esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The lack of extensive, varied, and thoroughly annotated datasets impedes the advancement of artificial intelligence (AI) for medical applications, especially colorectal cancer detection. Models trained with limited diversity often display biases, especially when utilized on disadvantaged groups. Generative models (e.g., DALL-E 2, Vector-Quantized Generative Adversarial Network (VQ-GAN)) have been used to generate images but not colonoscopy data for intelligent data augmentation. This study developed an effective method for producing synthetic colonoscopy image data, which can be used to train advanced medical diagnostic models for robust colorectal cancer detection and treatment. Text-to-image synthesis was performed using fine-tuned Visual Large Language Models (LLMs). Stable Diffusion and DreamBooth Low-Rank Adaptation produce images that look authentic, with an average Inception score of 2.36 across three datasets. The validation accuracy of various classification models Big Transfer (BiT), Fixed Resolution Residual Next Generation Network (FixResNeXt), and Efficient Neural Network (EfficientNet) were 92%, 91%, and 86%, respectively. Vision Transformer (ViT) and Data-Efficient Image Transformers (DeiT) had an accuracy rate of 93%. Secondly, for the segmentation of polyps, the ground truth masks are generated using Segment Anything Model (SAM). Then, five segmentation models (U-Net, Pyramid Scene Parsing Network (PSNet), Feature Pyramid Network (FPN), Link Network (LinkNet), and Multi-scale Attention Network (MANet)) were adopted. FPN produced excellent results, with an Intersection Over Union (IoU) of 0.64, an F1 score of 0.78, a recall of 0.75, and a Dice coefficient of 0.77. This demonstrates strong performance in terms of both segmentation accuracy and overlap metrics, with particularly robust results in balanced detection capability as shown by the high F1 score and Dice coefficient. This highlights how AI-generated medical images can improve colonoscopy analysis, which is critical for early colorectal cancer detection.  
medical imaging synthesis; polyp detection; text-to-image generation; image segmentation; generative AI; medical image synthesis; colorectal cancer detection; data augmentation; synthetic colonoscopy images; text-to-image generation; DreamBooth; Stable Diffusion; Low-Rank Adaptation (LoRA); polyp segmentation; Feature Pyramid Network; Vision Transformer; Segment Anything Model; medical diagnostic models; healthcare AI
# Real-Time Water Level Monitoring Based on GNSS Dual-Antenna Attitude Measurement
## 2 Methodology
### Fixing of Integer Ambiguity  
The carrier phase measurement in GNSS should ideally result in an integer number of cycles around the whole perimeter, but due to measurement imprecision, the least squares method may yield a fractional number. In order to obtain a more accurate integer solution, the LAMBDA algorithm can be employed to search for the nearest integer value of the obtained float point solution, thus providing a fixed whole perimeter integer ambiguity resolution. The search space for the integer ambiguity is defined as follows:  
\[\Omega_{\mathrm{a}}=\{\mathrm{a}\in Z^{\mathrm{n}}|(\ \hat{\mathrm{a}}- \mathrm{a})^{\mathrm{T}}\cdot\mathrm{Q}_{\hat{\mathrm{a}}}^{-1}\cdot(\ \hat{\mathrm{a}}-\mathrm{a})\leq\chi^{2}\} \tag{16}\]where \(\chi^{2}\) is a chosen positive constant, The boundary of this search space is an ellipsoidal plane with its center at \(\hat{\text{a}}\). The shape of the ellipsoid is determined by Q \(\hat{\text{a}}\) and its size is determined by \(\chi^{2}\). To improve search efficiency, the LAMBDA method applies a decorrelation transformation as follows:  
\[\text{z}=Z^{\text{T}}\text{-a};\text{ Q}_{\hat{\text{z}}}=Z^{\text{T}}\text{-Q}_{ \hat{\text{a}}}\text{-Z};\text{ }\hat{\text{z}}=Z^{\text{T}}\text{- }\hat{\text{a}} \tag{17}\]  
where z and Q\({}_{\hat{\text{z}}}\) are the vectors of a and Q\({}_{\hat{\text{a}}}\) after integer transformation, respectively. The \(Z\)-transformed search space is as follows:  
\[\Omega_{\text{a}}=\{\text{z}\in Z^{\text{n}}|(\hat{\text{z}}-\text{z})^{\text {T}}\text{-Q}_{\hat{\text{z}}}^{-1}\text{-}(\hat{\text{z}}-\text{z})\leq\chi^{ 2}\} \tag{18}\]  
The ambiguity search is then conducted in the new space. The correct ambiguities identified are transformed back into the original space using Equation (17) to obtain the integer ambiguity.  
