esa-sceva/satcom-chunk-collection huggingface dataset:
# [PERSON] Generative Adversarial Networks Based Data Augmentation for Radar Data Analysis
###### Abstract  
Ground-based weather radar can observe a wide range with a high spatial and temporal resolution. They are beneficial to meteorological research and services by providing valuable information. Recent weather radar data related research has focused on applying machine learning and deep learning to solve complicated problems. It is a well-known fact that an adequate amount of data is a positively necessary condition in machine learning and deep learning. Generative adversarial networks (GANs) have received extensive attention for their remarkable data generation capacity, with a fascinating competitive structure having been proposed since. Consequently, a massive number of variants have been proposed; which model is adequate to solve the given problem is an inevitable concern. In this paper, we propose exploring the problem of radar image synthesis and evaluating different GANs with authentic radar observation results. The experimental results showed that the improved Wasserstein GAN is more capable of generating similar radar images while achieving higher structural similarity results.  
Keywords:weather radar; data augmentation; generative adversarial networks; structural similarity +
Footnote †: journal: Applied sciences
## 3 Evaluation of the Energy Conversion Subsystem and Energy Storage Subsystem
### Conversion Process
#### 3.1.2 Hydrogen  
Hydrogen (H\({}_{2}\)) is a clean energy carrier, has a high gravimetric energy density, and can be used in fuel cells and combustion engines to get back energy, where the former has higher efficiency than the latter but does not reach the efficiency of batteries. The challenges in hydrogen-based storage are that it is expensive and the fact that it is the least dense element, which creates the need for either compression or liquefaction owing to volumetric constraints. Storage of hydrogen as a high-pressure gas (H\({}_{2}\)(g)) requires the use of composite materials for the vessel, whereas the liquefaction process for obtaining liquid hydrogen (H\({}_{2}\)(l)) is energy-intensive [21].
## 6 Performance Settings  
Three static path planning algorithms and one dynamic model were implemented to evaluate the performance of FLPPL. SCAN, LMAT and Z-Curves are the static models while the NLA_MB model was used as a dynamic algorithm.  
Two localization algorithms, Weighted Centroid Localization (WCL) [46] and Weight-Compensated Weighted Centroid Localization (WCWCL) [44], were implemented to analyze the effectiveness of the mobility models implemented. WCL is a simple localization algorithm that provide a localization  
Figure 9: The movement and node localization process in the MA.  
estimation with a relatively low communication consumption and a lower energy cost. The location estimation is based on the weights of the anchors and their estimated distance. WCL has been used as the main tool of localization in several path planning models. WCWCL is an extended and improved version of WCL. It gives more impact for the nearby anchor nodes to calculate the weights which therefore provides more accurate estimation. Figure 10 shows an enlarged example of the FLPPL mobility movement and the estimation of location of the same nodes deployment in the two algorithms.  
We evaluated the performance of the proposed FLPPL model using MATLAB environment and compared it with the other implemented models of SCAN, LMAT, Z_Curve and NLA_MB in terms of localization accuracy, localization precision and localization ratio and coverage respectively. The simulation tool and implemented parameters were chosen in accordance with some other similar works, we used them here for consistency. A square network area is assumed with a size, \(S\), of 100 m \(\times\) 100 m. A randomly distributed set of 250 nodes, \(N\), is used with only one mobile anchor, \(M\). The maximum movement, \(d_{max}\) indicates the maximum distance that the mobile anchor can take in travelling around the network. Different maximum movements were used to evaluate the models. A random starting point approach was used in each run for each model. A realistic wireless model was used by implementing the characteristics of a wireless node that is equipped with a Chipcon CC1100 radio module [47]. Such specification were already used in similar works including [8; 44].  
The rest of the parameters are shown in the following Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbol** & **Value** \\ \hline Network size (\(m\)) & \(S\) & 100 \(\times\) 100 \\ Number of mobile anchors & \(M\) & 1 \\ Number of unknown nodes & \(N\) & 250 \\ Maximum movement distance (\(m\)) & \(d_{max}\) & 35, 70, 105, 140, 175 \\ Path loss exponent & \(\beta\) & 3.5 \\ Power loss (\(dB\)) at \(d_{0}\) & \(PL(d0)\) & \(-60\) \\ Reference point (\(m\)) & \(d0\) & 1 \\ Standard deviation of noise & \(\sigma\) & \(3,5,7,9\) \\ Simulation run & & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Simulation values and parameters.  
