esa-sceva/satcom-chunk-collection huggingface dataset:
# Detectron2 for Lesion Detection in Diabetic Retinopathy
## 3 Methodology
### Dataset  
The study used 89 color fundus images from a publicly available database DIARETDB1 (89) [16], with 84 images having at least mild non-proliferative diabetic retinopathy (NPDR) and 5 images of normal eyes. Medical experts marked the images as ground truth. Annotations were created using Labelme, which were then converted to the Common Objects in Context (COCO) [17] format using a local script or the tool Roboflow [18]. The original data set was divided into a training set of 28 images and a test set of 61 images, with different confidence levels marking the affected areas of the images. The training set had 18 images with hard exudates, 6 images with soft exudates, 19 images with microaneuryss, and 21 images with hemorrhages, while the test set had 20 images with hard exudates, 9 images with soft exudates, 20 images with microaneuryss, and 18 images with hemorrhages. The ground truth confidence level in the DIARETDB1 data set was set to 0.75.  
Figure 3: The step-by-step process of detecting hemorrhages and locating DME/DR with Faster R-CNN (R50-FPN) and Mask R-CNN (R50-FPN) models.  
Figure 2: Percentage of studies with a focus on different aspects of DR, highlighting the lower percentage dedicated to lesion detection.  
Publicly available datasets such as MESSIDOR [19], APTOS 2019 Blindness Detection [20], and the kaggle [21] diabetic-retinopathy-detection database are often utilized for detecting diabetic retinopathy (DR) and diabetic macular edema (DME) in public domain. However, one drawback of these datasets is that they solely offer a severity rating ranging from 0 to 4 for each image, and do not include precise annotations of the lesions present in the images. This can pose difficulties when utilizing these datasets for tasks that require pinpointing the location of the lesions.
## 5 Experiments and Results
### Models  
We decompose the monolithic application into microservices using three different approaches listed below:  
1. Autoencoder with k-means (AE-K): This is the approach mentioned in the paper by [PERSON] et al. [14]. They used the autoencoder as the feature extractor and the K-means algorithm to cluster the classes into different microservices.
2. Autoencoder with fuzzy C-means (AE-C): This is the approach we came up with to outperform approach 1 [14]. In this approach, we use the autoencoder as the feature extractor and the C-means algorithm to cluster the classes into different microservices.
3. Variational autoencoder with fuzzy C-means (VAE-C): This is the approach we came up with to further optimize approach 2. In this approach, we use a variational autoencoder as the feature extractor and the fuzzy C-means algorithm to cluster the classes into different microservices.  
It is worth noting that the architectures of AE and VAE are the same. That is, an encoder consists of an input layer, a 29-neuron dense layer, and a 14-neuron dense layer. The latent-space layer has only 2 neurons. We use 2 neurons for simplicity for visualization of other works. Also, our goal is to compare our approach to the other baselines that also use 2 neurons. Therefore, this number of neurons is not significant to our results. Then, a decoder includes a 4-neuron dense layer, an 8-neuron dense layer, a 16-neuron dense layer, and an output layer.
# Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing
## 2 Material and Methods
### Ground Measurements  
The measured data were obtained via two micrometeorological towers installed approximately 4.5 km apart at the Instituto Nacional do Semiarido (INSA), located in Campina  
Figure 1: Land use and cover map of the study area and location of micrometeorological towers.  
Grande. The database used in this study is from 2013 and was used to validate the energy, water, and carbon fluxes estimate based on remote sensing estimates. The eddy covariance towers are located over different Caatinga areas with contrasting characteristics, i.e., one tower is located in a dense area of Caatinga (DC), and the second is installed in a sparse area of Caatinga (SC).  
