esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved MbICP Algorithm for Mobile Robot Pose Estimation
## 1 Introduction
[PERSON] proposed an adaptive rejection technique called the relative motion threshold (RMT) [22], which calculates the rejection threshold with a simulated annealing ratio. This technique is flawed in that the rejection threshold heavily relies on the translation vector, so a large sensor rotation may produce an incorrect rejection [20]. This paper proposes an improved MbICP algorithm for mobile robot pose estimation by using partially overlapping laser scans from a 2D laser scanner, which is commonly used because of its low cost and effectiveness in terms of data processing. Before matching, a resampling step is executed in the object scan to make the density distribution of point clouds relatively even. In terms of scan matching, finding the nearest point is still based on a metric distance, but the procedure of determining the correspondence is improved by combining the point-to-point and point-to-line metrics. Then, a distance threshold that is based on the MAD-from-median method is calculated to reject correspondences with distances above the threshold. Experiments are designed to compare the performance of the improved algorithm and standard algorithm in terms of robustness, accuracy and convergence. Furthermore, a series of real-world experiments are conducted to test its validity in realistic situations. The paper is organized as follows. In Section 2, the principle of the ICP algorithm and the metric-based distance as defined by MbICP are outlined, followed by a detailed explanation of the resampling method and improved algorithm. The experimental results are presented in Section 3 and discussed in Section 4. The last section describes several conclusions.
# Monitoring of Recovery Process at Yeongildae Beach, South Korea, Using a Video System
###### Abstract  
Once a beach is eroded by storm waves, it is generally recovered under milder wave conditions. To prevent or reduce damage, it is therefore important to understand the characteristics of the site-specific recovery process. Here, we present the results, based on a data set from a video monitoring system and wave measurements, of the recovery process in a pocketed beach located inside a bay where the shoreline retreated harshly (\(\sim\)12 m, on average, of beach width) during Typhoon TAPAH (T1917) in September 2019. It took about 1.5 years for the beach to be recovered to the level before the typhoon. During this period, the erosion and accretion were repeated, with the pattern highly related to the wave power (\(P_{w}\)); most of the erosion occurred when \(P_{w}\) became greater than 30 kWatt/m, whereas the accretion prevailed when \(P_{w}\) was no greater than 10 kWatt/m. The recovery pattern showed discrepancies between different parts of the beach. The erosion during storm events was most severe in the southern part, whereas the northern shoreline did not significantly even during TAPAH (T1917). In contrast, the recovery process occurred almost equally at all locations. This discrepancy in the erosion/accretion process was likely due to human intervention, as a shadow zone was formed in the northern end due to the breakwaters, causing disequilibrium in the sediment transport gradient along the shore. The results in this study could be applied in designing the protection plans from severe wave attacks by effectively estimating the size of coastal structures and by correctly arranging the horizontal placement of such interventions or beach nonurishment. Although the application of these results should be confined to this specific site, the method using wave energy parameters as criteria can be considered in other areas with similar environments, for future planning of beach protection.  
beach erosion; storm waves; recovery process; video monitoring; pocket beach
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 4 Analysis of Dynamic Response Characteristics of the Transmission System
### Load-Sharing Coefficients
#### 4.3.3 Dynamic LSCs under Different Input Torques  
Using the dynamic LSCs on the left side as an example, the variations in the LSCs in the fixed-axes PGT and the differential PGT under the different input torques are shown in Figures 24 and 25, respectively. The comparison of the dynamic LSCs under different input torques are shown in Figure 26.  
Figure 23: Comparison of the dynamic LSCs of the transmission system with and without external excitation.  
Figure 21: Variations in dynamic LSCs in the fixed-axes PGT under external excitation. (**a**) Left external meshing pairs; (**b**) Left internal meshing pairs.  
Figure 22: Variations in the dynamic LSCs in the differential PGT under external excitation: (**a**) Left external meshing pairs; (**b**) left internal meshing pairs.  
