esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Profile Monopole Meander Line Antenna for WLAN Applications
## 5 Results and Discussion  
Figure 7 shows a performance comparison between the conventional meander antenna in [32] and the proposed design, which has wider bandwidth. Figure 8a presents the meander line monopole antenna's simulated and measured reflection coefficients. According to the Figure, the measured and simulated results are reasonably consistent with the perceived difference caused by material and hardware fabrication tolerances. The designed antenna's bandwidth was discovered to be 94.2 MHz with a frequency range of 2.3685 GHz to 2.4643 GHz at less than \(-10\) dB. Figure 8b presents the reflection coefficient measurement setup.  
Figure 6: The effect of the middle\(-\)lower meander line length on the simulated reflection coefficient and the geometric of the lower ML length; the sketches on the most-right side in Figure 6 show the geometries of the meander line changes.  
Figure 8: (a) The simulated and measured reflection coefficient of the suggested printed ML monopole antenna and (b) the reflection coefficient measurement setup.  
Figure 7: Performance comparison between the basic meander line antenna with the proposed design.  
Prior to the selection and fabrication of a final structure, numerous essential parameters of the proposed antenna are analyzed to show better performance in terms of single-band operation, bandwidth, and compact size.  
Figure 9 illustrates the simulated and measured gain of the meander monopole antenna is 2.88 dB at 2.45 GHz. Furthermore, an agreement between the measured and simulated gains has been accomplished. Due to the meander line, the antenna gain with the meander line increased and was enhanced. Simulated and measured antenna efficiencies are shown in Figure 10. The proposed antenna has a maximum radiation efficiency of 99.4% at 2.4 GHz and a maximum total efficiency of 92% at 2.45 GHz.  
Figure 10: Simulated and measured efficiency of the proposed antenna.  
Figure 9: Simulated and measured gain for a monopole array antenna.  
Figure 11 depicts the simulated, and measured radiation patterns in the proposed antenna's E (\(\varphi\) = 0 deg) and H (\(\varphi\) = 90 deg) plans at 2.4 GHz. It indicates the radiated power's variation in various spatial directions. We are able to see very clearly that the patterns are quasi-omnidirectional, and this makes them well suited for the reception of signals by WLAN wireless communication terminals.  
Figure 11: _Cont._
# Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest
## 3 Materials and Methods
### Feature Extraction and Fusion
#### 3.4.3 SpO2 Features  
Six features were calculated from the SpO2 signal. These features are listed in Table 2. Based on statistical methods, _Smin_, _Smean_, and _Svar_ were calculated from SpO2 segments. Three commonly used nonlinear features (_ApEn_, _CTM_, and _LZC_) were also added to the SpO2 feature set.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Derived from** & **Feature** & **Description** \\
**RR** & **RAMP** & **Feature** & **Mean of RR intervals** \\ \hline \(\times\) & & _RRmean_ & **Root mean square of differences between adjacent** \\ \(\times\) & & _RMSSD_ & **RR intervals** \\ \(\times\) & & _SDNN_ & **Standard deviation of RR intervals** \\ \(\times\) & & _NN50_ & **Number of adjacent RR intervals exceeding** \\ \(\times\) & & _pNN50_ & **Ratio of NN50 to the number of RR intervals** \\ \(\times\) & & _HR_ & **Mean of heart rates** \\ \(\times\) & \(\times\) & _VLF_1, _VLF_2 & **Very low frequency (0–0.04 Hz) component of the** \\ \(\times\) & \(\times\) & _LF_1, _LF_2 & **Low frequency (0.04–0.15 Hz) component of the** \\ \(\times\) & \(\times\) & _LF_1, _LF_2 & **corresponding signal** \\ \(\times\) & \(\times\) & _HF_1, _HF_2 & **High frequency (0.15–0.4 Hz) component of the** \\ \(\times\) & \(\times\) & _LF/HF_1, _LF/HF_2 & **Ratio of LF to HF** \\ \(\times\) & \(\times\) & _LFnorm_1, _LFnorm_2 & **Normalized low frequency components** \\ \(\times\) & \(\times\) & _HFnorm_1, _HFnorm_2 & **Normalized high frequency components** \\ \hline \hline \end{tabular}
\end{table}
Table 1: RR intervals features and R-wave Amplitudes feature.  
