esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
[PERSON] was responsible for gathering and preprocessing ionospheric scintillation data collected during solar cycle 24 across the equatorial anomaly. His tasks included addressing data quality issues, detecting outliers, and ensuring that the dataset was well-prepared for machine learning analysis. [PERSON] focused on identifying and engineering key features that influence ionospheric scintillations, such as solar activity parameters, geomagnetic indices, and local time effects. [PERSON] led the development of machine learning models tailored for predicting ionospheric scintillations. He explored various algorithms, including Random Forest, XGBoost, Support Vector Machines, and Neural Networks, to identify the most effective models. His work involved hyperparameter tuning, model training, and validation to ensure robust and accurate predictions. [PERSON] was responsible for evaluating the performance of the developed models using metrics like accuracy and precision. He also interpreted the results in the context of space weather forecasting, analyzing how the model predictions aligned with observed scintillations during solar cycle 24. Additionally, [PERSON] contributed to discussions on the implications of these predictions for mitigating the effects of ionospheric disturbances. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The magnetic field observations used in this study were obtained from the Svulbard network through the International Monitor for Auroral Geomagnetic Effects (IMAGE) \"[[http://space.fmi.fi/image/www/index.php](http://space.fmi.fi/image/www/index.php)]([http://space.fmi.fi/image/www/index.php](http://space.fmi.fi/image/www/index.php)) (accessed on 17 May 2024)\". The solar wind parameter data used for prediction are obtained from OMNI Web via \"[[https://omniweb.gsfc.nasa.gov/](https://omniweb.gsfc.nasa.gov/)]([https://omniweb.gsfc.nasa.gov/](https://omniweb.gsfc.nasa.gov/)) (accessed on 8 June 2024)\". Kp and F10.7 data were also obtained from NASA data centers via OMNI Web. **Acknowledgments:** Our special thanks to the hardworking operators of the OMNI Web database for providing real-time data of the solar wind parameters to the public for scientific use and the Norwegian Polar Institute, responsible for maintaining the magnetometers at the Bear Island station of the Svalbard network within the International Monitor for Auroral Geomagnetic Effect (IMAGE) network. The corresponding author expresses sincere gratitude to [PERSON] and [PERSON] for their unwavering commitment and guidance during the writing of this essay. Finally, I extend heartful thanks to the TICAD 7 scholarship for providing the necessary funding that enabled my pursuit of a master's degree in space environment at the Egypt-Japan University of Science and Technology (E-JUST) in Egypt. **Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 4 Implementation Details
### Quantitative Experimental Results  
To verify the performance of our model in remote sensing image super-resolution reconstruction, we conducted experiments on the test dataset and compared our model with several state-of-the-art models, including SRCNN [3], IMDN [10], RFDN [11], CARN [9], CTN [40], FeNet [46], LGCNet [47], DCM [48], and HSENet [49]. All compared models were retrained on the same dataset to ensure fairness in the evaluation.
## Magnetic Properties of the Y(Co-Fe-Ni)\({}_{5}\) Alloys
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Material**} & \multicolumn{2}{c}{**Unit Cell Volume (Å\({}^{3}\))**} & \multicolumn{2}{c}{**c/a Ratio**} & \multicolumn{2}{c}{\(K_{1}\) (meV/cell)} & \multicolumn{2}{c}{\(K_{1}\) (MJ/m\({}^{3}\))**} \\ \cline{2-9}  & **GGA** & **LDA** & **GGA** & **LDA** & **GGA** & **LDA** & **GGA** & **LDA** \\ \hline YFe\({}_{5}\) & 84.84 & 77.46 & 0.79 & 0.79 & 0.51 & 1.17 & 0.96 & 2.42 \\ \hline YCo\({}_{5}\) & 82.65 & 76.38 & 0.80 & 0.80 & 9.89 & 6.44 & 19.17 & 13.51 \\ \hline YNi\({}_{5}\) & 81.96 & 74.94 & 0.81 & 0.81 & 1.40 & 0.48 & 2.74 & 1.03 \\ \hline YFe\({}_{3}\)Co\({}_{2}\) & 86.40 & 77.28 & 0.79 & 0.79 & 2.01 & 6.71 & 3.73 & 13.91 \\ \hline YFe\({}_{3}\)CoNi & 86.22 & 76.38 & 0.81 & 0.82 & 4.51 & 5.04 & 8.38 & 10.57 \\ \hline YFe\({}_{3}\)Ni\({}_{2}\) & 86.34 & 76.64 & 0.84 & 0.83 & 1.93 & 3.69 & 3.58 & 7.69 \\ \hline \end{tabular}
\end{table} 
Table 4: FPLMTO results. GGA and LDA refer to generalized gradient approximation and local density approximation, correspondingly. \(K_{1}\) reflects the magnetic anisotropy energy. methods. The results are very consistent for YFe\({}_{5}\), YNi\({}_{5}\) (small MAE), and YFe\({}_{3}\)CoNi (intermediate MAE -4.5-5 meV/cell), while for YCo\({}_{5}\), the GGA produces a value significantly greater than the LDA (9.89 and 6.44 meV/cell, correspondingly). For YFe\({}_{3}\)Co\({}_{2}\) and YFe\({}_{3}\)Ni\({}_{2}\), the LDA values are about three and two times larger than the GGA number, correspondingly. Apparently, the particular electron exchange and the correlation approximation are important for the anisotropy, and to explore this further, we applied several other popular formulations for YFe\({}_{3}\)Co\({}_{2}\); the resulting MAE varied very sensitively. This remarkable sensitivity has not been recognized before for these types of magnets and certainly warrants further studies.
# Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1
## 2 Theory and Method
### GNSS-R Observables
#### 2.2.1 Forward GNSS-R Observables  
One existing observable derived from \(\overline{DDM}(\tau,f)\) was defined in [45] and later was modified as weighted area in [44], which can be formulated as:  
\[Weightted-Area=\underset{\overline{DDM}(\tau,f)>\text{threshold}}{\sum}\overline {DDM}(\tau,f)\cdot\delta_{\tau}\cdot\delta_{f}, \tag{2}\]  
where \(\delta_{\tau}\) and \(\delta_{f}\) are delay and Doppler shift resolutions respectively. If \(\delta_{\tau}\cdot\delta_{f}\) is set as one, this observable represents the normalized power summation of the grids whose \(\overline{DDM}(\tau,f)\) values are greater than a preset threshold, which is defined through specific processes described in Section 2.4.2.  
Figure 1: Global Navigation Satellite System Reflectometry (GNSS-R) Delay-Doppler Maps (DDMs) collected over (**a**) sea ice field (close to a specular reflection point) and (**b**) sea water field obtained from the Space GNSS Receiver Remote Sensing Instrument (SGR-ReSI) receiver onboard the TDS-1 satellite on 21 January 2016.  
A similar observable modified from an observable defined in [45] is defined in [40] as the number of grids (i.e., pixel number) whose values greater than a given threshold:  
\[Area=\underset{\overline{\text{DDM}}(\tau,f)>threshold}{\sum}\delta_{\tau}\cdot \delta_{f}. \tag{3}\]  
If \(\delta_{\tau}\cdot\delta_{f}\) is set as one, this observable represents the pixel number of \(\overline{DDM}(\tau,f)\) whose component values are greater than a given threshold, which is defined through specific processes described in Section 2.4.2.
# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
## 3 Methods
### Performance Measures  
In this study, a total of 150 validation points (including 22, 37, 23, 12, 15, 13, 16, and 12 points for wheat, corn/sunflower, other crops, forest, water body, sand dune, residential area, and abandoned salinized farmland, respectively) were selected to validate the accuracy of the land-use identification and abandoned salinized farmland identification in 2019 with metrics of precision, recall, F1-scores, and accuracy. The validation points were labelled manually using very high spatial resolution imageries (satellite constellation from Planet Lab Inc. and Google Earth imageries). The spatial distribution of validation points is shown in Figure 1b. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of relevant instances that were retrieved. The F1-score can be understood as a combination of both precision and recall. It should be noted that accuracy takes into account only the total number of samples and thus may be a misleading metric for imbalanced datasets. In contrast, precision, recall, and F1-score consider the total number of samples for each class separately.  
\[\text{Precision}=\frac{\mathit{TP}}{\mathit{TP}+\mathit{FP}} \tag{15}\]  
\[\text{Recall}=\frac{\mathit{TP}}{\mathit{TP}+\mathit{FN}} \tag{16}\]  
\[\text{F1}-\text{Score}=\frac{2}{\mathit{1}/\mathit{Precision}+\mathit{1}/ \mathit{Recall}} \tag{17}\]  
\[\text{Accuracy}=\frac{\mathit{TP}+\mathit{FN}}{\mathit{TP}+\mathit{TN}+ \mathit{FP}+\mathit{FN}} \tag{18}\]  
where \(\mathit{TP}\), \(\mathit{TN}\), \(\mathit{FP}\), and \(\mathit{FN}\) represent the true positive, true negative, false positive, and false negative predictions, respectively.
