esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Water Level Monitoring Based on GNSS Dual-Antenna Attitude Measurement
###### Abstract  
Real-time and high-precision water level monitoring is crucial for the fields of hydrology, hydraulic engineering, and disaster prevention and control. The most prevalent method for measuring water level is through the use of water level gauges, which can be costly and have limited coverage. In recent years, Global Navigation Satellite System Reflectometry (GNSS-R) technology has emerged as a promising approach for water level monitoring due to its low cost and high coverage. However, a limitation of current GNSS-R technology is the extended time required to record signals, which hinders its potential for real-time application. This paper introduces a novel real-time water level monitoring method based on GNSS dual-antenna attitude measurement and develops a model to invert water level based on baseline vector. This method uses double-difference observations to eliminate errors caused by various factors, such as satellite and receiver clock, and ionospheric and tropospheric delay. To avoid the impact of detecting and correcting cycle slips during real-time operations, a single-epoch calculation method is introduced. In order to verify the stability and reliability of our method, field tests were carried out at Dongshahe Station in Beijing. We obtained water level data with a time resolution of 1 Hz through field experiments. Experimental data collected from 12 May to 8 June 2022 and from 4 July to 8 August 2022 showed good agreement with on-site water gauge measurements, with root mean square errors of 2.77 cm and 2.54 cm, respectively. Experimental results demonstrate that this method can achieve high-precision, high-temporal-resolution water level monitoring.  
GNSS-R; GNSS dual-antenna attitude measurement; water level +
Footnote †: journal: remote sensing
## 2 Theoretical Background
In contrast, for high values of permeability, the magnetization of the object dominates the response, and the Eddy currents are negligible. When the induced fields of an object are dominated by Eddy currents (low permeability), the induced field will oppose the incident field via application of [PERSON]'s law. However, for a magnetization dominated object, the induced fields will in fact enhance the incident fields. As such, this is an important qualitative feature, since it can be readily utilized to infer properties about a hidden object without significant signal processing. Figure 3 shows the \(H\) fields and induced surface current for (a) conductive and (b) ferromagnetic cylinders in the presence of a 50 Hz magnetic dipole transmitter. The relative permeabilities of the conductive and ferromagnetic cylinders are 1 and 1000. The cylinders have a 0.2 m radius and 0.2 m height with \(10^{6}\) S/m conductivity. The center of the bottom of the cylinders is \(x=y=z=0\). The incident magnetic field is assumed as a negative z-oriented uniform and perpendicular magnetic field on cylinder materials. Figure 3a shows that a counterclockwise directional surface current on the conductive cylinder is induced. With the induced Eddy current, the illuminating magnetic field is blocked,  
Figure 1:0 The amplitude (\(m_{p}\)) of induced magnetic dipoles on sphere objects with various relative permeability at 50 Hz, 200 Hz, and 1 kHz. A radius of the sphere object is verified from 0.1 to 0.4 m.  
and the field amplitude is reduced 3 dB compared to around the corner of the cylinder radius (\(x^{2}+y^{2}=0.2^{2}\), \(z=0\)). However, Figure 3b shows that a clockwise directional surface current on the ferromagnetic cylinder is induced. The illuminating magnetic field is gathered and the field amplitude is enhanced 3 dB compared to the boundary area at \(x^{2}+y^{2}=0.2^{2}\) and \(z=0\). This result shows the important phenomenon that high conductivity and high permeability have opposing effects. Specifically, a purely conductive object results in a magnetic depletion; i.e., the object essentially blocks the incident magnetic field. However, a purely permeable object focuses the field and creates an enhancement. The direction of the currents is dependent on a phase of the incident field in time and is opposite when a phase of the field is 180 degrees. For the non-magnetic object, the induced current has approximately the same phase as the incident field. For the magnetic object, the current is 180 degrees out of the phase with the incident field. For an object with mixed properties, this results in a complex dependence on the size, permeability, and conductivity of the object as alluded by Equation (2). However, the problem becomes considerably more complex with the introduction of an external shield.