Specifically, \(ApEn\) and \(LZC\) are suitable for small sample data and can reflect the complexity and chaos degree of the signal [27]. The optimal parameters for calculating \(ApEn\) were a tolerance of 0.25 and an embedding dimension of 2, while \(LZC\) is a non-parametric measurement. In addition, \(CTM\) calculates the ratio of the number of points falling into the center in the origin region with radius \(R\) to the total number of points through the second-order difference graph [20].  
After feature extraction, in order to eliminate the distribution differences between various types of features and speed up the convergence of the model, we normalized the features with the following equation:  
\[x^{*}=\frac{x-\widetilde{x}}{\sigma}, \tag{1}\]  
where \(x\) is the unnormalized feature, \(\widetilde{x}\) represents the mean of the feature, \(\sigma\) is the standard deviation of the feature, and \(x^{*}\) is the normalized feature.
# COMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
## 4 Discussion
### Effectiveness of Proposed Modules  
To validate the effectiveness of the proposed modules, we compared the performance gain of the proposed modules. As listed in Table 7, we found that the performance gain mainly originated from the CMM. Although the performance gain was observed when the BPM was applied, in the second row of each dataset, the CMM significantly improved the polyp detection performance. That is, the CMM contributed more to the performance improvement than the BPM when applying an individual module. In terms of information propagation, employing both modules could achieve an outstanding performance compared to using an individual combination. This is because the proposed network (Base + CMM + BPM) propagated the boundary information obtained from the BPM to the second decoder structure (CMM). This structure could contribute to obtaining more refined polyp regions than the individual combinations (Base + CMM and Base + BPM).
# A Lidar-Inertial Navigation System for UAVs in GNSS-Denied Environment with Spatial Grid Structures
## 3 Experiment
### Experiments and Data Analysis
#### 3.2.1 Analysis of Feature Extraction  
The parameter settings of the feature extraction of the navigation system in the experiment are shown in Table 1.  
Figure 6: The equipment of the experimental system.  
The number and total time consumption of feature extraction in each frame of the point cloud are shown in Figure 7. From Figure 7, the total time spent extracting feature points in each point cloud frame showed the same trend as the number of shell features. This implies that the time consumption when extracting features mainly occurred as a result of the extraction of the shell features. It also reflects that the proposed grid feature extraction algorithm, which only uses logical judgment, can perform fast and efficient operations even with limited airborne resources.  
We analyzed and compared the features extracted by the proposed algorithm and the LOAM algorithm from the spatial grid structure. The features extracted by each algorithm in the low-altitude take-off and landing stage and the high-altitude cruise stage of the UAV are shown in Figure 8. It can be seen from (e) and (f) that, in the low-altitude take-off and landing stage, the LOAM algorithm incorrectly extracted many points belonging to the ground as line features. It can also be seen from (g) and (h) that, in the high-altitude cruise phase, the LOAM algorithm incorrectly extracted the points that fell on the grid as plane features. The incorrect feature extraction introduced significant errors to the localization. In contrast, it can be seen from (a), (b), (c), and (d) that the proposed feature extraction algorithm was able to accurately extract grid and shell features from the surrounding spatial grid structures, regardless of whether the UAV system worked at low altitude or high altitude.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & \(\mathbf{L_{e\_th}}\) & \(\mathbf{U_{e\_th}}\) & \(\mathbf{L_{s\_th}}\) & \(\mathbf{U_{s\_th}}\) & \(\mathbf{d}\) \\ \hline
**Value /m** & 0.3 & 6 & 0.3 & 6 & 0.2 \\ \hline \end{tabular}
\end{table}
Table 1: Parameter settings of the feature extraction.  
Figure 7: The number and time consumption of feature point extraction.
# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 4 Classification Methodology  
We developed a new classification methodology using a multiple decision tree randomized structure to construct our MDTR algorithm. This algorithm is similar to Random Forest [50], a machine learning approach that builds multiple decision trees based on different combinations of input variables. The main difference between the Random Forest and the MDTR algorithm is the randomization of the number of inputs, which for Random Forest is fixed to a number selected by the user. The MDTR algorithm employs several GNSS-R observables and ancillary data as inputs, along with reference data for training and validation. We first define the information required by the MDTR algorithm, and then explain the algorithm in detail.