The DC study area has 675 ha, of which approximately 300 ha are well preserved in various stages of development. It is an area covered by sub-deciduous and deciduous forests, which lose their leaves in the driest months and are composed mainly of arboreal and shrub species. The trees have an average height of \(\sim\)5.1 m. The SC area is an anthropized area, located approximately 1 km from the urban area of the municipality of Campina Grande. The SC area has sparse vegetation, with a predominance of shrubby Caatinga, composed of shorter trees, up to 2 m in height, and the presence of bare soil and rocky outcrops [1]. The measurements of environmental variables were carried out using sensors installed in the two towers at a height of 2 m above the vegetation's canopy. The footprint of the towers is approximately 1 km, enough to capture vegetation heterogeneity with a predominance of _Mimosa hostilis_ trees, as described in [1].  
The data were stored in a Datalogger CR3000 (Campbell Scientific), with the average values stored at each half-hour interval. The three-dimensional fluctuation of wind speed was measured using a sonic anemometer (CSAT3A, Campbell Scientific). The fluctuations in water vapor and carbon dioxide concentration were measured using a gas analyzer (EC150, Campbell Scientific). The measurements were obtained at a frequency of 10 Hz. The radiation balance components were quantified with a radiometer balance (CNR 4, Kipp & Zonen); air temperature (HC253-L, Campbell Scientific), with measurements recorded every 5 s, with their average and total values stored every 30 min. Following the collection of the high-frequency data, the averages for half-hour intervals were calculated. Energy, water, and carbon fluxes were quantified using the eddy covariance technique, as described in [1; 30]. The detailed installation of the instruments is described by [9]. In addition to periodic instrument maintenance and calibration, the data underwent a rigorous post-processing process. Detailed information on data processing, quality control, and post-processing can be found in [1]. Details on obtaining water (ET) and carbon (GPP) fluxes from flux towers are described in [5].
###### Abstract  
In the field of educational data mining, the classification of students' grades is a subject that receives widespread attention. However, solving this problem based on machine learning algorithms and deep learning algorithms is usually limited by large datasets. The privacy problem of educational data platforms also limits the possibility of building an extensive dataset of students' information and behavior by gathering small datasets and then carrying out the federated training model. Therefore, the balance of educational data and the inconsistency of feature distribution are the critical problems that need to be solved urgently in educational data mining. Federated learning technology enables multiple participants to continue machine learning and deep learning in protecting data privacy and meeting legal compliance requirements to solve the data island problem. However, these methods are only applicable to the data environment with common characteristics or common samples under the alliance. This results in domain transfer between nodes. Therefore, in this paper, we propose a framework based on federated transfer learning for student classification with privacy protection. This framework introduces the domain adaptation method and extends the domain adaptation to the constraint of federated learning. Through the feature extractor, this method matches the feature distribution of each party in the feature space. Then, labels and domains are classified on each side, the model is trained, and the target model is updated by gradient aggregation. The federated learning framework based on this method can effectively solve the federated transfer learning on heterogeneous datasets. We evaluated the performance of the proposed framework for student classification on the datasets of two courses. We simulated four scenarios according to different situations in reality. Then, the results of only source domain training, only target domain training, and federated migration training are compared. The experimental results show that the heterogeneous federated transfer framework based on domain adaptation can solve federated learning and knowledge transfer problems when there are little data at the data source and can be used for students' grades classification in small datasets.  
federated learning; transfer learning; students' grades classification; domain adaptation; educational data mining +
Footnote †: journal: Applied sciences
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 4 Intelligent Edge Computing

Table 1: _Cont._Figure 2 shows the cutting-edge computing technology used to enable several next-generation applications. Differently from fog computing (a concept initiated by Cisco [45]), edge computing is associated with processing at the end of the device, rather than processing over the local network provided by fog computing. A fundamental difference between MEC and fog computing is that MEC works only in the autonomous mode, whereas fog computing has several interconnected layers and can interact with the distant cloud and the network edge [46; 47]. The combination of Artificial Intelligence (AI) and edge computing was introduced to manage several emerging future communication problems. However, with the limited availability of resources and storage, it is very difficult to operate highly complex AI-based algorithms that require huge data collection at endpoints. Research is needed to design new and thin AI algorithms for edge nodes. In addition, the development of effective mobile resource scheduling and transfer techniques to improve system performance is also necessary [12]. Thus, by combining edge computing, AI, and NS we are able to meet the latency requirements of critical services, ensuring efficient network operation and service delivery with improved user experience. Furthermore, intelligent edge computing is important for slice selection as it is able to alleviate the network core functions, as the response occurs during applications in real-time, therefore reducing bandwidth usage with improved performance. In this sense, Section 4.1 presents the slices classification techniques and Section 4.2 discusses slice composition with ML.