From Figures 24 and 25, it can be observed that the LSCs exhibited a sinusoidal-like variation pattern, fluctuating approximately every 18 meshing cycles. As the torque increased, the average values and magnitudes of the fluctuations of the LSCs exhibited a decrease. This is consistent with earlier observation that the smaller meshing forces were more susceptible to error-induced fluctuations. Figure 26 shows that with an increasing torque, the mean value of the LSC decreased from around 1.74 to around 1.23 for the fixed-axes PGT and from around 1.26 to around 1.08 for the differential PGT.  
To validate the results of this study, a comparison was made between the meshing forces obtained from the ADAMS dynamic simulation and those obtained from the dynamic model. After analyzing the operating principles of the transmission system and the motion relationships among its components, all components were treated as rigid bodies in the ADAMS dynamic simulation. All rotating components were constrained as revolute joints, and contact pairs were added between meshing gears. A driving torque was applied to the sun gear, _aL_, while a load torque was applied to the ring gear, _rR_, and the carrier, \(h\). The operating conditions were consistent with those in Section 4.1, and the constraints of the transmission system are illustrated in Figure 27.  
The simulation duration was set to 0.0218 s. Taking the meshing forces between the sun gear and planet gears on the left side of both the fixed-axes PGT and the differential PGT as examples, the meshing force curves were obtained, which are shown in Figure 28.  
By comparing Figure 28a with Figure 7a and Figure 28b with Figure 8a, it can be observed that the meshing force curves obtained from the ADAMS dynamic simulation are generally consistent with those calculated by the model in terms of the numerical values and trend variations. Within the allowable range of error, this validates the reliability of the theoretical calculations.
# Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review
## 2 Theoretical Background of Wavelet Transforms
### Continuous Wavelet Transform (CWT)  
The CWT is used for mapping the changing properties of non-stationary signals. However, CWT is a time-frequency representation of a signal \(f(t)\) that can be defined by the following equation:  
\[W_{f}(a,b)=\int_{-\infty}^{\infty}f(t)\psi_{a,b}^{*}(t)dt=\frac{1}{\sqrt{a}} \int_{-\infty}^{\infty}\psi_{a,b}^{*}(\frac{t-b}{a})f(t)dt, \tag{5}\]  
where \(\psi_{a,b}^{*}\) is the mother wavelet conjugate [7]. The wavelet coefficients \(W_{f}(a,b)\) are obtained by continuously varying the scale parameter and the position parameter in order to select the different portions of the signal and analyze the different scale variations [8; 9]. The constituent wavelets of the original signal are obtained by multiplying each coefficient by the appropriate scaled and shifted wavelet. The mostly used mother wavelet for CWT is the \"Morlet\" function which extracts features with equal variance in time and frequency.
## 3 Results
### Theme Evolutionary Analysis
#### 3.4.3 Strategic Coordinate Diagram  
To gain a deeper understanding of the evolving state of themes in each stage, strategic coordinate diagrams of the themes in the four stages are plotted with SciMAT. The strategic coordinate diagram utilizes a cross-coordinate system, with the horizontal axis representing Centrality and the vertical axis representing Density. The larger the centrality value, the stronger the evolutionary relationship between the theme and other themes, and the larger the density value, the higher the internal connection of the theme. The size of the nodes is proportional to the amount of literature related to each theme. Larger nodes signify themes that have attracted more attention. Last but not least, the diagram divides themes into four quadrants. The first quadrant contains themes with high centrality and density, known as the \"engine themes,\" which represent the research focuses of the field. The second quadrant includes themes with low centrality and high density, referred to as isolated/professional themes. The third quadrant comprises themes with low centrality and low density, which are emerging or dying themes and are not research foci during that stage. The fourth quadrant consists of themes with high centrality and low density, representing innovation themes with great potential 1 [62].  
