esa-sceva/satcom-chunk-collection huggingface dataset:
# Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data
## 4 Experiments
### Experimental Results  
In order to intuitively show the accuracy improvement of the model during the training process, the prediction accuracy of the test set was synchronously tested during the training process. The model is implemented based on the TensorFlow [31] framework. The visualization function in the tensorboard of TensorFlow was used to visualize the changes in the loss function expressed as Equation (17) and \(RTIME_{Dis}\) expressed as Equation (21) during the training process. The training curves are shown in Figure 5. The value of the loss function of the training set and test sets during the training process are partially shown in Table 1. Through 60,000 training steps, the loss function of the training set gradually decreases to convergence and stability; the performance metrics of the test set is almost consistent with that of the training set, and there is no so-called overfitting problem in which the loss of the training set decreases while the loss of the test set rises.  
Some of the predicted results of the test set on the well-trained model are shown in Table 2 below. Each row in the table is a sample record, including prediction duration, longitude error, latitude error, and distance error. In the last row, the statistical errors of all test samples are measured by RTIME expressed as Equation (21).  
Figure 5: Training curves: (**a**) the loss function variation curve of the training set and test set; (**b**) the \(RTIME_{Dis}\) variation curve of the training set and test set.  
It can be seen from Table 2 that the prediction time is extremely uneven, which includes a short-term prediction of about 30 min and a long-term prediction of about 3 to 5 h. Using RTIME as a statistical evaluation indicator, the statistical errors are 0.157\({}^{\circ}\), 0.118\({}^{\circ}\), and 20.610\({}^{\circ}\).  
The trajectory predictions of the test set are partly visualized in Figure 6. It can be seen from the figure that the ship trajectory has strong nonlinear characteristics, and the prediction result can better grasp the movement direction. Furthermore, the deviation of the prediction result is relatively stable and has a relatively reliable prediction result.
## 2 Materials  
### SAR Data  
The Sentinel-1 satellite series is a key component of the Copernicus program, designed by the European Space Agency (ESA) to provide continuous, all-weather, global Earth observation data. Currently, it primarily operates with Sentinel-1A equipped with a C-band SAR, as Sentinel-1B was lost in 2021, while Sentinel-1C is still in the commissioning phase and not yet operational. Sentinel-1 operates in four distinct imaging modes: Strip Map (SM), Interferometric Wide Swath (IW), Extra Wide Swath (EW), and Wave Mode (WM), offering four different polarization modes, including both co-polarization (horizontal-horizontal, HH; vertical-vertical, VV) and cross-polarization (horizontal-vertical, HV; vertical-horizontal, VH). The main characteristics of each imaging mode of Sentinel-1 SAR are shown in Table 1.  
The Sentinel-1 level-2 OCN products aim to provide geophysical parameters related to wind, waves, and surface current velocity for a wide range of end-users. These products include Ocean Swell (OSW), Ocean Wind Field (OWI), and Radial Velocity (RVL) components [16]. This paper uses the NRCS from the OWI component, Doppler frequency observations, and predicted (geometric) Doppler frequency from the RVL component, for wind vector retrieval using Sentinel-1 OCN products.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **SM** & **IW** & **EW** & **WV** \\ \hline Polarization mode & \multicolumn{2}{c}{HH, VV, HH + HV, VV + VH} & HH, VV \\ Angle of incidence & 18.3–46.8\({}^{\circ}\) & 29.1–46.0\({}^{\circ}\) & 18.9–47.0\({}^{\circ}\) & 21.6–25.1\({}^{\circ}\) \\ Azimuth resolution & 5 m & 20 m & 40 m & 5 m \\ Distance resolution & 5 m & 5 m & 20 m & 5 m \\ Swath width & 80 km & 250 km & 400 km & 20 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main characteristics of each imaging mode of the Sentinel-1 SAR.  
As the IW mode serves as the primary imaging mode for monitoring offshore areas, this study collected a dataset comprising 1803 Sentinel-1 level-2 OCN images acquired in IW mode between 2015 and 2024, and matched them with wind vectors from 27 NDBC buoys, as shown in Figure 1, resulting in 2826 comparable data pairs.  
During the preprocessing of Sentinel-1 OCN imagery, we implemented a vessel detection and mitigation procedure to ensure accurate wind field analysis. Vessel signatures were identified as localized high-backscatter anomalies in the SAR images. These contaminated pixels were then replaced using bilinear interpolation of surrounding unaffected values, effectively removing non-meteorological artifacts.