Figure 10: The FLPPL mobility movement and the estimation of location of the same nodes deployment in (a) WCL, and (b) WCWCL.
## 3 Results
### Meta-Analysis
#### 3.4.2 Sub-Group Analysis for Probing Pocket Depth (PPD)  
As no significant results were found while comparing the resorbable membrane group with the non-resorbable membrane group in terms of pocket probing depth, a subgroup analysis was performed based on the study design, using the included studies to evaluate any significant difference in these two groups. The subgroup analysis for probing pocket depth shows a significant difference in treatment effect in the split mouth study design group, with a mean difference of \(-\)0.60, 95% CI [\(-\)0.86, 0.35]; \(Z=4.69\) (\(p<0.00001\)), when compared to parallel mouth study design group showing a non-significant difference with a mean difference of 1.32, 95% CI [\(-\)0.15, 2.80]; \(Z=1.76\) (\(p=0.08\)). In the split mouth design, a significant result was seen in the non-resorbable group, whereas in the parallel mouth design, no significant result was seen. The heterogeneity is less in the studies with the split-mouth study design with Chi\({}^{2}=0.02\) and I\({}^{2}=0\%\) in comparison to the studies in the parallel study group with Chi\({}^{2}=91.55\) and I\({}^{2}=97\%\) (Figure 5).
## 3 System under Study  
The proposed MILP-based algorithm and the reference non-linear formulation are applied to evaluate the optimal scheduling of the DC microgrid proposed as the backbone of the Electric Vehicle Supply Infrastructure (EVSI). The EVSI is a modular solution integrating renewable sources and storage for intelligent charging processes of EVs [51]. As introduced in a previous study [52], its structure includes a common DC bus, where all devices are interfaced with proper converters.  
Figure 1: Flowchart of the iterative methodology to account for non-linear parameters in the mixed integer linear programming (MILP) algorithm.  
In particular, the DC microgrid under study comprises a PV plant, including polycrystalline modules, an energy storage system (ESS) based on Li-Ion batteries, a connection with the AC utility grid, and five charging stations for vehicles, according to the scheme reported in Figure 2. A bidirectional AC/DC converter is exploited to connect the utility grid and the EVSI, while six different bidirectional DC/DC converters connect the ESS and the EVs to the DC bus. Additionally, a unidirectional DC/DC converter interfaces the PV system.  
The DC microgrid test case is aimed to reproduce the conditions to be realized in the framework of Bari Port Authority, Italy. Table 1 shows the installed sized for the DC microgrid devices, determined according to a proper sizing procedure as developed in a previous study [48], with a further increase in PV and ESS size to improve the reliability of the system.  
With regard to the operating range of the devices, a variation between maximum capacity and 20% of capacity is assumed for EVs, whereas the limits are more stringent for the ESS (95% maximum, 25% minimum) in order to take into account an energy reserve that can be used for further regulation services in real time.
# Magnetic Communication Using High-Sensitivity Magnetic Field Detectors
## 2 Characteristics of Magnetic Field Detectors
### Receiver Coil (Induction)  
Coil-based magnetometers employ the principle of MI. This implies that a change of the magnetic flux density through a coil leads to a voltage difference between the clamps of the coil. The voltage difference is proportional to the alternating magnitude of the magnetic flux density. The magnetic flux density through a coil changes if it is exposed to a time-varying magnetic field. Based on the sensitivity analysis made in [12], the dynamic range of a coil-based detector is large compared to other magnetic sensors. By inserting a high-permeability coil core material and optimizing the size of the coil, magnetic flux densities \(B\) as weak as \(B_{\mathrm{min}}=20\) fT can be measured [12].
