esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Large Hadron Collider (LHC) is being prepared for an extensive upgrade to boost its particle discovery potential. The new phase, High Luminosity LHC, will operate at a factor-of-five-increased luminosity (the number proportional to the rate of collisions). Consequently, such an increase in luminosity will result in enormous quantities of generated data that cannot be transmitted or stored with the currently available resources and time. However, the vast majority of the generated data consist of uninteresting data or pile-up data containing few interesting events or electromagnetic showers. High-Luminosity LHC detectors, including the Compact Muon Solenoid (CMS), will thus have to rely on innovative approaches like the proposed one to select interesting collision data. In charge of data reduction/selection at the early stages of data streaming is a level 1 trigger (L1T), a real-time event selection system. The final step of the L1T is a global trigger, which uses sub-system algorithms to make a final decision about signal acceptance/rejection within a decision time of around 12 microseconds. For one of these sub-system L1T algorithms, we propose using quantized neural network models deployed in targeted L1T devices, namely, field-programmable gate arrays (FPGA), as a classifier between electromagnetic and pile-up/quantum chromodynamics showers. The developed quantized neural network operates in an end-to-end manner using raw detector data to speed up the classification process. The proposed data reduction methods further decrease model size while retaining accuracy. The proposed approach was tested with simulated data (since the detector is still in the production stage) and took less than 1 microsecond, achieving real-time signal-background classification with a classification accuracy of 97.37% for 2-bit-only quantization and 97.44% for quantization augmented with the data reduction approach (compared to 98.61% for the full-precision, standard network).  
CMS; Level 1 Trigger; HGCAL; Quantized neural network; hls4 ml; EM shower; classification +
Footnote †: journal: Applied sciences
## 3 Cross-Dipole Antenna Design  
In this study, nested dipole antennas were designed as a cross-dipole antenna. In design processes, Finite Integration Technique (FIT) based microwave simulation software is used. Designed cross-dipole  
Figure 1: Illustration of the neural network approach.  
antenna is illustrated with dimensions in Figure 2a. This type of antenna is designed for 2.40 GHz communication frequencies and each pole has a 30.75 mm length. Moreover, FR-4 dielectric material is used in dielectric layers which have a permittivity of 4.3 and copper is used in antenna resonators which have a conductivity of \(5.8\times 10^{8}\) S/m. Designed cross-dipole antenna has a good return loss of \(-17\) dB at 2.45 GHz, as illustrated in Figure 2b. Moreover, there is an undesired transmission between each antenna as shown, and the transmission value is nearly \(-15\) dB at 2.45 GHz. The purpose of this study was to reduce this undesired transmission characteristic and improve the isolation between two antenna elements forming the cross dipoles. For this reason, a resonator plane was designed and integrated to the cross dipole antenna, as presented in the next section.
# Phased Array Antenna Analysis Workflow Applied to Gateways for LEO Satellite Communications
## 3 System Architecture
### Comparative Assessment  
Once the constellations' systems have been described, a comparison is made. The frequency assignment is shown in Tables 1 and 2[24; 29; 31; 33], where \(\#_{CH}\) is the number of channels and \(\dagger\) is for shared frequencies. In the Telesat system, the lower and upper Ka-band spectrum is shared between the user and the gateway links, the number of beams and the bandwidth per beam being reconfigurable [2].  
Regarding the parabolic antennas, the most important values are summarized in Table 3. It should be noted that more than one antenna is needed to make an earth station, so they will be occupying a large surface, considering the diameter and the above ground level [24; 29; 31; 33]. The values of the effective isotropic radiated power (EIRP), maximum gain and path distance are taken from [2; 33; 34]. The minimum value of elevation needed to communicate with the satellites in LEO is obtained from [24; 29; 31; 33].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{5}{c}{**Gateway Uplink Links**} \\ \cline{2-5}  & \(BW_{CH}\) & \(\#_{CH}\) & \(BW_{TOT}\) & \(\mathbf{k}\) \\ \hline SpaceX & 500 & 8 & 4000 & 1 \\ \hline Telesat & \(\dagger\) & \(\dagger\) & 4200 & 2 \\ \hline OneWeb & 250 & 16 & 4000 & 1 \\ \hline Amazon & 50 & 50 & 2500 & 1 \\ \hline  & MHz & - & MHz & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the uplink frequency allocation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{5}{c}{**Gateway Downlink Links**} \\ \cline{2-5}  & \(BW_{CH}\) & \(\#_{CH}\) & \(BW_{TOT}\) & \(\mathbf{k}\) \\ \hline SpaceX & 250 & 9 & 2250 & 1 \\ \hline Telesat & \(\dagger\) & \(\dagger\) & 3600 & 2 \\ \hline OneWeb & 155 & 16 & 2480 & 1 \\ \hline Amazon & 100 & 23 & 2300 & 1 \\ \hline  & MHz & - & MHz & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the downlink frequency allocation.
