esa-sceva/satcom-chunk-collection huggingface dataset:
# Spherical-Model-Based SLAM on Full-View Images for Indoor Environments
## 5 Validation
### Accuracy Test  
To validate our system further and compare with the limited-view method (PTAM), we measured routes in a space with sparse features, as shown in Figure 8. The camera moved from the starting point to the ending point with no rotation as quickly as possible. The first 10 cm was used for map initialization; we provided the real scale for the system. During the test, the limited-view SLAM failed because the environment was sparse. Only a few features were captured in limited views. We tested PTAM [1], one of the most famous limited-view SLAM approaches, on perspective images generated directly from full-view images. Figure 9 shows that PTAM failed to track in a sparse feature environment, which was a wall in this test. However, our full-view SLAM tracked well in this situation because it can capture features from other place except the wall. The final map is shown as the left image in Figure 10. Two boxes were defined in the final map. The coordinate system on the map was the world coordinate system, which was also the starting point in this test. Each green point represents a keyframe. The trajectory, which was very close to the ground truth, is shown as the right image in Figure 10. The video can be seen at [[https://youtu.be/c1](https://youtu.be/c1) ER1 QqyjXM]([https://youtu.be/c1](https://youtu.be/c1) ER1 QqyjXM).  
Figure 8: A measured route in a sparse environment.  
Figure 7: Tracking recovery in the desk video. (**a**) Tracking well before occlusion; (**b**) Let the system lost tracking; (**c**) Tracking recovery even the location is changed.  
Figure 9: At the same position, PTAM fails to track as fewer feature points visible, while full-view SLAM tracks well.  
Then, we conducted an experiment to test our system's accuracy in a larger environment. We planned a new, measured route, as shown in Figure 11. The camera moved in a clockwise loop through 16 marked points, we randomly put a few objects around the route to generate a sparse space, plus, no objects are put around points 9-12. Point A was the starting point. The first 10 cm from A to B was used for map initialization. Using continuous tracking and mapping, we obtained the distance between point A and every marked point. We measured the ground truth using a laser rangefinder. The final errors for every marked point using our method are shown in Table 1 and Figure 12. As seen in Table 1, the accuracy of marked points (8-12) decreased compared to the other points. The reason is that we set few trackable features around these marked points to simulate a sparse environment. Our proposed method is based on full-view images. It uses the features from every direction, so this had only a small influence on our system. However, limited-view based methods, such as PTAM [1], immediately failed to track at these points because of a limited view with few features. The result shows that our method tracked in a sparse environment, and that the accuracy of our method was sufficient. The average error was 0.02 m.
## 1 Introduction
This paper proposes an integrated method based on complete ensemble empirical mode decomposition with adaptive noise and Kullback-Leibler divergence (CEEMDAN-KL) and adaptive moving neighbor binary pattern (AMNBP). This method addresses the problems of non-stationary, highly sensitive, and fading practical signals monitored using the \(\Phi\)-OTDR system. For the non-linear signal collected by the \(\Phi\)-OTDR system in a practical environment, CEEMDAN-KL is used to reduce the noise and to reconstruct the signal. Compared with empirical mode decomposition (EMD) and machine learning methods, CEEMDAN-KL solves the problem of the modal aliasing of EMD and significantly reduces the number of iterations [19; 20]. Through the calculation of the distribution difference between each intrinsic mode function (IMF) and the original signal, the most similar IMFs are selected to reconstruct the signal with better characteristic information. After the enhancement of the signals, the encoding sequence with the adaptive AMNBP algorithm reduces the signal dimension and preserves the differentiation information. AMNBP originates from an image processing method local binary pattern (LBP) [21; 22], which is applied to the \(\Phi\)-OTDR sensing system first. The reconstructed signals are dimensionally reduced and segmented through a custom window, and each window corresponds to four bits of binary code. By comparing the average value of the window and the means of each group, we can obtain the corresponding codes without the impact of abnormal data. After the adaptive gain adjustment, the key information in the signals is retained,thereby achieving the rapid positioning and accurate identification of events. The AMNBP is very suitable for the requirements of huge data volumes, fast response, and low signal-to-noise ratio (SNR) of the \(\Phi\)-OTDR sensing system. By adopting this method, the \(\Phi\)-OTDR sensing system obtained excellent experimental results. Three kinds of typical events and background noise were used for the experiments. Compared with the time-frequency analysis method and the radial basis function (RBF) method based on wavelets [5, 17], this integrated method significantly improved the average accuracy to 94.03% and the average efficiency by 20.0%. This method alleviated the above problems and enhanced the performance regarding the location and recognition done using \(\Phi\)-OTDR in practical environments.
# Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets
## 3 Problem Model  
The problem to be solved in this paper is defined as follows.  
Suppose that \(U=\{U_{1},U_{2},...,U_{N_{U}}\}\) and \(A=\{A_{1},A_{2},...,A_{N_{T}}\}\) are the sets of UAVs and reconnaissance task areas, respectively, where \(N_{U}\) and \(N_{T}\) refer to the size of the corresponding set. Given the location information of the elements in \(A\) and \(U\), each reconnaissance task area in \(A\) must be initially allocated to a UAV in \(U\). Then the reconnaissance sequence, \(q\), for each \(U_{i}\in U\) and the reconnaissance time, \(t\), for each \(A_{i}\in A\) must be planned so as to maximize the reconnaissance benefits \(f_{1}\) and minimize the flight distance \(f_{2}\).
## 5 Discussion and Conclusions  
This paper introduces a stacking-based hybrid model to forecast a stock's highest and lowest prices by employing three ML models as base learners and a meta-model for the final prediction. This method typically produces more consistent and precise outcomes than single ML models. While the current approach avoids sector-specific influences to simplify analysis, future research should explore the impact of industry-specific volatility on model performance. Including sectoral classification or volatility measures may provide deeper insights into how models generalize across heterogeneous financial environments. In three out of four instances, the results from the proposed model exceed those found in the reference study [15]. In the other case [15], its performance is similar to those of the state-of-the-art methods. Additionally, a further experiment validated the model's competitiveness, attaining MAPEs < 1 for every stock and index examined (Figures 4-6).In addition to MAPE, the model's performance was also evaluated using RMSE, MAE, and directional accuracy, as reported in Tables 3-8. These metrics provide complementary perspectives \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Parameters** & **MAPE (\%)** & **RMSE** & **MAE** & \begin{tabular}{c} **Directional** \\ **Accuracy** \\ \end{tabular} \\ \hline \begin{tabular}{c} Support Vector \\ Regression \\ \end{tabular} & Kernel = RBF, C = 2, Epsilon = 0.1 & 1.011 & 0.159 & 0.125 & 0.617 \\ \hline \begin{tabular}{c} Neural Networks \\ \end{tabular} & \begin{tabular}{c} Epochs = 250, Hidden Layer \\ Neurons = 5 \\ \end{tabular} & 0.749 & 0.747 & 0.121 & 0.09 \\ \hline \begin{tabular}{c} Random Forest \\ \end{tabular} & \begin{tabular}{c} Trees = 100 \\ \end{tabular} & 0.754 & 0.118 & 0.094 & 0.666 \\ \hline \
\begin{tabular}{c} Hybrid Model \\ \end{tabular} &
\begin{tabular}{c} Epochs = 250, Hidden Layer \\ Neurons = 3 \\ \end{tabular} & 0.834 & 0.131 & 0.104 & 0.666 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Prediction results for the maximum price of the CPFE3 stock (five selected features). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Parameters** & **MAPE (\%)** & **RMSE** & **MAE** & \begin{tabular}{c} **Directional** \\ **Accuracy** \\ \end{tabular} \\ \hline \begin{tabular}{c} Support Vector \\ Regression \\ \end{tabular} & Kernel = Polynomial degree 2, C = 8, \\ \begin{tabular}{c} Epsilon = 0.1, Gamma = 0.1 \\ \end{tabular} & 4.332 & 1796.5 & 1697.89 & 0.636 \\ \hline \begin{tabular}{c} Neural Networks \\ \end{tabular} & \begin{tabular}{c} Epochs = 250, Hidden Layer \\ Neurons = 15 \\ \end{tabular} & 0.3695 & 187.12 & 147.89 & 0.727 \\ \hline \begin{tabular}{c} Random Forest \\ \end{tabular} & \begin{tabular}{c} Trees = 80 \\ \end{tabular} & 0.437 & 234.36 & 171.37 & 0.58 \\ \hline \
\begin{tabular}{c} Hybrid Model \\ \end{tabular} &
\begin{tabular}{c} Epochs = 250, Hidden Layer \\ Neurons = 3 \\ \end{tabular} & 0.474 & 246.93 & 186.08 & 0.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Prediction results for the minimum price of the Dow Jones index (five selected features).
