esa-sceva/satcom-chunk-collection huggingface dataset:
# Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges
###### Abstract  
Generative AI (artificial intelligence) refers to algorithms and models, such as OpenAI's ChatGPT, that can be prompted to generate various types of content. In this narrative review, we present a selection of representative examples of generative AI applications in medicine and healthcare. We then briefly discuss some associated issues, such as trust, veracity, clinical safety and reliability, privacy, copyrights, ownership, and opportunities, e.g., AI-driven conversational user interfaces for friendlier human-computer interaction. We conclude that generative AI will play an increasingly important role in medicine and healthcare as it further evolves and gets better tailored to the unique settings and requirements of the medical domain and as the laws, policies and regulatory frameworks surrounding its use start taking shape.  
generative AI; large language models; ChatGPT; artificial intelligence; medicine; healthcare; human health +
Footnote †: journal:  
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models, GPT models can engage with patients, provide educational resources, and address medical queries, fostering better patient engagement and empowerment in managing their health [6]. Furthermore, the capacity of GPT models to streamline electronic health record (EHR) management and clinical documentation presents a potential avenue for alleviating administrative burdens, allowing medical practitioners to focus more on patient care [7].  
Despite the transformative potential of generative models, their integration in medicine and healthcare is not without challenges and ethical considerations. Ensuring the accuracy and reliability of AI-driven decisions remains a critical concern, particularly in critical medical contexts. The \"black box\" nature of some AI models, including generative models, raises questions about the interpretability of the decisions they generate, calling for greater transparency and explainability in AI systems utilized in healthcare. Moreover, ethical considerations concerning data privacy, patient confidentiality, and potential biases in AI models require careful attention [8]. As these models interact with sensitive medical information, patient privacy and data security are paramount to maintaining public trust in AI-enabled healthcare solutions.  
In light of recent developments, this review article provides an overview of existing industry and research efforts of applying generative AI models in medicine and healthcare. By highlighting their vast potential, advantages, challenges, and ethical considerations, this study seeks to contribute to the ongoing dialogue on harnessing AI's transformative capabilities responsibly for the betterment of medical practice and patient well-being. As we explore generative models' impact on healthcare and medicine, navigating the ever-evolving AI landscape with a commitment to ethical principles, patient-centred care, and a collaborative approach between AI developers, medical professionals, and policymakers is crucial.
# Cryptanalysis of a New Color Image Encryption Using Combination of the 1D Chaotic Map
## 3 Chosen-Plaintext Attack  
The attack scheme is proposed in this part. Compared with other chaotic encryption algorithms, the rotation step is added in [37]. The rotation amount \(lp\) is used as the security key. If \(lp\) can be acquired, the permutation and diffusion steps can be attacked by imitating the attack method in [17]. Hence, the proposed attack method can be divided into two phases. In the first phase, we find the rotating amount of \(lp\) and the diffusion matrix \(D\) by shifting the Xor operations. In the second phase, we attack the permutation with some special plaintext images by imitating the attack method in [17]. We denote the original image I and the corresponding encrypted image \(C_{\text{L}}\)
## 5 Discussion
Lastly, regulatory frameworks and industry standards should evolve alongside technological advancements, ensuring that new energy solutions and battery technologies are implemented safely and effectively. Collaboration between industry, academia, and regulatory bodies will be critical in setting these standards and fostering innovation in the rapidly evolving field of drone technology. Drones equipped with advanced energy management systems and next-generation battery technologies have vast potential across various application areas, including agriculture, logistics, environmental monitoring, and emergency response. In agriculture, drones can optimize crop monitoring and precision spraying by extending flight times and enabling more comprehensive coverage of large areas. For logistics, enhanced battery performance can improve the efficiency of delivery services, especially in last-mile delivery scenarios. In environmental monitoring, drones with longer flight durations can conduct more extensive surveys of ecosystems, contributing to more accurate data collection for conservation efforts. Emergency response teams can also benefit from drones with reliable, long-lasting power sources, enabling more effective search and rescue missions and disaster assessments. To maximize the benefits of these advancements, operators should prioritize adopting drones with adaptable power systems and AI-driven battery management. Routine training on energy management practices and understanding the capabilities of emerging battery technologies will be crucial for field personnel. Additionally, integrating drones with cloud-based platforms for real-time data analysis and battery performance monitoring will ensure optimal operational efficiency and safety. Collaboration with technology providers to customize drones for specific application needs can also lead to more tailored solutions, enhancing the overall effectiveness of drone deployments in various sectors.
