esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN
## 2 Related Studies
### Deep-Learning Based Studies  
Classifying fundus images by means of any of the learning-based methods is a distinct approach. Pre-processing of the fundus is minimized in these methods. Many image-processing tasks, such as segmentation and feature extraction, may be accomplished directly with a deep-learning architecture.  
In [7], an architecture of a deep learning model (DLM) for identifying HR was developed. They used 32 \(\times\) 32 scaled assignments of grayscale transformed retina images to train a CNN. The CNN's output was either HR or regular. The accuracy of the detection was 98.6%. In [33] they used an architecture that combined the random Boltzmann machine (RBM) and deep neural network (DNN) methods to measure the alteration of arterial blood vessels. The distinct characteristics for the deep learning method used the AVR ratio and the OD region. In a previous study [36] described a CNN-based approach for extracting the OD, retinal arteries, and fovea centralis. There were seven layers in the CNN architecture. The fovea centralis, OD, retinal arteries, and the background of the retina were represented by four nodes in the output of their CNN design. On the DRIVE dataset, a mean accuracy of classification of 92.6% was attained.  
CNN was used by several researchers to segment and categorize retinal vasculature into arteries and veins [37; 38; 39]. The accuracy of these approaches was high: 88.9% for 100-photos of a poor-quality dataset and 93+% for the DRIVE data set. Finally, [40] described a CNN automated approach for detecting exudates in retinography photos. During the CNN training procedure, all the features were retrieved. The CNN was fed odd-sized patches as input, with the processed pixel at the patch's center. Convolutional layers were employed to determine whether each separate pixel belongs to an exudate or not. Exudates do not appear in the OD region. Hence, they were excluded. Aside from the input and output layers, the CNN design has four convolutional and pooling layers. Using the DRiDB dataset, the approach was found to have identical performance measures for positive predictive value (PPV) and sensitivity while having an F-score of 77%. Like in [41], the authors used different layers of CNN model to recognize diabetic retinopathy.  
Table 1 summarizes the DL-based methods utilized for HR detection or at least the extraction of OD, computation of A/V and other HR aspects.
## 2 Methods
### Grid and Time Step Verification  
To ensure the accuracy of the simulation results, independent verification was conducted on the number of grid elements and the time step. A simulation with TSR = 0.6 was selected, and the torque generated by the wind turbine was monitored and compared. Figure 4a shows the torque generated by the wind turbine at different numbers of grid elements. When the number of grid elements was less than 1.4 million, the torque generated by the wind turbine was unstable, which affected the experimental results. However, when the number of grid elements exceeded 1.5 million, the torque generated tended to be balanced and was approximately 1.68. A finer grid resolution did not improve the calculation accuracy and wasted resources [27]. This result indicates that 1.5 million grid elements were the most suitable number of grid elements. In addition to the grid independence verification, research on the time step was also needed to ensure that the simulation results were independent of the time step [28]. Three times steps were used in the simulation, namely \(\Delta\)t = 0.00256 s, \(\Delta\)t = 0.00513 s, and \(\Delta\)t = 0.01026 s. These time steps corresponded to the time for the VAWT to rotate \(1^{\circ}\), \(2^{\circ}\), and \(3^{\circ}\) positions. Figure 4b shows the relationship between torque and azimuth angle for these time steps. The result with \(\Delta\)t = 0.00513 s was more consistent with the simulation result with \(\Delta\)t = 0.00256 s and consumed more reasonable resources. Therefore, \(\Delta\)t = 0.00513 s was selected as the appropriate time step for the simulation.  
Figure 4: Model validity verification: (**a**) torque changes at different grid cells; (**b**) torque changes at different time steps with rotation azimuth angle.  
Figure 3: Boundary conditions of flow field.
