esa-sceva/satcom-chunk-collection huggingface dataset:
# Precise Orbit Determination for GNSS Maneuvering Satellite with the Constraint of a Predicted Clock
###### Abstract  
Precise orbit products are essential and a prerequisite for global navigation satellite system (GNSS) applications, which, however, are unavailable or unusable when satellites are undertaking maneuvers. We propose a clock-constrained reverse precise point positioning (RPPP) method to generate the rather precise orbits for GNSS maneuvering satellites. In this method, the precise clock estimates generated by the dynamic precise orbit determination (POD) processing before maneuvering are modeled and predicted to the maneuvering periods and they constrain the RPPP POD during maneuvering. The prediction model is developed according to different clock types, of which the 2-h prediction error is 0.31 ns and 1.07 ns for global positioning system (GPS) Rubidium (Rb) and Cesium (Cs) clocks, and 0.45 ns and 0.60 ns for the Beidou navigation satellite system (BDS) geostationary orbit (GEO) and inclined geosynchronous orbit (IGSO)/Median Earth orbit (MEO) satellite clocks, respectively. The performance of this proposed method is first evaluated using the normal observations without maneuvers. Experiment results show that, without clock-constraint, the average root mean square (RMS) of RPPP orbit solutions in the radial, cross-track and along-track directions is 69.3 cm, 5.4 cm and 5.7 cm for GPS satellites and 153.9 cm, 12.8 cm and 10.0 cm for BDS satellites. When the constraint of predicted satellite clocks is introduced, the average RMS is dramatically reduced in the radial direction by a factor of 7-11, with the value of 9.7 cm and 13.4 cm for GPS and BDS satellites. At last, the proposed method is further tested on the actual GPS and BDS maneuver events. The clock-constrained RPPP POD solution is compared to the forward and backward integration orbits of the dynamic POD solution. The resulting orbit differences are less than 20 cm in all three directions for GPS satellite, and less than 30 cm in the radial and cross-track directions and up to 100 cm in the along-track direction for BDS satellites. From the orbit differences, the maneuver start and end time is detected, which reveals that the maneuver duration of GPS satellites is less than 2 min, and the maneuver events last from 22.5 min to 107 min for different BDS satellites.  
GNSS; precise orbit determination; satellite maneuver; clock prediction; reverse precise point positioning 2019111949; doi:10.3390/rs11161949
# Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations
## 2 Cluster Tracking in Mobile Measurements
### Measurement Setup and Data Processing
#### 2.1.2 Kalman Filter Part  
State model \(g_{c}^{(n)}\) is assumed as follows:  
\[g_{c}^{(n)}=\Phi g_{c}^{(n-1)}+N_{1}=I_{5}\otimes\begin{bmatrix}1&1\\ 0&1\end{bmatrix}\cdot g_{c}^{(n-1)}+\Delta^{(n|n-1)}+N_{1} \tag{8}\]  
where \(\Phi\) is the state-transition matrix, \(I_{i}\) is the identity matrix with \(i\) dimensions, \(\otimes\) is the product of the Kronecker matrix, \(N_{1}\) is the state noise, and \(\Delta^{(n|n-1)}\) is the gap-compensation matrix, which is given as  
\[\Delta^{(n|n-1)}=[\Delta\tau_{i}^{(n|n-1)}\quad\Delta\theta_{Tx,i}^{(n|n-1)} \quad\Delta\phi_{Tx,i}^{(n|n-1)}\quad\Delta\theta_{Rx,i}^{(n|n-1)}\quad\Delta \phi_{Rx,i}^{(n|n-1)}]\otimes[0\quad 1]^{T} \tag{9}\]  
The observation model is the information of cluster centroids themselves, \(\mu_{c}^{(n)}\), which is given as  
\[\mu_{c}^{(n)}=Jg_{c}^{(n)}+N_{2}=I_{5}\otimes[1\quad 0]\cdot g_{c}^{(n)}+N_{2} \tag{10}\]  
where \(N_{2}\) is the observation noise.  