The float solution of the baseline vector is adjusted by applying a fixed integer ambiguity. The baseline is corrected as follows:  
\[\text{b}=\hat{\text{b}}-\text{Q}_{\hat{\text{b}}\hat{\text{a}}}\text{-Q}_{ \hat{\text{a}}}^{-1}(\hat{\text{a}}-\text{a}) \tag{19}\]  
\[\text{Q}_{\hat{\text{b}}}=\text{Q}_{\hat{\text{b}}}-\text{Q}_{\hat{\text{b}} \hat{\text{a}}}\text{-Q}_{\hat{\text{a}}\hat{\text{b}}}^{-1}\text{-Q}_{\hat{ \text{a}}\hat{\text{b}}} \tag{20}\]  
After obtaining the fixed solution for the integer ambiguity, its reliability must be verified. Methods for confirming integer ambiguity include the F-Ratio test [39], R-Ratio test [40], difference test [41], and projection test [42]. In practice, the R-Ratio test is the simplest and most commonly used method [43].
# Activation Analyses of Disposal Options for Irradiated Be\({}_{12}\)Ti
## 2 Be\({}_{12}\)Ti Neutron Multiplier
### Beryllium Chemical Composition  
The impurities content in the original raw Be is the crucial issue for the future strategy of the industrial handling of the irradiated beryllium [6]. The concentration of various impurities significantly affects the results of the berillium activity accumulated during the irradiation phase. For the purposes of this work, the following assumption was made: the majority of the beryllium for the HCPB blanket manufacture will very likely come from the main suppliers, namely, from the Ulba (stands for JSC Ulba Metallurgical Plant of Kazakhstan) and/or from the Materion (which stands for Materion Corporation, Parkland Blvd., USA). The Be\({}_{12}\)Ti will be produced utilizing beryllium: (1) from one of them, (2) separately from each of them or (3) from the mixture of them. Listed in Table 2 are the chemical compositions of berillium from the Ulba, PTB-56 [8] and nuclear grade S-65 beryllium for the International Thermonuclear Experimental Reactor (ITER) from the Materion [9]. A chemical composition of Ti (RP-Ti grade OM-1, Polema [8]) is given in Table 3.  
\begin{table}
\begin{tabular}{l c} \hline \multicolumn{1}{c}{**Country**} & \multicolumn{1}{c}{**Mine Production**} \\ \hline USA & 180 \\ \hline Brazil & 3 \\ \hline China & 70 \\ \hline Madagascar & 1 \\ \hline Mozambique & 13 \\ \hline Nigeria & 1 \\ \hline Rwanda & 1 \\ \hline Uganda & 7 \\ \hline
**World total (rounded)** & **280** \\ \hline \end{tabular}
\end{table}
Table 1: World mine Be production and reserves [5].  
\begin{table}
\begin{tabular}{l c c} \hline \multicolumn{1}{c}{**Element**} & \multicolumn{1}{c}{**Ulba**} & \multicolumn{1}{c}{**Materion**} \\ \hline Be & _balance_ & _balance_ \\ \hline BeO & - & 0.9000 \\ \hline C & 0.0500 & 0.0900 \\ \hline Fe & 0.1000 & 0.0800 \\ \hline Al & 0.0200 & 0.0500 \\ \hline Si & 0.0300 & 0.0450 \\ \hline \end{tabular}
\end{table}
Table 2: Chemical compositions (wt%) of the Be from Ulba and Materion.  
An essential difference of the Be compositions presented in Table 2 is the uranium content. The presence of the uranium impurity is not of concern in case of the Be for the industrial applications without neutron irradiation. A quite different context is in nuclear facilities with the neutron irradiation where the utilization of beryllium will inherently result in an accumulation of \(\alpha\)-radioactive and toxic transuranic isotopes of U, Pu, Am and Cm [10]. This will dramatically change the handling strategy of the irradiated Be\({}_{12}\)Ti and its clearance according to IAEA recommendations [11] and could be unavoidable. Other impurities are also of high interest due to (a) the potential environmental risk and (b) the potential danger during a treatment.