## 4 Wireless Access for Vactrain
### Leaky Waveguide Solution  
We can draw a conclusion that the above wireless access solutions are not suitable for application in vactrain train-to-ground communication. Fortunately, solution based on leaky waveguide/coaxial cables may be appropriate for vactrain. The reasons are listed as follows.  
1. _Elimination of Doppler frequency shift_: As is mentioned above, the large and time-varying Doppler frequency shift and Doppler spread is one of the distinguishing factors of vactrain's train-to-ground communicaiton. Fortunately, the Doppler issue may be solved by introducing leaky waveguide. The leaky waveguide is installed on the ceiling of the vacuum tube, producing a cylindrical wave in the broadside direction of leaky waveguide [40]. Thus, the leaky wave always radiates vertically to the operating vactrain. Note that the wave radiated from different  
Figure 1: Simulation of the antennas methods: (**a**) Configuration of antennas methods. (**b**) Doppler spectrum of antenna methods.  
hole must have different wavefront when considering the propagation along the waveguide. To deal with this issue, the spacing between the adjacent holes is set to an integer multiple of the wavelength in the design of the waveguide. Thus, the phase of the wave arriving at each hole would be same, and so is the wave-front. Suppose the velocity of the receiving end is \(v\), then the Doppler frequency shift can be presented as \[f_{d}=f_{m}\cos\left(\alpha\right)=\frac{v}{c}f_{c}\cos\left(\alpha\right)\] (2) where \(f_{d}\) represents Doppler frequency shift, \(f_{m}\) is the maximum Doppler frequency shift, \(c\) is the speed of light, the carrier frequency \(f_{c}\) is 3.3 GHz, and \(\alpha\) represents the incidence angle of radio wave. As is mentioned above, the angle between the train moving direction and the radiation direction of the leaky wave is always 90\({}^{\circ}\), which means \(\alpha\) equals to 90\({}^{\circ}\). According to Equation (1), the Doppler frequency shift can be calculated as \(f_{d}=0\), indicating that the Doppler frequency shift can be greatly suppressed or completely eliminated.
2. _Even field distribution:_ As the distance between the train receiving antenna and the leakage waveguide is close (-40-50 cm), the signal emitted by leaky waveguide is strong, with the relatively weak multi-path effect of the reflecting or scattering wave. Thus, the field distribution of leaky wave can be uniform at the train receiving antenna.
3. _Simplification of communication mechanism:_ As is analyzed above, the Doppler frequency shift could be eliminated or decreased at a quite low level, which indicates that the fast time-variation of the fading channel could be greatly suppressed. In addition, the leaky wave field distribution is also even for vactrain. Thus, the wireless channel between train and the leaky waveguide will be conditioned stationary. This implies that the communication method can be simplified and the complicated communication coding technologies such as OFDM and LDPC can be avoided.
# AnyGesture: Arbitrary One-Handed Gestures for Augmented, Virtual, and Mixed Reality Applications
## 4 Discussion
### Key Takeaways from the Experiment  
From the analysis of the results, interviews, and the observation of the participants, the following conclusions can be drawn:  
* Hand gestures should be defined in such a way that they can be performed as easily as possible. For gestures S3, S10, and S13, subjects were asked to turn their hand about 180 degrees, which made some subjects uncomfortable. Gesture S3 was also very difficult for one subject to perform because the little finger could not be extended as required for the gesture. When designing gestures, it should be taken into account that people can extend their fingers to different extents.