## 4 Results  
### Experimental Setup and Dataset  
The proposed system is tested in an outdoor setup. The training and testing data were collected from Tainan City, Taiwan, from a residential building rooftop, as shown in Figure 4a,b. A total of 168 h (1 week) of data were collected through the thermopile array, ambient temperature sensor, cup anemometer and wind vane. The total height of the sensors from the ground was 30 m and a vertical pole was used as the fixture for the sensors. Figure 4c shows the top view of the proposed device and experimental setup. In this setup, thermopile array data are collected simultaneously with the cup anemometer  
Figure 3: (a) Estimation and working principle proposed wind speed and direction sensing system using ANN models. (b) Potential application of the proposed system.  
and wind vane at 2-s intervals to establish a one-to-one sampling correspondence between the proposed device measurements and the observed/actual wind speed with direction.  
In Figure 5, the thermopile array (64 channels), ambient temperature, wind speed and wind direction collected at 2 s intervals are shown. From the plots, it can be observed that the wind speed and wind direction data show a highly stochastic nature. However, the change in trends of wind speed corresponds to the change in the trends of the thermopile array data. This represents the relationship between these two variables. Wind direction does not show an obvious relationship. The vertical line (black colored) separating the plots shows the separation of datasets used for training and testing. The data on the left side of the vertical line are the 96-h dataset used for training the regression models, including ANN, SVM and LR, while the data on the right side are the 72-h data that are used for testing.  
Figure 4: Images of sensors installed for experimental setup. (**a**) Picture taken from below showing the cup anemometer, wind vane and proposed sensing system installed on a vertical pole and (**b**) picture taken from the rooftop showing the positions of different sensors on the fixture. (**c**) Picture taken from the top showing the arrangement of different sensors.
###### Abstract  
Ship detection in panchromatic optical remote sensing images is faced with two major challenges, locating candidate regions from complex backgrounds quickly and describing ships effectively to reduce false alarms. Here, a practical method was proposed to solve these issues. Firstly, we constructed a novel visual saliency detection method based on a hyper-complex Fourier transform of a quaternion to locate regions of interest (ROIs), which can improve the accuracy of the subsequent discrimination process for panchromatic images, compared with the phase spectrum quaternary Fourier transform (PQFT) method. In addition, the Gaussian filtering of different scales was performed on the transformed result to synthesize the best saliency map. An adaptive method based on GrabCut was then used for binary segmentation to extract candidate positions. With respect to the discrimination stage, a rotation-invariant modified local binary pattern (LBP) description was achieved by combining shape, texture, and moment invariant features to describe the ship targets more powerfully. Finally, the false alarms were eliminated through SVM training. The experimental results on panchromatic optical remote sensing images demonstrated that the presented saliency model under various indicators is superior, and the proposed ship detection method is accurate and fast with high robustness, based on detailed comparisons to existing efforts.  
ship detection; hyper-complex Fourier transform; visual saliency; panchromatic optical remote sensing images; rotation-invariant modified LBP +
Footnote †: journal: Remote Sensing
## 2 Data
### Data Levels  
The data format of the ASTRI project was originally defined in compliance with those of the Cherenkov Telescope Array, adopting the FITS format [23]. Detailed information on the levels of the ASTRI data processing can be found in [14]. ASTRI data levels of interest for the work presented here are as follows:  
* Data Level 1a (DL1a): Single-telescope calibrated event data. For each event, they contain intensity and time tag uncleaned images to be processed for the evaluation of morphological and time parameters; they are used as input in our tests;
* Data Level 2a (DL2a): Array-wise event parameter data. For each event, they contain the morphological and time-based parameters for all the telescopes in the array triggered by that event, plus its stereoscopic reconstruction parameters. At the moment, all data level files are produced with standard procedure implemented inA-SciSoft, except for the computation of the time parameters at DL2a, which is currently performed with a working-code being implemented in the standard data reconstruction chain;
* Data Level 2b (DL2b): Array-wise fully reconstructed files. For each event, they contain the final global reconstructed parameters, which define the probability to be a gamma-ray event, and its reconstructed energy and arrival direction in the sky.  