# Copper- and Iron-Based Nanoflowers in Cancer Theranostics
## 2 Synthesis Methods for Copper- and Iron-Based Nanoflowers
### Sol-Gel Method  
The sol-gel process (Figure 4) is a widely used wet chemical method for synthesizing various nanostructures, especially metal oxide nanoparticles. In this technique, a molecular  
Figure 3: (**A**) shows the molecular structures of the surface coatings used on SPIOs; (**B**) provides a schematic of SPIO synthesis using single or dual surfactants through co-precipitation and thermolysis; and (**C**) illustrates interaction types in 34 DABA-coated SPIO formulations. The interactions in these formulations are categorized as (**i**) intrafunctional and (**ii**) interfunctional groups, as well as (**iii**) interparticle interactions [63].  
precursor, typically a metal alkoxide, is dissolved in water or alcohol and undergoes hydrolysis or alcoholysis through heating and stirring, leading to condensation and the formation of a gel. Since the gel remains wet, it requires drying, which is tailored to achieve specific properties and applications. For example, in an alcoholic solution, drying may involve burning off the alcohol. After drying, the gel is powdered and calcined. This conventional and industrially relevant method enables the synthesis of nanoparticles with diverse chemical compositions. The process begins with forming a homogeneous sol from precursors, which transitions into a gel. During the drying stage, the solvent is removed, and the properties of the resulting dried gel are significantly influenced by the selected drying technique [65].  
Due to its simplicity, the sol-gel method is also commonly used in NF production. Sol is a type of colloidal suspension of solid particles in a liquid, while gel is a viscous 3D macromolecular network immersed in a solvent [34]. This process includes five stages: hydrolysis, polycondensation, aging, drying and calcination [33]. The first three steps are related to the gradual formation of the gel structure, while the other two are associated with the separation of aqueous and organic components and the removal of water molecules, respectively [34]. The sol-gel method produces MNPs with high purity and uniformity; however, it can also lead to impurities that are challenging to remove. Like other techniques, it offers substantial control over particle size and composition [42].
## 4 Experiments
### Evaluation Criteria  
A cross-validation was performed to evaluate the experimental results. After training and validation using the corresponding datasets, the final performance evaluation of the model was conducted using the test dataset. In the experiment, the time from the input to the output of the model (inference time), sum of all weights and biases in the neural network (params), billion floating-point operations/s (GFLOPS), and average accuracy/precision (AP) were used as performance indicators to evaluate the detector.  
True positives (TP) are correct identification of positive examples, while false positives (FP) are incorrect identification of negative examples as positive. False negatives (FN) are identification of positive examples as negative examples. Accuracy (P) evaluates the object detection precision of the model and is computed using Equation (2):  
\[\mathrm{P}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} \tag{2}\]  
The recall rate (R) is the proportion of correctly predicted objects. The recall rate can be calculated using Equation (3).  
\[\mathrm{R}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}} \tag{3}\]  
AP represents the area under the curve formed by accuracy and recall rates. The average accuracy, calculated using Equation (4), indicates the average precision across all samples.  
\[\mathrm{AP}=\int_{0}^{1}\mathrm{P(r)dr} \tag{4}\]  
Figure 5: Label distribution of dataset.  
The [EMAIL_ADDRESS] stands for the average accuracy across all categories with an IoU threshold of 0.5. Meanwhile, [EMAIL_ADDRESS]:0.95 represents the average accuracy across all categories within the IoU range of 0.5 to 0.95. Thus, [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 are the two most commonly used indicators for evaluating the performance of target detection algorithms.  
To enhance the description of the detector's capability in multiscale object detection, COCO evaluation indicators, such as average precision for small objects (APS), average precision for medium objects (APM), and average precision for large objects (APL) were used. An APS is a small object with an AP value area of less than 32 \(\times\) 32. An APM is an object with an AP value between 32 \(\times\) 32 and 96 \(\times\) 96. An APL is an object with an AP value greater than 96 \(\times\) 96.
## 2 Related Work
### Localization Based  
The authors in [1; 2; 4; 18; 19] discuss the geographic or localization-based routing. The work in [1; 2] review the works in which the concept of localization-based routing is used. Both of these above, discuss reliability and none of them work on mobility management or packet size management. Moreover, in [1; 2], the concept of single-hop and multi-hop is devised. The challenges which are discussed in these works are high interference, limited batteries of sensor nodes, low bandwidth and malicious attacks. The work in [18] achieves the higher PDR by finding the locations of alive nodes. Afterward, the data packets are sent to these alive nodes, accordingly. The challenges discussed in [19] are localization, feasible hardware, relevant simulation tools and low power gliders.