# Decision Optimization for Power Grid Operating Conditions with High- and Low-Voltage Parallel Loops
## 4 Evaluation for Parallel Loop Opening Schemes
### Fuzzy Evaluation Algorithm  
The comprehensive evaluation model founded above considers almost all of the important factors to evaluate candidate opening schemes. A powerful evaluation algorithm, however, is still needed to find the optimal opening scheme based on the founded model.  
Fuzzy evaluation algorithm is based on AHP and fuzzy mathematics [26], which not only includes engineering experience from experts, but also combines the advantages of qualitative analysis and quantitative calculation. Thus, use of fuzzy evaluation algorithms could avoid the disadvantages of expert systems, including uneasy knowledge representation and long computation time [27], which guarantees an effective and fast way to find the optimal opening scheme.  
The priority index \(P\) of any parallel loop opening scheme can be obtained by fuzzy evaluation algorithm, and the scheme with the largest priority index in the \(M\)-dimensional candidate opening scheme set is selected as the optimal one.
## 4 Emergency Maneuver Strategies and Blind 60 Spot Management  
### Types of Emergency Scenarios and Blind Spot Challenges  
Autonomous vehicles must be prepared to handle a variety of emergency scenarios and blind spot challenges that can arise suddenly during operation. These situations often require immediate and precise responses to ensure safety.  
One of the most critical emergency scenarios involves the sudden appearance of obstacles in the vehicle's path. These can include pedestrians, animals, fallen objects, or other vehicles [78]. [PERSON] et al. [79] developed a probabilistic prediction and planning framework that enables autonomous vehicles to navigate safely around obstacles, even in scenarios with limited visibility.  
System failures, such as sensor malfunctions or actuator errors, can also lead to emergency situations. [PERSON] et al. [68] proposed a reinforcement learning approach for developing robust driving policies that can gracefully handle multiple types of system failures.  
Complex emergency scenarios involving multiple vehicles require sophisticated coordination and decision-making [80; 81]. [PERSON] et al. [82] introduced a cooperative collision avoidance system based on vehicle-to-vehicle (V2V) communication, enabling coordinated emergency maneuvers among multiple autonomous vehicles.  
Blind spots pose unique challenges in emergency scenarios, as they limit the vehicle's awareness of its surroundings. [PERSON] et al. [83] developed an integrated perception system that combines data from multiple sensors to enhance blind spot detection and reduce the risk of collisions in these critical areas.
## 3 Methodology
### Analysis of SM-LST Coupling/Decoupling and Critical SM  
The temporal evolution of SM and LST has been analyzed using time-series of in situ and satellite observations together with the daily mean precipitation. The latter is computed from the average of the four weather stations located within the REMEDHUS area. A method to get an estimate of the critical SM (i.e., the SM value at the SM-LST coupling/decoupling transitions) is proposed. As a first step, both SM and LST are normalized as:  
\[X_{N}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{3}\]  
where \(X\) corresponds to the variable (SM or LST) and \(X_{min}\) and \(X_{max}\) are its minimum and maximum values along the entire time-series, respectively.  
As a second step, the SM-LST transition points are identified as those in which the normalized SM and LST have a difference of less than 0.01. Then, the SM and LST values of these transition points are denormalized to compute their maximum, minimum, mean and standard deviation. The critical SM and its uncertainty can be inferred from these statistics.
