esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Deformed Microstructure and Fracture Surfaces
#### 3.4.1 Analysis of Deformed Surfaces
This suggests that the eventual failure of the different forms of DMLS Ti6 Al4V(ELI) at high strain rate is because of the formation of ASBs which offer favourable regions for the initiation, growth and propagation of cracks. Figure 15: Secondary electron images showing adiabatic shear bands on sections of deformed surfaces of samples D at different strain rates and temperatures (the red arrows indicate the compression loading axis). The width of ASBs is seen in Figures 14-16 to vary for different forms of the DMLS Ti6 Al4V(ELI) alloy and at different test conditions of strain rate and temperature. A summary of the average width of ASBs for different forms of the alloy and at different testing conditions, is presented graphically in Figure 17, all together for ease of comparison. It is evident from this figure that the average width of the ASBs increases with increasing test temperature, almost invariably in linear relationships. A similar observation was reported by [PERSON] and [PERSON] (2018) for wrought Ti6 Al4V. The average width of the ASBs is also seen in the figure to increase with strain rate, however, the error bars are seen to overlap which could suggest that the increase in the width of the adiabatic shear bands is not significant between the strain rates of 750 and 1500 s\({}^{-1}\). Figure 16: Secondary electron images showing adiabatic shear bands on sections of deformed surfaces of samples E at different strain rates and temperatures (the red arrows indicate the compression loading axis). A significant difference in the average width of ASBs is noted in Figure 17 for different samples of DMLS Ti6 Al4V(ELI). At the same strain rate and temperature, samples C and D show the lowest and the highest values of width, respectively. This suggests that the heat treatment regimens of DMLS Ti6 Al4V(ELI) do influence the width of ASBs formed at conditions of high strain rate deformation. A critical factor that influences the width of ASBs as reported in the research of [PERSON] and [PERSON] [42] is the hardness of materials, as the width of the ASBs diminishes with increasing hardness of the material. Hardness is generally a measure of a material's resistance to localized plastic deformation and is normally proportional to material strength. The thermo-softening effect which results in the formation of ASBs is expected to reduce with increase in strength and related hardness of materials. The values of Vickers hardness for samples C, D and E shown in Figure 5 were 362.36 \(\pm\) 2.57 HV, 343.92 \(\pm\) 3.52 HV and 353.88 \(\pm\) 4.23 HV, respectively. This order of hardness is consistent with the order of width of ASBs observed here for the three microstructures. Therefore, the difference in the width of the ASBs observed in Figure 17 for samples C, D and E is because of the different microstructures formed by the different heat treatment processes and the arising values of strength and hardness.
# Modeling and Key Parameter Interaction Analysis for Ship Central Cooling Systems
## 4 Development of the Response Surface Model  
### Selection of Design Variables  
In order to develop a response surface model (RSM) capable of accurately predicting the energy consumption of the ship's central cooling system and revealing parameter interactions, based on the preceding calculation model, this paper selected the following five parameters as design variables: main engine power, seawater temperature, seawater pump speed, opening of the low-temperature loop three-way control valve, and low-temperature freshwater flow rate. The selection of these parameters aimed to cover key aspects such  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **High-Temperature** & **Main Engine Jacket Cooler** & **Low-Temperature Freshwater** \\
**Freshwater to Main Engine** & **Inlet Temperature** & **Inlet Temperature** & **Three-Way Valve Outlet** \\ \hline Design Value & 65 \({}^{\circ}\)C & 80 \({}^{\circ}\)C & 36 \({}^{\circ}\)C \\ Simulation Value & 65.8 \({}^{\circ}\)C & 78.5 \({}^{\circ}\)C & 36.3 \({}^{\circ}\)C \\ Relative Error & 1.23\% & 1.88\% & 0.83\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of errors of main system parameters under rated operating conditions.  
Figure 2: Central cooling system structure.  
as the main heat load source, external cooling conditions, core energy consumption units, and critical links in internal heat distribution and control while simultaneously providing a basis for the subsequent analysis of the influence of each parameter and their interactions on total energy consumption.