# Design of NO\({}_{2}\) Photoacoustic Detection System Based on Finite Element Simulation
## 3 Design of Photoacoustic System Simulation
### Effect of Air Inlet and Outlet on Photoacoustic Signal  
In order to simplify the model and reduce the amount of calculation, the inlet and outlet holes on the buffer cavity of the photoacoustic cell were ignored during the construction of the geometric model of the photoacoustic cell. However, the buffer cavity of the photoacoustic cell actually manufactured has an air inlet on the outlet hole. Therefore, the influence of the upper and outlet holes on the buffer cavity was simulated.  
Following the steps in Section 2.1 to build the finite element model of the photoacoustic cell, the geometric model of the photoacoustic cell containing the inlet and outlet pores was first built. The gas pipe used in the laboratory was a 6 mm diameter polytetrafluoroethylene tube, and the diameters of the inlet and outlet pores on the buffer cavity were consistent with the diameters of the gas pipe. The grid model is shown in Figure 6. The grid model shown in Figure 3 and the grid model shown in Figure 6 were adopted in the same simulation-computing environment, and the same simulation parameters and conditions were applied to simulate the characteristic frequency of its operation. Figure 7a shows the pressure change in the photoacoustic cell with an air inlet in the second-order mode, and Figure 7b shows the pressure change in the center line of the photoacoustic cell with an air inlet in the second-order mode. Compared with Figure 5, it is not difficult to see that the resonance frequency, sound pressure during resonance, and sound pressure distribution of the two are basically the same, with a small difference. Therefore, it can be concluded that the simplified model is feasible to optimize the inlet and outlet holes on the buffer cavity.
## 2 Related Work
### Resource Scheduling  
The current problem for resource scheduling and QoS optimization aspects in SAGIN has proved to be an NP-hard problem [16], and most of the existing solutions use Exact Solution Algorithms and Heuristic Algorithms to solve it.  
There are many excellent results in existing research on SFC scheduling. For example, [PERSON] et al. conducted simulation experiments in small-scale scenarios using Matlab SCIP [17], but this approach could be more effective in large-scale environments. [PERSON] et al. proposed a VNF remapping and scheduling algorithm based on Tabu Search to improve the request acceptance rate of SFC [18]. However, the only experiments were conducted in small simulation scenarios.  
Although there are many studies on SFC orchestration in other scenarios and excellent results have been achieved, there needs to be more studies on resource scheduling and QoS optimization in SAGIN. [PERSON] et al. proposed a joint learning-based algorithm to deploy SFC in SAGIN [19]. [PERSON] et al. proposed a heuristic SFC deployment algorithm with an inter-domain path calculation method based on surgical inter-domain path computation, aiming to reduce the load on the compute nodes [20]. [PERSON] et al. proposed a Location-Aware Resource Allocation (LARA) algorithm based on Greedy and IBM CPLEX 12.10 to reduce the average utilization of compute resources in satellite and terrestrial networks [21]. [PERSON] et al. proposed a DRL-based SFC deployment algorithm to reduce end-to-end latency in large-scale LEO networks [22]. [PERSON] et al. formulated the SFC embedding problem as a congestion game and proposed three algorithms suitable for different scenarios to meet users' latency requirements [23]. [PERSON] et al. proposed a load-aware SFC orchestration algorithm to improve service capacity and load balancing. However, these studies should have considered the energy consumption and delay of the infrastructure in SAGIN [24]. Due to the complex structure and variable topology of the SAGIN, its problems in SFC are more complex and need more profound research. In order to meet the challenges of complex and changing environments in SAGIN, this paper is dedicated to minimizing the service energy consumption while satisfying the strict constraints on network resources and delays imposed by VNF in SFC. To this end, we employ a DRL-based algorithm to quickly obtain a solution set of potentially optimal deployment scenarios. Subsequently, through further filtering by heuristic algorithms, we can find the optimal deployment scheme precisely.