The update step of KF is  
\[K_{k}=P_{\Phi}^{(n|n-1)}J^{T}(JP_{\Phi}^{(n|n-1)}J^{T}+R)^{-1} \tag{11}\]  
\[g_{c}^{(n)}\approx g_{c}^{(n|n-1)}+K_{k}(\mu_{c}^{(n)}-Jg_{c}^{(n|n-1)}) \tag{12}\]\[P_{\Phi}^{(n)}=(I-K_{k}I)P_{\Phi}^{(n|n-1)} \tag{13}\]  
The predict step of KF is  
\[g_{\mathcal{C}}^{(n|n-1)}\approx\Phi g_{\mathcal{C}}^{(n-1)}+\Delta^{(n|n-1)} \tag{14}\]  
\[P_{\Phi}^{(n|n-1)}=\Phi P_{\Phi}^{(n-1)}\Phi^{T}+Q \tag{15}\]  
where \(P_{\Phi}\) is the error covariance matrix, \(K_{k}\) is the optimal Kalman gain, and \(Q\) and \(R\) are the noise-covariance matrices.  
It is noted that the state space model was chosen for the basic model description since it is suitable for linear movement [18]. Therefore, in the processing, \(Q\), \(R\), and \(P_{\Phi}^{(0)}\) were set as the identify matrices.
## 3 Data Analysis  
Mobile traffic data consists of time-series records of communication volume across different media in specific regions. First, we analyze the Telecom Italia dataset [41; 42]. This is an open dataset that has been widely used in several studies [14; 15; 34; 43; 44; 45], giving it strong representativeness. Also, it consists of multiple traffic sub-datasets, Call, Internet, and SMS. We can train the model to predict various cases. These data were collected for 2 months, from 1 November 2013 to 1 January 2014 in Milan. The interval of recording was 10 min. But some records were missing, so we used data for training and analyzing with an interval of an hour by aggregating six of them. The left of Figure 1 shows the number of Call Detail Records (CDRs) regarding SMS, Call, and Internet. In legends of graphs, sms-in denotes the number of received SMS, sms-out denotes the number of sent SMS, call-in denotes the number of received calls, call-out denotes the number of issued calls, and internet denotes the number of CDRs that are generated when the connection lasts for more than 15 min or the user transfers more than 5 MB during a given time interval. And the right of Figure 1 shows the Internet traffic for a random hour in Milan divided by a 100 \(\times\) 100 grid, where each cell of the grid has a size of 235 \(\times\) 235 m\({}^{2}\). As shown on the left of Figure 1, all of the traffic data has a periodicity of approximately 24 h. Also, in the SMS and Call graph, the first 3 days have lower peaks than the other days. This means this data has uneven and semi-periodic temporal characteristics. And, as shown in the right of Figure 1, even if a particular time slot exhibits high traffic, the traffic in that region can differ significantly from other cells. This means this data has uneven spatial characteristics. Therefore, predicting traffic requires considering both these complicated temporal patterns and spatial dependencies.
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
###### Abstract  
The past decade has witnessed a growing demand for drone-based fire detection systems, driven by escalating concerns about wildfires exacerbated by climate change, as corroborated by environmental studies. However, deploying existing drone-based fire detection systems in real-world operational conditions poses practical challenges, notably the intricate and unstructured environments and the dynamic nature of UAV-mounted cameras, often leading to false alarms and inaccurate detections. In this paper, we describe a two-stage framework for fire detection and geo-localization. The key features of the proposed work included the compilation of a large dataset from several sources to capture various visual contexts related to fire scenes. The bounding boxes of the regions of interest were labeled using three target levels, namely fire, non-fire, and smoke. The second feature was the investigation of YOLO models to undertake the detection and localization tasks. YOLO-NAS was retained as the best performing model using the compiled dataset with an average mAP50 of 0.71 and an F1_score of 0.68. Additionally, a fire localization scheme based on stereo vision was introduced, and the hardware implementation was executed on a drone equipped with a Pixhawk microcontroller. The test results were very promising and showed the ability of the proposed approach to contribute to a comprehensive and effective fire detection system.  