## 3 Results
### Impact in Performance of Targeting One Service over the Non-Target Services
At the same time, the \"price\" in terms of performance of the not-targeted services that we have to pay if we want to obtain the best GC performance, is very big (with values up to +66.5% in carbon footprint, +80% regarding EC or \(-57.4\%\) in GPP). At the view of the above, it might not be worth it to use the EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.1}\) strategy, and rather it would be more beneficial to choose another one that produces a better compromise among all of the services. If we observe the strategy that presents the smallest under-performance values of all, is the GPP\({}_{\text{min}}\)-AnEn\({}_{\tau=0.4}\). Accordingly, we could say that this strategy presents the best compromise for all the services, followed by the EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.5}\) strategy. From this section, we conclude that targeting one service (with its best scheduling-strategy + forecasting-method) affects negatively the performance on the other services (i.e., the best performance of each service is only achieved when the best combination of scheduling-strategy + forecasting-method for that particular service is used). However, a good compromise can be found with some combinations that yield satisfactory performance for all services. At the view of the above we wonder: can these results be affected by different seasonal conditions? How can these best combinations of \"scheduling-strategy + forecasting-method\" be adapted for each season? Could we further improve the performance by applying seasonal EMS strategies rather than annual strategies? The answers to these queries are tackled in the following section.
## 2 State-of-the-Art Review
A combination of GCNs and temporal convolutional networks (TCNs) can be used to estimate multi-person camera-centric 3D poses that do not need to know the camera parameters [67]. As another alternative to CNN, [PERSON] et al. propose the use of a deep-reinforcement-learning architecture capable of estimating appropriate views in space and time to help with final frame pose estimation [68]. Considering the nature of the approach, conversion from 2D to 3D space leads to numerous artifacts in the results, as well as to limitations in the input data. The occlusion was partially solved by applying a Fisher hierarchical matrix distribution to the relative 3D joint rotation matrices of key points and a Gaussian distribution to the body shape parameters [69]. [PERSON] et al. [70] also suggested employing the estimated 2D confidence heat maps of joints, together with an optical-flow consistency constraint, then filtering out unreliable estimations of occluded joints. As an alternative to CNN-related problems of depth ambiguity and self-occlusion, [PERSON] et al. [71] suggest using a multihypothesis transformer architecture to learn spatiotemporal representations of multiple plausible poses. [PERSON] et al. [72] offer applying transformer-based architectures to solve the problem of occlusions and oblique viewing angles by integrating information from different views. Loss in keypoint information or even segmentation is another big issue, especially when trying to reconstruct clothed figurines, as was shown by [PERSON] et al., who used higher level semantic knowledge about clothing to distinctly penalize the clothed and unclothed image regions [73]. Out-of-domain human pose estimation was performed using the Bilevel Online Adaptation algorithm, which uses temporal constraints to compensate for the unavailable notation, and bilevel optimization procedures to solve the mesh generation [74].
# Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country
## 2 Prerequisites for Using the UAVs in Some Spheres of Economy
### Exploration of Minerals (EoM)
The technologies developed under the program will reduce exploration costs, which may result in significant savings on a global scale. The use of unmanned technologies, combined with artificial intelligence techniques, will speed up geological exploration on large areas. For example, on an area of 10,000 square km, a comparative calculation of the cost of the traditional ground-based method (using motor vehicles) with manual geochemical sampling (USD 335,000) and the use of UAVs equipped with a hyperspectral camera and compact magnetometer (USD 68,000) shows almost five-fold advantage of using UAVs for some types of geological exploration. Two main sedimentary basins have been identified in Southern Kazakhstan with the total area of 248,000 square km: the Shu-Sarysu basin (168,000 square km) and the South-Torgay basin (80,000 square km) [71]. Consequently, the application of the described technology can result in savings of about USD 6 million for the territory of Southern Kazakhstan only. In accordance with the program [72], it is necessary to study about 5.5% of the territory of Republic (149,000 square km) and to increase the degree of geological and geophysical exploration of sedimentary basins (900,000 square km [73]). The costs of such work using traditional methods of geological exploration will amount to about USD 42 million. The costs of these works with employment of IUAVT can amount to about USD 8.5 million. In total, for the territory of Kazakhstan, savings in geological exploration with the use of UAVs and machine learning methods may amount to more than USD 18 million compared to the traditional methods. At the same time, the increase in labor productivity will amount to more than 300%. We would like to note that this assessment concerns only the part of necessary work. We also assume that the use of a hyperspectral camera could reduce sampling for laboratory studies. At the same time, some types of deposits require sampling, laboratory studies and well drilling. The use of IUAVT for solving the tasks of minerals exploration has some prerequisites, the main of them are significant exploration costs that currently do not provide return [70] and the concept of the State program of geological exploration for 2021-2025 in Kazakhstan [72]. The estimated annual expenses are USD 41 million. It is necessary to develop the methods of mineral exploration based on the application of highly mobile platforms for the collection of heterogeneous data, computer vision systems, and new search criteria identified by machine learning. The following problems should be solved in this area: increased flight time; payload; accuracy and sensitivity of sensors; data processing software, including computer vision; and control systems. The evaluated economic effect of IUAVT employment is USD 33 million (see Appendix A Table A4).