# Dynamic Measuring Method of Laser Beam Incident Angle for Laser Doppler Vibrometer
###### Abstract  
Accurately measuring the incident angle of the laser Doppler vibrometer (LDV) laser beam is crucial for calculating the accurate dynamic response of a target. Nonetheless, conventional measuring methods may encounter limitations due to spatial constraints. To address this issue, a novel high-precision dynamic measuring method is proposed based on the measuring principle of LDV. Furthermore, a compact dynamic measuring device is constructed to facilitate this method. The proposed method involves the simulation of various tangential velocities utilizing a high precision rotating disk system. Subsequently, the laser beam incident angle is computed based on the projection relationship established between the average value of LDV measurements and the simulated velocities. To validate the feasibility of the dynamic measuring method and the correctness of the obtained incident angle, the paper compares this angle with that obtained through a conventional laser beam measuring method and device. This paper analyzes four key factors that may affect the angle measuring results theoretically and experimentally: environmental noise, laser spot position error, roll angle, and pitch angle of the rotating disk. The results indicate that the laser spot position error and the pitch angle of the rotating disk are more influential than the other two factors. Corresponding optimization measures are also proposed to improve the measuring accuracy.  
laser Doppler vibrometer (LDV); laser beam; incident angle; dynamic measuring; error analysis Article
## 2 Materials
### In Situ Wind Speed Data  
Wind vectors measured by NDBC buoys ([[https://www.ndbc.noaa.gov/](https://www.ndbc.noaa.gov/)]([https://www.ndbc.noaa.gov/](https://www.ndbc.noaa.gov/)) (accessed on 6 February 2025)) are used to verify the accuracy of wind vectors retrieved from Sentinel-1 SAR data. The buoy wind vectors used in this study are primarily located in southern North America and along the east coast of North America, with temporal collocation strictly maintained within \(\pm\)30 min of corresponding Sentinel-1 SAR overpasses. The locations of buoys and their corresponding SAR images are shown in Figure 1.  
This study employs CMD05.N as the GMF for wind field inversion, which produces the equivalent neutral wind field at 10 m height [17]. The equivalent neutral wind is the wind speed calculated using the stress and roughness length consistent with the observed atmospheric stratification, but with the atmospheric stability term set to zero in the modified  
Figure 1: Spatial coverage of SAR images and the collocated buoys.  
logarithmic wind profile [18; 19; 20]. However, buoys typically measure actual wind speeds at approximately 4 m height above the sea surface. To compare with the SAR-retrieved equivalent neutral wind speeds, the buoy-observed wind speeds must be converted to 10 m equivalent neutral winds based on the observation height and relevant atmospheric boundary layer parameters.  
The height correction of wind speed primarily employs the wind profile function proposed by [PERSON] and [PERSON] [18]. The integral form of the flux-profile relationship for near-surface wind speed is derived from Monin-Obukhov similarity theory [21; 22], expressed as [18]:  
\[u_{z}=\frac{u_{*}}{\kappa}\left[\ln\left(\frac{z}{z_{0}}\right)-\Psi\right] \tag{1}\]  
where \(u_{z}\) is the wind speed at a height of \(z\) m above the sea surface. \(\kappa\) is the von Karman constant, taken as 0.4. \(\Psi\) represents the atmospheric stability term. By setting \(\Psi=0\), the equivalent neutral wind speed can be obtained. The relationship between friction velocity \(u_{*}\) and roughness length \(z_{0}\) is calculated using the Charnock model [23; 24]:  
\[z_{0}=\alpha\frac{u_{*}^{2}}{g}+0.1\frac{v}{u_{*}} \tag{2}\]  
where \(\alpha=0.0185\), \(g\) denotes gravitational acceleration. \(v\) represents the aerodynamic viscosity, with a value of \(0.15\times 10^{-4}\) m\({}^{2}\)/s.