* The spatial understanding of the test persons differed. The tracing of a letter in the air with the index finger (the Z gesture D3) was sometimes performed incorrectly, which led to frustration for some people. The gesture was aborted if the finger left the path and had to be performed again. This often happened because people underestimated or overestimated the depth of the required path and thus left the gesture path. In general, mistakes should be allowed when users have to follow a path with the finger or a whole hand in order to provide a good user experience with little frustration.
* By observing and interviewing the participants, it can be said that users learned to perform the gestures rather quickly. Looking at the results presented in Figure 8 underlines this argument. Especially within the last repetition, users seemed to be faster at performing gestures. This learning process should be taken into account when evaluating hand-gesture-based interactions. It is therefore recommended to conduct a trial session before the experiment, where users can test and learn the gestures.
* It should always be considered that users perform gestures incorrectly. Regardless of the fact that the experiment was timed, some subjects tried to finish the test as quickly as possible. This led to some gestures being performed incorrectly, such as the thumbs up gesture instead of thumbs down or palm up instead of palm down. This should be taken into account when designing an experiment and recording results to be analyzed.
## 5 Prompt Structure Ablation Study
### Table Data Samples in Prompt  
Including data samples from the tables in the input prompt resulted in substantial cost increases across all models, as shown in Figure 4. Cost increases ranged from 81.9% (_Clude-3.5_) to 149.5% (_Qwen-2.5-72b_), demonstrating the significant token overhead associated with this approach. However, the performance improvements were inconsistent across models. High-capacity models such as _Qwen-2.5-72b_ and _GPT-4o_ achieved notable accuracy gains of 26.2% and 21.4%, respectively. In contrast, _Gemini-1.5-pro_ exhibited a slight decline in performance (\(-2.5\%\)), and _Llama3.3-70b_ showed a minimal improvement of just 2.1%.  
The performance drop of _Gemini-1.5-pro_ when provided with database examples may be due to overfitting, misinterpreting patterns, increased input complexity, or prompt sensitivity. The model might have prioritized example replication over generalization, leading to incorrect SQL. Additionally, longer inputs could have diluted focus, and formatting differences may have influenced interpretation.  
Models such as _Clude-3.5_ and _GPT-4o-mini_ achieved moderate performance increases (15.9% and 17.7%, respectively). Still, the high-cost overhead suggests that the inclusion of table samples in prompts should be carefully evaluated based on the specific task requirements and budget constraints.  
**Recommendation:** Table data samples should only be included in prompts when the task requires detailed context to resolve complex SQL queries. For more straightforward questions or when using cost-sensitive models, omitting table samples is advisable. High-capacity models like _Qwen-2.5-72b_ and _GPT-4o_ can leverage the additional context effectively, making them suitable for this configuration. However, for models like _Gemini  
Figure 4: Cost and accuracy increase by model when using a prompt with a data example for each table.  
1.5-pro_ and _Llama3.3-70b_, the high cost and limited or negative performance impact make this approach less practical.
## 3 Samples and Experimental Methods
### Fractal Dimension Feature Analysis  
The French mathematician [PERSON] first introduced fractal theory in the 1970s. By analyzing the fractal dimensions of reservoirs, this theory enables the characterization of the structural features of various complex objects in nature. The pore structures within reservoirs exhibit specific fractal characteristics. By calculating the fractal dimensions from the basic parameters obtained through HPMI experiments, a quantitative description of the heterogeneity and complexity of the pore structures can be obtained. Typically, the internal pore structures of porous rocks have noninteger fractal dimensions, with idealized fractal dimensions generally ranging from 2 to 3 [55]. A smaller fractal dimension indicates a more uniform pore-throat distribution and greater homogeneity of the reservoir. Conversely, a larger fractal dimension corresponds to a more complex pore-throat distribution and a greater degree of heterogeneity.  
