esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
## 3 A 4D Airspace-Temporal Grid Framework (GeoSOT-AS) for ADS-B Data Organization
### GeoSOT-AS Rules and Methods for Planar Dimensional Profiling
#### 3.1.1 Planar Dimensional Profiling Method
The 9 th-level grid is divided into 2 \(\times\) 2 grids, corresponding to 15\({}^{\prime\prime}\)\(\times\) 15\({}^{\prime\prime}\) (\(-\)464 \(\times\) 464 m at the Earth's equator), according to equal divisions of latitude and longitude. The 10 th-level grid is divided equally by latitude and longitude into 3 \(\times\) 3 grids, corresponding to 5\({}^{\prime\prime}\)\(\times\) 5\({}^{\prime\prime}\) (\(-\)154.6 \(\times\) 154.6 m at the Earth's equator). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Level**} & \multirow{2}{*}{**Planar Scale**} & \multicolumn{2}{c}{**Approximate Size**} & \multirow{2}{*}{**Aerial Chart Size**} & \multirow{2}{*}{**Proportion**} \\  & & **Near Equator** & & \\ \hline
1 & 6\({}^{\circ}\)\(\times\) 4\({}^{\circ}\) & \(\backslash\) & 6\({}^{\circ}\)\(\times\) 4\({}^{\circ}\) & 1:1,000,000 \\
2 & 3\({}^{\circ}\)\(\times\) 2\({}^{\circ}\) & \(\backslash\) & 3\({}^{\circ}\)\(\times\) 2\({}^{\circ}\) & 1:500,000 \\
3 & 1\({}^{\circ}\The 11 th-level grid is divided into 5 \(\times\) 5 according to latitude and longitude, corresponding to a 1\({}^{\prime\prime}\)\(\times\) 1\({}^{\prime\prime}\) grid (\(\sim\)31 \(\times\) 31 m at the Earth's equator). The profiling method is compatible with the Chinese specification of civil aeronautical charting at multiple levels, showing good inheritance and compatibility between multiple map levels (Table 1); thus, it provides a solid basic framework for existing ADS-B data management.
## 3 Results and Discussion  
### Number of Oxygen Vacancies in a Defect  
The dependence \(C_{O}\)(\(C_{d}\)) shown in Figure 3 agrees with Expression (1), which testifies to the formation in the TiO\({}_{2}\) nanoparticles of defects consisting of oxygen vacancies. Processing by the least-square method gave the number of vacancies in a defect \(N\) = 12.5 \(\pm\) 0.9.  
Figure 2: The spectra of products of the nuclear reactions \({}^{16}\)O(d,p\({}_{1}\))\({}^{17}\)O* and \({}^{2}\)H(d,p)\({}^{3}\)H for the nanopowders synthesized by sol–gel method and annealed in vacuum. The temperatures and annealing times are shown in the figure.  
As follows from Figure 3, the formation in the dioxide titanium nanoparticles of defects containing \(N=12\) oxygen vacancies is invariant with respect to experimental conditions. First, it takes place in the nanopowders synthesized by different technologies and characterized by different mechanisms of defect formation. In the technology of SG method, in the as-synthesized powders of composition close to stoichiometric, the formation of defects occurs in the course of vacuum annealing through the oxygen depletion of nanoparticles. Upon LE, oxygen-deficient defects were formed directly in the course of synthesizing. Second, the powders strongly differ in the number of defects per unit volume of nanoparticles. This follows from Figure 2 since this parameter decreases with decreasing concentration \(C_{O}\). Finally, the powders under study differed in phase composition. In the case of the SG method, it was anatase, whereas upon LE, the mixture of TiO\({}_{2}\) phases. In particular, in the powders with S \(>\) 200 m\({}^{2}\)/g, brookite with small additions of anatase and rutile predominates, whereas at S \(<\) 200 m\({}^{2}\)/g, quite the opposite, brookite was not observed at all, only anatase and rutile. Since the number of vacancies in a defect \(N=12\) does not depend on the synthesis technology, mechanism of defect formation, phase composition, and other characteristics of nanoparticles, one can suggest that the formation of such defects, denoted as TiO\({}_{6}\), is conditioned by thermodynamic reasons. This question needs further investigation.