## 3 Wireless Localization Technology
### Range-Based Techniques
#### 3.1.1 Received Signal Strength (RSS)  
One of the simplest methods for distance measurement is the utilization of received signal strength (RSS). The distance between the device and the anchor through RSS can be obtained through the following equation.  
\[P_{R}=P_{0}-10n\log(\frac{d}{d_{0}}). \tag{1}\]  
\(d\), \(d_{0}\), \(P_{R}\), \(P_{0}\), and \(n\) are the distance between the device and the anchor, the reference distance, the actual RSS, the reference RSS, and the path loss exponent, respectively. Here, \(P_{0}\), \(d_{0}\), and \(n\) are fixed values, thus the distance \(d\) can be simply calculated by substituting \(P_{R}\). Although this method is quite simple regarding hardware implementation, there is an issue of knowing the path loss exponent \(n\) in advance, and it is very vulnerable to the signal-to-noise ratio (SNR).  
Figure 2: An illustration of the concept of trilateration.
## Appendix A  
In SDR2, we have made use of the Xilinx PRC IP core and the ICAP port to apply the run-time partial reconfiguration for a design clocked at 100 MHz. Based on the test, validation, and extensive debugging of the system, we have spotted a hardware limitation in the USRP X310 board that deters it from running the PR flow using the PRC and ICAP port. In contrast, the PR works well in SDR2 when we reconfigure the same static and partial bitstreams through the JTAG port (i.e., using the Vivado Hardware Manager). The following points describe the debugging procedure and the limitation we found in the USRP X310 board:  
* A process in MicroBlaze fetches the partial bitstream (.BIN file format) from the host PC via TFTP and copies the contents in the DDR3 memory.
* Using a breakpoint in the executed code, we are able to verify in SDK that the contents in the DDR3 are the correct ones.
* The PRC is triggered by a process in the MicroBlaze to access the.BIN stored in the SDRAM through its m_axi_mem port. Using an ILA core, we monitor that the transaction takes place without any problems.
* The PRC IP core pushes the.BIN partial bitstream to the ICAP port to apply partial reconfiguration. Another ILA debug core monitors the ICAP port (Figure A1). By reading the ICAP \"O\" Port bits, we check the status bits as follows (see also Table A1):
* Initial status: 1001 The ICAP receives the sync word: 1101 The ICAP receives the DESYNC command: 0101 The 'icap_csib' port produces a value of '1': 0001 The latter means that the ICAP indicates an error directly after receiving all the data of the partial bitstream (.BIN) as it could be seen in Figure 18 (i.e., ILA screen capture).
* At that stage of debugging, we went deeper to analyze why the partial reconfiguration was not completed successfully.
* In order to make the partial reconfiguration work through the ICAP interface, we need to activate the Slave SelectMAP configuration interface in our system [45; 46]. Table 2-1 in [45] (page 17) indicates that ports M [2:0] need to have a '110' value to set the Slave SelectMAP configuration.
* However, when revising the schematic diagrams of the USRP X310 board (page 9), we realized that the M [2:0] ports take the value '111', which in fact is a hardwired value (thus, not reprogrammable by software).
* This value must change in order to be able to use the PRC and ICAP on this SDR board.
* Looking at the schematic diagrams, the only work around is to remove the R70, in which case, M2_0 will be connected to GND through the R71 resistance (i.e., likewise setting the M [2:0] with the required '110' value).
## 3 Results
### Comparison Structural and Functional-ROI
#### 3.3.2 Images Analysis  
Figure 5 shows that the optical imaging combined with the utilization of a functional-ROI is capable of detecting subtle variations in the lateral distribution of activation foci. Moreover, these brain regions are situated within deep sulcal structures, posing challenges for optical approaches to access them. The loci distributions for both the GLM using a structural-ROI and GLM using a functional-ROI results are along the sensorimotor and motor cortices.  
Although the activation patterns show the strongest qualitative similarities in both methods, consistent differences worth mentioning were also observed. GLM using functional-ROI shows the major number of voxels distributed across the postcentral gyri (BA1, BA2 and BA3), which covers the primary somatosensory cortex for DOT and fMRI measurements. This fact matches prior studies in fMRI during finger tapping, which showed higher regional homogeneity in the sensory cortex than in the motor cortex itself [17]. The premovement sensory cortex shows a slight delay in encoding information about the impending activity of the forelimb muscles compared to the motor cortex. As a result, the sensory cortex receives information regarding motor output before the arrival of sensory feedback signals. This suggests that sensory processing engages in the real-time processing of somatosensory signals through interactions with anticipatory information [18].  
