esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles
## 4 Future Challenges
### Multi-Source Information Acquisition and Processing  
Traffic information is highly time-variant, random, and uncertain. Moreover, the information gathered from various sensing devices in HEVs is often unstructured, irregular, and unstandardized. Hence, it is a significant challenge to integrate information from multiple sources, filter it, and fuse it to meet the computing platform's information format  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strategy** & **References** & **Advantages** & **Disadvantages** \\ \hline Rule-based & [56; 64; 172; 173; 56] & Low technical difficulty of the method; low online calculation volume; wide industrial application. & Difficult to ensure charge maintenance; requires extensive tuning; poor robustness. \\ ECMS, PMP & [174; 179; 105; 175; 176; 177; 178; 179; 105; 177; 174; 175; 176; 177; 178; 179] & Little calculation; no need for full working conditions information; good fuel economy. & No guarantee of global optimality; high impact of equivalence factors \\ MPC & [174; 180; 181; 137] & Combining real-time and optimality; a wide range of applications. & Computationally intensive; over-simplified model \\ DP & [182; 183; 184; 185; 186; 187; 188; 189; 190; 184; 183; 188; 184] & Obtains the global optimal solution; performs directly multi-objective optimization. & Requires a high amount of computational power; no real-time optimization possible \\ Learning-based & [185; 186; 187; 188; 163] & Excellent adaptability and robustness; suitable for high non-linearity. & Complex calculations and low maturity. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Vehicle performance with different control strategies over WLTP.  
requirements. Additionally, the energy management of HEVs requires different decision and control platforms, such as human-vehicle-road-cloud, to operate together seamlessly, which requires effective communication and coordination among them. Therefore, the development of an efficient and robust system that can handle such complex tasks is crucial for the successful implementation of energy management in HEVs. To address these challenges, researchers have proposed various approaches, such as data fusion, machine learning, and cloud computing, which can help integrate and analyze the data from different sources to make effective energy management decisions. Ensuring real-time interactions between platforms with low latency and high reliability is crucial. Additionally, it is essential to establish effective communication mechanisms among various heterogeneous platforms. Therefore, the accurate acquisition and efficient processing of information is a prominent prerequisite for the efficient use of energy.
###### Abstract  
This study compared two approaches to integrating leveling and GNSS data to develop relative vertical movements of the Earth's crust. Novel approaches were tested using transformation and hybrid grid adjustment. The results from double-leveling measurements in Poland were used as test data, and GNSS measurements developed using the PPP technique were used as Supplementary Data. The least squares method was used for the adjustment, and the isometric, conformal and affine methods were used for the transformation, with and without Hausbrandt correction. So-called pseudo-nodal points, i.e., points identified as common in both networks, whose weight was determined according to the assumptions of scale-free network theory, were used as integration points. Both integration methods have similar results and are suitable for integrating leveling and GNSS data to determine the relative vertical movements of the Earth's crust. The average unit error m\({}_{0}\) of the transformation was 0.1 mm/yr and the average error after adjustment of the hybrid network was 0.1 mm/yr. The use of the Hausbrandt correction does not significantly improve the transformation results. A 12-parameter affine transformation is recommended as the transformation method.  
hybrid network; data integration; GNSS triangulation network (GTN); double-leveling network (DLN); vertical crustal movements Article
## 2 Materials and Methods
### Data
#### 2.1.4 Climate Data  
The spatially interpolated grid data used in this study were the annual mean air temperature (TAVG) and annual total precipitation (PRCP) from 1980 to 2018. Details on data interpolation can be found in [PERSON] et al. [42]. The data were used to analyze the impacts of climate changes through a random forest regression. The 8-day mean total downward shortwave radiation (SWRad) was estimated through the algorithm from [PERSON] [43] using the actual sunshine hours interpolated according to [PERSON] et al. [42]. The annual total SWRad was calculated for the period from 1980 to 2018 and used to explore the impact of radiation [44].  