# Artificial Intelligence Algorithm-Based Arrangement Optimization of Structural Isolation Bearings
## 5 Discussion
### Seismic Decrease Coefficient  
Similar to the variation trend of maximum story drift ratio, the seismic decrease coefficient shows three obvious trends during the optimization process: when the weight coefficient of seismic decrease coefficient \(\alpha\) = 0.0, the seismic decrease coefficient does not decrease significantly in comparison to the initial value, and especially increases gradually in the case of 0.0-0.8-0.2, as shown in Figure 15a,b. In addition, the seismic decrease coefficient shows an increasing trend, increasing by 1.7%, 5.6%, 13%, and 1.3%, respectively, when \(\alpha\) = 0.2, \(\gamma\) = 0.0, 0.2, 0.4, and 0.8, compared to the initial value. Except in the case \(\gamma\) = 0.6, the seismic decrease coefficient decreases by 1.3%, which could be ignored. When weight coefficient \(\alpha\) > 0.2, the seismic decrease coefficient exhibits a descending trend. Fixing the weight coefficient of seismic decrease coefficient \(\alpha\) at 0.4, when weight coefficient of maximum story drift ratio \(\lambda\) increases from 0.2 to 0.4, the seismic decrease coefficient decreases by 1.8%.  
Therefore, when the seismic decrease coefficient is mainly considered, the weight coefficient of the seismic decrease coefficient \(\alpha\) should be larger than 0.2. Meanwhile, the seismic decrease coefficient would acquire 1-2% decrease percent for every increase of 0.2 in weight coefficient \(\lambda\).
## 3 Algorithm and Methods
### CNN Location Classification Model
In the online stage, the observation images of the test points are input into the CNN location classification model. Because the test point can appear at any position in positioning area, the estimated position \(L\) of the test point is obtained by using the probability weighted centroid method, which is expressed as:  
\[L=\frac{\sum_{k\in\Omega}p_{k}(x_{k},y_{k})}{\sum_{k\in\Omega}p_{k}} \tag{6}\]  
We sort the probabilities of all reference points in descending order, and \(\Omega\) is the set of the first \(K\) reference points with high probability. \(p_{k}\) is the probability of the \(k\)th reference point. \((x_{k},y_{k})\) is the coordinates of the \(k\)th reference point.
# A Physical Approach to Simulate the Corrosion of Ceramic-Coated Magnesium Implants
## 2 Materials and Methods  
### Saturation Tests  
The modified simulated body fluid (m-SBF) prepared according to the procedure described in [21] is one of the solutions that best mimics the level of inorganic compounds in the blood plasma. The solution is prepared using ultrapure water and reagent grade salts including NaCl (5.403 g/L), NaHCO\({}_{3}\) (0.504 g/L), Na\({}_{2}\)CO\({}_{3}\) (0.426 g/L), KCl (0.225 g/L), K\({}_{2}\)HPO\({}_{4}\).3H\({}_{2}\)O (0.23 g/L), MgCl\({}_{2}\).6H\({}_{2}\)O (0.311 g/L), HEPES (buffering agent, 17.892 in 100 mL of 0.2M NaOH), CaCl2 (0.293 g/L), Na\({}_{2}\)So\({}_{4}\) (0.072 g/L), and 1M NaOH (15 mL/L). To prepare a liter of the solution, all glassware were cleaned using neutral detergent followed by 1M HCL and eventually ultrapure water. The salts were dissolved in the same order in 500 mL of ultrapure water under heating and stirring using a magnetic stirrer and heating plate. The pH was adjusted to 7.4 at 37 \({}^{\circ}\)C, and then ultrapure water was added to complete the 1 L volume.  
Ten milliliters of this solution was tested for its saturation limit of MgCl\({}_{2}\) under a body temperature of 37 \({}^{\circ}\)C. Reagent grade Anhydrous MgCl\({}_{2}\) was used as an approximation to magnesium ions, the species under concern, which was also used to provide an estimation for the solubility limit in water in the literature. Ten milliliters of the solution was put in an open glass beaker, and a heating and magnetic stirrer plate was used to hold the temperature at 37 \({}^{\circ}\)C and dissolve the added increments of the salt. An amount of the salt was placed in an air-tight HDPE 250 mL bottle to be used for adding the salt until saturation. The bottle and the salt weight were measured before and after running the test using a precision scale with an accuracy of 1 mg. MgCl\({}_{2}\) salt has an exothermic dissolution nature in water, which increases the solution temperature above the target temperature at each increment. Hence, the test solution was left to cool down before adding the next increment. The process was repeated until visible crystals of the salt were no longer dissolving under stirring. The test was repeated for 10 runs. The separation of the precipitates was not possible, and their weight was not deducted due to the precipitation of other compounds than the added salt.  
