esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 7 Conclusions  
In this paper, we proposed an on-board ship detection scheme based on deep learning and a COTS component, which can be used to achieve near real-time on-board processing by micro-nano satellite computing platform. Many novel strategies were designed for on-board processing. Square anchors was used to enhance the quality of proposals and global average pooling layer replaced the full connection layers in the network to reduce the model size. In addition, bigger-left non-maximum suppression method was adopted to ensure the integrity of targets patches. We also constructed a computing platform in a micro-nano satellite and a ground demonstration and verification system to verify the feasibility and effectiveness of our scheme. Our method achieved the performance with 95.9% recall and 80.5% precision in our dataset and the target patches downlinked by our scheme have a better quality. Experiments showed that the scheme has a good application prospect for micro-nano satellites. In our future work, a more specific network will be designed to further reduce the model size and improve the computational efficiency. Another important work is the design of some protective measures for the reliability of COTS component.  
Figure 8: The result of the proposed scheme for one image in our dataset. The red boxes are the detection results of our ship detection method and the white boxes are the target patches extracted by our network. The two numbers are the image coordinates of the central point for each ships.  
**Author Contributions:** [PERSON], [PERSON], [PERSON], and [PERSON] conceived of this study. [PERSON] performed the experiments and wrote the manuscript. [PERSON] and [PERSON] supervised the work and revised the manuscript. [PERSON] contributed with field experience in micro-nano satellite and on-board processing.  
**Funding:** This work was supported in part by the National Key Research and Development Program of China (2016 YFB0501300 and 2016 YFB0501302), the National Natural Science Foundation of China (Grant Nos. 61771031, 61501009 and 61371134), and the Fundamental Research Funds for the Central Universities.  
**Acknowledgments:** The authors would like to thank all the colleagues in the lab, who generously collected the dataset with ground truth. Besides, we would also like to thank the editor and the three anonymous reviewers for their important comments and helpful suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 3 Results
### Cardiovascular Response Results  
The results of SampEn RR, SV, and CO are shown in Table 2. Six participants reached the SV maximum in the first stage; hence, the number of sub-max stages was 24 (E = 8, N = 8, S = 8). SampEn RR at rest was significantly different to the sub-max stage and ACP stage (\(p<0.05\)). SampEn RR increased during exercise (1.24 to 1.35). However, SampEn RR in the sub-max stage is not significantly different than that in the ACP stage. Hence, SampEn RR may not be an effective indicator for identifying the sub-max stage and predicting the ACP stage. The SampEn SV results are listed in Table 2 (E = 8, N = 8, S = 8). SampEn SV at rest, in the sub-max stage, and in the ACP stage were all significantly different (\(p<0.01\)). SampEn SV increased during exercise (1.43 to 2.15). These results suggest that SampEn SV can be used to predict the ACP stage because the SampEn SV is the greatest in the ACP stage; moreover, the SD of SampEn in the ACP stage was the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **S (Sedentary)** & **N (Normal)** & **E (Exercise)** \\ \hline Exercise time/week (hour) & 0.6 \(\pm\) 0.52 * & 3.00 \(\pm\) 1.00 * & 11.64 \(\pm\) 8.37 \\ Gender (male/female) & 4/6 & 4/5 & 9/2 \\ Body mass index & 22.52 \(\pm\) 3.57 & 22.14 \(\pm\) 2.71 & 23.59 \(\pm\) 2.25 \\ Riding time (min) & 20.50 \(\pm\) 4.88 & 21.67 \(\pm\) 3.61 & 22.91 \(\pm\) 2.43 \\ ACP stage & 2.6 \(\pm\) 1.17 & 2.00 \(\pm\) 0.71 * & 4.45 \(\pm\) 2.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Participant data.  
smallest. These results suggest that SampEn SV could be used as an effective indicator in exercise-related health care. The SampEn CO results are presented in Table 2 (E = 8, N = 8, S = 8). SampEn CO at rest, in the sub-max stage, and in the ACP stage were all significantly different (\(p\) < 0.01). SampEn CO increased during exercise (1.38 to 2.18). This result indicates that SampEn SV and SampEn CO can be used to predict the ACP stage; SampEn CO can also be used in exercise health care.  
The Pearson correlation coefficient test results for SampEn RR and HRV and for SampEn SV and SVV are displayed in Table 3. SampEn RR and RR have a strong positive correlation in the rest stage (\(R\) = 0.51, \(p\) < 0.01) as do SampEn SV and SVV nHF (\(R\) = 0.46, \(p\) < 0.05).  