# Radar Voxel Fusion for 3D Object Detection
## 3 Methodology
### Distance Threshold  
Similar to [21], we argue that an IoU-based threshold is not the optimal choice for 3D object detection. We use both an IoU-based and a distance-based threshold to distinguish between the positive, negative, and ignore bounding box anchors. For our proposed network, the positive IoU-threshold is empirically set to 35% and the negative threshold is set to 30%. The distance threshold is set to 0.5 m.
## 2 Literature Review
### Mie Scattering  
The theoretical models behind Mie Scattering can be directly derived from the Maxwell equations, given that the extinction and backscattering efficiencies \(Q_{\text{ext}}\) and \(Q_{\text{back}}\) depend on the particle geometry and refractive index of the particle material. To compute the extinction coefficient \(\alpha\) or backscattering coefficient \(\beta\) requires integration over the particle diameter D [17]:  
\[\alpha=\frac{\pi}{8}\int_{0}^{\infty}D^{2}Q_{\text{ext}}(D)N(D)\;dD \tag{7}\] \[\beta=\frac{\pi}{8}\int_{0}^{\infty}D^{2}Q_{\text{back}}(D)N(D)\;dD \tag{8}\]  
where \(D^{2}\) denotes the particle surface factor, \(Q\) the efficiency, and \(N(D)\) is a weather-specific particle density function. The factor \(\pi/8\) before the integral is related to the cross section of a sphere (\(\pi r^{2}\)) and the conversion from radius \(r\) to diameter \(D\) for these equations.  
Figure 2: Bistatic beam setup of a lidar. Adapted from [9].  
Exemplary density functions can be found in the literature, e.g., [9; 18]. Figure 3 shows the scattering anisotropy for water and SiO\({}_{2}\), the main mineral of sand. The scattering anisotropy is dimensionless and is denoted as \(g\) and takes values between \(-1\) and \(1\), where \(1\) stands for completely forward scattering, \(0\) for an uniform scattering into all directions, and \(-1\) for total backscattering. As SiO\({}_{2}\) has a higher refractive index, the particle will scatter less light into the forward direction compared with water.
## 2 System Description
### Orientation of Normal Vectors to the Mesh  
When the first surface is created in the virtual environment, all the normal vectors in its mesh are oriented in the same direction. The generation of subsequent surfaces triggers a method that reorients the direction of the normal vectors (if necessary) of all meshes of the current surfaces. The goal is to keep all normal vectors pointing toward the outside of a possible solid object.  
The algorithm implemented by this function checks each mesh on the surfaces in the scene to determine the number of intersections between a line passing through the center of gravity of a triangle defined by the vertices of the mesh and the mesh of other surfaces in the direction of the normal vector. If the total intersection points is an odd number in any mesh of the surface, the normal vectors of all the meshes in that surface are reversed. An example of how the algorithm works is shown in the process illustrated in Figure 2.  
The sweeping operation can also be performed on surfaces generated in a previous step. In this case, a new surface is produced directly, which is a copy of the original and with the normal vectors in opposite directions. The normal vectors on the first surface are assigned the direction that is opposite the motion of the controller, and those on the second surface are assigned the direction of motion of the controller. Likewise, a new surface is  
Figure 1: Types of surfaces supported by EVM.  
Figure 2: Structure of the surfaces represented in the scene.  
generated for each entity that defines the contour of the surface being swept. Once again, the normal vectors will point toward the outside of the enclosed volume, as shown in Figure 3.
## 5 Identification and Prediction of Major Fractures
### Results of the Main Rupture Prediction  
In order to reduce the influence of different fractures sample quantities on the identification results, 2300 AE data were randomly selected from both the major fractures and other fractures, respectively. Among those AE events, 4000 AE data were used as the training data, and 600 AE data were used as the test data. Each datum contains 21 feature information of acoustic emissions, including amplitude, frequency, energy, rise time, and so on. The constructed neural network consists of one input layer, one output layer, and four hidden layers, with each hidden layer containing sixteen units. The activation function is the logarithmic sigmoid transfer function; the training method used is gradient descent, and the number of training epochs is set to 6000.  