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 4 Cons and Pros of Each Network for Liver or Liver Lesion Segmentation

Table 4: Summary of integrated methods for simultaneous liver segmentation and lesion detection. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Pros** & **Method** & **Cons** \\ \hline Dense V-Net & Strong for 3D volumetric analysis. & High computational demands. \\  & Preserves fine details. Handles complex & Sensitive to noise. Requires large \\  & lesions effectively. & annotated datasets. \\ \hline U-Net & Simple, efficient, and effective for liver segmentation. & Limited performance for small or \\ \hline U-Net++ & Superior accuracy with nested and dense & Complex lesions without enhancements. \\  & skip connections. Effective for small and & Complex lesions. \\  & complex lesions. & Increased complexity and \\ \hline U-Net 3+ & High accuracy and robustness for liver and & High computational demands. Risk of \\  & liver lesion segmentation. Suitable for & overfitting on small datasets. \\  & clinical applications. & \\ \hline RA-UNet & Excellent for small or irregular lesions. & High computational requirements. \\  & Combines residual and attention & Requires high-quality datasets. \\  & mechanisms to improve accuracy. & \\ \hline Conditional GANs (cGANs) & Robust to noise and class imbalance. & Complex training. High resource \\  & Captures complex shapes and fine details. & demands. Requires paired data. \\ \hline Pix2 Pix & Handles intricate details and complex & Dependency on paired data. Training \\  & structures effectively. & instability. Resource-intensive. \\ \hline Swin-Unet & Excellent for small and irregular lesions. & High memory and computational \\  & Preserves liver boundaries with global & requirements. Long training times. \\  & context understanding. & Not as accurate as complex \\ \hline MSL-UNet & Balanced efficiency and accuracy good for & Architectures. Limited performance on \\  & small/irregular lesions. Suitable for & noisy datasets. \\  & real-time applications. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
# Application of Areal Topography Parameters in Surface Characterization
## 2 Areal Topography Parameters from Definition to Application
These findings highlight the importance of areal topography parameters, in assessing corrosion behavior, emphasizing their practical relevance in industrial settings where ideal surface finishing cannot always be achieved. Although ISO 25178-2 provides standardized definitions of surface parameters, it assumes ideal measurement conditions. In real-world applications, measurement systems must be calibrated, adjusted, and verified in accordance with the principles outlined in  
Figure 5: The ratio of the material on the observed surface [49]. ISO 25178-700 [53] to ensure accurate and traceable results. For atomic force microscopes (AFMs), which operate at the nanometer scale, such a metrological approach is not optional; it is essential. The aim of this review is to provide a standards-aligned and metrologically grounded overview of selected areal surface topography parameters, with particular emphasis on their behavior in atomic force microscopy (AFM) measurements and the role of uncertainty estimation an aspect often underrepresented in similar reviews. The following chapter presents simulation and experimental results analyzing how lateral inaccuracies, geometric variations, and measurement noise affect surface parameters such as _Sa_, _Sq_, _Sz_, _Sdq_, and _Sdr_. The findings confirm theoretical assumptions and highlight the importance of rigorous calibration and the application of ISO 25178-700 guidelines in precise surface metrology. An essential part of such analysis is the evaluation of measurement uncertainty in accordance with the principles outlined in the Guide to the Expression of Uncertainty in Measurement (GUM). This study applies a Monte Carlo-based approach, as described in JCGM 101, which enables a realistic estimation of uncertainty under complex models and nonlinear influences. Such quantitative evaluation is crucial for the reliable interpretation of surface parameters, for the comparison of results between different laboratories, and for assessing compliance with specification limits.
## 4 Results and Discussion
### The Relationship between Stability Number (m) and Factor of Safety (FoS)  
The stability number is a dimensionless parameter that is used to evaluate the stability of an embankment against failure\". The value of the stability number was calculated as follows  
\[\mathrm{m}=\frac{\mathrm{c}}{\gamma\times\mathrm{H}}\]  
where (c) is the cohesion of the embankment soil, (\(\gamma\)) is the unit weight of the soil and (H) is the height of the slope.  