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
###### Abstract
[PERSON] and [PERSON] [18] derived candidate constellations (e.g., L245 three-satellite and L1245 four-satellite configurations) that meet all the cislunar coverage requirements based on L2/L4/L5 point geometries. [PERSON] et al. [19] proposed Earth-Moon satellite constellation designs using multi-revolution elliptic Halo orbits (ME-Halo) near L2 within an elliptic restricted three-body problem framework. Additionally, orbital resonance phenomena in near-Earth constellations have been investigated. [PERSON] et al. [20] studied the resonance between satellite orbital periods and Earth's rotation period using GPS as a case study. [PERSON] et al. [21] optimized regional coverage constellations by analyzing revisit resonance in coverage performance under periodic requirements. Current Earth-Moon navigation constellation designs face challenges including heterogeneous orbital types, non-uniform design parameters, and excessive optimization search spaces, with existing methods being limited to localized cislunar regions rather than providing full-space coverage. This paper addresses these limitations through a period resonance-based constellation design, enabling full-cycle performance analysis and simplified system management/optimization. This work introduces a method for generating optimal navigation resonance constellations targeting distinct cislunar regions via the construction of an operational database and DOP evaluation. Key innovations include the following: (1) a classification strategy prioritizing the baseline orbit, which is the trajectory with the minimal orbital period within the resonant constellation and intra-constellation orbital type composition in the Earth-Moon-Spacecraft three body system, enabling the systematic generation and categorization of resonant combinations into constellations; (2) the identification of optimal resonant configurations for near-Earth and lunar systems; and (3) an analysis of DOP distributions over time and the orbital radius. The paper is structured as follows: Section 2 details the circular restricted three-body problem (CRTBP) dynamics, CRTBP-based operational orbit database generation, DOP calculation formulas, and definitions/gridding methods for critical cislunar regions. Section 3 comprehensively describes the resonance constellation design methodology using the orbit database. Section 4 applies this method to derive five superior and five inferior constellations across target regions, with case studies analyzing the relationships between orbital radius/DOP values and temporal DOP evolution in the combined region. The conclusions provide actionable recommendations for the implementation of an Earth-Moon navigation constellation.
## 4 6G Convergence with Green AI Perspective in Terrestrial and Non-Terrestrial Applications
### Application 1: Green AI Enabled 6G Virtual Emotion Barrier  
Definition: Green-AI-Enabled 6G Virtual Emotion Barrier, referred as _GreenAI-6G-VEmoBAR_, is a barrier with a _Green AI_ view, which specific emotion type is detected by a series of heterogeneous devices consisting of UAVs, mobile robots, smart devices. Furthermore, it focuses on economic, environmental, social cost in 6G environment.  
System GoalBasically, _GreenAI-6G-VEmoBAR_ looks for favorable trade-off between performance and efficiency by identifying key factors of task accomplishment, computational cost with minimum reduction in performance, environment protection, reduction in spent resources. The first objective is to minimize computation cost for the number of devices in _GreenAI-6G-VEmoBAR_, which meet the given emotion detection accuracy requirement toward appropriate emotion based services in smart cities. The second goal is to reduce 6G communication resources such as communication messages, transmitted signals when _GreenAI-6G-VEmoBAR_ is activated in the given area. Furthermore, the third aim is to minimize energy expenditure when _GreenAI-6G-VEmoBAR_ is constructed as well as is restored from failures.  