# Federated Learning and Its Role in the Privacy Preservation of IoT Devices
###### Abstract  
Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized problem-solving technique that allows users to train using massive data. Unprocessed information is stored in advanced technology by a secret confidentiality service, which incorporates machine learning (ML) training while removing data connections. As researchers in the field promote ML configurations containing a large amount of private data, systems and infrastructure must be developed to improve the effectiveness of advanced learning systems. This study examines FL in-depth, focusing on application and system platforms, mechanisms, real-world applications, and process contexts. FL creates robust classifiers without requiring information disclosure, resulting in highly secure privacy policies and access control privileges. The article begins with an overview of FL. Then, we examine technical data in FL, enabling innovation, contracts, and software. Compared with other review articles, our goal is to provide a more comprehensive explanation of the best procedure systems and authentic FL software to enable scientists to create the best privacy preservation solutions for IoT devices. We also provide an overview of similar scientific papers and a detailed analysis of the significant difficulties encountered in recent publications. Furthermore, we investigate the benefits and drawbacks of FL and highlight comprehensive distribution scenarios to demonstrate how specific FL models could be implemented to achieve the desired results.  
federated learning; artificial intelligence; privacy; security; machine learning +
Footnote †: journal: _Future Internet_  
4. Notifying devices after the global model-based improvement and preparing for the next training session.  
In the meantime, devices represent digital assets on a secure server and can apply the training model to their data [5]. When the server accesses a model, each device initiates the training process. Then, a set of communication rules is used to send the learning results to the server. Compared to single classification algorithms, this approach is radically different. High-performance processes usually begin with small data samples transmitted to a data center [6].  
It is possible for several actors to develop robust learning models that do not incorporate the distribution of information, which allows them to address many significant concerns, such as the security of data, the privacy of data, and access to data [7]. The local and global models in FL are shown in Figure 1.
# GPP of a Chinese Savanna Ecosystem during Different
## 3 Results
### Evaluation of the HLS and MODIS VPM Results  
The VPM was applied to the processed HLS and MODIS data. The GPP\({}_{\text{HLS-VPM}}\) and GPP\({}_{\text{MODIS-VPM}}\) were compared with the GPP measured at the Yuanjiang flux tower  
Figure 7: Comparison between the interannual variations in the EC tower-derived gross primary production (GPP\({}_{\text{EC}}\)) and the simulated GPP (GPP\({}_{\text{VPPm}}\)) from the two remote sensing data sources: (**a**) MODIS and (**b**) HLS.  
(Figure 8; Table 2). During the 2015-2018 period, compared with those of the GPP\({}_{\text{EC}}\) curves, the slopes of the linear regression curves (ideally 1.00) varied between 1.12 and 1.8 for the GPP\({}_{\text{HLS-VPM}}\) (Figure 8a-d; Table 2). For the MODIS VPM, the GPP\({}_{\text{MODIS-VPM}}\) was 91% greater than the GPP relative to the GPP\({}_{\text{EC}}\) (Table 2, slope = 1.91). The HLS overestimation was 45% lower than the MODIS overestimation (Table 2, slope = 1.46). The VPM results for very few of the MODIS data fell below the 1:1 line (Figure S2a), indicating that the GPP\({}_{\text{MODIS-VPM}}\) values were higher than the EC observations. The VPM results for the HLS data occurred on both sides of the 1:1 line (Figure S2b), indicating the robustness of the GPP\({}_{\text{HLS-VPM}}\) in matching the EC-based GPP. The use of the HLS-based VIs in the VPM improved the predictability of the GPP, as represented by the reduced RMSE (decreased from 2.83 to 2.25) of the MODIS VPM results compared with that of the GPP\({}_{\text{EC}}\) (Table 2). In terms of the coefficient of determination (R\({}^{2}\)), except for 2015, the differences between the GPP\({}_{\text{HLS-VPM}}\) and GPP\({}_{\text{MODIS-VPM}}\) were not significant (Figure 8; Table 2), indicating the need to further evaluate the performance of GPP simulations with data of two distinct spatial resolutions on a finer time scale (i.e., the seasonal phenological phase).
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 2 Power of the Battery and Transmitter
### Battery Power  
We consider two types of batteries with different BUFs which can be described as follows.  