The variation of the factor of safety (FoS) of a clayey sand embankment with the stability number (m) for various geogrid elastic moduli (E) at various values of the slope angle (\(\beta\)) is addressed in Figure 9a-c. It has been observed that the factor of safety (FoS) tends to rise linearly as the stability number (m) increases. Furthermore, it is discovered that the FoS is inversely proportional to the slope angle (\(\beta\)), which means that a rise in the slope angle causes a fall in the FoS [36].
## 6 Functional Properties and Applications of Nanocomposites
### Piezoelectric actuators and sensors  
Piezoelectric ceramics like lead zirconate titanate (PZT) are widely used in microsystems technologies. They are characterized by outstanding applicability as microactuator converting electrical energy into mechanical movement, vice versa as sensor or for energy harvesting as well as for exploiting the pyroelectric properties [178, 185, 186]. Despite the simplified device realization in comparison to pure ceramic samples, polymer-PZT-composites are seldom investigated. To achieve an effective mechanical response after applying an electrical field the device must be electrically conductive at the surface when connected to a frequency generator. In addition the solid load should exceed the percolation threshold; otherwise the mechanical response of the PZT domains would be mainly absorbed by the polymer matrix. PVDF is the only commercially available polymer showing piezoelectricity. [PERSON] and coworkers investigated composites, consisting of polyvinylidentifio-trifluorethylene (PVDF-TrFE) and PZT, prepared by solvent casting and subsequent compression molding, with respect to their piezoelectric and pyroelectric behavior [187]. The resulting composite pyroelectric and piezoelectric coefficients cannot be derived from a simple mixing rule applying the individual material values of the matrix and the active filler. The dielectric constant as well as the mechanical properties influenced the composite properties, also [187]. In addition, theoretical calculations delivered the impact of the piezoelectric anisotropy of the active filler on the resulting composite behavior [188].
# A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages
## 5 Experimental Results
### Results of Different Languages  
To validate the effectiveness and reliability of the proposed method, we conducted experiments on Uyghur and Uzbek languages, and the results are shown in Table 5. On Uyghur and Uzbek languages, our proposed method achieved relatively better experimental results. Because of the short duration of the audio data for the two languages mentioned above, for the selection of negative samples, the original model is more likely to select negative samples with similar pronunciation for \(c_{i}\). This results in a relatively low quality of the negative sample set, but using the FNIE method can significantly improve the quality of the negative sample set. We believe that the assimilation of false negative samples is more effective in improving the model's ability to learn speech representations for longer speech clips, and the deletion of false negative samples is more effective for shorter speech clips. Additionally, this experiment demonstrates that our proposed method is widely applicable and has good effectiveness and reliability, and also verifies the feasibility of our proposed method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**loss**} & \multirow{2}{*}{\(K\)} & \multirow{2}{*}{\(K^{\prime}\)} & \multirow{2}{*}{\(N\)} & \multicolumn{2}{c}{**English**} \\ \cline{5-6}  & & & & & _CER\%_ & _WER\%_ \\ \hline \multirow{2}{*}{**FNIE**} & ass & 101 & 100 & 1 & 11.694 & 23.315 \\  & del & 102 & 100 & 2 & 11.805 & 23.353 \\ wav2 vec 2.0 & - & 100 & 100 & 0 & 11.753 & 23.998 \\  & - & 101 & 101 & 0 & 11.294 & 24.587 \\  & - & 102 & 102 & 0 & 11.328 & 24.909 \\  & - & 104 & 104 & 0 & 11.517 & 25.145 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different numbers of negative samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{\(K\)} & \multirow{2}{*}{\(K^{\prime}\)} & \multirow{2}{*}{\(N\)} & \multicolumn{2}{c}{**Mandarin**} \\  & & & & _CER\%_ \\ \hline \multirow{2}{*}{wav2 vec 2.0} & 100 & 100 & 0 & 27.853 \\  & 101 & 100 & 1 & 26.725 \\  & 102 & 100 & 2 & 27.9 \\ \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{\(K\)} & \multirow{2}{*}{\(K^{\prime}\)} & \multirow{2}{*}{\(N\)} & \multicolumn{2}{c}{**English**} \\ \cline{5-6}  & & & & _CER\%_ & _WER\%_ \\ \hline \multirow{2}{*}{wav2 vec 2.0} & 100 & 100 & 0 & 11.753 & 23.998 \\  & 101 & 100 & 1 & 11.694 & 23.315 \\  & 102 & 100 & 2 & 11.862 & 24.023 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of assimilating different numbers of false negative samples.