In this work, we considered two DL2b data samples: the first was achieved by reducing data up to DL2b without any time-based parameters as training parameters in the Random Forest method for gamma/hadron separation, while the second by including in the Random Forest procedure, in addition to the standard ones, the time-based parameters described in Section 4.  
The DL2b files of the two data samples are then further processed, separately, using appropriate tools of the ASTRI data reduction and scientific analysis software in order to derive the global properties of the final dataset in terms of sensitivity, angular resolution,and energy resolution. In this way, we are able to assess the impact of the time parameters in the final performance of the system.
## 4 Discussion
### Positioning of the Proposed Approach Against Other XAI Methods  
To contextualise the present study, it is necessary to position the proposed methodology within the broader landscape of XAI techniques. This work does not aim to develop a new state-of-the-art classifier; instead, it introduces an XAI methodology that offers a distinct approach to model interpretation.  
For example, widely used techniques such as Grad-CAM [33] are effective in generating saliency maps that highlight the pixels on which a CNN model focuses for a specific prediction, thus providing an explanation of attribution or the 'what.' In contrast, the controlled variation example-based approach presented here is designed to answer a different question: 'how' and 'why' a model's prediction changes in response to systematic variations in a semantic feature (i.e., the size of the polyp). This methodology is therefore particularly well suited to probe model robustness and identify sensitivity thresholds, offering a more dynamic form of explanation.  
Consequently, this approach should be seen not as a replacement for saliency-based methods, but as a complementary tool. While a technique such as Grad-CAM [33] can explain a single static prediction, the proposed methodology facilitates a more dynamic understanding of a model's behaviour across a spectrum of controlled scenarios. This process can reveal potential biases and limitations in a way that static attribution methods cannot.
# A Narrow-Down Approach Based on Machine Learning for Indoor Localization
## 3 System Design
### Sub-Clustering Algorithms
#### 3.4.3 Multi-Variate Regression-Based DNN Algorithm  
A deep neural network (DNN) model consists of multiple stacked hidden layers and it is able to approximate any arbitrary function (i.e. linear/non-linear) to any degree of accuracy [38]. The training process is involved in finding the optimal weights, so that the loss function is minimal [39]. We develop our multivariate regression approach based on the lightweight deep neural network (MVR-DNN). A quick response, less training time, high localization accuracy, and generalization are the key considerations. The process of finding the optimal network size is very complicated, and no reliable method exists in the literature to find the proper size of the neural networks [40]. Researchers use their intuition and experience to find the best architecture to solve their problems. A small network requires less memory to store weights, involves less computational work, and shows a fast response as there are very short propagation delays [41]. In contrast, larger networks exhibit poor generalization. [PERSON] et al. [42] proposed that a single hidden-layer network is enough to approximate a non-linear decision boundary. We utilized a single hidden layer to construct our proposed model and utilized rectified linear function (Relu) as the activation function since they help reduce the vanishing gradient problem [43]. We have applied the mini-batch gradient descent (GD) optimization technique to boost the training process, as batch gradient descent (BGD) can be very slow since they are involved in redundant computations for large datasets. Although, stochastic gradient descent resolves this redundancy, it falls prey to local minima. Mini-batch gradient descent has two advantages [44]: (1) it shows a more stable convergence and (2) it makes computing the gradient very efficient. Common mini-batch sizes range between 50 and 256. Our proposed model is a supervised training algorithm, where the training samples are split according to their measuring space. We trained our proposed model on each sub-area's dataset separately; consequently, each sub-area has its own trained model, and this strategy works well to reduce computational complexity and increase localization accuracy, as shown in the Section 4.4. Each model consists of an input layer, a hidden layer(s), and an output layer. Each neuron in the input layer represents an input variable from the training samples, so the number of input layer neurons is equal to the length of the input feature vector (i.e., RSS Vector). The output layer has two nodes because the response of the DNN-based MVR model is the actual 2D position of the object.