We tried to find the threshold value of sub-max by using the ROC curve module. The input is the result of the SampEn of sub-max and rest (lower limit), and the result of SampEn of sub-max and ACP (upper limit). Figures 4-6 are the results of the ROC curve in SampEn RR, SV, and CO. The SampEn RR sub-max threshold is 0.7971-1.0482. The SampEn SV sub-max threshold is 1.3218-1.8813. The SampEn CO sub-max threshold is 1.4520-1.9286.
## 3 Numerical Analysis and Simulation Calculation
### Thermal Effects Optimization Measures
#### 4.3.3 Compensating for Thermal Errors in the Transmission System  
1. Feedback interception method: By inserting a feedback loop into the servo system, thermal error compensation is achieved by adjusting the position of the tool holder;
2. Zero drift method: Calculating the compensation amount based on the established thermal error model and then sending it to the CNC controller. Finally, the compensation amount is added at each position as a command signal to the servo loop for feedforward compensation.
# Investigation of Rain-On-Snow Floods under Climate Change
## 2 Materials and Methods
### Climate Change Scenarios  
The pre-processing of climate change data used in this study was carried out in the scope of the project entitled The Estimation of the Average and Extreme Meteorological and Hydrological Conditions in Slovenia Over the 21 st Century, by the Slovenian Environment Agency [12]. Under this project the bias-corrected regional climate model projections ensemble from the EURO-CORDEX database was analyzed [27]. The following combinations of the global/regional climate models (GCM/RCM) were used: CNRM-CM5-LR/CCLM4-8-17 (Case 1), MPI-ESM-LR/CCLM4-8-17 (Case 2), EC-EARTH/HIRHAM5 (Case 3), IPSL-CM5A-MR/WRF331F (Case 4), and MPI-ESM-LR/RCA4 (Case 5). The focus was only on the moderately optimistic scenario RCP4.5. High-resolution models were used in this study (i.e., resolution approx. 140 km\({}^{2}\)). Firstly, the observed historical data for the baseline period (i.e., 1981-2010) were interpolated to the same grid as that used by the RCM models. In the next step, bias-correction was applied to model data for each variable (i.e., precipitation and air temperature) separately. Quantile mapping was applied as a method for precipitation bias-correction using the methodology proposed by [28] and qmap package in combination with R software [21]. Additionally, to obtain trends in temperature series the quantile delta mapping (QDM) method was applied using the QDM function available in the MBC package [29]. The same data were also used for investigating the impact of climate change on low- and high-flow characteristics of the karst catchments in Slovenia [30].  
The period from 1981 to 2100 was selected to investigate the potential future changes in ROS floods' characteristics. The 1981-2010 period was defined as a baseline period and future projections were analyzed for three 30-year periods: 2011-2040, 2041-2070, and 2071-2100.  
The projection results for the air temperature series in Slovenia predict an increase for all future periods (i.e., 2011-2100), where the increase is generally lower for the period 2011-2040 (i.e., up to 1 \({}^{\circ}\)C compared to the baseline period) compared to periods 2071-2100 and 2041-2070 (up to 2-3 \({}^{\circ}\)C compared to the baseline period) [12]. The spring and winter temperature increase could be more pronounced than the summer and autumn increase [12]. The projection results demonstrate a small difference in the projected temperature increase among the investigated catchments of this study.  
Furthermore, projections indicate that precipitation could change from -5% to 5% for the 2011-2040 period compared to the baseline period. A potential increase for the precipitation amount for the 2041-2070 and 2071-2100 periods could be in the range between 5% and 20% compared to the baseline period [12]. Moreover, winter precipitation could increase in the future while, on the other hand, summer precipitation could decrease in the future, especially for the 2011-2040 and 2041-2070 periods [12].
