esa-sceva/satcom-chunk-collection huggingface dataset:
# Electromagnetic Induction Imaging with Atomic Magnetometers: Progress and Perspectives
## 3 Application Areas and Limitations of Inductive Coils  
### Security  
Luggage, cargo and vehicle screening play an important role in ensuring transport security. X-ray based scanners are a very effective tool for this application, because of their ability to image through barriers. However, the use of ionising radiation requires adopting precautions for the operators as well as for the members of public. This constitutes a potentially serious drawback of X-ray scanners for security. In this context, electromagnetic induction imaging was considered due to its harmless nature. The demonstration [6; 7; 8] of the ability of electromagnetic imaging to penetrate through metallic barriers validated its potential use for security applications. However, increased penetration requires lowering of the operating frequency, typically below the 1 MHz level, as from the expression (1) for the skin depth. Conventional inductive sensors are severely challenged at such low frequencies, with a very significant loss of sensitivity.
# A Fiber-Optic Six-Axis Force Sensor Based on a 3-UPU-Compliant Parallel Mechanism
## 5 Experiments and Results Analysis
### Results and Analysis
#### 5.3.3. Linear Range and Strength Limits  
Experimental analysis is conducted to investigate nonlinear effects within a large load range. Forces \(F_{x}\), \(F_{y}\) and \(F_{z}\) are applied with a step size of 6 N within the range of \(-96\) N to 96 N, and the torques \(T_{x}\), \(T_{y}\) and \(T_{z}\) were applied with a step size of 0.25 N\(\cdot\)m within the range of \(-4.25\) N\(\cdot\)m to 4.25 N\(\cdot\)m. Figure 26 illustrates the measurement results. The results show that the linear ranges of force loading \(F_{x}\), \(F_{y}\) and \(F_{z}\) are \(\pm 60\) N, \(\pm 72\) N and \(\pm 90\) N, and the linear ranges of torque loading \(T_{x}\), \(T_{y}\) and \(T_{z}\) are \(\pm 2.5\) N\(\cdot\)m, \(\pm 3.25\) N\(\cdot\)m and \(\pm 3.75\) N\(\cdot\)m, respectively.  
Figure 26: _Cont._  
Figure 25: The results of repeatability experiment.  
Here, ANSYS Workbench is used to simulate the strength limits and material lifespan. The simulation results show that the maximum stress occurs at single-sided circular flexible hinge. The material of the force-measuring branches is 7075 aluminum alloy and its allowable stress [\(\sigma\)] = 419 MPa. Therefore, the maximum stress at the single-sided circular flexible hinge should be less than this allowable stress. The results of the strength limits of sensor in all directions are shown in Table 1. Although the maximum loads are significantly greater than the loads of the linear range, it is generally not allowed to exceed the load of the linear range when using sensors. The material lifespan simulation result shows that the fatigue life is greater than 1.2 \(\times\) 10\({}^{6}\) under the maximum linear load. To investigate whether nonlinear effects occur under material fatigue aging conditions, the maximum linear load (90 N) is applied in the \(Z\)-direction repeatedly. The experiment results show that the sensor still maintains a good linear output after 5000 loading cycles.
## 4 Methodology
### Model Specifications  
#### 4.1.1 Multinomial Logit Model  
The MN-logit model formulation was discussed in [PERSON] and [PERSON] [56]. For a given dataset of rural SV crashes, the probability of driver injury level \(k\) in crash \(i\) is given as:  
\[P_{i,k}^{w}=P(U_{i,k}^{w}\geq U_{i,j}^{w})\ \ \forall k\
eq j, \tag{1}\]  
where \(P_{i,k}^{w}\) denotes the probability of \(i\)th crash with injury level \(k\) on the \(w\)th road segment; \(j\) is the set of possible injury severities; \(U_{i,k}^{w}\) is a propensity function of covariates that determines the likelihood of crash \(i\) resulting in crash severity \(k\). To clarify this likelihood, a function that defines the severity probability needs to be specified and a linear function was assumed:  
\[U_{i,k}^{w}=\beta_{k}X_{i}^{w}+\epsilon_{i,k}, \tag{2}\]  
\[\beta_{k}X_{i}^{w}=\beta_{k,0}+\beta_{k,1}x_{i,1}^{w}+\beta_{k,2}x_{i,2}^{w}+ \ldots+\beta_{k,\ L}x_{i,L}^{w}=\beta_{k,0}+\sum_{l=1}^{L}\beta_{k,l}x_{i,l}^ {w}, \tag{3}\]  
where \(X_{i}^{w}\) is a vector of independent variables, such as driver gender, driver age, drunk driving, weather, road surface, etc., which determines the injury outcome \(k\); \(x_{i,l}^{w}\) is the value of predictive variable \(l\) for \(i\)th crash; \(\beta_{k}\) denotes a vector of estimable coefficient corresponding to the injury outcome \(k\); \(\beta_{k,l}\) is the coefficient of the predictive variable \(l\); \(\beta_{k,0}\) denotes the model intercept; \(\epsilon_{i,k}\) is an error term, which follows type I extreme value distribution.  