The recognition model established through the BP neural network is used to recognize both the training and testing data. As shown in Table 5, the model has an average accuracy of 85.47% for the training data, and an average accuracy of 82.83% for the testing data. The overall average accuracy of the model for both the training and testing data is above 80%, indicating that the established model is effective in recognizing various forms of fractures. The highest accuracy for identifying the main fracture in the testing data is for specimen 1-1, with 93.33%, and the lowest accuracy is for specimen 1-2, with 83.68%. The average accuracy for identifying the main fracture in the testing data is 87.22%, while the average accuracy for identifying other fractures in the testing data is 78.44%. The accuracy for identifying the main fracture is significantly higher than the accuracy for identifying other fractures.  
Figure 12: Model of neuron.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{3}{c}{**Accuracy for Triandata/\%**} & \multicolumn{3}{c}{**Accuracy for Testdata/\%**} \\ \cline{2-7}  & **Whole** & **Main Fracture** & **Other Fracture** & **Whole** & **Main Fracture** & **Other Fracture** \\ \hline
1-1 & 96.33 & 95.70 & 96.96 & 95.18 & 93.33 & 97.00 \\
1-2 & 81.25 & 78.75 & 83.75 & 76.67 & 83.68 & 69.67 \\
1-3 & 78.83 & 88.30 & 69.35 & 76.67 & 84.67 & 68.67 \\ Average Value & 85.47 & 87.58 & 83.35 & 82.83 & 87.22 & 78.44 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Identification results of fracture mode through the BP neural network.
## 9 Obesity Biomarkers and Risk Assessment
### Serological Markers  
Given the key implications of leptin in the mechanisms governing appetite and weight regulation, it becomes evident that measuring its circulating levels could aid in tracking the evolution of weight excess. Leptin levels may also aid in evaluating an individual's responsiveness to weight reduction programs [331].  
In addition to leptin, adipocytes secrete a wide array of biologically active factors, or adipokines. FABP4 falls into this category. FABP4 has been linked to weight control, metabolism, and formation of atherosclerosis, and has demonstrated increased circulating levels in patients with obesity, cardiovascular diseases, or metabolic syndrome [130; 332]. Adiponectin is an adipokine with antiatherogenic, anti-inflammatory, insulin-sensitizing, and cardioprotective effects. Its serum concentration can provide details regarding the atherosclerotic process in children, as its decrease correlates with premature thickening of carotid walls in pediatric subjects, as well as regarding insulin sensitivity assessment, due to its relation to circulating insulin levels and the HOMA-IR [333].  
HOMA-IR, defined as: \(\frac{\left(\text{Serumglucose}\left(\frac{\text{mml}}{\text{m}}\right) \right)\left(\text{Serum insulin}\left(\frac{\text{mult}}{\text{m}}\right) \right)}{22.5}\) can, in and of itself, provide information on the initially subclinical stages of diabetes, particularly in obese patients [334].  
The atherogenic index of plasma (AIP), defined by the following formula:  
\(\text{AIP}=\text{lg}\frac{\text{Serum Triglycerides (mmol/L)}}{\text{Serum HDL cholesterol (mmol/L)}}\), is a good measure of the balance between the harmful effects of hypertriglyceridemia and the cardio-protective properties of HDL-cholesterol. AIP has shown a potentially stronger correlation to cardiovascular risk when compared to its individual components, and may aid in the quantification of treatment response [335; 336; 337].  
Certain markers for hepatic injury also foretell the ill effects of obesity on metabolism. An increase in ALT (alanine aminotransferase) levels has been linked to insulin resistance and altered glucose tolerance as well as to increased levels of circulating free fatty acids and triglycerides [338]. The AST/ALT (aspartate aminotransferase/ALT) ratio has been proposed as a potential marker for screening adolescents with increased cardiometabolic risk. Gama-glutamyl peptidase (GGT) may also provide useful information regarding hepatic involvement in obesity [339].  
Other potential biomarkers for obesity include isoleucine, glyceric acid, serin, 2,3,4-trihydroxybutyric acid, and phenylalanine [301].
## 4 Results and Discussion
### Maintenance Phase
#### 4.2.2 Collaborative Communication  
In this project, we used BIM and MR for achieving accurate transmission of multi-dimensional model information and the collaborative communication of the model by multiple parties in virtual space. Multiple designers can simultaneously observe the three-dimensional effect of the landscape after the planting of plants in advance and visualize clashes with the park structure and site preset pipeline in the process of plant growth using Hololens2 devices. While protecting the aesthetic effect of landscape design, the conflict of plants is avoided in advance, and the collaborative communication and management of multi-professional models are achieved, which indirectly reduces the later maintenance cost.