_Battery with IDC-based BUF_: the battery's instantaneous inner current is  
\[I_{\text{in}}(t)=\frac{I_{\text{b}}(t)}{\mu(I_{\text{b}}(t))} \tag{2}\]  
where \(I_{\text{b}}(t)\) represents the instantaneous discharge current, \(\mu(I_{\text{b}}(t))=1-\omega I_{\text{b}}(t)\) is the BUF depending on \(I_{\text{b}}(t)\), and \(\omega\) is a BUF-related parameter. In such a case, the battery's instantaneous power consumption is  
\[P_{\text{bat}}(t)=V_{\text{b}}I_{\text{in}}(t)=\frac{P_{\text{out}}(t)}{1- \gamma P_{\text{out}}(t)} \tag{3}\]  
where \(V_{\text{b}}\) is the battery's discharge voltage, \(P_{\text{out}}(t)=V_{\text{b}}I_{\text{b}}(t)\) and  
\[\gamma=\omega/V_{\text{b}} \tag{4}\]  
is the battery characteristic parameter connecting \(P_{\text{bat}}(t)\) with \(P_{\text{out}}(t)\). From the above equations, it can be seen that even when \(P_{\text{out}}(t)\) is fixed, \(P_{\text{bat}}(t)\) still increases as \(\gamma\) increases.  
_Battery with MDC-based BUF_: the battery's instantaneous inner current is  
\[I_{\text{in}}(t)=\frac{I_{\text{b}}(t)}{\mu(I_{\text{mean}})} \tag{5}\]  
where \(I_{\text{mean}}\) denotes the average of \(I_{\text{b}}(t)\) over the discharge process, \(\mu(I_{\text{mean}})=1-\omega I_{\text{mean}}\) is the \(I_{\text{mean}}\)-dependent BUF. In such a case, the battery's instantaneous power consumption is  
\[P_{\text{bat}}(t)=V_{\text{b}}I_{\text{in}}(t)=\frac{P_{\text{out}}(t)}{1- \gamma P_{\text{mean}}} \tag{6}\]  
where \(P_{\text{mean}}\) represents the average of \(P_{\text{out}}(t)\) over the discharge process. From the above equations, it can be seen that even when \(P_{\text{out}}(t)\) and \(P_{\text{mean}}\) are fixed, \(P_{\text{bat}}(t)\) still increases as \(\gamma\) increases.  
Note that for the above two types of batteries, \(\gamma\) is strictly greater than 0, thus \(P_{\text{bat}}(t)\) is strictly greater than \(P_{\text{out}}(t)\), meaning that the above two batteries always has an energy waste during discharge process. When \(\gamma=0\), \(I_{\text{in}}(t)\) is always equal to \(I_{\text{b}}(t)\) and \(P_{\text{bat}}(t)\) is always equal to \(P_{\text{out}}(t)\), which corresponds to an ideal battery without energy waste during discharge process.
# Structure-Function Coupling Reveals Seizure Onset Connectivity Patterns
## 2 Materials and Methods
### Image Processing to Obtain Structural Connectomes  
The T1 images were processed using a modified version of Freesurfer's recon-all (v6.0) [35], alongside an in-house skull-stripping tool (Sydney Neuroimaging Analysis Centre). Each subject was inspected, and minor segmentation errors were manually corrected. A 5 tissue-type (5 TT) image [36] was generated using MRtrix3 [37]. The T1 image was registered to the mean \(b\)0 image; the warp was used to register the 5 TT image, and the DK [32] parcellation image to the diffusion image.  
Diffusion image processing was conducted using MRtrix3 [37]. The diffusion pre-processing included motion and distortion correction [34; 38], bias correction using ANTs [39]. The dhollander algorithm [40] was used to estimate the response functions of the white matter, grey matter, and cerebral spinal fluid, from which constrained spherical deconvolution was used to estimate the fibre orientation distributions using MRtrix3 Tissue [37]. The intensity of the white matter fibre orientation distributions was normalised [37], and used for anatomically constrained whole-brain tractography [41] (along with the registered 5 TT image). The tractography specifications were as follows: 15 million tracks were generated, iFOD2 probabilistic fibre tracking [42], dynamic seeding [43], maximum length 300 mm, backtrack selected and crop at grey-matter-white-matter interface selected. For quantitative analysis, the corresponding weight for each streamline in the tractogram was derived using SIFT2 [43]. The streamlines and corresponding SIFT2 weights were used to create a weighted, undirected structural connectome (\"SC\") using the registered parcellation image. All image processing steps are shown in Figure 1 (Step 1, a).