As noted, the dependent variable was a tripartite injury result; hence, we have \(Y_{i}^{w}=(Y_{i,1}^{w},Y_{i,2}^{w},\ Y_{i,3}^{w})\); \(Y_{i}^{w}=1\) denotes no injury and was considered as the reference category, while \(Y_{i}^{w}=2\) and \(Y_{i}^{w}=3\) indicate slight injury and FS injuries, respectively. Let \(P_{i,k}^{w}=Pr\big{(}Y_{i}^{w}=k\big{)}\) denote the probability of \(Y_{i}^{w}=k\). Therefore, we have:  
\[Y_{i}^{w}\sim categorical(P_{i,1}^{w},P_{i,2}^{w},P_{i,3}^{w}). \tag{4}\]  
Assuming that the probabilities \((P_{i,1}^{w},\ P_{i,2}^{w},\ P_{i,3}^{w})\) of crash severity obey a multinomial logistic distribution, the structure of MN-logit model can be shown as follows.  
\[logit\Big{(}P_{i,k}^{w}\Big{)}=\log\frac{P_{i,k}^{w}}{P_{i,1}^{w}}=\beta_{k,0} +\sum_{l=1}^{L}\beta_{k,l}x_{i,l}^{w},\ (k=2,\ 3), \tag{5}\]  
where all variables in Equation (5) are the same as those defined earlier.
## 2 Materials and Methods
### Models  
GRNDVI, GNDVI, NDVI, and DVI are vegetation indices which can better eliminate the images of soil and the cotton canopy background in cotton density areas, thus showing a better ability to extract cotton information [14]. Cotton growth is affected by soil salinization in cotton fields. With a more serious degree of soil salinization in cotton fields, the growth of cotton will be inhibited. The vegetation index can be used as a basis to indirectly reflect the degree of soil salinization in cotton fields [15]. The study found that the green band, red band, and short-infrared waves have better responses to soil salinization information. The formula of the spectral index and the formula of the remote sensing salinity monitoring index model are shown in Table 1.
## 3 Discussions of Experimental Results  
The greater the wind speed, the more the energy required to achieve CFJ and DCFJ technologies. Considering the existing experimental capabilities, the wind speed is selected as 8 m/s for all experiments and the chord Reynolds number is about 410,000.
## 2 Materials and Methods
### Monomer and Contour Extraction
#### 2.5.1 Contour Set Generation Based on Horizontal Slicing of Grid Model Data  
Our basic principle behind building contour extraction was to consider a building contour collection using sections from different elevations; geometric similarity of the contour line is determined to extract the optimal contour line and perform logical monomer processing (Figure 4).  
In the oblique photographic 3D model, the horizontal plane where the highest point of a building is located is taken as the starting level. The model is \"sliced\" horizontally at certain intervals between the highest and lowest points of the building (Figure 5). Note that no physical or geometric cuts of the model are involved; purely the projection of all grid mesh data above the cutting plane elevation is obtained for the horizontal cutting plane at each elevation interval to facilitate subsequent contour extraction.  
The triangular plane of the model was projected onto the elevation plane and rasterized to allow the extraction of a binarized raster orthographic projection image on each plane. The size of grid cells selected for rasterization was 0.1 m \(\times\) 0.1 m, balancing extraction efficiency and accuracy (for large cell sizes, the extraction accuracy is low, but the calculation efficiency is high). The process of projecting the triangle onto the elevation plane and performing binarization is summarized in Figure 6.  
Figure 4: Monomer and contour extraction process of a 3D model building.  
Figure 5: Real model diagram of 3D building elevation segmentation.  
A binarized raster image of building contours was obtained for each elevation plane. Examples of binarized raster images are shown in Figure 7.  