# Investigation of a Gravel-Bed River's Pattern Changes: Insights from Satellite Images
###### Abstract  
Changes in river pattern have been documented in the literature. The recognition of islands and vegetated patches, which is indicative of the evolution processes, requires simultaneous information at different points and at different times and field studies are still difficult and scarce. The present work, focusing the attention on a gravel-bed river, explores the possibility to conduct a first-step pattern changes analysis by identifying the evolution of the morphological features and taking information of islands and vegetated patches from satellite images easily available from Google Earth. The study is conducted by combining the information taken from both the satellite images and the field photos and the hydrological data. Here analysis concerns a reach of the Tagliamento River, in Italy, in a decadal timescale characterized by evident stream pattern changes. Results confirm that a combination of different factors determine favorable conditions for the river's evolution, but the frequency of occurrence of high-magnitude events is the most important factor to consider in a first-step analysis. This is because the frequency of occurrence of high-magnitude events affects, in turn, some of the other influencing factors. In particular, the present study highlights that different morphological features form in time periods characterized by different frequency of occurrence of high-magnitude events. On one hand, this could be related to the fact that high-magnitude events could influence the sediment transport and deposition mechanisms, modifying the ratio coarse-to-fine sediments and the grain-to-grain interactions, with consequences in the sediment flux variation and in the migration of the morphological features. On the other hand, the frequency of occurrence of high-magnitude events could affect the vegetation distribution and growth, contributing to determine a variation of the typology of the vegetation characteristics depending on the tolerances of the vegetation species. A sort of interface between the vegetated-dominated area, where the flow resistance is significant, and the flood-disturbance-dominated area, where the action by flow is significant, is determined with a progressive reduction of the number of active channels and development of the meandering bend.  
prediction; river; morphological evolution; images; monitoring +
Footnote †: journal: Applied Sciences
# Research on Semi-Active Vibration Control of Pipeline Based on Magneto-Rheological Damper
## 3 Research on Semi-Active Pipeline Vibration Control System
### Comparative Analysis of Simulation of Different Vibration Control Algorithm
#### 3.3.1 Two-State Vibration Control Simulation  
In order to reduce the vibration response of pipeline as possible and make the pipeline always move to the equilibrium point after disturbance deviation, the following two-state vibration control method is established.  
\[\mathbf{U}=\begin{cases}F_{2}&\dot{x}\dot{x}\geq 0\\ F_{1}&\dot{x}\dot{x}<0\end{cases} \tag{19}\]  
Equation (19) shows that when the pipeline is away from the equilibrium position under external disturbance, the MR damper is controlled to output a large control force; and when the pipeline is close to the equilibrium position, a small control force is output. However, the control algorithm takes the pipeline vibration balance point as the switching condition. When the pipeline vibration is in the initial, final or small amplitude, the two-state vibration control algorithm can easily lead to the overshoot of the control force and local amplification of the dynamic response of the pipeline. In order to solve the disadvantage of poor control effect of two-state vibration control near the pipeline equilibrium point, a dead zone is set up near the equilibrium point, that is, when the pipeline vibrates in the dead zone, only the minimum control force is applied to the pipeline. When the pipeline is far from the equilibrium position, and when the displacement of the pipeline exceeds a certain displacement limit, the MR damper is controlled to output a large control force. The two-state vibration control with dead zone is shown in Equation (20).  
\[\mathbf{U}=\begin{cases}F_{2}&\mathbf{x}\dot{\mathbf{x}}\geq 0\;\text{and}\;|\mathbf{x}|\geq[ \mathbf{x}]\\ F_{1}&\mathbf{x}\dot{\mathbf{x}}<0\;\text{or}\;|\mathbf{x}|<[\mathbf{x}]\end{cases} \tag{20}\]  
where \([\mathbf{x}]\) is the minimum displacement limit.  
The Simulink simulation model of the two-state pipeline vibration control system with dead zone based on MR damper is shown in Figure 7a. When the sinusoidal excitation amplitude is 2 mm and the frequency is 5 Hz, the vibration displacement response and control force simulation results of the pipeline under the two-state vibration control are shown in Figure 7b,c.  
As shown in Figure 7, the two-state vibration control has better control effect on pipeline vibration, and the amplitude of the first peak of the transient response of the controlled pipeline system is higher than that of the uncontrolled one. When the vibration amplitude of the pipeline is equal to the minimum limit of the two-state vibration control, the system has a brief chattering. The transient process time of the controlled pipeline system is basically the same as that of the uncontrolled system. In comparisons with the uncontrolled system, the attenuation amplitude of the pipeline vibration adopting the steady-state two-state vibration control system is approximately 64.65%.
## 2 Reflective In-Band Full-Duplex with NOMA in beyond 5G  
The proposed R-IBFD aims to assist the relay in forwarding the signal in the IBFD mode. The proposed algorithm is explored for NOMA for improving the SE of the system with minimal interference and better security. For obtaining the benefits of IBFD and NOMA, a reflective IBFD (R-IBFD) cooperative communication algorithm is proposed. The R-IBFD supports the IBFD mode device to forward the message with cooperative communication, ideally without interference. It is slightly different from the existing Decode-and-Forward IBFD (DF-IBFD) cooperative communication. The user in the IBFD mode decodes its own signal by using SIC (real or imaginary) and encodes the signal that needs to be forwarded to an opposite component (real or imaginary) then forwards/reflects the signal to the distant user. The trusted IBFD mode user contains the CSI of other users. Before reflecting, the IBFD mode user adds AN for preventing the security issues. The use of R-IBFD reduces the decoding complexity and co-channel interference at the IBFD mode user.