## 3 Results
### Evaluating the Capability of SIF in Estimating GPP  
To evaluate the relationship between GPP and SIF at different timescales, we examined the relationships between our simulated SIF and flux tower GPP at different wavelengths at both instantaneous and daily timescales for the Park Falls site (Figure 12). The model predicted SIF exhibited consistently strong linear correlation with the flux tower GPP for different wavelengths (757 nm, 771 nm, and 740 nm) at both timescales (\(p\) value \(<\) 0.001). Meanwhile, for each wavelength, the RMSE was lower at the daily timescale than at the hourly timescale.  
Figure 11: Comparison of simulated SIF (in GOME-2 SIF wavelength) against GOME-2 SIF observations from 2007 to 2017. The black points and blue points represent the SIF simulation with default parameters and that with the optimized parameters, respectively.  
Figure 12: Relationships between SIF simulated by the SCOPE model and flux tower GPP at the Park Falls site at both instantaneous and daily timescales.  
We also examined how well the simulated SIF by the optimized SCOPE model could be used to estimate GPP based on the relationship between these variables (Figure 13). Flux tower GPP had strong linear relationships with the satellite retrieved SIF and MODIS-derived EVI (\(p\) value < 0.001) (Figure 13a-d). The OCO-2 SIF at 757 nm was more strongly correlated with tower GPP than the OCO-2 SIF at 771 nm. The OCO-2 SIF and MODIS EVI data were more strongly correlated with tower GPP than GOME-2 SIF. We further used these relationships between satellite SIF and tower based GPP (Figure 13a-c) to predict the time series of GPP from our simulated SIF time series over the period 2003-2017 for different wavelengths (Figure 13e-g). For comparison purposes, we also calculated EVI from the surface reflectance simulated by the optimized SCOPE model for the period 2003-2017 and then used the relationship between tower GPP and MODIS EVI (Figure 13d) to predict GPP from EVI for the 15-year period (Figure 13h). The SIF-GPP models exhibited a better performance than the EVI-GPP model. Furthermore, the relationship between OCO-2 SIF and GPP had a better performance in predicting GPP than that between GOME-2 SIF and GPP. Our results showed that the SIF data simulated by the optimized SCOPE model can be used to predict GPP continuously.
## 4 Properties of Treated Liquids from Waste Cooking Oil as an Alternative Insulating Liquid
### Influence of WCO Treatment on Thermal Properties
#### 4.1.4. Acidity of Treated WCO  
The acidity of insulating liquids is quantified as the amount of potassium hydroxide (KOH), in milligrams, required to neutralize the acids present in one gram of the liquid, commonly referred to as the total acid number (TAN). A high acid value in insulating liquids can degrade the dielectric strength of the oil and accelerate the deterioration of solid insulation materials, such as Kraft paper. Furthermore, the synergistic effect of acids and moisture within the transformer can lead to corrosion of metallic components and the hydrolytic degradation of cellulose-based insulation [158; 159; 160]. Although elevated acidity in natural esters is often considered less aggressive toward electrical components due to their inherent chemical structure, excessive acid content can promote oxidative degradation, increase electrical conductivity, and ultimately impair the dielectric performance of the insulating liquid.  
The comparative summary of waste cooking oil's acid values before and after transesterification using various catalysts is presented in Table 11. A significant reduction in acid value was observed across all studies, confirming the effectiveness of transesterification in enhancing the chemical stability of WCOs for transformer applications. The improvement is primarily attributed to the conversion of free fatty acids and glycerides into methyl esters. Heterogeneous catalysts such as CaO and coal ash achieved the highest reductions, with coal ash exhibiting a remarkable 98.94% decrease. Homogeneous catalysts like NaOH  
Figure 10: (**a**) Wax crystallization of oil without depressants; (**b**) crystallization of oil with depressants.  
and KOH also demonstrated substantial improvements, though generally slightly lower in comparison. It is to be mentioned that samples with initially low acid values, like in Refs. [82; 141], show modest reductions, indicating that transesterification benefits both normal and highly degraded oils. However, when benchmarked against the IEC 62770 standard for natural ester-based insulating liquids [87], which stipulates an acid value of 0.06 mg KOH/g, only the modified oils from Refs. [82; 100; 141] meet the standard. While the other samples exhibit promising reductions, further purification or process optimization may be necessary to bring their acid values within the acceptable range for safe and reliable application in power transformers.