognize and classify visual information.  
To sum up, the parallelism of the biological vision system and the cyber-physical vision system highlights the potential of Artificial Intelligence to replicate and even improve upon the natural abilities of the human visual system. By leveraging the strengths of both systems, researchers can create more efficient and effective vision systems that have a wide range of applications in fields such as robotics, autonomous vehicles, and surveillance. Further to that, computer vision is best suited for simple mechanical tasks or periodic tasks, such as defect detection in objects, pattern recognition, fraud detection. However, human vision outperforms machine vision in tasks requiring perception of objects and context understanding. A comparison is presented in Figure 9.
# A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms
## 3 Experimental Validation
### Results  
The obtained results are visualized in Figures 9 and 10. They show the model performance of the experiments from Section 3.1 with 1 and 2 noise mixture components, respectively.  
In Figure 9 the output SNR, PESQ and STOI are calculated for a varying input SNR for both models of Section 2.2 with 1 noise mixture component. Besides the Wiener filter, the baseline performance, which corresponds to the output signal of the FIR compression filter for unity gain, is also plotted. The offset in the baseline output SNR with respect to the input SNR is caused by the frequency-warped filter. The input SNR is calculated with respect to the signals that enter the frequency-warped filter. After the processing by the filter the frequency-dependent phase delays lead to slightly degraded output SNRs, resulting in a vertical offset in the input and output SNR relationship.  
The PESQ scores for an input SNR of \(-10\) dB for the baseline and Algonquin model are incorrect as they by far outperform the Wiener filter for high noise situations. Therefore these points are regarded as outliers, possibly due to computational stability issues of the PESQ metric, as it was originally intended for narrowband telephone speech. From the figures it becomes evident that the Wiener filter yields the highest source separation performance in terms of output SNR, PESQ and STOI. This is expected as the Wiener filter requires full knowledge about the underlying signals in the observed mixture. In terms of PESQ scores, the Gaussian scale sum-based model attains better performance in comparison to the baseline signal.  
In Figure 10 the output SNR, PESQ and STOI are calculated for a varying input SNR for both models of Section 2.2 with 2 noise mixture components, including the baseline performance and performance obtained with a Wiener filter. From all three plots it can be noted that the performance with respect to the baseline model has improved, especially for high input SNRs. In comparison to Figure 9, we can also see that the performance has increase when introducing an additional noise mixture model components. This behaviour is expected as we can model the source more accurately.
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 4 Societal Benefit
### Fisheries  
\"Satellite remote sensing has been an important technique in fisheries research, management and harvesting, because it provides synoptic ocean measurements for evaluating environmental influences on the abundance and distribution of fish population and allows ecological analyses at community and ecosystem scales [109; 110; 111; 89]. Satellite images combined with other data can be analyzed to find ocean environmental conditions suitable for fish aggregation. Therefore, forecast models depend heavily on inputs from satellite sensors, in addition to _in situ_ measurements and observations. Fisheries-related satellite data include SST, ocean color (productivity), turbidity, sea surface salinity, ocean fronts and gyres, sea surface currents, winds and waves\" [90].  
Early studies showed that satellite-derived fishery-aid charts could reduce 25%-50% of some US commercial fisheries search time [112]. Now, confronting the situation that nearly 80% of the world fish stocks are either fully exploited or overexploited, effective monitoring and management is required for (1) sustainable use of marine resources, e.g., to monitor fishing fleets at night due to their use of lights to prevent illegal fishing in protected regions of the ocean [113] and (2) protection of fish habitat and ocean ecosystem, such as coral reefs [114], and satellite remote sensing continues to fulfill this requirement.  
With the relatively long CDR data set, humans may achieve a more comprehensive knowledge of fish dwelling and migration, and their timing, and hence more strategic fisheries management.
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 2 Related Literature
### Post-Quantum Cryptography (PQC)
#### 2.2.1 In the Classical Space  
The main algorithms in contemporary use are not currently known to have fundamental flaws that can be exploited by attacks based on classical computing. Whilst classical computing capabilities have risen exponentially, this increase has been largely driven by Moore's Law [20] and the self-governed targets of the semiconductor industry, as outlined in reports such as the IEEE International Roadmap for Devices and Systems [21]. Specialised computing branches, including massively parallel GPUs, ASICs, and FPGAs, have achieved performance gains that outpaced Moore's Law for general-purpose computing hardware, further contributing to the potential for stronger cryptographic algorithm attacks. Despite these improvements, the underlying mathematical problems on which cryptographic techniques rely have remained largely intact, with key sizes increasing to meet user expectations for security, and the required window of protection. One such mathematical characteristic is the computational hardness of large-number factorisation, a principle that protects both key exchange and the integrity of digital signatures. By using appropriately chosen cryptographic parameters, data protected by these traditional methods are expected to remain secure for decades, centuries, or even millennia, due to the time and energy required to mount a successful attack using classical computing methodologies.
## 11 Conclusions  
We reviewed recent development of the Vaca Muerta formation in this article, which can be structured around the following five key points:  
* In terms of geological characterization, the Vaca Muerta formation's geological analysis reveals significant hydrocarbon potential, with TOC values ranging from 3% to 8%, indicating rich organic content. The formation varies in thickness (60-520 m) and exhibits diverse porosity (4-14%), emphasizing its complexity.