U  
A _Applied_  
_sciences_  
**Citation:** [PERSON]; [PERSON].;  
[PERSON]; [PERSON]; [PERSON]; [PERSON] Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models. _Appl. Sci._**2023**, _13_, 11548. [[https://doi.org/10.3390/app132011548](https://doi.org/10.3390/app132011548)]([https://doi.org/10.3390/app132011548](https://doi.org/10.3390/app132011548))
## 2 Materials and Methods
### Spectral Preprocessing  
#### 2.3.1 Savitzky-Golay Filter  
The SG (Savitzky-Golay) filter is widely used for smoothing and denoising spectral data, effectively handling data in most experimental areas [37; 38]. It eliminates noise while preserving the width and shape of the signal. This filter is based on local polynomials  
Figure 2: Method flowchart.  
and uses a weighted moving average through a convolution window to achieve the least squares method to fit the curve. The formula is shown in Equation (1).  
\[\widehat{y_{j}}=\frac{\sum_{i=-m}^{m}C_{i}X_{j+i}}{n} \tag{1}\]  
where \(\widehat{y_{j}}\) is the smoothed data, \(X_{j+i}\) is the data to be processed, \(C_{i}\) is the smoothing coefficient, \(m\) is the window size, and (\(n=2m+1\)). In this study, the smoothing coefficient is set to 3, and the window size is set to 21.
# Robust and Secure Data Transmission Using Artificial Intelligence Techniques in Ad-Hoc Networks
## 4 Proposed Work
### Use of ML Techniques  
In this research, the dual mechanism of an ML approach including ANN and SVM is used. This helps to provide double security to the network. SVM is used as a binary classifier that helps to select only supportable features of sensor nodes and these features help to train the network with more efficiency and more accurate data. Therefore, during the transmission of data packets, ANN can select better nodes in the network and the network efficiency will be better compared to the single AODV approach. Here, the supportable sensor nodes feature acts as an input of the ANN in terms of node properties like coordinates, required time, and energy to transmit or receive the data packets. Based on these properties, ANN can decide to segregate the nodes into two categories, such as communicating and non-communicating nodes within the route.
## 2 Case Study
### Modelling an Equivalent Plane Strain
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\eta=k_{h}\)/\(k_{s}\) & \begin{tabular}{c} **Clay Silt** \\ **(Layer 1)** \\ \end{tabular} & \begin{tabular}{c} **Silty Clay** \\ **(Layer 2)** \\ \end{tabular} & \begin{tabular}{c} **Soft Clay** \\ **(Layer 3)** \\ \end{tabular} & \
\begin{tabular}{c} **Silty Clay** \\ **(Layer 4)** \\ \end{tabular} &
\begin{tabular}{c} **Clay Silt** \\ **(Layer 5)** \\ \end{tabular} \\ \hline
1 & \(3.46\times 10^{-4}\) & \(3.51\times 10^{-4}\) & \(2.87\times 10^{-4}\) & \(2.94\times 10^{-4}\) & \(2.87\times 10^{-4}\) \\
3 & \(2.09\times 10^{-4}\) & \(2.12\times 10^{-4}\) & \(1.73\times 10^{-4}\) & \(1.78\times 10^{-4}\) & \(1.73\times 10^{-4}\) \\
6 & \(1.34\times 10^{-4}\) & \(1.36\times 10^{-4}\) & \(1.11\times 10^{-4}\) & \(1.14\times 10^{-4}\) & \(1.11\times 10^{-4}\) \\
9 & \(1.01\times 10^{-4}\) & \(1.02\times 10^{-4}\) & \(8.36\times 10^{-5}\) & \(8.58\times 10^{-5}\) & \(8.36\times 10^{-5}\) \\
10 & \(9.34\times 10^{-5}\) & \(9.48\times 10^{-5}\) & \(7.75\times 10^{-5}\) & \(7.95\times 10^{-5}\) & \(7.75\times 10^{-5}\) \\
20 & \(5.68\times 10^{-5}\) & \(5.77\times 10^{-5}\) & \(4.71\times 10^{-5}\) & \(4.84\times 10^{-5}\) & \(4.71\times 10^{-5}\) \\
30 & \(4.32\times 10^{-5}\) & \(4.39\times 10^{-5}\) & \(3.59\times 10^{-5}\) & \(3.68\times 10^{-5}\) & \(3.59\times 10^{-5}\) \\