# Integrated GNSS/IMU-Gyrocompass with Rotating IMU. Development and Test Results
## Reference Frame Definitions and Main Notations  
Below we introduce the notations for the reference frames used in the paper (see Figure 2):  
\(X_{i}Y_{i}Z_{i}\)--inertial frame (i-frame);  
\(X_{e}Y_{e}Z_{e}\)--Earth-Centered-Earth-Fixed Frame (e-frame);  
_ENU_--n-frame, a local geographic frame (eastward-northward-upward);  
\(X_{b}Y_{i}Z_{b}\)--b-frame bound with the integrated GNSS/IMU gyrocompass body;  
\(X_{s}Y_{s}Z_{s}\)--s-frame bound with the antenna baseline and measurement axes of accelerometers and gyros, with the antenna baseline \(\overset{\rightarrow}{B}\) oriented along axis \(Y_{s}\) (Figure 3), s-frame rotates with respect to b-frame about axis \(Z_{b}\) coinciding with axis \(Z_{s}\) at angular velocity \(i\).  
Figure 2: Reference frames: \(X_{i}Y_{i}Z_{i}\)—inertial frame (i-frame), \(X_{e}Y_{e}Z_{e}\)—Earth-Centered-Earth-Fixed Frame (e-frame), _ENU_—eastward-northward-upward (n-frame), \(\Omega\)—angular velocity of Earth about its polar axis, \(t\)—time.  
Also we assume that  
\(\mathbf{p}=(\phi,\lambda,h)^{T}\) and \(\mathbf{\delta p}=(\delta\varphi,\delta\lambda,\delta h)^{T}\) are the vectors of geographic coordinates and their errors; \(\mathbf{V_{n}^{e}}=[V_{E},V_{N},V_{U}]^{T}\), \(\mathbf{\delta V_{n}^{e}}=[\delta V_{E},\delta V_{N},\delta V_{U}]^{T}\) is the vector of object linear velocity with respect to the Earth projected onto n-frame axes and its error vector;  
\(K,\psi,\theta\) are the heading, roll and pitch angles;  
\(\beta,\gamma,\alpha\) are INS errors in modeling a local geographic frame; \(\beta\) and \(\gamma\) are pitch and roll errors, and \(\alpha\) is a heading error;  
\(\mathbf{\omega_{s}^{i_{s}}}=[\omega_{\chi_{s}},\omega_{\gamma_{s}},\omega_{Z _{s}}]^{T}\) and \(\mathbf{\delta\omega_{s}^{i_{s}s}}=[\delta\omega_{X_{s}},\delta\omega_{Y_{s}},\delta\omega_{Z_{s}}]^{T}\) are the projections of gyros angular velocity with respect to i-frame projected onto their measurement axes s-frame and their errors;  
\(\mathbf{f_{s}}=[f_{X_{s}},f_{Y_{s}},f_{Z_{s}}]^{T}\) and \(\mathbf{\delta f_{s}}=[\delta f_{X_{s}},\delta f_{Y_{s}},\delta f_{Z_{s}}]^{T}\) are the specific force vector projected onto its measurement axes (s-frame) and its errors;  
\(\rho^{i}\), \(\dot{\rho}^{i}\), \(\Delta\phi^{i}\) are the pseudorange, radial velocity and single difference of carrier phase received by the spaced GNSS antennas for each observed i-th satellite.
## 3 Results and Discussion
### Univariate Histogram Analysis of the Three Modalities  
The first column of Figure 9 displays the virtual tomography slices registered to the selected histology slice. In the second column, related univariate histograms using a linear scale are depicted. K-means clustering was applied to each dataset, and the related clusters are visualized on the univariate histograms (middle) and the slices themselves (right). As expected, the pixel-intensity distributions of the selected modalities are diverse. The origin of the histogram peaks from hard X-ray tomography are discussed above. Whereas tomography data represented by grayscale have only one channel, histology data can be analyzed with respect to three color channels. Thus, k-means clustering based on the three RGB values can yield overlapping clusters when displayed on a univariate histogram of greyscale intensity, as shown with the colored bars of the middle column bottom histogram. For the conventional \(\upmu\)CT of non-decalcified tissue, we reasonably found only two clusters representing bone and environment, which are blue-colored and transparent on the slice in the third column of Figure 9. For the SR\(\upmu\)CT and histology data, five clusters were selected. Using black, green, red and blue, similar regions were colored accordingly. The pixels indicated in gray within the histograms were made transparent in the third column's slices.  