## 2 Preliminaries
### Physical Unclonable Function  
The physical unclonable function (PUF) [16] generates an output based on physical characteristics, such as delay, resistance, capacitance, or reflection properties. Since the output of the PUF is based on slight randomness and unevenness in the manufacturing process, it is difficult for an adversary to generate the same response sequence as the original PUF via copying or simulation. Therefore, the PUF has a high degree of security and protection in security systems, and it is widely used to protect sensitive information in cryptographic authentication protocols.  
During the registration phase of the authentication protocol, the user or device generates a unique identifier or a unique key \(C\) via PUF and stores it securely. In the login phase, PUF generates a response: \(R\), \(R=PUF(C)\). Response \(R\) is compared with a previously registered identifier or key. If \(R\) matches the key, the login can be seen as a success, and the user is allowed to send the authentication request to the GWN. This PUF-based user-authentication mechanism takes advantage of the physical properties and unclonability of the PUF in order to ensure a secure, unique, and difficult-to-forge identity verification process. In the proposed protocol, we use a physical unclonable function \(PUF_{sum}(\cdot)\), which configures the embedded trigger sum for the user, where the sum refers to the number of times that the user is allowed to try to use \(PUF_{sum}(\cdot)\) in the event that the user forgets the secret key.
# Q-Learning Based Fair and Efficient Coexistence of LTE in Unlicensed Band
## 3 LTE-U Coexistence Mechanism  
There is approximately 600 MHz of spectrum below the 6-GHz unlicensed bands, which can be further subdivided into multiple channels of the same or different bandwidths. Because Wi-Fi is provisioned to connect to a channel with less interference, the traffic load offered by Wi-Fi in such channels will differ. Therefore, simply considering the single best unlicensed channel for studying a coexistence scenario does not meet the requirements of a practical environment. This paper considers the number of different channels in the unlicensed band and creates a utility function by considering the network efficiency and fairness factor. The duty cycle of LTE-U is adaptively manipulated using the Q-learning mechanism to increase the performance of the systems, thereby allowing LTE-U to occupy various unlicensed bands for a suitable duration.
# GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study
###### Abstract  
Currently, eight million metric tons of plastic end up in the oceans every year, and microplastics in different forms are present in almost all water systems in the world: streams, rivers, lakes, or oceans, and even in our blood. Detection of marine litter is an urgent task. Some works have recently reported the potential of GNSS-Reflectometry to detect marine plastic litter from space. This study presents the results of a controlled field experiment conducted under the auspices of ESA at the \"Atlantic Basin\" at the Deltares research institute (Delft, The Netherlands). Several types of wave conditions were created: sinusoidal and with JONSWAP spectrum, with different significant wave heights, and with different types of plastics and marine litter collected from the Dutch coast. Experimental results show the difficulty in detecting marine plastic litter based on a change of the reflected power. However, a statistical analysis of the GNSS-R estimated reflectivities (amplitude and phase) computed with very short integration times (coherent integration time \(\mathrm{T}_{\mathrm{coh}}=1\) ms, and no incoherent averaging: \(\mathrm{N}_{\mathrm{incch}}=1\)) show that it may be possible to detect large accumulations of some types of marine litter that dampen the water waves, such as nets, bottles in a net, food wraps, and bags.  
GNSS-R; scattering; polarization; statistical parameters; water flume; marine litter 202315637. Article
## 2 Expressing Service Security Requirements
Upon the specification of security properties, an important aspect of the assurance process is the definition of requirements over the mechanisms used to provide evidence for those properties. We can verify the service support for a given property using two different approaches: _a test-based approach_ providing evidence that a test carried out on the software has given a certain result, and _a formal approach_ providing evidence based on an abstract model of the service. In this paper, we focus on test-based evidence: each security property can be associated with one or more classes of tests, which in turn contain a set of test types (e.g., equivalence partitioning, fuzzy/mutation) used to generate the test cases providing the evidence that the property is supported by the service. As for security properties, test-based evidence (including the results of test execution) is stored in a certificate awarded to a service and can be compared with users' requirements, to search and discover services on the basis of their non-functional characteristics. Section 3.1 presents our approach to service security certification using a test-based mechanism for certifying that a service possesses a concrete security property that can be integrated into the lightweight Web service discovery technique presented in Section 4.