40 & \(3.61\times 10^{-5}\) & \(3.67\times 10^{-5}\) & \(3.00\times 10^{-5}\) & \(3.08\times 10^{-5}\) & \(3.00\times 10^{-5}\) \\
100 & \(2.28\times 10^{-5}\) & \(2.31\times 10^{-5}\) & \(1.89\times 10^{-5}\) & \(1.94\times 10^{-5}\) & \(1.89\times 10^{-5}\) \\
200 & \(1.82\times 10^{-5}\) & \(1.85\times 10^{-5}\) & \(1.51\times 10^{-5}\) & \(1.55\times 10^{-5}\) & \(1.51\times 10^{-5}\) \\
300 & \(1.66\times 10^{-5}\) & \(1.69\times 10^{-5}\) & \(1.38\times 10^{-5}\) & \(1.42\times 10^{-5}\) & \(1.38\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: An equivalent vertical permeability in m/day. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Symbol** & **Value** \\ \hline The width of the unit cell plane strain, m & \(B\) & \(0.75\) \\ The equivalent drainage diameter, m & \(d_{w}\) & \(0.052\) \\ Smear zone diameter & \(d_{s}\) & \(0.176\) \\ Horizontal permeability ratio of undisturbed soil and smear, (\(k_{h}\)/\(k_{c}\)) & \(\eta\) & \(3\) \\ The ratio of the diameter of the smear zone and drainage, (\(d_{u}\)/\(d_{w}\)) & \(s\) & \(3.38\) \\ Unit cell diameter, m & \(D_{e}\) & \(1.58\) \\ The ratio of the diameter of the unit cell and drainage, (\(D_{e}\)/\(d_{w}\)) & \(n\) & \(30.3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of equivalent horizontal and vertical permeability.
## 4 Experimental Results
### Overall Comments  
When the numerical values from the seven spectral metrics for three different test sites (Tables 11, 13 and 15) were evaluated, the proposed CIELab had a consistent behavior for different metrics and for different land categories, and ranked as the first among all methods. On the other hand, for the other Pan sharpening methods, different metrics provided various accuracies and did not show a consistent manner for different metrics and even, for different images, the same metric. As an example, the GS method generally had the second ranking for the ERGAS metric for different test sites. However, in the case of the RASE metric, the GS method had second ranking just for some images, while the GIHS method took the second rank for the remaining images. This phenomenon is similar for the worse results; there is no one method that can be mentioned as the worst for all test sites and all metrics. All facts about the consistent manner of the proposed method can also be asserted for the spatial metrics. The CIELab method had the best spatial performance for the all ten test sites, while second through seventh rankings were variable across different metrics and different images. As an outcome, it is evident that the proposed method presents the best results considering spectral and spatial quality metrics and visual interpretation for ten different sites having different landscape characteristics. Moreover, it provided efficient spectral conservation performance according to comparative evaluation performed with binary classification of VARI index. An overall ranking
## 3 Methodology
### Indoor Room Segmentation  
We propose a method that simulates the work of laser scanner to segment indoor space. The specific segmentation process is as follows:  
Step 1: The restored wall segment projection raster and the free-space evidence raster are fused;  
Step 2: Select a random point within the free-space evidence raster as the laser scanner site location \(\mathbf{L}\);  
Step 3: Simulate the laser scanner at point \(\mathbf{L}\) to emit \(\mathbf{n}\) rays uniformly clockwise and record the wall segments hit by the rays;  
Step 4: Extend the wall segment that was hit by the ray to form a plurality of virtual enclosed areas. Then, the enclosed area where \(\mathbf{L}\) is located is used as the area to be processed in the subsequent steps;  