# GIS-Based Solar Radiation Mapping, Site Evaluation, and Potential Assessment: A Review
## 2 Solar Radiation Mapping Using GISs
### Solar Radiation Model  
Solar radiation models integrated with GISs are also employed to generate spatial DBs. Solar radiation information over large territories can be provided by these models by considering surface inclination, aspect, and shadowing effects. In particular, these models can be useful for predicting solar radiation in areas without measured data.  
The r.sun solar radiation model was developed for an open source environment of the geographic resources analysis support system GIS [19]. The model computes three components (i.e., the beam, diffuse, and reflected components) of global solar radiation for clear-sky or overcast conditions. The model can be applied to large regions, and the shadowing effects of the terrain can be modeled using a shadowing algorithm. In a case study, the model was applied to PV system planning in Central and Eastern Europe and for solar radiation modeling in mountainous terrain in Slovakia.  
By integrating the clear-sky index based on a multivariate interpolation method with the r.sun model, the radiation estimation ability was improved [20]. This method can be especially helpful for data at higher resolutions and in regions lacking ground measurements. A study that models solar radiation considering the interaction over topographic and plant canopies was performed using both a GIS (SOLARFLUX) and an image processing system (ATM Model) [21]. The effects of topography and plant canopies on solar radiation were analyzed with various options for obtaining the data. Design issues, computational problems, and error propagation were considered to implement the model for mountainous areas.  
An algorithm was presented for calculating the slope gradient, aspect, and cell surface area as a normal vector using the DEM [22]. Based on this algorithm, the sun position, the direct component of insolation, and the hillshade can be calculated. In addition, the horizon angles and sky view factor can be calculated more economically than they could with previous algorithms. A 3D urban solar model was developed for the calculation and visualization of building potential [23]. Light detection and ranging (LiDAR) data were used to build a DSM of an urban region (the University of Lisbon campus), and a shadow algorithm was developed to calculate shadow maps and sky view factors both for roofs and facades. In this study, climatic observations based on the typical meteorological year data were utilized for direct and diffuse solar radiation mapping at each point on the ground, roof, and facades.
## 2 Materials and Methods
### Fluorescence Microscopy Imaging Data
#### 2.1.2 2018 Data Science Bowl  
With the goal of validating ACDC on imaging data coming from different sources, we considered a selection of the training set of the Data Science Bowl (DSB) dataset [37; 46], which was a competition organized by Kaggle (San Francisco, CA, USA). We used only the human-annotated training set because the gold standard for the test set is not publicly provided by the organizers. The annotations were manually performed by a team of expert biologists in a collaborative manner, where a single expert outlined the nuclei and the other collaborators reviewed the result.  
The goal of the DSB regarded the detection of the nuclei of the cells to identify each individual cell in a sample, a mandatory operation to understand the underlying biological processes. This dataset includes a large number of labelled nuclei images acquired under a variety of conditions, magnification, and imaging modality (i.e., bright-field and fluorescence microscopy). Image size varies among \(256\times 256\), \(256\times 320\), \(260\times 347\), \(360\times 360\), \(512\times 640\), \(520\times 696\), \(603\times 1272\), and \(1040\times 1388\) pixels.  
The DSB dataset aimed at evaluating the generalization capabilities of computational methods when dealing with significantly different data. Therefore, to run tests with ACDC, we first extracted the fluorescence microscopy images from the training set; then, we selected images where the maximum size of the region cells segmented in the ground truth was equal or less than 1000 pixels to obtain a magnification factor roughly comparable to the Vanderbilt University dataset, for a total of 301 images. According to the work presented in [37], we considered only the small fluorescent nuclei images (e.g., those obtained using a microscope objective of \(10\times\) or \(20\times\)), which are very common in biomedical research.  
Three examples of input images with highly different characteristics are shown in Figure 2.  
Finally, Figure 3 shows the boxplots the reveal the high variability of the analyzed microscopy imaging datasets, in terms of the number of cells and cell coverage, to support the validity of the experiments carried out in this study.  
Figure 3: Boxplots depicting the distribution for both the analyzed datasets in terms of: (**a**) total number of cells, and (**b**) coverage of the cell nuclei regions.  
Figure 2: (**a–c**) Examples of the analyzed microscopy fluorescence from the DSB dataset.