# Ultrasound and Magnetic Resonance Imaging of the Tongue in Obstructive Sleep Apnoea
## 2 Materials and Methods
### Mri  
MRI measurements were carried out at the Medical Imaging Centre of Semmelweis University using a Philips Ingenia 1.5 T MRI device. MRI scans were conducted from the posterior nasal spine to the hyoid bone. As part of the neck MRI study, measurements of sagittal T2 TSE, STIR and T1 TSE, coronal T1 TSE (each with a slice thickness of 3.5 mm without gaps), and axial T2 SPIR, T1 TSE, and DWI (with a slice thickness of 3 mm with a gap of 1.5 mm) were performed. A Philips IntelliSpace Portal (Philips Healthcare, Best, The Netherlands) was used to analyse the scans and an experienced radiologist interpreted the results. The patients were examined in supine position and instructed to breathe normally through their noses.  
The sagittal diameter of the tongue was measured in the anteroposterior directions, as the longest diameter between the apex and the base of the tongue. Tongue thicknesses were defined as the longest diameters between the origin of the genioglossus muscle and the surface of the tongue. Both calculations were based on T2 and T1 TSE weighted images.  
The coronal and axial diameters were defined as the longest lateral diameters in the coronal or axial direction in coronal T1 TSE, axial T2 SPIR and T1 TSE scans. The midsagittal area (cm\({}^{2}\)) means the surface measured in the middle line of the sagittal axis, which was determined using the DICOM viewer software. The volume of the tongue was calculated using a semiautomatised analysis software (Philips IntelliSpace Portal Multi-Modality Viewer). The measurement was based on the calculation of the Region of Interest and volume rendering.  
Tongue Fat% was defined as the ratio of the area on the midsagittal axis that shows the density of fat and the total surface of the tongue. In T1 weighted images, continuous adipose tissue in the tongue can be relatively well differentiated, and tongue Fat% was calculated using the fat area in the middle of the tongue using sagittal images and scaled to the midsagittal tongue area (Figure 2).  
Figure 1: US image of the tongue in grayscale mode B. The axial, sagittal diameters and volume are visualised (i.e., D1 axial, D2 coronal and D3 sagittal diameters; taken from our data).
# Differential Evolution Applied to a Multilevel Inverter--A Case Study
## 2 Materials and Methods
### Structure of the Seven-Level Cascaded Multilevel Inverter: Case Study  
Reference [27] provides a systematic review of publications using metaheuristic methods to minimize THD in multilevel inverters. The publication highlights that the most used topology in combination with the differential evolution algorithm is the single-phase cascaded multilevel inverter with seven levels.  
The most outstanding features of CHBMILI are:  
* The generation of its switching signals is simple, allowing the use of any PWM technique based on multi-carrier or vector spaces [28,29].
* Switching states can be changed to compensate for faults.
* Input capacitors have no voltage balance problems [30].  
Considering the advantages, the topology selected as a case study for this paper is the seven-level single-phase CHBMILI with an RL load connected to the output, as shown in Figure 3. MATLAB Simulink software was used to perform the simulation of the case study. The differential evolution method is used to minimize THD.  
Figure 2: CHBMILI waveform with seven levels and three switching angles.  