Another approach was also used based on thermal feedback (see Figure 2), rather than visual feedback that relies on precipitation of non-dissolving crystals. The exothermic dissolution nature of the MgCl\({}_{2}\) was utilized by monitoring the temperature change above 37 \({}^{\circ}\)C after each salt increment. When the change was insensible by the used thermometer, saturation was assumed. This approach was repeated for another 10 runs.
# The Role of Surveillance Imaging in Patients with Advanced Laryngeal Cancer
###### Abstract  
Background: While almost one-third of patients treated for laryngeal cancer will experience disease recurrence, the current National Comprehensive Cancer Network guidelines do not recommend routine post-treatment imaging follow-up. We aimed to examine the yield of post-treatment imaging surveillance among patients with primary advanced laryngeal cancer. Methods: Patients treated for advanced-stage laryngeal carcinoma between 2000 and 2020 in a tertiary care medical center were retrospectively reviewed. Data collected included demographics, clinical and pathological features, treatment modalities, tumor recurrence data including the diagnostic modality (patient's symptoms, physical examination, or imaging), and outcome. Results: The cohort included 123 patients with advanced-stage laryngeal cancer. Thirty-five (28.7%) patients experienced disease recurrence with fourteen recurrences (40%) diagnosed by imaging. Patients diagnosed by imaging had a shorter time to recurrence diagnosis (23.8 vs. 45.9 months, \(p\)-value = 0.016), with similar 5-year overall and disease-specific survival (20.6% vs. 26.6%, log-rank \(p\)-value = 0.53 and 62.1% vs. 59.7%, log-rank \(p\)-value = 0.87, respectively). Conclusions: Performance of routine post-treatment imaging follow-up in patients with advanced laryngeal cancer was associated with a shorter time to diagnosis of recurrence and similar survival rates. Imaging follow-up should be considered in cases where clinical follow-up is limited and according to the judgment of the treating physician.  
laryngal cancer; imaging; tumor recurrence; PET-CT +
Footnote †: journal: Medical Cancer
## 3 Simulation
### Effect of Diameter on MPPT Signal  
As shown in Figure 8, the baseline value of the curve decreases as the external diameter of the steel pipe increases, and the concave feature of the signal becomes weaker. For the steel pipes with the same outer radius, the larger the internal diameter of the pipe, the more significant the differences in the MPP of the defect.  
Figure 6: Characteristics of MPP and eddy current distribution at different magnetizing currents.  
Figure 7: Signal diagram: (**a**) Signal features; (**b**) effect of DC current on the signal.  
Simulation studies were conducted for three wall thickness cases of 8 mm, 10 mm, and 12 mm. The corresponding magnetizing current densities are 2.2 \(\times\) 10\({}^{5}\) A/m\({}^{2}\), 2.86 \(\times\) 10\({}^{5}\) A/m\({}^{2}\), and 3.49 \(\times\) 10\({}^{5}\) A/m\({}^{2}\), respectively, for obtaining the approximate same magnetization degree of the steel pipe. The outer diameters \(\varphi\) of the pipes used at each wall thickness are 50 mm, 55 mm, and 60 mm, respectively. The external diameter of the pipe remains the same, and only the internal diameter is changed. The \(\Delta Vpp\) of the signals are extracted and fitted, as shown in Figure 9.  
As shown in Figure 9, the \(\Delta Vpp\) of the axial crack in the internal wall increases with the increase in the internal diameter of the steel pipe, and the \(\Delta Vpp\) values of the steel pipe with a wall thickness of 12 mm are the smallest. In addition, the signals of different depth defects with a change in the internal diameter of the pipe show the same trend, but the signal amplitude is more affected by the depth of the defect when the wall thickness is smaller, or the diameter of the pipe is larger.  