The raster image data of the projected building surfaces were obtained through binarization. To obtain building contours, building edges were extracted from the raster data. This involves the vectorization of raster data using relatively mature algorithms. However, in contrast to the classification of alternative image data, ground objects are complex and include many different linear and areal entities. The binary raster data processed in this study contained simple information and a large image range; the vector objects generated were all polygons. Considering these characteristics, we used a method based on image edge detection [43] and boundary tracking [44] to extract polygon contours from the binary imagery.  
Figure 6: 3D model projection and binarization process.  
Figure 7: Binary images formed by triangular rasterization (elevation = 31 m).
## 3 Results and Discussion
### MBR Plants in Italy
#### 3.1.3 Type of Membrane  
Regarding the type of membrane, 59% of all MBR plants analyzed in this survey use flat-sheet membrane, and 38% adopt hollow-fiber membranes. Recently, the hybrid membrane technology has been introduced on the market: this type is currently adopted by 3% of the MBR installations.  
In municipal MBR plants, the flat-sheet membranes are mainly used for small and very small installations, and the hollow-fiber membranes are usually adopted for medium and large capacity (Figure 3a). Moreover, in the industrial applications, the flat-sheet membranes are prevalent in small and medium MBR plants (Figure 3b).  
This aspect is mainly related to the advantages and disadvantages of hollow-fiber membranes with respect to the flat-sheet ones. Hollow-fiber configurations, compared to the flat-sheet, are usually designed for higher fluxes, operated at lower mixed liquor suspended solid (MLSS) concentrations, cleaned more frequently (both mechanically--backwash--and chemically) and must be protected by more severe pre-treatment [27]. Moreover, the prevalent use of hollow-fiber membranes for larger installations is related to both their lower aeration energy demand and their higher specific surface with the possibility to reduce the tanks size/footprint [28; 29]. As concerns this point, the average specific surface area is reported to be 292 m\({}^{2}\)/m\({}^{3}\) and 118 m\({}^{2}\)/m\({}^{3}\) for commercial hollow-fiber and flat-sheet membranes, respectively [30].
# SSKM_DP: Differential Privacy Data Publishing Method via SFLA-Kohonen Network
## 4 The Proposed Data Publishing Method
### SSKM_DP Multi-Sensi five Attribute Data Publishing Mechanism  
The operation mechanism of the differential privacy data publishing method based on the SFLA-Kohonen network is shown in Figures 3 and 4 as a detailed flow chart of the proposed method.  
The steps of the SSKM_DP data publication method are as follows:  
1. Attribute Clustering  
The Kohonen network is optimized, the original data are clustered by using the improved SFLA-Kohonen network, and the data are reasonably divided into multiple sub-data to achieve the differentiation of sensitive attributes from individuals to groups to reduce the data and query sensitivity and reduce the noise required to meet the differential privacy.
2. Attribute Correlation Judgment  
Part of the sensitive attribute exists on a strong affinity, by inferring sensitive attributes, introducing the largest information coefficient sensitive to data with the sensitive attribute. The connection between each child data clustering partition cluster with the sensitive property has a strong correlation between the sensitive attributes. Add an appropriate amount of noise to the subdataset cluster to protect such non-sensitive attributes and further prevent the privacy leakage of sensitive data.  
Figure 4: Detailed flow chart of the proposed method.  
Figure 3: SSKM_DP data publishing framework satisfying differential privacy protection.  
Data Noise  
The privacy budget satisfying differential privacy is allocated to the subset cluster obtained by SFLA-Kohonen network clustering. Then, the corresponding noise is added to the cluster of sensitive attributes and the cluster of non-sensitive attributes associated with sensitive attributes, to reduce the required noise amount and improve the availability of data.  
Algorithm 1 is the process algorithm for proposing the model.  
```
Input: dataset \(U=\{x_{1},x_{2},\ldots,x_{n}\}\), the number of neurons in the input layer of [PERSON]'s network \(t\), the number of frogs \(N\), learning rate \(\eta\), Maximum learning times \(T\). Output: published dataset \(\widehat{U}=\{x_{1},x_{2},\ldots,x_{n}\}\).