OperationThe system assumes that the basic human emotion types for joy, pleasure, neutral, sadness, fury are detected by wireless signal and its reflection. It is given that the heterogeneous components consisting of UAVs, mobile robots, smart devices, AI agent at edge computing in 6G-assisted area have random locations initially. Firstly, the system accepts the required detection accuracy from system administrator or users. Furthermore, in order to build _GreenAI-6G-VEmoBAR_, we take into account the minimum number of elements to schedule which components move from initial positions to specific location within the planned multiple formations of _GreenAI-6G-VEmoBAR_ where sleep-wakeup scheduling is applicable on condition that the required detection accuracy is attained. After a multitude of _GreenAI-6G-VEmoBAR_ are set up in 6G environment, the detected emotion information by _GreenAI-6G-VEmoBAR_ is transmitted to the closest AI agents with reduced 6G communication in spent resources. Then, by the cooperation of multiple AI agents and the implementations of deep/machine learning schemes at edge side, the meaningful emotion information is generated for emotion-based services. Furthermore, due to dynamic movement property of heterogeneous devices in the system, the formed _GreenAI-6G-VEmoBAR_ during specific time can be destroyed temporarily. To solve those provisional failures, _GreenAI-6G-VEmoBAR_ takes care of minimizing energy consumptions for movements of UAVs, mobile robots and other mobile devices from current locations to failed positions instead of thinking over re-construction delay. It follows that the failure recovery of _GreenAI-6G-VEmoBAR_ only deals with energy consumption of heterogeneous devices because the electricity usage of each movable component is different.
## 5 Parametric Analysis
### Effect of 2b  
To determine the effect of 2\(b\), parameter analysis was performed via a change (using 4 m, 6 m, 8 m, and 12 m widths) while keeping the other parameters constant. Figure 11 shows the lateral displacement and internal force diagram calculated under different 2\(b\) values. In Figure 11a, it is noticeable that, within the range of influence when keeping the burial depth constant, increasing the top area of the upstream scour hole from 28 m\({}^{2}\) to 84 m\({}^{2}\) brings about a notable increase in the lateral displacement of a single pile. Figure 11b emphasizes that the influence of 2\(b\) on the bending moment of the single pile is the most significant in the range of buried depth of 5-10 m. As 2\(b\) increases, the pile's point of contraflexure shifts downward.  
Analyzing Figures 8 and 9 reveals that the fluctuation in the scour hole width, 2\(b\), exerts a comparatively minor effect on the lateral displacement and internal forces of the single pile in contrast to variations in the scour depth. Further insights are drawn from Table 5, which outlines additional stresses on the soil encircling the pile at the bottom of the scour hole, considering different widths of the upstream bottom, 5_11, and scour hole widths, 2\(b\). From the table, it is evident that, with 2 times, 4 times, and 6 times expansion in the upstream bottom area of the scour hole, the additional stresses on the soil around the pile at the bottom of scour hole plummet by approximately 72.52%, 80.50%, and 82.59%, respectively. When the upstream top area of the scour hole grows by 0.5 times, 1.25 times, and 2 times, the additional stresses on the soil around the pile at the bottom of the scour hole lessen by around 33.65%, 52.94%, and 61.45%, respectively. When maintaining a constant scour depth, the changes in the upstream bottom area of the scour hole exert a more pronounced influence on the lateral displacement capability of the single pile compared to alterations in the upstream top area of the scour hole. Moreover, this method can quantitatively evaluate the change in the additional stress of pile-side soil, which is of guiding significance for further study and the evaluation of the effectiveness of different scour reduction techniques in various cases. In practice, the scour around the pile can be regularly detected. The diversion structure can be set around the pile to guide the water flow away from the pile, reducing the scour effect and protecting the stability of the pile foundation. The timely resolution of any erosion problems through maintenance activities is critical to maintaining the lateral bearing performance of the pile and ensuring the long-term effectiveness of the protection measures.  
Figure 11: Lateral bearing performance of pile at varying 2\(b\) values: (**a**) lateral displacement curves and (**b**) bending moment curves.