Figure 4: Power spectrum for the time series of BOLD (green lines); HbO (red lines); HbR (blue lines) signals in the cerebral activation maps generated by (**a**) GLM using structural-ROI, (**b**) GLM using functional-ROI. Vertical axis: Normalized Power Spectrum; Horizontal axis: Frequency (Hz).
# Present and Future Marine Microwave Satellite Missions in China
## 1 Introduction
On 16 August 2011, China's first oceanic dynamic environment satellite (HY-2A) was successfully launched. The performance of the satellite reached the international advanced level [9], filling the gap in China's real-time acquisition of oceanic dynamic environment information. Subsequently, on 10 August 2016, the \"GF-3\" satellite was successfully launched at Taiyuan Satellite Launch Center, which was mainly used for marine applications [10; 11]. As a result, the gap in China's independent high-resolution multi-polarized SAR marine remote sensing data acquisition was successfully filled. In this introduction, the development history of oceanic microwave satellite remote sensing technology during the past 70 years is reviewed. Then, the new achievements in the field of marine satellite remote sensing technology in China are introduced. Finally, the future development of China's marine microwave remote sensing satellite technology is analysed.
# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 4 Results
### Spatial Distributions of Paddy Rice Maps
#### 4.4.2 PMI-Based Paddy Rice Mapping Using Google Earth Engine  
PMI was applied to a larger area (paddy rice cultivated area in California State; Figure 9) using the threshold of 0.2. Figure 9 shows PMI based paddy rice maps on the Google Earth Engine platform. The reference paddy rice map was obtained from USDM CDL 2016. Paddy rice was well detected through PMI, although there were some false alarms that were caused by clouds. These results indicate that PMI can effectively map and monitor yearly paddy rice areas at a national or larger scale through Google Earth Engine through the improvement of computing capacity. We also tested the PMI-based paddy rice mapping for other regions where rice is cultivated (Pohang in South Korea, Acadia county in Louisiana State, and Arkansas county in Arkansas state, United State; Supplementary Figure S1). Paddy rice was well detected through PMI where the fields are flooded during the transplanting season (Pohang and Acadia County).
###### Abstract  
Framed slotted aloha (FSA) is a multiple access protocol widely used in wireless communication for its simplicity and effectivity. The theoretical maximum channel utilization of FSA is approximately equal to 0.37 irrespective of the capture effect. In fact, the capture effect is a common phenomenon in wireless communication, which can increase the channel utilization. In this paper, we derive the closed-form expressions of capture probabilities under Rayleigh, Rician and Nakagami-\(m\) fading channels, respectively, and further give the optimal frame length which maximizes the channel utilization in the FSA-based networks. The numerical results are given for indicating the capture probabilities under different fading channels and their impacts on the optimal frame length and the maximum channel utilization.  