* Technological advances in drilling has shown the shift to horizontal drilling, with laterals extending 2500-2800 m, and even up to 4000 m in some projects, marking a significant advancement. This approach, despite challenges in wellbore stability and tortuosity, has led to improved production efficiency.
* Hydraulic fracturing innovations include the application of tailored fracturing fluid compositions and injection protocols, based on reservoir conditions, has optimized fracturing processes. The use of polyacrylamide-based viscosifying friction reducers in some regions has reduced operational costs by 34%.
* Regarding economic considerations and investment, the formation continues to attract significant investment, despite high operational costs due to logistical constraints. The decrease in extraction costs and the introduction of efficient technologies have enhanced its global competitiveness.
* Future prospects and challenges are revealed. Projected advancements in technology and favorable policy shifts could significantly increase the formation's contribution to global hydrocarbon production. Continued research and development are essential to overcoming the operational and environmental challenges.
## 6 Analysis and Output  
### Slump Flow Diameter  
The test that is most frequently used to gauge SCC flow performance is the slump flow test. During this test, the slump flow diameter of the SCC mix is measured. When using the EFNARC SCC criterion, the slump flow diameter should meet one of the classifications listed in the standard. A slump flow diameter of 550 to 650 mm is considered in slump flow class 1, 650 to 750 mm is considered in slump flow class 2, and 750 to 850 mm is considered to be in slump flow class 3 [54]. Any slump flow diameter that is both less than 550 mm and more than 850 mm cannot be classified as SCC, according to EFNARC guidelines. With a median slump flow diameter of 672 mm and minimum and maximum slump flow diameters of 550 mm and 850 mm, respectively, all of the mixes can be regarded as SCC, per 401 data (Table 1) from prior studies. Figure 7 depicts the relationship between the slump flow diameter and the CS of self-compacted concrete [4].
# Matching Quality Detection System of Synchronizer Ring and Cone
## 4 Detection System of Matching Quality between Synchronizer ring and Cone  
### System Device  
Detection system of matching quality between synchronizer ring and cone is mainly composed of detection host, computer and electrical control cabinet, and lubricating oil filling station. Its schematic diagram is shown in Figure 9. The technical parameters are as follows: the range of synchronous ring end force is 0-2000 N (adjustable), 0-500 N, with an accuracy of \(\pm\)1%; the rotation speed of synchronizer cone is 0-50 r/min (adjustable), with a measurement accuracy of \(\pm\)1 r/min; the measurement range of synchronizer cone drive torque is \(\leq\) 20 N m, with an accuracy of \(\pm\)0.1%.  
The detection host of the system is shown in Figure 10. It consists of a drive motor, servo mechanism, torque displacement sensor, data display, and control cabinet.  
The detection host can detect the friction torque of the synchronizer ring and its displacement relative to the cone when the clamping state of shifting is simulated, thereby realizing the detection of the synchronizer gap and the fit degree. The aim is to first detect the backup gap and then the friction torque. After the detection is completed, the system automatically saves the detected data to the database, including the backup gap data and average torque data.  
The partial operation results of the system program are shown in Figure 11.  
Figure 8: Synchronizer measurement steps.  
Figure 9: Schematic diagram of the synchronizer detection device.
## 6 Findings and Future Research Scope
### Future Research Direction  
The previous section lists the results of the current study. In addition to the findings, the author identified a range of directions for future researchers by analyzing the screened deep learning-based papers.  
* Improve the efficiency of data annotation: Create unsupervised learning techniques that reduce the need for manual annotation. Generate diverse datasets that include unusual pavement features, such as hairline cracks and scratches, to improve the reliability of detection systems.
* Optimized model structures: To improve the performance of the architectures, incorporate purpose-specific functional modules and multi-scale features to achieve higher levels of accuracy. while simultaneously considering the demands of lightweight and mobile deployment.
* Enhanced feature extraction: Improve shadow removal and segmentation accuracy. And increase detection speed, explore attention mechanisms, and explore different model compression approaches.
* Quantification and indexing: Use crack quantification techniques to measure crack length, width, and depth, which can help generate numerical pavement condition indices such as the Pavement Condition Index (PCI).
* Multi-sensor fusion: Integrate multiple vision sensors for more efficient area coverage.
* Better automation: To make detection models more automated, combine classification and segmentation modules to make the most of additional data on edge features and break severity.
## 3 Experimental Results
### Data Transmission Performance
Based on basic principles of trigonometric and optical measurements, it is speculated that the 2 \(\times\) 2 point clustered LED-DCC system should only double the maximum data transmission distance compared with the previous LED-DCC system. However, the experimental results show that the data transmission distance achieved by the proposed LED-DCC system is about 12 times that of the previous LED-DCC system under the 7% FEC limit, which clearly demonstrates that the LED-DCC point clustering scheme and the lightweight, back-forth, fast image processing algorithm effectively improve the data transmission distance, as analyzed in Section 2. It is anticipated that a much longer distance can be achieved if more adjacent LED display points are clustered and the proposed image processing algorithm is utilized. However, considering the characteristics of the optical wireless channel [16; 17], the performance of the system would degrade with the decline in channel capacity as the distance increases, which is challenging in practical applications.