1:\(W_{ij}\gets SFLA\) optimizes the initial weight of [PERSON] network \((N,\ t)\)
2:\(FModel\gets SFLA-K\)onen network to achieve data clustering data clustering \(\left(W_{ij},\eta,T\right)\)
3:\(V=v_{1},v_{2},\ldots,v_{m}\gets FModel(U)\)
4:\(V_{c}=v_{c1},v_{c2},\ldots,v_{cq}\) and \(V_{s}=v_{s1},v_{s2},\ldots,v_{sp}\leftarrow\) Attribute correlation determination method
5: published dataset \(\widehat{U}=\{x_{1},x_{2},\ldots,x_{n}\}\leftarrow Noise(V_{c},V_{s})\)
```  
**Algorithm 1** SSKM-DP
## 4 Experiments and Results
### Results Analysis
#### 4.4.4 Performance Comparison for Different Models
It not only reduces the computational complexity of the original EfficientNet model but also further improves the model's performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Input Image Size**} & **Top-1** & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**FLOPs**} \\  & & **Accuracy** & & \\ \hline AlexNet [31] & 600 \(\times\) 600 & 71.9\% & 61 MB & 5 G \\ \hline GoogleNet [18] & 600 \(\times\) 600 & 80.6\% & 13 MB & 10 G \\ \hline VGG16 [19] & 600 \(\times\) 600 & 88.1\% & 138 MB & 110 G \\ \hline ResNet34 [15] & 600 \(\times\) 600 & 86.3\% & 36 MB & 26 G \\ \hline EfficientNet-B7 [26] & \multirow{6}{*}{600 \(\times\) 600} & \multirow{2}{*}{92.0\%} & \multirow{2}{*}{66 MB} & \multirow{2}{*}{38 G} \\ (+SE attention mechanism) & & & & \\ \hline CA-EfficientNet [32] & \multirow{6}{*}{600 \(\times\) 600} & \multirow{2}{*}{92.6\%} & \multirow{2}{*}{67 MB} & \multirow{2}{*}{39 G} \\ (+Coordinate attention mechanism) & & & & \\ \hline Triplet-EfficientNet & \multirow{6}{*}{600 \(\times\) 600} & \multirow{2}{*}{93.2\%} & \multirow{2}{*}{64 MB} & \multirow{2}{*}{36 G} \\ (+Triplet attention mechanism) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison for different network models in the test set.
# A Novel Dual-Band Implantable Antenna for Pancreas Telemetry Sensor Applications
## 2 Methods and Results
### Antenna Simulation in Anatomical Models
#### 2.3.2 Specific Absorption Rate  
The specific absorption rate (SAR) is the most important parameter for quantifying the electromagnetic radiation in human tissues in IEEE C95.1-2019, and [19] specified that the SAR 1 g and 10 g averages should not exceed 1.6 W/kg and 2 W/kg, respectively.  
We set the input power equal to 1 W. In the male anatomical model, at 402.5 MHz the maximum SAR values for mass 1 g (pSAR1g) and 10 g (pSAR10g) were 148.38 W/kg and 59.6 W/kg, respectively. At 2.45 GHz the maximum values of SAR in mass 1 g (pSAR1g) and 10 g (pSAR10g) were equal to 217.4 W/kg and 57 W/kg, respectively. In the female anatomical model, at 402.5 MHz the maximum SAR values for mass 1 g (pSAR1g) and 10 g (pSAR10g) were 192.34 W/kg and 63.48 W/kg. At 2.45 GHz the maximum values of SAR in mass 1 g (pSAR10g) and 10 g (pSAR10g) were equal to 258.5 W/kg and 61.62 W/kg, respectively.  
As we can observe, the values of SAR were high, compared against the specified limits. This was expected for 1W input power. However, for implanted sensors, antennas are expected to operate at much lower power, usually not exceeding 0 mW. As shown in the summary Table 4, the antenna was safe to use, with a maximum permissible input power (Pmax) much higher than 1 mW.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Human** \\ **Phantom** \\ \end{tabular} & **Frequency** & \begin{tabular}{c} **SAR, 1 g** \\ **(W/kg)** \\ \end{tabular} & \begin{tabular}{c} **Pmax, 1 g** \\ **(mW)** \\ \end{tabular} & \
\begin{tabular}{c} **SAR, 10 g** \\ **(W/kg)** \\ \end{tabular} &
\begin{tabular}{c} **Pmax, 10 g** \\ **(mW)** \\ \end{tabular} \\ \hline \multirow{2}{*}{Male} & 402.5 MHz & 148.38 & 10.8 & 59.6 & 33.6 \\  & 2.45 GHz & 217.4 & 7.4 & 57 & 35.1 \\ \hline \multirow{2}{*}{Female} & 402.5 MHz & 192.34 & 8.3 & 63.48 & 31.5 \\  & 2.45 GHz & 258.5 & 6.1 & 61.62 & 32.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specific absorption rate and maximum permissible input power.  