## 2 Materials and Methods
### SEIR-H Model
#### 2.1.3 Dynamics of Required Hospital Resources  
The dynamics of reported cases, highlighted in green in Figure 1, is mainly focused on the number of reported individuals in need of general hospitalisations and Intensive Care Units (ICUs). Hence, it is important to analyse the portion of infected individuals that develop severe cases of the disease as well as the fatality rate.  
A compartmental model is considered: (i) the compartment \(H\) includes general hospitalisation of COVID-19 patients, (ii) the compartment \(U\) includes all those reported individuals requiring ICU, and (iii) the compartment of the deceased \(F\). In this work, we use the letter \(F\) for the deceased because it is the first letter of the translation in Spanish of the deceased (_Fallecido_).  
The equations that describe the dynamics in hospitals can be expressed as follows:  
\[\frac{dH}{dt} =\lambda_{I\to H}\gamma I-\lambda_{H\to U}\delta_{H\to U}H-\lambda_{H\to F} \delta_{H\to F}H-\lambda_{H\to O}\delta_{H\to O}H, \tag{7}\] \[\frac{dU}{dt} =\lambda_{H\to U}\delta_{H\to U}H-\lambda_{U\to F}\varphi_{U\to F}U- \lambda_{U\to O}\varphi_{U\to O}U,\] (8) \[\frac{dF}{dt} =\lambda_{I\to F}\gamma I+\lambda_{H\to F}\delta_{H\to F}H+ \lambda_{U\to F}\varphi_{U\to F}U. \tag{9}\]  
The dynamics of the portion of infected individuals who develop severe cases and need to be hospitalised, is represented in Equation (7), by using a parameter \(\lambda_{I\to H}\). A portion of those who are hospitalised, given by the parameter \(\lambda_{H\to U}\), require intensive care. The average length of stay at a normal bed before requiring an intensive care unit, is given by \(1/\delta_{H\to U}\). However, hospitalised individuals are dying with portion \(\lambda_{H\to F}\) with average staying in a hospital of \(1/\delta_{H\to F}\). The rest of the portion \(\lambda_{H\to O}=1-\lambda_{H\to U}-\lambda_{H\to F}\) is recovered with average staying in hospital of \(1/\delta_{H\to O}\).  
The Equation (8) represents the dynamics of the occupation of ICU. The admission in ICU is restricted to individuals with severe cases who are initially hospitalised in normal beds. The portion of dying individuals from the ICU is \(\lambda_{U\to F}\), with an average stay in ICU of \(1/\varphi_{U\to F}\). The recovered portion is \(\lambda_{U\to O}=1-\lambda_{U\to F}\), with average stay in ICU of \(1/\varphi_{U\to O}\).  
The last compartment corresponds to accumulated death, given by the Equation (9). Here, it is included those who die from those hospitalised and also those who were in ICU. In addition, a certain portion given by \(\lambda_{I\to F}\) dies without admission in the hospitals.
# Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy
## 4 Fine-Level Focusing and Classification of MEO SAR Ship Targets
### Ship Target Fine Classification Based on Local Features  
The second-level classification network is designed to achieve fine target classification through local structural features. The input data consist of fine-refocused local target slices. Under MEO imaging conditions, these slices typically range from tens to hundreds of pixels in size, which does not impose significant computational burdens. Furthermore, to meet integrated processing efficiency requirements, a lightweight deep learning model based on MobileNet-v3 [36; 37] is implemented to achieve fine classification of ship targets. MobileNet-v3 employs depthwise separable convolutions combined with residual connections to achieve efficient inference. The detailed architecture is illustrated in Figure 10.  
MobileNet-v3 employs a hybrid neural architecture search (NAS) approach combining block-wise and layer-wise search strategies. The framework first establishes the macro-architecture through block-wise search, then performs micro-architecture refinement via layer-wise search, achieving complementary global-local optimization for inference efficiency. To enhance classification robustness for localized target features, we train the model using multi-scale target slices, including: full, 1/2, 1/3, and 1/4 of target regions. Figure 11 illustrates the extraction of local region slices for a bulk carrier.  