Figure 8: Location of the selected histology slice within the tomography data, see top row. In a first step, the approximate location was identified manually. In a second step, landmark pairs, i.e., SRuCT, and histology slices, guide surface extraction and 2D-2D pre-registration. An automatic non-rigid 2D-2D registration refines correspondences.
## 6 Simulation Experience and Verification
### Experimental Setup
#### 6.1.1 Cooperative Communication  
In this task scenario, there are two types of agents, speakers and listeners, placed around several landmarks with different colors. The listener can know the distance between the landmark and itself and the color of the landmark through observation, but it does not know what color the target landmark is, but the speaker can tell the target by observing the listener's color. Therefore, the listener must communicate with the speaker and reach the target according to the speaker's guidance. The listener will obtain the corresponding reward according to the distance between it and the target to guide it to the target. In Section 6.2, we compare DDPG, MADDPG and SMADDPG in this task environment in the case of fixed agent scale or dynamic agent scale, and give experimental results.
## 4 Advanced Positioning Methodologies Using UWB and Wi-Fi RTT Signals
### Integrated Positioning of Data Fusion
In this case, the accuracy can be improved by replacing the Gaussian assumption with a more flexible model, such as a Gaussian mixture model or a robust error model. In addition, adaptive filtering techniques that dynamically adjust noise parameters based on environmental conditions can also help improve performance in dynamic and unpredictable environments [92, 93, 94]. Factor graph optimization makes full use of time correlation information by constructing and optimizing a factor graph containing current and historical data. Factor graph optimization gradually approaches the optimal solution through multiple iterations, especially when the measurement distribution assumption is not fully met, as it can significantly improve positioning accuracy. Due to its flexible error modeling capabilities, it can handle non-Gaussian distributions and complex environments, and effectively reduce linearization errors through multiple iterative optimizations in nonlinear scenarios [95]. However, factor graph optimization has high computational complexity, and batch processing limits its real-time performance. It requires technical optimization such as sliding windows or marginalization to reduce the amount of data processed each time, thereby improving the efficiency of real-time applications. In addition, factor graph optimization is sensitive to initial values and may fall into local optimality. Through smarter initialization strategies, such as machine-learning-based methods or heuristic algorithms, the risk of falling into local optimal solutions can be reduced and robustness can be improved. In addition, integrating real-time adaptive error models into factor graph optimization can also improve the adaptability of factor graph optimization in response to environmental changes.
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 3 Experiments
### Results  
To evaluate the suggested model's performance along with other existing solutions, we establish a constant LWL while varying the horizon lengths. For datasets other than ILI, the LWL was set to '96', with horizon lengths varying in the range of 96, 192, 332, and 720. Similarly, for the ILI dataset in the multivariate setting, the LWL was set to 36, with horizon lengths of 24, 36, 48, and 60.
## 6 Evaluation and Analysis
### Experimental Evaluation
As the number of nodes increases, the performance advantage of MT-SOTA over the traditional SOTA process becomes more noticeable. For example, when there are only eight nodes per ECU, MT-SOTA improves the process by 80% regardless of the size of the software clusters. Additionally, MT-SOTA demonstrates improved efficiency when removing a software cluster, as it eliminates the need for hash calculations on the removed software. Instead, the algorithm solely updates the Merkle tree contents. Consequently, the time depicted in Figure 13 becomes unnecessary, and MT-SOTA decreases the time interval from multiple milliseconds to microseconds. Figure 14: Execution time for setting up the Merkle tree. Figure 13: Processing time for generating hash of the entire software image. As the number of software clusters or the size of the software increases, the required time also increases. Figure 15: Execution time for adding/updating individual software clusters with MT-SOTA. Figure 16: Execution time for removing individual software clusters with MT-SOTA. Figure 17: Time execution percentage improvement with MT-SOTA vs. standard SOTA. Figure 18: Comparing execution times: updating software clusters with MT-SOTA vs. standard SOTA. Looking into memory needs, the overhead of the MT-SOTA scheme depends on the software clusters number as well as the hash algorithm used. In Table 4, we provide a summary of memory utilization for various hash output sizes and maximum node counts across our study cases.