## 3 Topological Map Representation  
The process of map preservation is shown in the block diagram on the right side of Figure 1, from which it can be seen that the representation method of the topological map is a process of combining the metric map with the topological map. The method is inspired by the ROS package [27], which expands from a gray value to an RGB color, so that the occupied information and the topological information is saved efficiently while saving and reusing the topological map. In this paper, the parameter TM and the parameter OM are used to represent the topological information and the occupied information, respectively; then, the information converter is used to convert the parameters into the RGB value of the color picture, so that the obtained RGB value can not only be used to display and identify the topology area in real time, but can also contribute to save the map information as a color image.
## 1 Introduction
However, consecutive historical data are necessary for the prediction of between-epoch ionospheric variations under high ionospheric activities or in the case of a large sampling interval/data gap. As for the satellite-borne GPS receiver, the ionospheric residuals in carrier phase observations clearly vary and cannot be removed by differences in measurements in adjacent epochs, and consecutive historical ionospheric residual data are hardly available, which makes many of the methods related to the ionosphere ineffective. [PERSON] et al. [28] and [PERSON] et al. [1; 29] used a priori orbit as a reference to detect cycle slips with ionosphere-free linear combinations. However, because carrier phase measurements are essential for precise GPS orbit determination, it is an iteration process that detects cycle slips, and the detection effect is affected by the accuracy of the a priori orbit. In this research, we present a second-order, time-difference, geometry-free (STG) approach to detect and repair cycle slips for LEO satellite GPS carrier phase observations with orbit dynamic model information. According to a reformulation of [PERSON]'s equation of motion, the second-order time difference of the LEO satellite's position (STP) is only related to the satellite's acceleration and the sampling interval, regardless of velocity. Therefore, the STP value can be computed, with an accuracy that is only affected by the accuracy of the a priori acceleration. Moreover, the precise dynamic orbit determination of the GPS/LEO satellite [3; 5; 30; 31] shows that it is possible to obtain a high-precision acceleration estimate of satellites with the known orbit dynamic models. By substituting the STP values into the carrier phase observations, the STG observations are derived. In this paper, unlike the traditional HMW combination and the geometry-free phase combination, four STG combinations with different levels of ionospheric residuals are adopted together to detect and repair cycle slips. The remainder of the paper is organized as follows: In Section 2, the STG cycle slip detection and repair approach is presented. The experiments and analysis are presented in Section 3 to assess the performances of the proposed method in various cases. Finally, concluding remarks are summarized, and an outlook for future research is presented in Section 4.
## 4 Conclusions  
In conclusion, we have measured the SLP values of iron oxide nanoparticles in aggregates containing various quantities of iron oxide and cerium oxide nanoparticles. As shown, aggregation of magnetic nanoparticles decreased the SLP values. The use of electrostatic bonds between both nanoparticles and oppositely charged polyelectrolytes allowed for the preparation of aggregates with homogeneous distributed iron oxide and cerium oxide nanoparticles. By diluting iron oxide nanoparticles in aggregates with cerium oxide nanoparticles, we were able to increase the SLP value back up to 79% of the SLP value of well-dispersed nanoparticles.  
This model is of prime importance for designing materials with _a priori_ stable magnetic properties toward new delivering systems for therapy applications. It would be of prime importance to look at different systems with directed linked (covalent bonds) or with more controlled architectures and their impact on hyperthermia efficiency.  
[PERSON] prepared sample and achieved SLP measurements. [PERSON] performed TEM on cerium oxide and iron oxide nanoparticles. [PERSON] performed TEM on aggregates and the chemical mapping. [PERSON] conceptualized the experiments, analyzed the results, supervised the scientific discussions, and wrote the draft.  
This research received no external funding.  
Figure 5: Specific loss power (SLP) versus the ratio between Fe\({}_{2}\)O\({}_{3}\)/CeO\({}_{2}\) ratio. Red line corresponds to the SLP of well dispersed IONPs. Error bars correspond to standard deviation of measurements of at least two samples.
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
## 5 Estimation Results  
This section presents various estimation results when using the proposed ellipsoidal enclosure technique based on an a posteriori evaluation of the measured data for both DDboats A and B. Here, we distinguish the localization of the DDboats without and with using information on the measured heading. In all presented results, the confidence parameter \(r=3\) is used in Equations (58)-(60).