After the network is constructed, three groups of fine-classification network weight parameters corresponding to the three coarse-classification results are trained. For aircraft carriers, the fine-classification network performs aircraft carrier confirmation. For other military ships, the network accomplishes further classification of cruisers and destroyers. For other civilian ships, the network achieves further classification of tankers, bulk carriers, and container ships.  
Figure 12 shows the whole flowchart of the proposed multi-level focusing-classification method. After the ship SAR data in the range-compressed domain are obtained, Doppler parameter estimation is conducted. Then, RCMC and azimuth focusing are performed using the estimated Doppler centroid frequency and Doppler FM rate. Next, the coarsely-focused  
Figure 11: Local region slices for a bulk carrier: (**a**) 1/4 local region slice; (**b**) 1/3 local region slice; (**c**) 1/2 local region slice; (**d**) full region slice.  
Figure 10: MobileNet-v3 architecture.  
SAR image is input to the coarse-classification network to complete coarse classification. Based on the result, the image local region slice is extracted, and fine focusing is conducted via space-variant high-order phase error compensation. Finally, the fine-focused image slice is input to the fine classification network to achieve the final fine classification.
# Parameter Combination Framework for the Differential Evolution Algorithm
## 2 Related Works  
### Classical Differential Evolution  
Differential evolution (DE) is a population-based heuristic random search algorithm. It has three steps in each generation of evolution: mutation, crossover, and selection [1].  
The population can be described as \(P_{g}=\{x_{1,g},\,x_{2,g},\,\ldots,\,x_{Np,g}\}\), where g is the current generation of the evolution process, and NP is the population size. \(X_{i,g}=\{x_{1,i,g},\,x_{2,i,g},\,\ldots,\,x_{D,i,g}\}\) is an individual vector, where \(D\) represents the dimension size of the solution space. The population is initialized uniformly, after which DE enters the iterative evolutionary process until the termination condition.  
(1) Mutation step: each individual in the population as a target vector generates a corresponding mutation vector through the mutation strategy. There are many variants of the mutation strategy. The naming convention is DE/x/y, where x is the selection method of the base vector and y is the number of difference vectors. The mutation strategy of classical DE is named DE/rand/1 as follows:  
\[v_{i,g}=x_{r1,g}+F\cdot(x_{r2,g}-x_{r3,g}) \tag{1}\]  
where \(x_{i,g}\) is the target individual, \(x_{r1,g},\,x_{r2,g},\,x_{r3,g},\,x_{r4,g}\), and \(x_{r5,g}\) are random individuals selected from the population, which differ from one other and from \(x_{i,g}\). \(x_{rb,g}\) is the best individual in the current generation, \(v_{i}\) is the generated mutation vector, and \(F\) is the scale factor parameter.  
(2) Crossover step: the mutation vector \(v_{i}\) and target vector \(x_{i}\) exchange internal components to generate candidate vector \(u_{i}\). There are generally two methods for crossover operations: binomial crossover and exponential crossover. Binomial crossover is a commonly used method, and its equation is as follows:  
\[u_{i,g}=u_{j,i,g}=\left\{\begin{array}{ll}v_{j,i,g}&\text{if}(\;\;rand_{j}(0, 1)\leq CR\;\text{or}\;j=j_{rand})\\ x_{j,i,g}&\text{otherwise}\end{array}\right. \tag{2}\]  
where \(CR\) is the crossover probability parameter, \(u_{i}\) is the trail vector, and j is one of the dimensions of the vector (j = 1, 2,..., \(D\)).  