Keywords:FSA; capture effect; Rayleigh; Rician; Nakagami-\(m\); frame length; channel utilization +
Footnote †: journal: Applied Sciences
# AU-Guided Unsupervised Domain-Adaptive Facial Expression Recognition
## 2 Related Work
### Cross-Domain FER  
It is inevitable that distribution divergences among different facial expression datasets will exist, due to variant collecting conditions and annotating subjectiveness. In past decades, cross-domain FER (CD-FER) has recieved more attention [16; 18; 41; 42; 43; 44; 45; 46; 47; 48]. Generally, the CD-FER can be divided into semi-supervised-based, unsupervised-based, and generation-based methods. Semi-supervised-based methods [45; 49] apply a convolutional neural network (CNN) model to train a classification model using limited labeled samples from a target domain. For the unsupervised-based methods, [PERSON] et al. [50] use a Gabor feature-based landmark detector to localize facial points and track them in facial sequences to model temporal facial activation for facial expression recognition. They trained the recognition model using the CK database and performed the test in the MMI database for cross-validation. [PERSON] et al. [48] propose a transductive transfer subspace learning method using labeled source domain images and unlabelled auxiliary target domain images to jointly learn a discriminative subspace. For generation-based methods, [PERSON] et al. [18] propose a domain regeneration framework (DR) that aims at learning a domain regenerator to regenerate samples from source and target databases, respectively. [PERSON] et al. [51] introduce an unsupervised domain-adaptation method using a generative adversarial network (GAN) on the target dataset, and dynamically assign the unlabelled GAN-generated samples distributed pseudo-labels according to the current prediction probabilities. In order to understand the conditional probability distributions' differences between the source and target datasets, [PERSON] et al. [42] develop a deep emotion-conditional adaption network that simultaneously considers the conditional distribution bias and expression class imbalance problem in CD-FER. [PERSON] et al. [17] propose an adversarial graph representation adaptation (AGRA) framework that unifies graph representation propagation with adversarial learning for cross-domain holistic-local feature co-adaptation. Different from the above works, our work utilizes AU information as auxiliary cues to bridge the gap between different FER datasets, and is expected to learn a generic feature space for a source and a target dataset.
## 2 Numerical Model  
### Flow Model  
We have developed the direct-forcing fictitious domain (DF/FD) method [29] for the direct numerical simulation of particle-laden flows, based on the modification of the original distributed-Lagrange-multiplier-based FD method of [PERSON] [30]. The key idea of the FD method is to fill the interior domain of particles with fictitious fluids, and distribute a pseudo-body force (i.e., distributed Lagrange multiplier) over the solid region to enforce the rigid-body constraints [30]. Our FD method has been applied to the motion mode of a flexible particle in a two-dimensional DLD device [22] and a wide range of particle-laden flows (e.g., [31; 32; 33; 34]). However, it is not possible to apply our FD code to a three-dimensional DLD geometry with a great many number of posts and inlet-outlet boundaries due to huge computational costs. Thus, following [PERSON] [26] and [PERSON] [27], we adopt a DLD cell with the periodic boundary condition in the lateral (\(y\)) direction and a shifted periodic boundary condition in the streamwise (\(x\)) direction, as shown in Figure 2. The no-slip wall boundary condition is imposed in the spanwise (\(z\)) direction, since we plan to examine the spanwise spatial constraint effect on the motion of the bioparticle in the DLD device [35] in the future. A pressure gradient is imposed in the streamwise direction to maintain a constant fluid mean velocity \(U_{m}\). The size of the computational domain (i.e., DLD cell) is \(L_{x}\times L_{y}\times L_{z}\). The cylinder with the diameter of \(D\) is located at the center of the \(x-y\) cell. In the present study, we set \(L_{z}=L_{x}/2=D\), and \(L_{x}=L_{y}=2D\), so that the gap distance between vertical neighboring posts (i.e., \(L_{g}\) in Figure 1) is \(L_{g}=D\). For a row shift of \(\Delta S\), the shifted periodic boundary condition implies that  
\[u(x,y)=u(x+L_{x},y+\Delta S). \tag{1}\]  
The row shift fraction is defined as \(\varepsilon=\Delta S/L_{y}\), and the periodicity of the DLD system is \(N=L_{y}/\Delta S=1/\varepsilon\) when \(L_{y}/\Delta S\) is an integer.  
Two significant modifications of our previous FD method are required. One is on the fast solver for the pressure Poison equation specified for the shifted periodic boundary condition, and the other is that our previous half-staggered finite-difference scheme was found unstable for this problem, and so the conventional staggered finite-difference scheme is then adopted. In the following, we describe our modified FD method for the motion of one particle in the DLD cell.
# Mobile Robot Path Planning Considering Obstacle Gap Features
## 2 Path Planning Method with Clearance Characteristics
### The Simulation Experiment of Global Path Planning  
In this section, a 30 \(\times\) 30 grid environment is set up, and the performance of the proposed algorithm is compared with the algorithms in references [6; 16].  