The atmospheric CO\({}_{2}\) concentration data used in this study were from scrippsco2 [45] for the period from 1980 to 2018. The data were observed at Mauna Loa station (19.5\({}^{\circ}\)N, 155.6\({}^{\circ}\)W), Hawaii, and are considered to be the longest time series of global CO\({}_{2}\)[46]. Though CO\({}_{2}\) concentration data are still being observed at Mount Waliguan, which represents the CO\({}_{2}\) baseline for Chinese terrestrial ecosystems, data were only available for the period from 1990 to 2016. Therefore, the data at Mauna Loa station were applied in this study since the data were found to be consistent on magnitudes and average rates of change with the Mount Waliguan baseline data [47; 48].
## 2 Materials and Methods
### Validation and Accuracy Assessment  
To assess the accuracy of our results, 200 polygons were randomly created for each image using the Multiple ROI Creation tool provided by an SCP plugin. The parameters employed to define them were the same as the initial ROIs. The polygons were manually assigned to their respective LULC land cover classes following a visual inspection of the image and ground-truth area interpretation. Thus, this data was used as a reference to compare with our results. In order to assess the improvement due to our post-processing, the classification accuracy before and after application of the procedure is compared.
## 2 Spatially Fixed Virtual Sensing Algorithms
### The Kalman filtering virtual sensing method
\begin{array}{c}\tilde{\mathbf{z}}(n |n-1)\\ \mathbf{u}(n)\\ \mathbf{e}_{p}(n)\end{array} \right],\] (39) where the Kalman gain matrix \(\tilde{\mathbf{K}}_{ps}\) and the virtual innovation gain matrix \(\tilde{\mathbf{M}}_{vs}\) are calculated as follows \[\tilde{\mathbf{K}}_{ps}=\left(\tilde{\mathbf{A}}\mathbf{X}_{s} \tilde{\mathbf{C}}_{p}^{\mathrm{T}}+\tilde{\mathbf{K}}_{s}\left[ \begin{array} []{c}\tilde{\bar{\mathbf{R}}}_{pv\epsilon}\\ \tilde{\bar{\mathbf{R}}}_{pv\epsilon}^{\mathrm{T}}\end{array} \right]\right)( \tilde{\mathbf{C}}_{p}\mathbf{X}_{s}\tilde{\mathbf{C}}_{p}^{\mathrm{T}}+\tilde{ \bar{\mathbf{R}}}_{pv})^{-1},\] (40) \[\tilde{\mathbf{M}}_{vs}=(\tilde{\mathbf{C}}_{v}\mathbf{X}_{s} \tilde{\mathbf{C}}_{p}^{\mathrm{T}}+\tilde{\bar{\mathbf{R}}}_{pv\epsilon}^{ \mathrm{T}})(\tilde{\mathbf{C}}_{p}\mathbf{X}_{s}\tilde{\mathbf{C}}_{p}^{ \mathrm{T}}+\tilde{\bar{\mathbf{R}}}_{pv})^{-1},\] (41) with \(\mathbf{X}_{s}=\mathbf{X}_{s}^{\mathrm{T}}>0\), the unique solution to the discrete algebraic Riccati equation given by \[\mathbf{X}_{s}=\tilde{\mathbf{A}}\mathbf{X}_{s}\tilde{\mathbf{A}}^{\mathrm{T} }-\tilde{\mathbf{K}}_{ps}(\tilde{\mathbf{C}}_{p}\mathbf{X}_{s}\tilde{\mathbf{ C}}_{p}^{\mathrm{T}}+\tilde{\bar{\mathbf{R}}}_{p\epsilon})^{-1}\tilde{\mathbf{K}}_{ ps}^{\mathrm{T}}+\tilde{\mathbf{K}}_{s}\tilde{\mathbf{R}}_{\epsilon}\tilde{ \mathbf{K}}_{s}^{\mathrm{T}}.\] (42)The Kalman filtering virtual sensing method is optimal in its estimation of the virtual error signals given a known or measured noise covariance. Also, instead of using a number of FIR or IIR filter matrices to compute an estimate of the virtual error signals, one compact state space model is used. This virtual sensing algorithm is also derived including measurement noise on the sensors [8]. The Kalman filtering virtual sensing method is however, limited to use in systems of relatively low order. The performance of this virtual sensing algorithm in generating a zone of quiet at a virtual microphone 10 cm from a physical microphone has been investigated in real-time broadband feedforward experiments conducted in an acoustic duct over a 50 - 500 Hz frequency range [8; 12]. The state space model of the plant was first estimated using subspace model identification techniques [54] with a microphone temporarily placed at the virtual location. Combining this virtual sensing algorithm with the filtered-x LMS algorithm [14] achieved an overall attenuation of 19.7 dB at the virtual location. This is compared to an attenuation of 25.1 dB being achieved by directly minimising the error signal at the virtual location. The 5.4 dB difference was attributed to the fact that the primary disturbances at the physical and virtual locations were not completely causally related, which is a requirement in this virtual sensing algorithm.