(3) Selection step: this step determines which vectors enter the next generation. The DE algorithm uses binary greedy selection between the target vector \(xi\) and trail vector \(u_{i}\). The equation is as follows:  
\[x_{i,g+1}=\left\{\begin{array}{ll}u_{i,g}&f(u_{i,g})\leq f(x_{i,g})\\ x_{i,g}&\text{otherwise}\end{array}\right. \tag{3}\]
## 4 Modeling and Implementation
### Numerical Implementation
#### 4.2.2 Proposed Characterization Methodology  
The proposed methodology for characterizing the different parameters (\(\mathrm{C_{s}}\), \(\mathrm{D}\) and \(\alpha\)) is illustrated in Figure 5. The Bayesian networks shown in Figure 4 allow us to integrate the observed information on the child nodes (chloride measurements at different depths) in order to update the a posteriori probabilities of their parent nodes. The proposed methodology is composed by two steps, the first one aims at determining \(\mathrm{C_{s}}\) and \(\mathrm{D}\) from unloading test data. It is supposed that the parameters identified during this step are representative of the exposure conditions (i.e., \(\mathrm{C_{s}}\)) and chloride diffusivity (i.e., \(\mathrm{D}\)) for uncracked concrete. The factor \(\alpha\) is identified in Step 2 using the evidence of tests with loading (static or cyclic). The a posteriori histograms of \(\mathrm{C_{s}}\) and \(\mathrm{D}\) in Step 1 are used as prior information of Step 2. This procedure allows us to focus the characterization process on the acceleration factor \(\alpha\). At the end of each step, a posteriori histograms of the variables are used to characterize the distribution, mean and standard deviation of each parent node.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Number of States** & **A Priori Distribution** & **Boundaries** \\ \hline \(\mathrm{C_{s}}\) (\% \(\mathrm{Cl^{-}}\)) & 50 & Obtained from a & [0; 2] \\ \(\mathrm{D}\) (mm\({}^{2}\)/day) & 50 & Unloading test & [10\({}^{-12}\); 4] \\ \(\alpha\) & 50 & Uniform & [1; 6] \\ \(\mathrm{C}\)(\(\mathrm{x_{i}}\), \(\mathrm{t}\)) (\% \(\mathrm{Cl^{-}}\)) & 60 & - & [0; 2] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Discretization of nodes and a priori information for the Bayesian network under loaded conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Number of States** & **A Priori Distribution** & **Boundaries** \\ \hline \(\mathrm{C_{s}}\) (\% \(\mathrm{Cl^{-}}\)) & 50 & Uniform & [0; 2] \\ \(\mathrm{D}\) (mm\({}^{2}\)/day) & 50 & Uniform & [10\({}^{-12}\); 4] \\ \(\mathrm{C}\)(\(\mathrm{x_{i}}\), \(\mathrm{t}\)) (\% \(\mathrm{Cl^{-}}\)) & 60 & - & [0; 2] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Discretization of nodes and a priori information for the Bayesian network under unloaded conditions.
## 3 Methods
### S-Transform  
S-transform, introduced by [PERSON] et al. [28], is a time-frequency analysis method, which is deduced from short-time Fourier transform and continuous wavelet transform. S-transform is given by the equation  
\[S(\tau,f)\ =\ \int_{-\infty}^{\infty}ECG_{T}(t).\frac{\left|f\right|}{\sqrt{2\pi}}e ^{\frac{-(\tau-t)^{2},\sigma^{2}}{2}}.e^{-i2\pi ft}dt \tag{1}\]  
where \(ECG_{T}(t)\) is the ECG signal in the time domain, \(\tau\) denotes the time of spectral localization and \(f\) is the frequency in the Fourier transform.  
The Fourier transform of \(ECG_{T}(t)\) is  
\[ECG_{F}(f)\ =\ \int_{-\infty}^{\infty}ECG_{T}(t).e^{-i2\pi ft}dt \tag{2}\]  
Figure 1: Block diagram of the pace pulses detection algorithm. The 10 s electrocardiogram (ECG) buffer input is passed through S-transform, Shannon energy (ShE) calculation and the threshold-based rule. Fs stands for the sampling frequency. The output is the pace pulses positions within the analyzed 10 s ECG epoch.  
The S-transform could be represented as operations on the Fourier spectrum \(ECG_{\rm F}\)(_f_) of the time signal \(ECG_{\rm T}\)(_t_):  
\[S(\tau,f)\ =\ \int_{-\infty}^{\infty}ECG_{\rm F}(f+\alpha).e^{\frac{-2\pi^{2} \alpha^{2}}{f^{2}}}e^{2\pi ft}d\alpha \tag{3}\]  
where \(f\
eq 0\) and \(\alpha\) is a frequency variable.  
The S-transform of a discrete time series, for which \(f=\eta/NT\), \(\tau=jT\) and \(\alpha=m/NT\), is given by  
\[S\Big{(}jT,\frac{n}{NT}\Big{)}\ =\ \sum_{m\ =\ 0}^{N-1}ECG_{\rm F}\Big{(}\frac{m +n}{NT}\Big{)}e^{\frac{-2\pi^{2}\alpha^{2}}{\pi^{2}}}.e^{\frac{2\pi mj}{N}} \tag{4}\]  
The output of S-transform is a matrix of complex values with rows indicating the frequency of the analyzed ECG signal and columns indicating the time.