The strategic coordinate diagrams indicate that engine themes for 2013-2014 are INTELLIGENT-TRANSPORTATION-SYSTEMS and AS-HOC-NETWORKS, while the innovation theme is SYSTEM; engine themes from 2015 to 2017 are VEHICLES, and AUTONOMOUS-VEHICLES, while innovation themes are NETWORKS, VEHICULAR-NETWORKS, and VEHICULAR-AS-HOC-NETWORK(VANET), the theme shifting from an innovation theme to an engine theme is INTELLIGENT-VEHICLES and the theme changing from isolated theme to engine theme is IMPACT. In the next two stages, a lot of themes prosper in all quadrants, and the size of nodes is quite balanced in the first and fourth quadrants, indicating that research in this field has experienced leapfrog growth since 2018, which is consistent with the growing trend of publications in the previous section. From 2018 to 2020, engine themes include ELECTRIC-VEHICLES, AUTOMOBILES, INTELLIGENT-TRANSPORTATION-SYSTEMS, DRIVER-BEHAVIOR, and UAV, while innovation themes include INTELLIGENT-VEHICLES, NEURAL-NETWORK, PREDICTION, and SENSOR. VEHICULAR-COMMUNICATION and ATTACKS are the two largest nodes moving from the second quadrant to the first quadrant. In 2021-2022, engine themes include AUTOMOBILES, SIMULATION, ANALYTICAL-MODELS, IMPACT, INTELLIGENT-TRANSPORTATION-SYSTEM, and UNMANNED-AEERIAL-VEHICLES. ELECTRIC-VEHICLES is moving into the second quadrant. Innovation themes in this stage include ALGORITHM, SMARTCITIES, ARCHITECTURE, and ACCIDENTS. It is worth noting that ACCIDENTS come into sight for the first time in 2021-2022, and IMPACT appears in both stages of 2015-2017 and 2021-2022, indicating research on road safety in the field of ICV is attracting people's attention (Figure 15).  
Figure 14: The thematic evolution map of intelligent connected vehicle traffic accidents.
## 3 Proposed Framework
### Reference View Extension
#### 3.1.1 Calculation of the Extended Region  
The pinhole camera model is used in the 3D warping process. Assuming that the world coordinate system coincides with the reference camera coordinate system, the relationship between the associated pixels in the reference image and the virtual image can be defined as:  
\[Z^{\prime}m^{\prime}=ZA^{\prime}RA^{-1}m+A^{\prime}t, \tag{1}\]  
where \(m\) and \(m^{\prime}\) are the corresponding pixels of the 3D space point \(M\) in the reference image and the virtual image, i.e., \((u_{r},v_{r})\) and \((u_{v},v_{v})\). \(Z\) and \(Z^{\prime}\) are the depth values of two pixels. \(A\) and \(A^{\prime}\) are intrinsic matrixes of the reference camera and the virtual camera. The rotation matrix \(R\) and the translation matrix \(t\) represent the transformation from the reference view to the virtual view.  
After the virtual image is obtained by the DIBR algorithm, we mark the hole pixels on the image boundary and inversely map them to the reference view by using (1) to calculate the size of the extended region. It is noted that the depth values of these hole pixels are unknown. Our calculation is under the assumption that these boundary pixels have similar depth values to those of the reference image if there are no additional foreground objects in the extension region. Therefore, the depth values of the boundary pixels in virtual image come from the processing result of the reference image boundary with a Gaussian lowpass filter.  
Figure 3: Positional relationship between reference view and virtual view: (**a**) Horizontal translation; and (**b**) Rotation and translation.  
The coordinate range of original reference image is \((0,0)\)-\((w-1,h-1)\). After the hole pixels on the virtual image boundary are re-projected into the reference image plane, the minimum coordinate \((u_{\text{min}},v_{\text{rm}})\) and maximum coordinate \((u_{\text{rm}},v_{\text{rm}})\) of these pixels can be obtained. Since these pixels are outside the imaging range of the reference view, the dimensions of the extended region in the horizontal and vertical directions are formulated as:  
\[\left\{\begin{array}{l}Width\_Ex=\max(0-u_{\text{rm}},u_{\text{rm}}-w+1)\\ Height\_Ex=\max(0-v_{\text{rm}},v_{\text{rm}}-h+1)\end{array}\right., \tag{2}\]  
The extended reference image and its depth image are shown in Figure 4a,b, respectively. The extended region is marked and then filled by the inpainting method in the later section. It can be seen that the original reference image is located in the center, which facilitates the 3D warping computation and the extraction of the virtual image.