## 3 Finite-Time Disturbance Observer and Controller Design
### FTDO-Based Sliding Mode Controller Design and Stability Analysis
Part 2 (Global stabilization): As \(E_{\varpi_{i}}(t)\) converges to a bounded neighborhood of zero according to Theorem 1 for \(t>T_{d}\), (30) is rewritten as  
\[\dot{S}_{i}(t)=-k_{1}|S_{i}(t)|^{1/2}\,\text{sgn}\left(S_{i}(t)\right)-\int_{ 0}^{t}k_{2}\,\text{sgn}\left(S_{i}(\tau)\right)d\tau \tag{35}\]  
According to Lemma 3, (35) is finite-time stable, which means that \(S_{i}(t)\) converges to a bounded neighborhood of zero for \(t<T_{s}\). From (24), as \(s_{i}(t)\) and \(S_{i}(t)\) exhibit the same convergence, \(s_{i}(t)\) also converges to a bounded neighborhood of zero for \(t<T_{s}\). Then, (33) is rewritten as  
\[\dot{e}_{i}(t)=-\ell_{i}e_{i}(t) \tag{36}\]  
Choose the Lyapunov function \(V_{e_{i}}(t)=\frac{1}{2}e_{i}^{2}(t)\), and its derivative is  
\[\dot{V}_{e_{i}}(t)=\dot{e}_{i}(t)e_{i}(t)=-\ell_{i}e_{i}^{2}(t)=-2\ell_{i}V_{e _{i}}(t) \tag{37}\]  
The above analysis indicates that \(\dot{e}_{i}\) converges to a bounded neighborhood of zero, which means that the string stability of the vehicle platoon is guaranteed. An approach similar to [35] is utilized to demonstrate strong string stability of the entire platoon. As \(S_{i}(t)\) converges to a bounded neighborhood of zero, we have  
\[\gamma s_{i}(t)-s_{i+1}(t)=0 \tag{38}\]Substituting (23) into (38), we have  
\[\gamma\bigg{(}(e_{i}(t)+\int_{0}^{t}\ell_{i}e_{i}(\tau)d\tau)\bigg{)}=e_{i+1}(t)+ \int_{0}^{t}\ell_{i+1}e_{i+1}(\tau)d\tau \tag{39}\]  
The Laplace transform of (39) yields  
\[\gamma\bigg{(}E_{i}(s)+\frac{\ell_{i}}{s}E_{i}(s)\bigg{)}=(E_{i+1}(s)+\frac{ \ell_{i+1}}{s}E_{i+1}(s)) \tag{40}\]  
where \(\ell_{i}\) is the design parameter. Assume \(\ell_{i}=\ell_{i+1}\) such that \(G_{i}(s)=E_{i+1}(s)/E_{i}(s)=\gamma\). Then, if \(0<\gamma\leq 1\) holds, \(|G_{i}(s)|\leq 1\) is satisfied, which indicates that strong string stability is ensured for the vehicle platoon. The proof of Theorem 2 is complete.
# Research on Lifespan Prediction Methods Using Ultrasonic Microimaging for Electronic Packaging
## 3 Ultrasonic Image Data Processing and Feature Extraction
### Feature Extraction of Solder Joint Image
In contrast, the method used in this paper consistently delineates the damage regions with high precision, providing a robust foundation for failure feature extraction. After determining the solder joint damage regions, data from over 400,000 solder joint images were selected for feature extraction. These images were obtained from 56 flip chips distributed across four sample boards, each containing 109 solder joints. For each solder joint image, the mean intensity of the damage region was computed, taking solder joint 107 from the second flip chip on sample board number 1 as an example, as shown in Figure 10.  
Figure 11 presents the curve of mean intensity over the thermal cycling periods obtained by the proposed method. The blue curve represents the original, unprocessed data, the green curve corresponds to data after intensity compensation, and the brown curve represents data after comprehensive processing, including intensity compensation and dy  
Figure 10: Ultrasonic image of solder joint 107 on flip chip 2 of experimental sample 1. Figure 9: Comparison of edge detection results for solder joint damage areas. namic range normalization. It can be observed that after a series of image processing steps, the extracted solder joint failure features evolve from initially disordered and irregular patterns to a trend that is positively correlated with the number of thermal cycling periods. This trend aligns with objective physical phenomena, laying a solid foundation for solder joint failure modeling and lifetime prediction. In comparison, prior research [8] encountered significant challenges in accurately characterizing the solder joint failure process. The use of overly aggressive thermal cycling conditions caused samples to fail prematurely, resulting in an insufficient number of sampling points. Additionally, the image processing techniques employed lacked the capability to precisely delineate the boundaries of solder joint damage regions. By addressing these issues, the proposed method ensures robust sampling, employs accurate feature extraction, and enhances the reliability of results. These improvements enable the method to generate physically consistent and meaningful results that better capture solder joint failure features and support solder joint failure modeling and lifetime prediction.