The curve of signal amplitude versus wall thickness was redrawn as shown in Figure 10. The signal decreases with the increase in wall thickness under the condition of the same external diameter. In the process of increasing the wall thickness from 10 mm to
# FPGA Implementation of ECT Digital System for Imaging Conductive Materials
## 5 System Implementation
### FPGA Platform and Software Tools  
The implementation of the ECT system is carried out on an Altera Cyclone V SoC FPGA platform. This platform has the Cyclone V 5 CSXFC6D6F31C6 Revision D FPGA device, DDR3 memory, and common interface controllers. A multicore ARM processor subsystem is integrated into the FPGA fabric, in addition to a Hard Processing System (HPS) has a dual-core ARM Cortex-A9 MPCore processor running at 925 MHz frequency. A standard AXI4 bridge connects the HPS to the FPGA fabric, which provides communication means between masters on the FPGA fabric and slaves in the HPS logic. The Matlab HDL Coder [30] integrated with the Altera suite of development tools, Quartus II, and the SoC Embedded Design Suite (EDS) are the tools used in the implementation of the proposed system [31].  
Figure 4: Block diagram of the proposed system.  
Currently, the embedded system design is applied on the system-level rather than the RTL-level in order to reduce difficulties associated with the RTL-level design and benefit from the immediate integration between the HW/SW in the system design phase. Therefore, sophisticated embedded SoC design tools such as Altera Qsys integrated to its Quartus II CAD system have been developed. These tools integrate components such as the processor, DSPs, FPGA IPs, and I/O peripherals on the FPGA platform. In addition, other tools such as SoC Embedded Design Suite (SoC EDS) help in the software design on the ARM processor [31]. However, there are some difficulties when working with these tools such as having two separate workflows. The hardware workflow is implemented using Quartus II CAD tool integrated with the HDL language for modeling custom hardware IPs, whereas the software workflow is managed by the SoC EDS toolset.  
The HDL Coder and Embedded Coder from Mathworks are Model-Based design workflow toolboxes. These two tools generate synthesizable HDL code and Embedded C code, respectively, from MATLAB code as well as from Simulink model. Therefore, the HDL coder integrated with the Altera Quartus II can generate an optimized hardware IP targeting the Cyclone V SoC FPGA device that can be integrated with a larger design using Qsys tool as well as generating the design bitstream file ready to be downloaded to the FPGA fabric. Similarly, linking with Altera SoC EDS allows the Embedded Coder to generate an executable \".elf\" file downloadable to the ARM processor on the FPGA platform.
# A Photoplethysmogram Dataset for Emotional Analysis
###### Abstract  
In recent years, research on emotion classification based on physiological signals has actively attracted scholars' attention worldwide. Several studies and experiments have been conducted to analyze human emotions based on physiological signals, including the use of electrocardiograms (ECGs), electroencephalograms (EEGs), and photoplethysmograms (PPGs). Although the achievements with ECGs and EEGs are progressive, reaching higher accuracies over 90%, the number of studies utilizing PPGs are limited and their accuracies are relatively lower than other signals. One of the difficulties in studying PPGs for emotional analysis is the lack of open datasets (there is a single dataset to the best of the authors). This study introduces a new PPG dataset for emotional analysis. A total of 72 PPGs were recorded from 18 participants while watching short video clips and analyzed in time and frequency domains. Moreover, emotional classification accuracies with the presented dataset were presented with various neural network structures. The results prove that this dataset can be used for further emotional analysis with PPGs.  
emotion recognition; photoplethysmogram; dataset; physiological signals; deep neural network +
Footnote †: journal: Applied Sciences
# Performance Analysis of Post-Quantum Cryptography Algorithms for Digital Signature
## 4 Implementation
### Application  
The implemented application can sign a selected file and verify digital signatures using post-quantum algorithms. Its main functionality is based on the pq-sign command, which can be used in the terminal. An example of the sign and verification process is presented in Figure 2. An important function is generate-keypair, which generates a private key used to sign documents and a public key, which can be used to verify signatures. The sign command is a central feature of the application--it lets users digitally sign documents, ensuring their authenticity and integrity. Verification of the digital signature is carried out with the pq-sign verify command. Additionally, the pq-sign ls command has been designed to provide a comprehensive list of all post-quantum cryptographic algorithms currently supported by the application.  
Table 1 provides specifications of the available algorithms [19]. It details the algorithm names along with their corresponding security strengths, represented by the NIST security levels [21]. The security levels of cryptographic algorithms or systems are determined based on a numerical value associated with the amount of work or operations required to compromise them. These levels range from Level 1, offering the lowest level of security, to Level 5, which provides the highest level of security [22]. The numbers at the end of the Dilithium algorithm name correspond to NIST's declared security level. The table also provides the sizes of the public keys, secret keys, and signatures, all expressed in bytes. And it is the size of the generated signature that is crucial regarding distinguishing SPHINCS+ algorithms. It is important to note that comprehending the trade-offs between the security level and resource requirements is imperative for users. This understanding can assist in selecting an appropriate algorithm for a specific security context.