# Analysis of the Chip Geometry in Dry Machining of Aeronautical Aluminum Alloys
## 3 Results and Discussion
### Shrinkage Factor (\(\zeta\))  
Figure 12 shows the evolution of the mean values of \(\zeta\) as a function of \(f\), for each alloy tested. First of all, these values have been calculated by Equation (2), through the experimental measurement of the shear angle (\(\varphi\)). As can be observed from this figure, \(\zeta\) values (calculated via Equation (2)) have the same trend observed for \(\varphi\), as expected. Its value tends to remain constant in terms of \(f\), at around a value of \(\zeta\) = 0.8. This behavior is similar in both alloys. Nevertheless, previous studies of these alloys have revealed tool geometry alterations during machining, due to secondary adhesion wear phenomena (Built-Up-Edge, BUE and Built-Up-Layer, BUL), which result in variations of \(\gamma\)[22; 32; 33]. Because of this, Equation (2) (where \(\gamma\) has been considered constant) is not suitable for calculating \(\zeta\) in this case.  
Because of all of the information mentioned above, the shrinkage factor has been also calculated through the experimental values of the chip's cross-section area (\(S\)) and the theoretical value of the chip's cross-section area before machining (\(S_{0}\), calculated via \(f\) and \(a_{p}\)), Equation (5). The values of \(\zeta\) obtained in this way are plotted in Figure 12.  
\[\zeta=\ S_{0}/S\ =\ \big{(}f\cdot a_{p}\big{)}/S, \tag{5}\]  
As it can be observed, \(\zeta\) values calculated via Equation (5) are lower than those calculated by Equation (2), which means a higher chip strain grade. For both alloys, \(\zeta\) values fluctuate between 0.55 and 0.65, with a general increasing trend with \(f\). In addition, for the UNS A97075 alloy, the lowest values of \(\zeta\) are obtained for the lowest\(f\) values (0.05 mm/r and 0.1 mm/r), which indicates a higher grade of chip deformation. This grade of deformation becomes lower once the shear limit has been reached (0.2 mm/r and 0.3 mm/r). This fact is less noticeable in the UNS A92024 alloy, due to its lower plasticity.  
Figure 11: Chip width (\(b\)) as a function of feed.
# Methodology for Studying the Reliability of Interlocking Devices in Bulgarian Railways
## 5 Conclusions  
The article analyzes the failures and their duration of signalling devices working on Bulgarian railways, using two approaches over a three-year period:  
* statistical analysis of data on the failures and their duration, with special attention paid to interlocking devices (RCIDs, RRIDs, EMIDs, RSKDs) and compared to external station devices (traffic lights, point machines, rail current circuits, and counter points), interstation devices (Type ALS, type PAB, AB with axle counters, AB with rail circuits, automatic crossing devices), telecommunications devices (automatic telephone exchanges, external lines);
* reliability analysis of interlocking devices, including the determination of the time for failure-free operation \(\tau(\Delta t)\), the statistical assessment of the failure flow intensity \(\hat{\omega}_{i}(\Delta t)\), the Mean Time Between Failures _MTBF_ and finally--the stationary indicators of availability and unavailability.  
Reliability analysis provides a much more accurate evaluation of the operational reliability of devices than statistical data processing. Its use makes it possible to determine the elements of signalling systems that need to be upgraded, modernised, or replaced sooner. They also can be taken into account during the planning of technical maintenance of the specific devices. If data are available for specific lines, the methodology can be used to determine the reliability indicators for each individual section.  
The proposed approach can be used to analyse the reliability of all other devices in signalling systems, as well as to determine the reliability indicators of any other systems, different in type, application, and structure, of course if the necessary data are available.  
**Author Contributions:** Conceptualization, methodology, resources, project administration, funding acquisition, E.D.; writing--original draft preparation, formal analysis, E.D. and V.D.; software, investigation, data curation, writing--review and editing, supervision, V.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Bulgarian National Science Fund, grant number KP-06-H57/12.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: Reference documents of the railway network, NRIC, 2019, 2020, 2021, [[https://rail-infra.bg/bg/92](https://rail-infra.bg/bg/92)]([https://rail-infra.bg/bg/92](https://rail-infra.bg/bg/92)) (accessed on 2 March 2025). Annual consolidated activity reports, NRIC, 2020, 2021, 2022, [[https://rail-infra.bg/bg/216](https://rail-infra.bg/bg/216)]([https://rail-infra.bg/bg/216](https://rail-infra.bg/bg/216)) (accessed on 2 March 2025).  
**Conflicts of Interest:** The authors declare no conflicts of interest.