In recent years, satellite communication systems (SCSs) have rapidly developed in terms of their role and capabilities, promoted by advancements in space launch technologies. However, this rapid development has also led to the emergence of significant security vulnerabilities, demonstrated through real-world targeted attacks such as AcidRain and AcidPour that demand immediate attention from the security community. In response, various countermeasures, encompassing both technological and policy-based approaches, have been proposed to mitigate these threats. However, the multitude and diversity of these proposals make their comparison complex, requiring a systemized view of the landscape. In this paper, we systematically categorize and analyze both attacks and defenses within the framework of confidentiality, integrity, and availability, focusing on specific threats that pose substantial risks to SCSs. Furthermore, we evaluate existing countermeasures against potential threats in SCS environments and offer insights into the security policies of different nations, recognizing the strategic importance of satellite communications as a national asset. Finally, we present prospective security challenges and solutions for future SCSs, including full quantum communication, AI-integrated SCSs, and standardized protocols for the next generation of terrestrial–space communication.

Otolaryngological diagnoses, such as otitis media, are traditionally performed using endoscopy, wherein diagnostic accuracy can be subjective and vary among clinicians. The integration of objective tools, like artificial intelligence (AI), could potentially improve the diagnostic process by minimizing the influence of subjective biases and variability. We systematically reviewed the AI techniques using medical imaging in otolaryngology. Relevant studies related to AI-assisted otitis media diagnosis were extracted from five databases: Google Scholar, PubMed, Medline, Embase, and IEEE Xplore, without date restrictions. Publications that did not relate to AI and otitis media diagnosis or did not utilize medical imaging were excluded. Of the 32identified studies, 26 used tympanic membrane images for classification, achieving an average diagnosis accuracy of 86% (range: 48.7–99.16%). Another three studies employed both segmentation and classification techniques, reporting an average diagnosis accuracy of 90.8% (range: 88.06–93.9%). These findings suggest that AI technologies hold promise for improving otitis media diagnosis, offering benefits for telemedicine and primary care settings due to their high diagnostic accuracy. However, to ensure patient safety and optimal outcomes, further improvements in diagnostic performance are necessary.

Otitis media (OM) is a common disease in childhood that may have aftereffects such as hearing loss. Therefore, early diagnosis and proper treatment are important. However, the diagnostic accuracies of otolaryngology and pediatrics are low, at 73% and 50%, respectively. Therefore, clinical work that supports the early diagnosis of diseases, such as computer-aided diagnostic (CAD) systems, can be helpful. However, CAD systems for diagnosing ear diseases require an automatic tympanic membrane (TM) segmentation model to assist in diagnosis. This is because it is difficult to detect the TM and affected areas in an endoscopic image of the TM owing to irregular lighting. In this study, we propose a ResNet152 UNet++ image segmentation network. The proposed method applies the ResNet152 layer structure to the encoders in the UNet++ model to detect the location of the TM and affected area with high accuracy. Furthermore, the TM and affected regions can be segmented better than when using the previously proposed UNet and UNet++ models. To the best of our knowledge, this study is the first to use a UNet++-based segmentation model to segment TM areas in endoscopic images of the TM and evaluate its performance. The experiments revealed that ResNet152 UNet++ outperforms conventional methods in terms of segmentation of the TM and affected areas.

In the case of neonates, especially low birth weight preterm and high-risk infants, portable X-rays are frequently used. However, the image quality of portable X-rays is significantly lower compared to standard adult or pediatric X-rays, leading to considerable challenges in identifying abnormalities. Although attempts have been made to introduce deep learning to address these image quality issues, the poor quality of the images themselves hinders the training of deep learning models, further emphasizing the need for image enhancement. Additionally, since neonates have a high cell division rate and are highly sensitive to radiation, increasing radiation exposure to improve image quality is not a viable solution. Therefore, it is crucial to enhance image quality through preprocessing before training deep learning models. While various image enhancement methods have been proposed, Contrast Limited Adaptive Histogram Equalization (CLAHE) has been recognized as an effective technique for contrast-based image improvement. However, despite extensive research, the process of setting CLAHE’s hyperparameters still relies on a brute force, manual approach, making it inefficient. To address this issue, we propose a method called Bayesian Optimization CLAHE(BO-CLAHE), which leverages Bayesian optimization to automatically select the optimal hyperparameters for X-ray images used in diagnosing lung diseases in preterm and high-risk neonates. The images enhanced by BO-CLAHE demonstrated superior performance across several classification models, with particularly notable improvements in diagnosing Transient Tachypnea of the Newborn (TTN). This approach not only reduces radiation exposure but also contributes to the development of AI-based diagnostic tools, playing a crucial role in the early diagnosis and treatment of preterm and high-risk neonates.

Chronic otitis media is characterized by recurrent infections, leading to serious complications, such as meningitis, facial palsy, and skull base osteomyelitis. Therefore, active treatment based on early diagnosis is essential. This study developed a multi-modal multi-fusion (MMMF) model that automatically diagnoses ear diseases by applying endoscopic images of the tympanic membrane (TM) and pure-tone audiometry (PTA) data to a deep learning model. The primary aim of the proposed MMMF model is adding "normal with hearing loss" as a category, and improving the diagnostic accuracy of the conventional four ear diseases: normal, TM perforation, retraction, and cholesteatoma. To this end, the MMMF model was trained on 1,480 endoscopic images of the TM and PTA data to distinguish five ear disease states: normal, TM perforation, retraction, cholesteatoma, and normal (hearing loss). It employs a feature fusion strategy of cross-attention, concatenation, and gated multi-modal units in a multi-modal architecture encompassing a convolutional neural network (CNN) and multi-layer perceptron. We expanded the classification capability to include an additional category, normal (hearing loss), thereby enhancing the diagnostic performance of extant ear disease classification. The MMMF model demonstrated superior performance when implemented with EfficientNet-B7, achieving 92.9% accuracy and 90.9% recall, thereby outpacing the existing feature fusion methods. In addition, five-fold cross-validation experiments were conducted, in which the model consistently demonstrated robust performance when endoscopic images of the TM and PTA data were applied to the deep learning model across all datasets. The proposed MMMF model is the first to include a category of normal ear disease state with hearing loss. The developed model demonstrated superior performance compared to existing CNN models and feature fusion methods. Consequently, this study substantiates the utility of simultaneously applying PTA data and endoscopic images of the TM for the automated diagnosis of ear diseases in clinical settings and validates the usefulness of the multi-fusion method.