# Stabilization of Road Embarkments on Peat Soils Using Oil Shale Ash and Pozzolanic Additives
## 5 Conclusions  
Using kukersite oil shale ash to stabilize peat soils has been largely unsuccessful in studies published so far. However, the current study shows that excellent results can be achieved when the chemistry of the process is altered. For this purpose, two changes are necessary:  
First, the pH of the mixture must be raised to over 13 to allow the retarding influence of humic acids to be eliminated.  
Second, pozzolanic agents must be added to form the C-S-H phase which binds the organic fibers together and allows the organic fibers to function as a reinforcement.  
Based on an experiment and tests, it can be said that the strength of the test embankment material is sufficient up to pressures of 100 kPa, and the arrangements accompanying its loading do not pose a threat to transport. The load-bearing capacity can be increased by using a geotextile, but this needs to be evaluated with corresponding plate load tests.  
In any case, the achieved compression strength is sufficient for building roads with a moderate load capacity, such as roads serving natural reserves or military objects. The building costs of such roads are several times lower than those using traditional methods. For full-scale applications, a machine with a continuous mode of operation must be constructed. A matter of importance is also the significantly lower environmental impact of this method; the water regime of peat soils remains presumably unchanged, thus preventing GHG emissions and reducing negative impacts on biodiversity in comparison with traditional mass-stabilization methods. The tests were planned to be carried out sequentially in order to reach a satisfactory result with a minimum amount of time, labor, and material consumption. Each subsequent series of tests was based on the previous series, on the basis of which a limited number of test samples were selected for further studies; therefore, the results cannot be summarized.  
Conceptualization, V.P. and J.L.; methodology, E.R.; validation, M.S.; formal analysis, T.T.; investigation: field and lab testing V.P. and A.L.; XRD/XRF: H.M.; writing--original draft preparation, J.L. and V.P.; writing--review and editing, E.R.; supervision, J.L.; project administration M.M.; funding acquisition: T.T. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by project KIK-15401 \"Stabilization of soil containing humic substances in roads with using oil shale ash\".  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.
# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
## Study Area and Data
### Satellite Data and Preprocessing
#### 2.2.2 Landsat Data  
Both Landsat 7 Enhanced Thematic Mapper plus (ETM+) and Landsat 8 Operational Land Imager (OLI) images were used in the study. Due to the failure of the Scan Line Corrector (SLC) in Landsat 7 since 31 May 2003, an estimated 22% of any given scene has been lost [35]. The SLC-off effects are most pronounced along the edge of the scene and gradually diminish toward the center of the scene so the central part of the image, approximately 22-km wide, is less affected by the SLC-off problem. The cloud-free central parts of Landsat 7 ETM+ images without the SLC-off effect can still be used as pair images. The edge parts of scene are affected by the SLC-off problem and are not directly suitable as pair images in data fusion without gap-filling; however, the remaining valid pixels in the ETM+ images can be used as reference data to evaluate the fused results.  
Study site 1 is covered by two Landsat scenes in World Reference System 2 (WRS-2) p28r31 (path 28 and row 31) and p29r31 (path 29 and row 31), located in the sidelap area between these two Landsat scenes (Figure 1a). All cloud-free Landsat 8 images, including eight from p28r31 and seven from p29r31 (red points in Figure 2), were selected as pair images and used in the STARFM model. Fifteen cloud-free Landsat 7 ETM+ images with SLC-off effects from both p28r31 and p29r31 (green points in Figure 2) were only taken as validation images. Since experimental site 2 is located in the central area of the Landsat scene p25r36 (path 25 and row 36), there are no data gaps in the SLC-off Landsat 7 ETM+ images. Thus, all ten cloud-free images from Landsat 7 and 8 from this scene (p25r36) were used as pair images (Figure 2).  
Landsat 7 and 8 surface reflectance products were ordered and downloaded through the U.S. Geological Survey (USGS) Earth Explorer website [36]. They have been calibrated and atmospherically corrected using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) [37; 38]. Landsat surface reflectance quality assessment band [37; 38] can be used to define the clear and valid pixels. In this study, scenes with less than 1% cloud fraction (including cloud, cloud shadow and snow) are considered to be clear. A subset of each Landsat scene, larger than the study site, was processed to reduce possible image edge effects due to partial moving window near the image edge in the STARFM data fusion [6].