Figure 23: Resonance performance at **(a)** 402.5 MHz and **(b)** 2.45 GHz of the female human phantom.  
Figure 22: Resonance performance at **(a)** 402.5 MHz and **(b)** 2.45 GHz of the male human phantom.
# Tomographic Inversion Methods for Retrieving the
## 1 Introduction
Its original formulation, by [PERSON], dates back to the '60 [21,22] and has been revisited and analyzed in the following decades by [PERSON] himself [23], [PERSON] [24] and [PERSON] [25, 26, 27] with applications to radiology and non-destructive testing. The mathematical problem posed in [21] is inverting the Radon transform of a 2D field calculated on a compact annular region. In our specific problem, the unknown 2D field to be retrieved is the WV concentration in the aforementioned annular region of the orbital plane of the LEO satellites while its Radon transform corresponds to the ensemble of IWV estimates made available by the time series of NDSA measurements. The nature of this inversion method--referred to as External Reconstruction Tomographic Algorithm (ERTA) in this paper--poses profoundly different problems with respect to LSATR, but also leads to quite different retrieval performances. Objective of this paper is investigating the application of the ERTA approach to the IWV measurements inversion scenario as well as comparing its performance with respect to the LSATR method. The analysis shown here is part of the study that been developed within the SATCROSS project funded by the Italian Space Agency (ASI), aimed at providing a feasibility study for future missions. Figure 1: Acquisition scheme of the NDSA measurements of IWV for a system of co-rotating LEO satellites (\(\hbar_{\text{M}}\) is the thickness of the annular region where it is possible to retrieve the WV field). In the example, three satellites with a Tx onboard and three satellites with a Rx onboard enable the creation of nine tropospheric links (of which only six are shown in the figure); thus, nine simultaneous measures of IWV are available after every integration interval. This paper is structured as follows. In Section 2, we briefly recall the NDSA measurement approach and the geometry of the acquisition system, constituted by a constellation of co-rotating LEO satellites. In Section 3, we introduce ERTA, based on the previous work by [PERSON], [PERSON] and [PERSON], whereas in Section 4 we present its specific application to our measurement scenario. Section 5 shows some examples of WV retrieval by using both ERTA and LSATR in the presence of realistic signal impairments. In Section 6, some concluding remarks are drawn.
# A Study of Cellular Traffic Data Prediction by Kernel ELM with Parameter Optimization
###### Abstract
However, artificial neural networks are difficult to train, and they can easily fall into the local optimum. In order to achieve higher performance, [PERSON] et al. [8] employed multiple recurrent neural network (RNN) learning models to construct traffic prediction models by exploring spatiotemporal correlations between base stations. The structure of RNN neurons is more complex than traditional artificial neural networks. This method constructs multiple RNNs, which makes the prediction model more complicated. In addition, the model's training and testing only take 15 days, and its performance regarding flow prediction needs to be further verified. Therefore, a novel and efficient flow forecasting method is proposed in this paper  
Extreme learning machine (ELM) [10], a typical single-hidden layer feedforward neural network (SLFN), learns extremely fast and does not fall into the local optimum compared with backpropagation (BP) algorithms. In order to further improve the performance of ELM, [PERSON] et al. [11] proposed a kernel ELM (kELM) by referring to the idea of feature mapping of a support vector machine (SVM), which retains the high efficiency of ELM and inherits the excellent learning ability of SVM. The technique has been successfully used for classification [12; 13; 14; 15; 16] and regression tasks [17; 18; 19; 20; 21]. Therefore, in this study, we employed kELM to predict cellular flow data. There are four kernel functions in the classical SVM. In order to achieve optimal prediction of traffic data by kELM, four classical kernel functions were used for flow prediction. The meta-inspired optimization algorithm [22] was used for parameter optimization, and the kernel function with the optimal result was selected. [PERSON] and [PERSON] [23] as well as other authors have proven that none of the optimization algorithms can search for the optimal results of all optimized problems. Therefore, we chose the classic particle swarm optimization method (PSO) [24] as well as two heuristic optimization algorithms with strong searchability, namely,multiverse optimizer (MVO) [25] and moth-flame optimization (MFO) [26], for parameter optimization of ELM with different kernel functions. The rest of the paper is structured as follows. In Section 2, network accidents caused by traffic are described, and the proposed method is introduced. Section 3 outlines the experimental preparation for validation of the proposed method. In Section 4, the experimental results are presented and analyzed. Section 5 concludes the paper.