# Zernike Coefficient Prediction Technique for Interference Based on Generation Adversarial Network
## 2 Methods
### Generative Adversarial Network (GAN)
#### 2.2.4 Testing GAN Model  
We also use the Colab service provided by Google to test the GAN model (pre-trained model). The test process includes two different fringe datasets in the paper, based on the formula as an ideal image and optics software as an approximate actual image, to estimate the GAN model. The fringe datasets input the Generator network to test the model, and the Generator predicts fake pictures. The testing datasets in the ideal images are different from the training datasets. According to the image-to-number translation, 32 Zernike coefficients are calculated from a fake photo and then use the quantified ground truth (target image) and the Generator's output (fake image) to calculate the RMSE. When the RMSE is smaller, the fake photo is closer to the target image. The process of testing and transfer learning is shown in Figure 5.  
Testing_1 with an ideal image: The testing datasets are generated randomly by Python with the optics formula for testing the Generator. The testing datasets have 1000 photos and are inputted into the Generator. Additionally, then, the Generator's outputs, fake images, are translated to Zernike coefficients to obtain an averaged RMSE from 1000 tests.  
Testing_2 with a simulated image: The testing datasets are generated randomly by VL with a Fizeau interferometer for testing the Generator. The testing datasets have ten simulated images and are inputted into the Generator. Additionally, then, the Generator's outputs, fake photos, are translated to Zernike coefficients to obtain an averaged RMSE from ten tests.  
Transfer learning with the simulated image: The training datasets are generated randomly by VL with a Fizeau interferometer for retraining the pre-trained model. The training datasets have 100 simulated images and are inputted into the pre-trained model to acquire a new GAN model. The training datasets (100 simulated images) do not include the testing datasets (ten simulated images). After finishing the transfer learning, we use  
Figure 5: The flowchart of the testing and transfer learning.  
the Testing_2 process to test the new Generator with the same testing datasets to gain the new testing result to increase accuracy.
# Comparison of Machine Learning Algorithms Used for Skin Cancer Diagnosis
## 2 Related Works  
Currently, the leading method in the field of recognition and classification of digital images, also in the field of diagnosis and diagnosis of skin cancers, are deep neural networks of the CNN type--Convolutional Neural Network. The networks use sets of convolutional filters, by means of which from input images characterized by relatively large dimensions and small depth (usually 3 channels--R, G, B), tensors are obtained--high depth maps of image features.  
In [17] authors propose the use of the CNN network consisting of four convolutional layers placed in blocks of two layers intertwined with max pooling layers. After the features vector is flattened, two Fully Connected layers are used for classification. The applied architecture as well as the performed preprocessing and segmentation of skin lesions allowed the authors to obtain an accuracy of 81%.  
Another effective solution presented in the literature is the use of transfer learning. Authors use convolutional neural networks pre-trained on vast databases, and then modify the classifier's part so as to adapt the network to solve a new classification problem. The technique allows for a higher validation accuracy with a relatively small data set [18]. Authors in [19] raised the important issue of the diagnosis of skin cancer in people with different skin colors, which is currently poorly represented in the literature using, among others: SqueezeNet. In [20] authors used the GoogLeNet network, achieving an accuracy of 93.2%, while in [21] authors compared the results for different neural network architectures when solving the problem of multi-class classification.  
Another approach also used in this paper is the use of machine learning algorithms to diagnose skin cancer. It is connected with the necessity of performing the image feature extraction with the use of different descriptors beforehand. In [22] the authors used classifiers: Support Vector Machine (SVM), Multilayer perceptron (MLP), k-nearest neighbors, and Adaboost, achieving an accuracy of 93% for the last classifier, while in [23] authors used the range of different algorithms to diagnose breast cancer and then compared their accuracy achieving the best results on the test set for the Decision Tree and Random Forest algorithms.  
Authors in [24] proposed a combined solution. They used pre-trained ResNet architecture [25] in order to deep feature extraction and Fisher Vector Encoding Strategy to aggregate the local deep representations into a single image representation. Principal component analysis (PCA) was also used to reduce the dimensionality of deep feature vectors. Finally, the authors used SVM to classify data which allowed them to obtain an accuracy of classification of over 86%.