## 5 Conclusions  
We have demonstrated the possibility to accurately estimate the concentration ratio of two fluorescent probes implanted several millimeters under the skin in living mice. This study has a higher complexity than our previous study, which tested the estimating accuracy in vitro and ex vivo. Here the concentration ratio estimation takes into account the absorption and scattering spectra of an in vivo tissue, pulsatile blood flow and respiration movements. It is a preparation for in vivo studies that will be performed on AD mice models with markers injected to the blood and reaching the CSF. The proposed method may estimate in the future the CSF A\(\otimes\)42:P-tau ratio without the need to penetrate the dura matter and collect CSF samples. It does not require any a priori information regarding the anatomical and optical properties of the lumbar tissue. Considering these advantages, this technique could be used as an annual test, which may assist in early AD diagnosis and increase the efficacy of treatments. In recent years there are reports on additional potential CSF bio-markers that may be used as early detection [35; 36]. Our method is generic and only relies on having a fluorescence marker that can be attached to those disease markers. Therefore, our proposed method can also function on other Alzheimer's disease markers, so it will be even more powerful than relying on the ratio of the two markers that we have discussed in this work.  
**Author Contributions:** [PERSON] performed all the experiments, calculations, and simulations in this paper. [PERSON] helped with the animal experiments. [PERSON] is the head of the group. He initiated the research and advised [PERSON], who was his doctorate student at the time.  
**Funding:** \"The founding sponsor (A small stipend to support [PERSON]) 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\".  
**Acknowledgments:** [PERSON] acknowledges the generous support of the Israeli Ministry of Science and Technology and the support of the Herczeg Institute on Aging for this research. We also thank [PERSON] from the Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, for his advice during the animal studies.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Symbol-Level Selective Channel Estimation in Packet-Based OFDM Systems
## 3 Related Work
### PLS Channel Estimation Scheme  
The PLS scheme selectively uses the better channel estimation scheme of two candidate schemes with good performance in their corresponding SNR regions on a packet-by-packet basis [20]. First, the long training symbols are used to obtain the channel estimate \(H(0,k)\) defined in Section 3. \(H_{i}(0,k)\) is then obtained by using a candidate channel estimation technique \(i\) in \(H(0,k)\). The modified mean squared error (MSE) for the candidate channel estimation scheme is defined as follows:  
\[\tilde{\delta}_{i}=\frac{1}{N}\sum_{k=-26}^{26}\left|H(0,k)-H_{i}(0,k)\right|^{ 2}, \tag{11}\]  
where \(N\) is the total number of data and pilot subcarriers in each OFDM symbol. The selection criterion is then defined as follows:  
\[\zeta_{i}=\operatorname*{arg\,min}_{i\in\mathbb{C}}\left|\frac{1}{\overline{ \gamma}}-\tilde{\delta}_{i}\right|. \tag{12}\]  
Here, \(\mathbb{C}\) denotes a set of two candidate schemes, and \(\overline{\gamma}\) represents the average SNR. By finding \(i\) that minimizes \(\left|1/\overline{\gamma}-\tilde{\delta}_{i}\right|\) in \(\mathbb{C}\), the PLS scheme can choose a better technique. The selected channel estimation scheme using the preamble is employed to estimate the channel of all data symbols in the packet.  
Assuming that \(H_{i}(0,k)\) is similar to a perfect CR, the modified MSEs are defined as \(1/\overline{\gamma}\). Therefore, the better the channel estimation scheme between the candidate schemes, the closer the channel estimate is to the perfect CR, and \(\tilde{\delta}_{i}\) of the better scheme is approximated to \(1/\overline{\gamma}\).  
The PLS scheme achieves a performance gain by selectively using two channel estimation schemes with superior performance in different SNR regions. However, the selected channel estimation scheme at the beginning of a packet has difficulty in precisely estimating the channel variations within the packet over time in vehicular environments. In addition, because the method of selecting a better channel estimation scheme in the PLS scheme is based on the long preamble in front of the packet, it cannot be extended to a scheme for changing the channel estimation scheme in the middle of the packet.
# Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN
## 5 Experimental Results
### Simulation Settings  
We consider the UAV taking off from the BS, the maximum flight time \(T\) to be set at 10 min. The sensor nodes are randomly distributed within a \(400\times 400\) m\({}^{2}\) square two-dimensional area. The UAV flies at an altitude of 10 m, with a maximum data transmission radius \(R_{d}=10\) m and a maximum charging radius \(R_{c}=30\) m. The maximum flight speed of the UAV is set to 20 m/s [20]. The coordinates of the nodes are randomly generated and remain fixed. The nodes' maximum data storage capacity is \(Q_{max}=100\) Mb [40]. The total energy of the nodes is set to \(E_{max}=800\) J [40], and their initial energy is randomly generated within the range of [0 J, 800 J] [40]. The transmission energy consumption for CM nodes is \(e_{t}=0.4\) J [43], and the reception energy consumption for CH nodes is \(e_{r}=0.5\) J [43]. Table 3 and Table 4, respectively, show the network parameters of MODDPG and the main environmental parameters, as specified in reference [20]. The operating system environment of the simulation experiment is TensorFlow 2.5.0, TensorLayer 2.2.5, and Python 3.7 on a Linux server with four NVIDIA 3090 GPUs.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Episodes (\(M\)) & 400 \\ Actor network structure & \(400\times 300\times 300\) \\ Critic network structure & \(400\times 300\) \\ Actor network hidden layers & 3 \\ Critic network hidden layers & 2 \\ Batch size & 64 \\ Learning rate for actor (\(lr_{a}\)) & \(1\times 10^{-3}\) \\ Learning rate for critic (\(lr_{c}\)) & \(1\times 10^{-3}\) \\ Discount factor (\(\gamma\)) & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Network parameters.
# Fingerprint Classification through Standard and Weighted Extreme Learning Machines
## 2 Related Works
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Authors** & **Year** & **Feature Extractor** & **Classifier** & **Database** & **Accuracy (\%)** & **Evaluation Time (s)** \\ \hline [PERSON] et al. [1] & 2019 & None & Bayesian deep CNNs & NIST-DB4 (3300 samples) & 96.1 and 95.5 & 4393 and 3801 \\  & & & & and FVC2002 (1600 samples) & & \\ \hline [PERSON] et al. [29] & 2019 & Conic Radon transform & CNNs (4 convolutional layers with & NIST-DB4 (3300 samples) & 95.0 & 0.06 \\  & & (image functions are & 3 max-pooling layers followed by a & & & \\  & & integrated over conic & fully-connected layer) & & & \\ \hline [PERSON] et al. [21] & 2018 & Orientation field with & Basic ELM with the radial & FVC2004 (3520 samples) & 98.7 & Not reported \\  & & histograms of oriented & activation function & & & \\ \hline [PERSON] et al. [3] & 2018 & None & CNNs (a new network and a & NIST-DB4 (3300 samples) & 93.73 and 94.58 & 960 and 2,306 \\  & & & modification of the CaffeNet & and SFINCE (120,000 samples) & & \\  & & & CNN [30] with softmax & samples) & & \\  & & probabilities for the last layer & & & \\ \hline [PERSON] [4] & 2017 & Normalized orientation & CNNs with various convolutional, & NIST-DB4 (3300 samples) & 95.4 & Not reported \\  & & angles & max-pooling, and fully connected & & & \\ \hline [PERSON] et al. [14] & 2017 & None & Deep CNNs with 6 diverse layers & NIST-DB4 (3,300 samples) & 97.9 & Not reported \\ \hline [PERSON] et al. [13] & 2017 & None & Pre-trained CNNs known as & NIST-DB4 (3300 samples) & 94.4 and 95.95 & 32,600 and 108,000 \\  & & & VGG-F and VGG-S [31] & & & \\ \hline [PERSON] et al. [32] & 2016 & Minutae extraction & Support vector machines & FVC2000 and FVC2002 & 92.3 and 92.8 & Not reported \\  & & & & (each has 880 samples) & & \\ \hline [PERSON] et al. [33] & 2016 & Orientation field based on & Deep CNNs with 3 complex & NIST-DB4 (3300 samples) & 98.4 & Not reported \\  & & a support vector machine & hidden layers & & & \\ \hline [PERSON] et al. [34] & 2015 & A combination of the & Support vector machines & FVC2004 (1600 samples) & 97.9 & 2.6 for input features \\  & & orientation field, & & & \\  & & directional filtering, and & & & \\  & & Poincare index & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the state-of-the-art approaches for the fingerprint classification problem.