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
###### Abstract  
In this paper, we present a comparison between several algorithms for oil spill classifications using fully and compact polarimetric SAR images. Oil spill is considered as one of the most significant sources of marine pollution. As a major difficulty of SAR-based oil spill detection algorithms is the classification between mineral and biogenic oil, we focus on quantitatively analyzing and comparing fully and compact polarimetric satellite synthetic aperture radar (SAR) modes to detect hydrocarbon slicks over the sea surface, discriminating them from weak-damping surfactants, such as biogenic slicks. The experiment was conducted on quad-pol SAR data acquired during the Norwegian oil-on-water experiment in 2011. A universal procedure was used to extract the features from quad-, dual- and compact polarimetric SAR modes to rank different polarimetric SAR modes and common supervised classifiers. Among all the dual- and compact polarimetric SAR modes, the \(\pi/2\) mode has the best performance. The best supervised classifiers vary and depended on whether sufficient polarimetric information can be obtained in each polarimetric mode. We also analyzed the influence of the number of polarimetric parameters considered as inputs for the supervised classifiers, onto the detection/discrimination performance. We discovered that a feature set with four features is sufficient for most polarimetric feature-based oil spill classifications. Moreover, dimension reduction algorithms, including principle component analysis (PCA) and the local linear embedding (LLE) algorithm, were employed to learn low dimensional and distinctive information from quad-polarimetric SAR features. The performance of the new feature sets has comparable performance in oil spill classification.  
oil spill; SAR data; compact polarimetric mode; image classification; feature selection +
Footnote †: journal: Applied Sciences
# Vector Signals and Invariant Systems: Re-Tooling Linear Systems Theory
## 5 Frequency Domain Analysis for LRITI Systems
### Example 1 (Illustrating Non-Orthogonal Bivector Exponential via Rotations)  
Suppose we are given two bivector exponential signals, denoted by \(\mathbf{x}_{1}(t)\) and \(\mathbf{x}_{2}(t)\), each rotating in distinct planes with different angular frequencies \(\omega_{1}\) and \(\omega_{2}\). Specifically, \(\mathbf{x}_{1}(t)\) rotates in the \(\mathbf{e}_{1}\mathbf{e}_{2}\) plane, while \(\mathbf{x}_{2}(t)\) rotates in the \(\mathbf{e}_{2}\mathbf{e}_{3}\) plane:  
\[\mathbf{x}_{1}(t) = \mathbf{e}_{1}\mathbf{e}^{\mathrm{j}\omega_{1}t},\quad\mathrm{j }_{1}=\mathbf{e}_{1}\mathbf{e}_{2} \tag{48a}\] \[\mathbf{x}_{2}(t) = \mathbf{e}_{2}\mathbf{e}^{\mathrm{j}2\omega_{2}t},\quad\mathrm{j }_{2}=\mathbf{e}_{2}\mathbf{e}_{3}. \tag{48b}\]  
In GA, the _orthogonality_ of the rotations is expressed by their commutativity [33]. Here, the rotations are defined by the bivector exponentials \(\mathbf{e}^{\mathrm{j}\,\omega_{1}t}\) and \(\mathbf{e}^{\mathrm{j}\,\omega_{2}t}\). To determine whether the given rotations are orthogonal, we need to see whether the commutativity holds:  
\[\mathbf{e}^{\mathrm{j}\,\omega_{1}t}\mathbf{e}^{\mathrm{j}\, \omega_{2}t} = \mathbf{e}^{\mathrm{j}\,\omega_{2}t}\mathbf{e}^{\mathrm{j}\, \omega_{1}t}. \tag{49}\]  
To verify the commutativity in (49), we examine whether the bivectors \(\mathbf{j}_{1}\) and \(\mathbf{j}_{2}\) commute:  
\[\mathbf{j}_{1}\mathbf{j}_{2} = (\mathbf{e}_{1}\mathbf{e}_{2})(\mathbf{e}_{2}\mathbf{e}_{3}) \tag{50a}\] \[= \mathbf{e}_{1}\,(\underline{\mathbf{e}_{2}\mathbf{e}_{2}})^{ \top}\mathbf{e}_{3}^{\mathrm{j}}\] (50b) \[= \mathbf{e}_{1}\mathbf{e}_{3}\] (50c) \[\mathbf{j}_{2}\mathbf{j}_{1} = (\mathbf{e}_{2}\mathbf{e}_{3})(\mathbf{e}_{1}\mathbf{e}_{2})\] (50d) \[= (-\mathbf{e}_{3}\mathbf{e}_{2})(-\mathbf{e}_{2}\mathbf{e}_{1})\] (50e) \[= \mathbf{e}_{3}\,(\underline{\mathbf{e}_{2}\mathbf{e}_{2}})^{ \top}\mathbf{e}_{1}^{\mathrm{j}}\] (50f) \[= \mathbf{e}_{3}\mathbf{e}_{1}. \tag{50g}\]  