## 3 Results and Discussion
For the current study, it was not feasible as it requires tremendous resources beyond the scope of this study. Thus, as an alternative to full-scale clinical testing, it uses approximate pulse pressure waveforms of 15-85 years of age, obtained by a mathematical model based on in vivo data measured on about 1000 subjects [26], to analyze the pulse waveforms produced by the simulator. The in vivo data were measured by a robotic tonometry system at the Korea Institute of Oriental Medicine in a separate study [26]. Figure 5 shows the approximate pulse waveforms for ages 15 through 85 with an increment of 10 years. Figure 6 compares the pulse pressure waveforms reproduced by the developed simulator and approximate pulse pressure waveforms after normalizing them by age. Overall, they match well, and, particularly, the initial slope and the first peak coincide well. The L\({}^{2}\) errors and the AI values of each case are calculated, and the results are summarized in Table 1. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Age** & **15** & **25** & **35** & **45** & **55** & **65** & **75** & **85** \\ \hline L\({}^{2}\) error & 9.10\% & 9.62\% & 6.44\% & 4.90\% & 5.63\% & 5.19\% & 5.67\% & 6.74\% \\ \hline AI error & 7.71\% & 7.29\% & 2.86\% & 5.92\% & 9.37\% & 2.73\% & 0.28\% & 1.64\% \\ \hline \end{tabular}
\end{table} 
Table 1: Errors between human pulse pressure waveforms and artificial waveforms generated by 3D cam simulator. Figure 5: Approximated blood pressure waveforms by age: (**a**) Pulse pressure waveforms from 15 to 45 years old. (**b**) Pulse pressure waveforms from 55 to 85 years old. In Table 1, it can be confirmed that the L2 error is less than 9.6% at all ages, and the AI error is at most 9.4% or less at all ages. These errors fall within the acceptable level of accuracy in generating actual radial pulse pressures. As a matter of fact, in vivo data (systolic pulse pressure) obtained by a tonometric method using hundreds of subjects by age show an average standard deviation of 11.7% [26]. Thus, the error analysis results indicate that the developed simulator is capable of generating realistic human pulse waveforms. Moreover, the low AI error results indicate that the pulse pressure waveforms generated by the 3D cam-based pulse pressure simulator can be used to estimate AI values accurately when clinically judging arteriosclerosis.
# Frequency Domain Full Waveform Inversion Method of Acquiring Rock Wave Velocity in Front of Tunnels
## 2 Method
### Frequency Domain FWI
#### 2.2.1 Build Objective Function  
The objective function is obtained by minimizing the difference between the forward data and the observation data, thereby obtaining the best model data. [PERSON] [33] compared four minimization functions. Experimental tests show that the L2 norm is a relatively stable objective function when dealing with noisy data with the best inversion effect. Therefore, this paper adopts the objective function based on the L2 norm for inversion.  
When the wavefield residuals of field observation seismic records and forward seismic records obey Gaussian distribution, the objective function based on the L2 norm is [33]:  
\[E(v)=\frac{1}{2}\|d_{obs}-d_{mod}\|^{2}=\frac{1}{2}\Delta d^{\intercal}\Delta d ^{\intercal}, \tag{7}\]  
where \(d_{obs}\) is the field observation seismic data, \(d_{mod}\) is the full wavefield seismic record obtained from forward modeling, \(d_{mod}\) is the function of the speed model \(v\), \(\Delta d\) is the wavefield residual between the observation data and the calculated data, \(T\) is the transpose operator, and * represents conjugate.