Table 1 shows the parameters used for the design and simulation of the cascaded multilevel inverter:
# Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
## 2 Proposed System
### Texture Analysis and Classification
#### 2.3.4 Growth and Direction Analysis  
In several cases, the texture of a smoke region's border is poorly defined. Then, it is possible to find some regions with similar texture to smoke or at least equivalently described by LBP. This fact inevitably produces incorrect classification. To improve the system performance and reduce the false alarms, an analysis of the ascendant and expanding moving agglomerations over time is proposed. To this end, a series of images is analyzed considering the difference between frames, searching for regions with smoke texture that increase their size in an upward direction. If such region maintains its upward growth over time, it can be considered as smoke region and then an alarm is triggered. This is accomplished by overlapping and connecting blocks with smoke texture and eliminating noisy blocks, where the noisy blocks are those classified as smoke, but scattered and isolated and disappearing and reappearing during the video sequence. This can be accomplished using the scheme proposed by [PERSON] et al. [7], which is given as follows:  
\[\hat{A}_{t}^{k}=\left\{\begin{array}{ll}\hat{A}_{t-1}^{k}\cup\hat{A}_{t}^{k}& \text{if}&\hat{A}_{t-1}^{k}\cap\hat{A}_{t}^{k}\
eq\varnothing\\ \hat{A}_{t}^{k}&\text{otherwise}\end{array}\right.,\qquad k=1,2,\cdots,K \tag{26}\]  
\[\hat{H}_{t}=\bigcup_{k=1}^{K}\hat{A}_{t}^{k} \tag{27}\]  
where \(A_{t}^{k}\) is an area or region classified as smoke texture in the frame \(t\), \(\hat{A}_{t-1}^{k}\) is a region marked as smoke texture at frame \(t-1\) and \(k\) is the label of each region. \(\hat{H}_{t}\) is the resulting image with new overlapping or connected regions. \(H_{t}\) is the image with smoke texture regions, which is analyzed to determine the regions that overlap with the same region of the previous picture at time \(t-1\). The connected regions at time \(t\) acquire the same label as the previous region.  
Figure 4: Smoke blocks detected that meet the characteristics of movement and color.
## 3 Seismic Control Effectiveness of the TID for Base-Isolated Structures
### Structure Information  
To illustrate the control effectiveness of the TID, three 5-, 10-, and 15-storey structures have been considered for this study. Table 4 lists the structural parameters of three types of structures modeled using the lumped mass method, including storey mass, stiffness, and damping properties [56]. Specifically, a dead load factor of 1.0 and a live load factor of 0.5 were applied to calculate the lumped mass for seismic response. It is important to note that the first storey, functioning as a seismic isolation storey, does not serve the same purpose as the upper storeys. The first-order modal dynamic characteristics of these three structures are presented in Figure 10, in this case, the mode-shape vectors are all normalized such that the first element, corresponding to the base storey, is set to one. The last parameter \(\gamma_{1}\) is the modal mass participation coefficient, which reflects the contribution of the first-order modal to the seismic response of the structure. Considering that interstorey deformation primarily occurs in the isolation storey, while the upper structure predominantly remains elastic, the laminated rubber bearings can be approximated as springs with viscous damping, exhibiting linear elastic behavior. Therefore, the seismic response analysis of the base-isolated structure in this study is conducted using a linear elastic model [57; 58]. However, when lead rubber bearings or friction pendulum bearings are used, the nonlinear behavior of the isolation storey must be considered, which will be the focus of our future research.
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 3 Results and Discussion
### Seasonal Variations of Forecasting Models  
To compare the forecasting ability of seasonal variations of foF2, samples of data at 00:00 UT and 12:00 UT in 2015 and 2019 are selected, respectively.  
Figure 10 are samples of seasonal variations of observed and forecasting foF2 values for 4 stations at DAR, BRI, CAN, and HOB for the low solar activity year in 2019. Similar samples of comparations between observed and forecasting foF2 values for 4 stations at SAY, SHY, WUH, and BEJ stations for high solar activity year in 2015. The observed values are shown together with the forecasting values obtained from BPNN, GABP, IRI2016, and LSTM models. As can be seen from Figures 10 and 11, all four prediction models can capture the general trend of seasonal variabilities of foF2 no matter during low or high solar activity year. However, the variation trend of the LSTM model is closer to the observations, the BPNN and GABP model are between the LSTM model and IRI2016 model, while the IRI2016 model deviates from the observation curve to some extent, indicating that the LSTM model is much better than BPNN, GABP, and IRI2016 models.  
Figure 10: Samples of comparisons of the seasonal variation between observed and 4 models forecasting foF2 values at 00:00 UT (**right panel**) and 12:00 UT (**left panel**) for selected verification stations in 2019.