Clearly \(\mathbf{j}_{1}\mathbf{j}_{2}\
eq\mathbf{j}_{2}\mathbf{j}_{1}\). Consequently, the bivector exponentials in (49) do not commute either, i.e., \(\mathbf{e}^{\mathrm{j}\,\omega_{1}t}\mathbf{e}^{\mathrm{j}\,\omega_{2}t}\
eq \mathbf{e}^{\mathrm{j}\,\omega_{2}t}\mathbf{e}^{\mathrm{j}\,\omega_{1}t}\). Therefore, the rotations generated by these bivector exponentials are non-orthogonal.
# Propagation, Scattering and Defect Detection in a Circular Edge with Quasi-Edge Waves
## 4 Directivity Pattern of Scattered Waves
### Influence of Location and Size of Partially Through-Thickness Edge Defect
However, the slope becomes less steep when the width increases from 4 to 5 mm. Figure 17c shows the backward-scattered-wave amplitudes in the directions of \(-67.5^{\circ}\), \(-45^{\circ}\), \(-22.5^{\circ}\) and \(0^{\circ}\). For the backward scattered waves in the direction of \(-67.5^{\circ}\), the scattered wave amplitudes rise with the defect width and exhibit a similar trend as normal scattered waves (as shown in Figure 17b), while magnitudes are relatively smaller than those in the normal direction. For backward scattered waves in the directions of \(-45^{\circ}\) and \(-22.5^{\circ}\), the wave amplitudes grow when the defect width increases from 1 to 4 mm and decreases when the defect shape approaches through-the-thickness geometry. In the incident wave direction (\(0^{\circ}\)), the amplitudes of backward scattered waves mainly increase with the defect size, but the dependence is quite weak for a defect width between 3 and 4 mm. Additionally, other than \(-180^{\circ}\) direction, the magnitude of backward scattered waves is relatively greater than for forward scattered waves, which is shown in Figure 17a. Figure 17d-f present the normalised amplitudes of forward, normal and backward scattered waves at the type II edge defect, with various widths (see Figure 8d). Figure 17d shows the normalised amplitudes of forward scattered waves in the directions of \(-180^{\circ}\), \(-157.5^{\circ}\), \(-135^{\circ}\) and \(-112.5^{\circ}\), respectively. It can be observed from Figure 17a that forward scattered waves in the direction of \(-180^{\circ}\) significantly increase with the defect width. Meanwhile, the forward scattered waves in the other directions show a similar trend as for type I edge defects. Figure 17e presents the normal scattered waves (\(-90^{\circ}\)). The overall responses are identical to the corresponding results of the type I edge defect (see Figure 17b), while the change in scattering amplitude is relatively larger when the width of the edge defect increases from 4 to 5 mm. Figure 17f shows the backward scattering amplitudes in \(-67.5^{\circ}\), \(-45^{\circ}\), \(-22.5^{\circ}\) and \(0^{\circ}\) directions. It can be observed that the backward scattered waves in the direction of \(-67.5^{\circ}\) are relatively stronger than the other three different propagation angles, which is similar to the corresponding tendency shown in Figure 17c. The aforementioned results and analysis indicate that forward, normal and oblique backward directions are all suitable for detection and characterising internal, outer and through-the-thickness edge defects.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### HetNet Deployment with Uniformly Distributed Small Cells
#### 3.2.4 Spectral Efficiency  
To see the impact of the variation in the \(P_{\rm max}\) limit on the system performance, the spectral efficiency for the cell average and at the specific position of the UE in the cell was computed from Equations (12) and (13) by using the received SIR given in Equation (20).