## 4 Simulation Results and Complexity Analysis
### Simulation Results and Discussion
Apart from this, when \(\beta\) is 16, the BER distance approach of CAOR to conventional MMSE is a negative dB value, which means that the BER distance between CAOR and conventional MMSE was drastically reduced and approached the optimal result. Of course, the smaller the dB value, the better and the closer to the optimum. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & \(\beta=4\) & \(\beta=8\) & \(\beta=16\) \\ \hline SOR & 9.43 dB & 15.19 dB & 24.52 dB \\ modified SOR & 9.91 dB & 15.60 dB & 24.82 dB \\ SSOR & 7.34 dB & 7.89 dB & 3.93 dB \\ AOR & 9.13 dB & 14.86 dB & 24.30 dB \\ CAOR & 4.58 dB & 3.74 dB & \(-0.738\) dB \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the degree to which the BER performance approaches that of conventional MMSE among all detectors in terms of dB value under different \(\beta\) as \(i=2\) and SNR at 40 dB. Figure 7: BER vs. \(\beta\) for different detection schemes when \(i=2\) and SNR is 40 dB. Next, in Table 4, we also compare the degree to which the BER performance approaches conventional MMSE among all detectors in terms of the dB value under different iteration numbers when \(N_{R}\times N_{T}=128\times 16\) and SNR is 45 dB. Similarly to Table 3, to obtain a more demarcated numerical comparison, we have also taken \(10 log(\cdot)\) for the BER distance between the individual detector and conventional MMSE. Moreover, this table demonstrates that our proposed CAOR scheme has the smallest dB value, which means that the CAOR's BER performance is the closest to the conventional MMSE scheme regardless of the iteration number. Furthermore, we show the performance improvement of various receivers with the increase in antennas in Table 5, which refers to Figure 7 at the same time.
# Calculation Method of Rotational Constraint Stiffness for a New Tower-Pier Connected System
## 2 The Plane Rotation Model of UHS
### Parametric Analysis of RCS
#### 3.2.4 Influence of Friction Coefficient of Contact Surface on RCS  
The rotational performances of UHS were studied with friction coefficients of 0.03, 0.06, 0.09, 0.12, and 0.15. The curvature radius took the value of \(R_{0}=1.5\) m, central angle, and vertical force \(F=10\) MN. The responses of UHS are shown in Figures 12 and 13.  
Figure 12 shows that the critical torque increased with the increase in the friction coefficient. Similarly, the related curve of RCS-\(\mu\) can be acquired, as shown in Figure 13.  
Figure 11: The critical torque under different \(R_{2}\) or \(R_{0}\).  
Figure 12: Torque–displacement curves under different friction coefficients.  
Figure 13: Variation curve of RCS with the friction coefficient.  
It can be concluded that RCS increases with the increase in the friction coefficient in an approximate line when the vertical force, radius of curvature, and central angle are not changed.  
The related curve of RCS and the friction coefficients is fitted using the least square method to obtain the following expression equation:  
\[K_{M}=8246074\mu=\gamma\times\mu \tag{10}\]  
where the unit of RCS \(K_{M}\) is kN\(\cdot\)m/rad, and \(\gamma\) is a coefficient with dimensions of (kN\(\cdot\)m/rad).
## Nomenclature  
**Variables**  
\(a\)Matrix [\(a\)] coefficients  
A Area [m\({}^{2}\)]  
\(b\)Matrix [\(b\)] coefficients  
\(c\)Matrix [\(c\)] coefficients  
\(C_{p}\)Specific heat capacity at const. pressure [J/kg\(\cdot\)K]  
\(C_{v}\)Specific heat capacity at const. volume [J/kg\(\cdot\)K]  
\(C\)UCapacitance utilization  
\(D\)Diameter [m]  
\(F_{f}\)Fluid fraction  
H Regenerator height [m]  
\(h\)Convective heat transfer coefficient [W/m\({}^{2}\cdot\)K]  
mMass [kg]  
mMass flow rate [kg/s]  
MMolar mass [g/mol]  
\(N\)Number of cells  
\(R\)Storage effectiveness  
\(R_{f}\)Specific gas constant [J/kg\(\cdot\)K]  
T Temperature [\({}^{\circ}\)C]  
\(th\)thickness [m]  
U Thermal transmittance [W/m\({}^{2}\cdot\)K]  
\(V\)Volume [m\({}^{3}\)]  
\(w_{j}\)Mass concentration  
\(E\)Specific effectiveness  
\(\eta_{T}\)Thermal efficiency  
\(p\)Density [kg/m\({}^{3}\)]  
T Regenerator inversion time [s]  
**Subscripts**  
_air_Air  
_ch_Channel  
_exh_  
Exhaust gas  
\(f\)  
Fluid  
_in_  
Inlet  
_max_  
Maximum  
_out_  
Outlet  
\(s\)  
Solid  
_sf_  
Solid-fluid  
ss Solid-solid  
**Abbreviations**  
CC Combustion chamber  
CFD Computational fluid dynamic  
EP End Port  
RC Regenerative chamber  
WGR Waste Gas Recirculation