## Abstract  
The current study presents an adaptive control approach to solve the tracking trajectory problem for a robotic manipulator that uses a gripper based on bimorph piezoelectric actuators. The development of an adaptive gain state feedback form that considers the state restrictions is proposed using a novel class of barrier Lyapunov function that drives the effective control of joints and piezoelectric actuators. The proposed method allows for the inclusion of complex combinations of state restrictions in the Lyapunov function, yielding the construction of differential forms for the gains in the controller that can handle the evolution of trajectories of the robotic arm inside the restricted region. The proposed control design successfully tracks reference trajectories for both joints of the robotic arm as well as the motion of the piezoelectric device during several operative scenarios. A comprehensive experimental study evaluates the effect of introducing state-dependent gain considering state restrictions of the ellipsoidal type. The comparison of the mean square error confirms the contributions of the developed control action, showing better tracking quality for less control power with the same evaluation, which is a desirable characteristic in the controlled motion of micromanipulators. The proposed controller solves the tracking trajectory problem for the micromanipulation system, satisfies the motion restrictions, and allows better tracking performance to be enforced. Furthermore, comparison of the obtained trajectories seems to validate the proposed controller's contribution concerning a feedback form with fixed gains.  
Keywords:automatic manipulator; piezoelectric actuator; adaptive control; state restrictions +
Footnote †: journal: Applied
###### Abstract  
In this study, the tribological behavior of both liquid (oil) and semi-liquid (grease) lubricants enhanced by multilayer graphene nano platelets and titanium dioxide nano powder was evaluated using ball-on-disk and shaft-on-plate tribo-meters. Oil samples for both 2D graphene nano platelets (GNP) and titanium nanopowders (TiNP) were prepared at three concentrations of 0.01 %_w/w_, 0.05 %_w/w_ and 0.1 %_w/w_. In addition, 0.05%_w/w_ mixtures of GNP and TiNP were prepared with three different ratios to analyze collective effects of both nano additives on friction and wear properties. For semi-liquid lubricants, 0.5%_w/w_ concentrations were prepared for both nano additives for shaft-on-plate tests. Viscosity and oxidation stability tests were conducted on the liquid-base lubricants. Nano powders of both additive and substrate were analyzed using transmission electron microscopy (TEM) and scanning electron microscopy (SEM). In addition, Raman spectroscopy was conducted to characterize the graphene and titanium dioxide. The study shows that adding graphene and titanium dioxide individually sacrifices either the wear or friction of lubricants. However, use of both additives together can enhance friction resistance and wear preventive properties of a liquid lubricant significantly. For a semi-liquid lubricant, the use of both additives together and individually reduces friction compared to base grease.  
titanium dioxide; graphene; grease; base oil; friction; wear Article
## 3 Results
### Validation of CET-WV-Extracted coastline with reference UAV Data  
The validation of CET-WV-extracted coastline with reference UAV data has shown that a coastline derived from WV models is significantly generalized (Figure 10). Such generalization is mainly the result of the spatial resolution of WV models, which are not detailed enough for the detection and representation of the majority of smaller coastline curvatures. Consequently, the total length of the coastline derived from WV models is considerably shorter than the coastline derived from UAV models. In comparison, the length of the CET-WV coastline is 405.01 m, while the total length of the CET-UAV-extracted coastline is 634.14 m, which is 36.13% longer. The level of generalization of the CET-WV-extracted coastline is furthermore expressed through the difference in the total number of vertices that represent derived coastlines. While the CET-UAV coastline has in total 2930 vertices, the CET-WV coastline has only 240, which is less than 10% of all vertices of the CET-UAV coastline.  
Despite significant generalization, the extracted CET-WV coastline managed to accurately follow and define the predominant shape and direction of the coastline.  
This was further confirmed through the conducted validation of the coastline horizontal accuracy. Within the validation performed by DSAS, 310 transects were created in total,  
Figure 10: Coastlines within validation area derived by CET from WV-derived (red) and UAV-derived (green) models.  