## 3 Data and Methods
### Principle and Applicability of Restoring the Denotation Thickness via the Sonic Transit Time Method  
Currently, the sonic transit time method is a commonly used denudation thickness restoration method, which first originated from the exponential relationship model between mudstone porosity and depth proposed by [PERSON] in 1930 [24]. In consolidated rock formations with a uniform porosity distribution, the porosity and travel time of sound waves can be compared. Therefore, in 1976, [PERSON] proposed an exponential model between the sonic transit time and formation burial depth [25], which can be used to estimate the formation denudation thickness, as shown in Equation (1):  
\[\Delta\mathrm{t}=\Delta\mathrm{t}_{0}\;\mathrm{e}^{-\mathrm{kD}}, \tag{1}\]  
where \(\Delta\mathrm{t}\) represents the mudstone sonic transit time (\(\mathrm{\mu s/m}\)); \(\Delta\mathrm{t}_{0}\) represents the sonic transit time at initial mudstone deposition (\(\mathrm{\mu s/m}\)); \(\mathrm{k}\) is an exponential constant; and \(\mathrm{D}\) represents the burial depth (m).  
By extrapolation, we can obtain Equation (2):  
\[\mathrm{In}\Delta\mathrm{t}=-\mathrm{kD}+\mathrm{In}\Delta\mathrm{t}_{0}, \tag{2}\]  
Equation (2) shows that, under the semilogarithmic coordinate system, there is a linear relationship between the depth and the sonic transit time corresponding to the depth [26], which is the theoretical basis for estimating the formation denudation thickness via the sonic transit time. Therefore, we can perform linear fitting on these two parameters to extend the mudstone compaction trend line below the uncomformity surface to the paleo-surface, that is, at \(\Delta\mathrm{t}_{0}\). The distance between the paleo-surface and the uncomformity surface is the thickness of the denuded strata (Figure 3):  
The key to recovering the denudation thickness by using the sonic transit time method is whether the static pressure of the denuded strata on the stratum below the uncomformity  
Figure 3: Schematic diagram of the denudation thickness restored via the sonic transit time method.  
surface is greater than the static pressure of the newly deposited strata on the stratum below the unconformity surface [27]. If this condition is satisfied, it can be ensured that the compacted framework of the denuded stratum under the new sediments has not changed; that is, the sonic transit time is used to restore the thickness of the denuded stratum in this area. According to the drilling data in the Ordos Basin, the new stratum overlying the Cretaceous erathem is only 0-300 m thick. It is a set of semi-consolidated loess deposits [28] whose thickness is far less than that of the denuded strata, and its density is much lower than that of the undenuded strata below the unconformity; therefore, the static pressure generated by the newly deposited strata is less than that generated by the denuded strata. Therefore, the sonic transit time is suitable for restoring the thickness of denuded formations in northeastern part of the Sulige gas field.
## 3 System Model  
In our paper, we consider a direct communications link between ground-mounted designated base station (BS) and an aircraft, see Figure 3. Both BS and aircraft are assumed to be equipped with transceivers with perfectly aligned beams. The aircraft of interest is assumed to be an altitude of \(d\) km. We consider the system in stationary conditions, where the movement of the aircraft is assumed to produce no additional impairment for communications. Due to the high flying altitude, we neglect the height of BS.  
For certainty, we consider uplink transmission from ground BS to aircraft. By accounting for atmospheric absorption by water vapor and oxygen, the received power air aircraft antenna can be expressed as  
\[P_{out}(f,d)=P_{in}\left(\frac{4\pi fd}{c}\right)^{-2}\frac{G_{R}G_{T}}{L_{A}( f,d)L_{C}(f,d)}, \tag{9}\]  
where \(P_{in}\) is the emitted power at ground BS, \(G_{T}\) and \(G_{R}\) are transmit and receive antenna gains, \(L_{A}(f,d)\) is the attenuation due to absorption in the atmosphere, \(L_{C}(f,d)\) is the  
Figure 3: A direct communications link between ground-mounted designated BS and an aircraft.  
propagation losses due to cloud blockage, \(f\) is the carrier frequency, \(c\) is the speed of light, and \(d\) is the distance between the BS and the aircraft.  
The propagation model in (9) features two unknowns, the average attenuation coefficient due to absorption in the atmosphere, \(L_{A}(f,d)\), and the overall losses due to cloud blockage, \(L_{C}(f,d)\). In the next section, we will determine these unknowns.