Step 5: The proportion \(\partial\) of real wall segments in the boundary of the virtual enclosed area is calculated according to Equation (6). If the proportion \(\partial\) exceeds the threshold \(\varepsilon_{\partial}\), the virtual enclosed area is labeled as the area where the room or corridor is located. Instead, the area is discarded and step 2 is executed;  
Step 6: Check whether the virtual enclosed area in step 4 has an adjacency or containing relationship with the already labeled area. If so, the area will be fused with the existing area and labeled as the same area;  
Step 7: If the proportion of the labeled area to the free-space evidence raster exceeds \(\alpha\), the segmentation process is complete. The unmarked area is the connecting transition space.  
\[\partial=\frac{W_{r}\cap W_{v}}{W_{v}} \tag{6}\]  
where \(W_{r}\) represents the real wall and \(W_{v}\) represents the boundary of the virtual enclosed area.  
As shown in Figure 7, when scanning within a room, the scanner can usually obtain a larger amount of wall surface data. In this case, it is sufficient to form a virtually enclosed area by adding a small number of wall segment extensions. Whereas, if the scanner is located in a connecting transition space between rooms, it will only obtain less wall surface data to segment room.
## 2 Experimental Procedure
### Materials
#### 2.1.2 Adherends  
* Metallic adherend  
The aluminum alloy (AA6061-T6) was used as the metallic adherend material. This type of aluminum alloy is widely used in the manufacture of new car bodyshells. Test samples were cut from a rolled plate. Table 2 presents the mechanical properties of the aluminum alloy.  
* Composite adherend  
Carbon-fiber-reinforced polymer (CFRP) composite laminates were employed as the composite adherend in this study, showcasing their significance in modern engineering applications. These CFRP adherends were cut from a unidirectional composite plate. The primary composite plate was fabricated using the hand layup process, as depicted in the schematic presented in Figure 1. The hand layup process involved the sequential layering of 12 unidirectional carbon-fiber sheets and the application of LY-5052 epoxy resin, each accounting for 50% of the total composite mass. This process yields a composite plate distinguished by its controlled fiber orientation and customized material properties. The mechanical properties of the fabricated carbon-fiber sheets were determined via standard tests. As per the guidelines outlined in ASTM D3039/D3039M [35], the composite's tensile properties can be evaluated in multiple directions, both along the fiber orientation (referred to as the 1-axis in Figure 1e) and perpendicular to the fiber direction (referred to as the 2-axis in Figure 1e). These tests are conducted under quasi-static conditions, offering a comprehensive understanding of the composite's mechanical behavior in different orientations. Furthermore, to determine the composite's shear properties, ASTM D3518/D3518M [36] provides a reliable framework and methodology. The schematics of the performed tests are displayed in Figure 2. Table 3 represents the measured mechanical properties of the fabricated CFRP plate.  
Figure 1: The hand layup process for fabrication of CFRP laminate, (**a**) Schematic of the hand layup process, (**b**) Cutting process of carbon fiber sheet, (**c**) Mixing the resin and hardener, (**d**) Hand layup step, (**e**) Fabricated samples (Indices 1 and 2 shown in figure (**e**) indicate the parallel and perpendicular directions of the fibers, respectively).
# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 4 CNN-Based or Deep Learning-Based Approaches  
Brian MRIs can be captured at different angles, and under different magnetic strength conditions. Therefore, the image parameters contain a lot of very diverse information. Consequently, robustness to these changes is very crucial in the development of algorithms. In general, deep learning-based skull stripping approaches are divided into two distinct groups: 2D skull-stripping methods and 3D skull-stripping methods. Although 3D skull-stripping is thought to produce better results because contextual information between adjacent slices can be additionally used, it is computationally very expensive, so 2D skull-stripping has been more commonly adopted than the 3D method. 2D skull-stripping methods are categorized into two groups. The first group contains a voxel-wise neural network and the second group comprises fully connected CNNs. The networks of the first group predict the class of a voxel from multiple image patches, whose center the voxel is located using multiple 2D CNNs and a fully connected network. The networks of the second group adopt an encoder-decoder structure. The encoder part reduces the size of the image simultaneously extracting features, and the decoder expands the image in order to get high resolution segmentation results. One of the most used networks in medical image segmentation, U-Net [108], employes this encoder-decoder structure. Generally, the second group's methods are more efficient than the first ones because fully convolutional networks can recognize both local and global features. Moreover, the first group's methods have the disadvantage that the size of the input image is fixed, but fully convolutional networks are not limited to this condition and are also fast.
## 4 Multi-Attribute Machine Learning Model Selection
### Evaluation of Machine Learning Models Fitting Capabilities
GPR perfectly fits the target data for 1000 samples case, where the accuracy indicators show perfect results, \(R^{2}\) taking unity value and MSE being zero. Figure 9 displays the predicted motor losses (total, winding, iron, stator back iron, stator teeth, rotor back iron and the magnets) over their actual target values (original) obtained from the GPR 1000 samples machine learning model. The obtained values are specific to  
Figure 8: GPR 250 samples accuracy. Predicted vs. original values for stator modes and frequencies. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Samples/**} & \multicolumn{2}{c}{**Mode 0**} & \multicolumn{2}{c}{**Mode 2**} & \multicolumn{2}{c}{**Mode 3**} & \multicolumn{2}{c}{**Mode 4**} & \multicolumn{2}{c}{**Mode 5**} & \multicolumn{2}{c}{**Mode 6**} \\ \cline{2-13} \multicolumn{2}{c}{**Models**} & \(R^{2}\) & **MSE** & \(R^{2}\) & **MSE** & \(R^{2}\) & **MSE** & \(R^{2}\) & **MSE** & \(R^{2}\) & **MSE** & \(R^{2}\) & **MSE** \\ \hline \multirow{3}{*}{250} & SVR & 0.96 & 4 & 0.96 & 4 & 0.96 & 4 & 0.96 & 4 & 0.96 & 4 & 0.96 & 4 \\  & GBR & 0.98 & 2 & 0.99 & 0 & 0.99 & 1 & 0.99 & 1 & 0.99 & 1 & 0.99 & 1 \\  & GPR & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 \\ \hline \multirow{3}{*}{500} & SVR & 0.98 & 2 & 0.98 & 2 & 0.98 & 2 & 0.98 & 2 & 0.98 & 2 & 0.98 & 2 \\  & GBR & 0.99 & 1 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 0.99 & 1 \\  & GPR & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 \\ \hline \multirow{3}{*}{750} & SVR & 0.99 & 1 & 0.99 & 1 & 0.99 & 1 & 0.99 & 1 & 0.99 & 1 & 0.99 & 1 \\  & GPR & 0.99 & 1 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 \\ \hline \multirow{3}{*}{1000} & SVR & 0.99 & 1 & 0.99 & 1 & 0.99 & 1 & 0.99 & 1 & 0.99 & 1 & 0.99 & 1 \\  & GPR & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Machine learning models performance to predict natural frequencies of mode-shapesthe described number of stator DOFs. For less DOFs, a reduced number of feasible designs is needed to build the data-driven model. Correspondingly, when the number of DOFs is increased, the number of analysed designs must be enlarged to keep the same accuracy.