The experimental results of scenario 1 are shown in Figure 5, and the experimental parameters are shown in Table 1. It can be seen that compared with the traditional A* algorithm, the proposed algorithm reduces the number of nodes, path length, path optimization times, and time consuming through the path optimization strategy significantly. And only a part of the redundancy points of the path are removed by the algorithm proposed by reference [16]. Compared with reference [6], although the length of the path is the same, the algorithm proposed in this study has a better comprehensive performance, and the final path generated by it is more consistent with the kinematic law of mobile robots. In addition, the key point selection range of the global method can be further optimized by the parallel Bernstein equation, which is more conducive to the subsequent selection of the local path planning range.  
The experimental results and parameters are shown in Figure 6 and Table 2. In the global search, the proposed algorithm can achieve the global optimal search without redundancy in a neutral posture. Compared with reference [5], the number of search nodes and optimization times of the proposed algorithm are significantly reduced.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **Reference [6]** & **Reference [16]** & **Improved Algorithm** \\ \hline Node Number & 205 & 57 & 39 \\ Path Length & 40.8666 & 43.1124 & 40.8666 \\ Optimization Times & 12 & 18 & 4 \\ Optimization Time & 0.104 & 0.133 & 0.004 \\ IAE (Path Length) & 0 & 2.25 & - \\ ITAE (Path Length) & 0 & 0.2993 & - \\ ISE (Path Length) & 0 & 5.06 & - \\ ITSE (Path Length) & 0 & 0.6726 & - \\ IAE (Optimization Time) & 0.1 & 0.13 & - \\ ITAE (Optimization Time) & 0.0104 & 0.0170 & - \\ ISE (Optimization Time) & 0.01 & 0.0169 & - \\ ITSE (Optimization Time) & 0.00104 & 0.00222 & - \\ \hline \end{tabular}
\end{table}
Table 1: Experimental results for Scenario 1.  
Figure 5: Global Path Simulation Experiment for Scenario 1. (**a**) Algorithm from reference [6]; (**b**) Algorithm from reference [16]; (**c**) The algorithm in this paper.
# Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks
## 5 Simulation Results and Comparative Analysis
It can be seen from Figure 8 that the multi-point cooperative transmission mechanism of a vehicular network based on revenue learning is better. In brief, the simulation results show that the overall throughput of our proposed collaborative multi-point transmission mechanism has an average growth rate of 7.00% in different base stations compared with the traditional dual-link vehicular networks. Against the joint anti-jamming mechanism, the overall throughput of our proposed collaborative multi-point transmission mechanism has an average growth rate of 3.17% with different base stations. Figure 8: Comparison of fairness coefficients under no policy. Figure 7: Growth rate curve.
# Cybersecurity for Blockchain-Based IoT Systems: A Review
## 5 Discussion
### Blockchain for IoT: Potential Applications for Smart Cities
#### 5.2.3 Smart Contracts and Automation  
Smart contracts, which are self-executing contracts with predefined rules and conditions, can be deployed on a blockchain to automate interactions and transactions between IoT devices and systems. In the context of smart cities, smart contracts can facilitate various processes, such as energy trading, parking management, waste management, and more. For example, in an energy trading scenario, IoT devices can autonomously negotiate and execute transactions based on predefined conditions stored in smart contracts [49]. By leveraging blockchain-based smart contracts, smart cities can achieve greater efficiency, transparency, and accuracy in their operations.
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 5 Performance Analysis
### Communication Overhead  
In the procedure of an LBS query, the vehicle user sends service query to the LBS server through RSU. The communication overhead at the vehicle user is \(O(k)\).  
RSU needs to broadcast the service request probability, the service semantics and other information. The communication overhead is \(O(I\!U)\). At the same time, RSU needs to forward the service query to the LBS server and return \(kn\) service query results to the vehicle user. Therefore, the communication overhead at the RUS is \(O(I\!U+kn+k)\).  
The LBS server needs to send the service request probability, service semantics and other information to RSU. The communication overhead is O(\(I|U\)). Receiving the service query, the corresponding service results are returned to RSU. The communication cost is \(O(kn)\). Therefore, the communication overhead at LBS server is \(O(I|U+kn)\).  
The performance of proposed RR-DLS method in terms of computation overhead and communication overhead is listed in Table 1.