# Removal of Color-Document Image Show-Through Based on Self-Supervised Learning
## 2 Related Work
### Cycle Consistency Loss  
CycleGAN is a special variant of traditional GAN proposed by [PERSON] et al. [6] in 2017. There are some problems in previous GANs, such as difficult training, unstable generation results, and the need for paired training data. To solve these problems, CycleGAN introduces the cycle consistency loss function.  
[PERSON] et al. [20] proposed an end-to-end unsupervised multi-document image blind denoising method, which provides a unified model that can remove various artifacts from various document types without the need for paired clean images. [PERSON] et al. [21] proposed a new image-transformation model that achieves better performance while maintaining cycle consistency constraints, solving problems in the field of unpaired image transformation. The model uses a vision transformer and adopts necessary training and regularization techniques to achieve better performance. [PERSON] et al. [22] proposed an unsupervised method based on cycle consistency for blind image restoration. By constraining low-frequency content, optimizing the content of training data, and using model-averaging techniques, the quality of image restoration is improved. [PERSON] et al. [23] proposed the UDoc-GAN framework to solve the problem of uncontrolled lighting affecting document images captured on mobile devices. UDoc-GAN performs document light correction in non-paired settings, learning the relationship between normal and abnormal lighting domains by predicting environmental light features and redefining cycle consistency constraints. [PERSON] et al. [24] proposed a single-image bleed-through image-restoration method based on self-supervised learning. By leveraging the cyclic consistency of generative adversarial networks and the transfer-learning capability of constraints, this method achieves model training without paired image sets. The approach incorporates a self-supervised-learning module designed to extract supervised information from large-scale unsupervised data, effectively enhancing the quality of texture, edges, and other detailed information in the bleed-through image content, thus enabling the removal of bleed-through from a single image.  
The above methods have verified the effectiveness of CycleGAN and cycle consistency loss. Based on this finding, we also considered that CycleGAN does not have accurately matched label supervision during the training process. Therefore, we designed a network to provide pseudo-similarity loss and used double-cycle consistency loss to constrain the training process.
## 2 Materials and Methods
### Promoter and Fiber Selection  
Promoters were synthesized by Genscript (Pistacaway, NJ, USA) with _Not_I/(_XhoI-Stu_I) flanking restriction sites and cloned in the _Not_I/_Stu_I sites of the vector pShuttle-I-XP-Luc [23] to obtain pShuttle-Pr1-Luc and pShuttle-Pr2-Luc. The vectors pAd-SV40-Luc and pS-CMV-Renilla were previously described [23]. For the selection of the most appropriate promoter, HEK293T cells grown in 24-well plates were co-transfected with 1 ug of the different plasmids and 100 ng of pS-CMV-Renilla, using Lipofectamine 2000 (Thermo Fisher Scientific, Carlsbad, CA, USA). Twenty-four hours later, the cells were collected and assayed for Firefly and Renilla Luciferase activities using the Dual-Luciferase Reporter Assay System (Promega, Madison, WI, USA) and measured in a Genius luminometer (TECAN, Maennedorf, Switzerland). Each experiment was performed at least three times. hAdV5/3-Luc and hAdV5-Luc replication-deficient adenoviral vectors were already described [23]. Hs 729T and THP-1 cells were transduced with hAdV5-Luc and hAdV5/3-Luc viruses at MOI 500. Forty-eight hours later, the cells were collected and assayed for Renilla Luciferase activity as described.