The relevant data are obtained from HPMI experiments and the fractal dimension is calculated via the following Equation (3) [56]:  
\[LgS_{w}=(D-3)lgP_{c}+(3-D)lgP_{min} \tag{3}\]  
In Equation (3), \(S_{w}\) (%) is the wetting phase saturation, with \(S_{w}=1-S_{Hg}\) (\(S_{Hg}\) is the mercury saturation in the HPMI experiment); \(D\) is the fractal dimension; \(P_{c}\) (MPa) is the capillary pressure; and \(P_{min}\) (MPa) is the capillary pressure when mercury first enters the pores during the HPMI experiment, corresponding to the maximum pore-throat radius.  
Equation (3) shows that in double logarithmic coordinates, the wetting phase saturation \(S_{w}\) is linearly related to the capillary pressure (\(P_{c}\)). The slope of this line is (\(D-3\)), which allows for the determination of the corresponding fractal dimension. Given that the pore structure of tight sandstone reservoirs exhibits multifractal characteristics [57], first delineating the fractal intervals on the intersection plot is necessary. Within each fractal interval, segmental linear regression can be performed to determine the slope \(K\) for each interval. The fractal dimension \(D\) can then be calculated based on the slopes obtained from different fractal intervals as follows in Equation (4):  
\[D=K+3 \tag{4}\]In Equation (4), \(D\) represents the fractal dimension and \(K\) represents the slope for each interval.
# Flickering-Free Distance-Independent Modulation Scheme for OCC
## 5 Methodology
### Experimental Setup
Additionally, the successful transmission probability was calculated based on two elements: frame error rate and symbol error rate. The first part took into account the relation between the frames that have not been correctly received and processed, and the total transmitted frames. The second part is defined as the probability of receiving the symbol A given that another symbol was transmitted. Nevertheless, it is possible to obtain a correct symbol even when some frames are not adequately received or processed. In this cases, an error for the success rate calculation is assumed.
## 2 CCD-Based Fine Tracking Optical System  
The configuration of the FTOS is illustrated in Figure 1. The light source emits light to simulate the target, which is detected by a CCD to calculate the LOS error. The inertial sensors like MEMS accelerometers and FOG could acquire the angular acceleration and velocity of the mirror. The controller receives the feedback signals, implements control algorithm and controls the motors to track the target and suppress the disturbance from the pedestal.  
The FTOS acceleration open-loop transfer function as follows is composed of a quadratic differential element, a mechanical resonance part with natural frequency \(\vec{\omega}_{n}\), and a inertial element with electrical time constant \(T_{e}\)[8; 18]:  
\[G_{a}(\mathbf{s})=\frac{\theta(\mathbf{s})}{U(\mathbf{s})}=\frac{K\mathbf{s}^{2 }}{\frac{\vec{\omega}_{n}^{2}}{\vec{\omega}_{n}^{2}}+\frac{\vec{\omega}_{n}^{ 2}}{\vec{\omega}_{n}^{2}}+1}\cdot\frac{1}{T^{\delta}+1} \tag{1}\]
## 2 Background and Related Work
### Experimental Validation Methodology
#### 2.4.2 Performance Metrics  
Primary metrics used for algorithm evaluation are as follows:  
Success Rate: The percentage of transmitted frames whose timing errors remain below a predefined threshold.  
Convergence Time: The number of iterations required for the algorithm to achieve stable and consistent performance.  
Robustness: The degree to which algorithm performance degrades when subjected to adverse conditions, such as noise, drift, or burst errors.  
Computational Efficiency: A combined measure of latency and hardware resource utilization, reflecting the algorithm's suitability for real-time embedded deployment.
## 3 Methodology and Accuracy Assessment  
This research proposes a new Pan sharpening method that relies on the CIELab transform, which modifies the color components of images to be used for Pan sharpening of VHR satellite images. The flowchart of the proposed method is given in Figure 1, and the details of the proposed method are described in the sub sections.