Figure 9: Two very small rocks successfully mapped by CET from WV-derived models.  
along the 310.86 m long baseline. The average calculated deviation of the CET-WV coastline in regard to the reference CET-UAV coastline was 0.73 m, with a maximum deviation reaching up to 5.7 m. Calculated deviation of CET-WV coastline was under 0.68 m in 49.35% of all transects, while deviations above 1.13 m were present within only 8.38% (26 transects) of all analyzed transects (Figure 11). It should be noted that the highest coastline deviation rates were calculated within very shallow parts of the evaluated coastline, where detected deviations could be related to the fluctuations in Sea level caused by different tidal regimes (between CET-UAV and CET-WV). Calculated deviations have shown that the horizontal accuracy of the CET-WV coastline is very high, which confirms that WV-derived models can serve as a perfect basis for a highly accurate large-scale coastline mapping.  
Furthermore, the performed validation has shown that the accuracy of the CET coastline extraction within the whole Iz-Rava island group (Section 3.1) would be even better if WV imagery extraction was applied on VHR models created from the UAV photogrammetric data. However, acquisition of such UAV photogrammetric data for the whole study area would require difficult and lengthy field work.
# IoT's Tiny Steps towards 5G: Telco's Perspective
## 5 IoT in 5G Enabling Technologies
### Energy Related Technologies  
IoT applications span a broad range of domains including home automation, healthcare, surveillance, transportation, smart environments, etc. One of the most important obstacles for implementing such an impressive scheme is supplying adequate energy to operate the network in a self-sufficient way without compromising QoS [180]. Since energy efficiency is of most importance to battery constrained IoT devices, researches have focused their work on the development of the device energy saving mechanisms.  
**Energy harvesting** is a new paradigm which uses solar, thermal, wind and kinetic energy sources to power sensor nodes and consequently prolong network lifetime [181]. Among different energy harvesting methods, wireless energy harvesting (WEH) has proven to be one of the most promising solution for energy aware IoT devices, because of its simplicity, ease of implementation, and availability [180]. This concept will improve some of the 5G high network communication requirements, like reliability of IoT communications, as presented in [182]. Authors in [252] have shown that the integration of social awareness and energy harvesting in D2D communication results in higher system capacity. Network architectures proposed in [100; 253] have shown better energy efficiency in 5G wireless network. Researchers could work on improving this architecture and integration with various 5G technologies in the future.  
**UAV** implies a flying vehicle without the driving presence of a human pilot to control it on-board [113]. Drones are a possible example of UAVs, although many other robotics related applications could be part of this application group [113]. UAVs can play a vital role in IoT scenarios where devices are unable to transmit over a long distance due to their energy constraints. In this case UAVs play the role of moving aggregators which fly toward one IoT devices, collect the data, and transmit it to other devices [81]. In [183] authors presented UAV-based floating relay (FR) for cell dynamic and coverage improvement. Deployment of unmanned aerial base stations in 5G heterogeneous architecture can improve throughput [254], coverage [183; 184; 185; 255], connectivity [184; 185], and 5 th percentile spectral efficiency [254]. On-demand wireless systems with low-altitude UAVs are in faster to deploy, more flexibly reconfigured, and likely to have better communication channels due to the presence of short-range line-of-sight links [186] when compared to terrestrial communications or those based on high-altitude platforms. The utilization of highly mobile and energy-constrained UAVs for wireless communication introduces many new challenges [186], some of them are listed in [82]. To effectively use UAVs for IoT, several challenges must be addressed such as optimal deployment, mobility and energy-efficient use of UAVs [81].
# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
###### Abstract  
The Reed-Solomon algorithm is well known in different fields of computer science. The novelty of this study lies in the different interpretation of the algorithm itself and its scope of application for remote sensing, especially at the preparatory stage, i.e., data fusion. A short review of the attempts to use different data fusion approaches in geospatial technologies explains the possible usage of the algorithm. The rationale behind its application for data fusion is to include all possible information from all acquired spectral bands, assuming that complete composite information in the form of one compound image will improve both the quality of visualization and some aspects of further quantitative and qualitative analyses. The concept arose from an empirical, heuristic combination of geographic information systems (GIS), map algebra, and two-dimensional cellular automata. The challenges are related to handling big quantitative data sets and the awareness that these numbers are in fact descriptors of a real-world multidimensional view. An empirical case study makes it easier to understand the operationalization of the Reed-Solomon algorithm for land use studies.  
R 2020 13 188 10.3390/a13080188