## 3 Experiment
### Measurement Device  
The ultrasonic system (Figure 2) consisted of several functional units: three transducers (one transmitter (T) and two receivers (R1 and R2)), a pulser/receiver, and a computer (PC). The pulser/receiver generates high-voltage electrical pulses. Driven by the pulser, the transmitting transducer produces high ultrasonic energy with a high frequency. The sound energy is introduced and emitted through the 304 stainless steel in the form of an LCR wave. The LCR wave is converted by the receiving transducers (R1 and R2) into an electrical signal, which is shown on the computer screen. The pulse received at R2 appears on the digital screen later than the pulse received at R1. Double receiving transducers are advantageous in that they eliminate environment effects (e.g., temperature and coupling conditions, among others). Time-of-flight was measured using the zero-crossing method between the two receiver echoes R1 and R2. This measuring device showed a time resolution of 0.5 ns.  
Three groups of transducers with different frequencies were used to detect residual stresses of 10 samples. This means that there were three groups, and each group consisted of three transducers. The specimens were numbered from S1 to S10, with S1 being the reference specimen free of stresses determined in advance. Samples S2 through S10 were tested for travel time using the LCR technique,  
Figure 2: Schematic of the experimental critically refracted longitudinal (LCR) wave system (T: transmitting transducer, R1: the first receiving transducer, R2: the second receiving transducer, and PC: personal computer).  
and each travel time was compared to the travel time of S1. The residual stresses were evaluated using Equation (5). The parameters of the different frequency transducers are shown in Table 2.  
The results obtained with ultrasonic and X-ray diffraction methods were compared in order to validate our measurements. After the measurement of residual stress using the X-ray diffraction method, the sample was electrochemically polished (because the X-ray beam penetrates to a depth of only a few microns), and the measurement was repeated. The thickness of each etched layer was approximately 20 \(\upmu\)m. The surface was etched 10 times, which enabled us to reach a depth of 200 \(\upmu\)m below the machined surface.  
An X-ray stress analyzer from Proto Corporation was used herein, and the main parameters of the device are shown in Table 3. Before the experiment, the X-ray stress analyzer was used to detect the residual stress in the reference bar (S1). A value of \(-0.78\pm 3.45\) MPa was obtained, indicating that the analyzer was sufficiently accurate for our purposes.
## 3 Test Procedures for GBPT Performance Assessment
### Laboratory Tests  
For the laboratory tests, radio frequency constellation simulators (RFCSs) were exploited to define the scenario in a controlled and repeatable way. Several parameters, such as the pre-defined  
Figure 1: Global navigation satellite systems (GNSS)-based positioning terminal (GBPT) system architecture. IMU: inertial measurement unit.  
Figure 2: GBPT performance assessment.  
trajectory, the satellite geometry, the simulation of the errors (e.g., ionosphere, troposphere, multipath), and the power signal level, were under the user's control. The appropriate radio frequency (RF) output was thus determined by using mathematical models. The laboratory test was performed in a dedicated suitable area, usually an anechoic chamber or via a cable directly connecting the simulator to the GNSS antenna. During the execution of the test, the desired measurements were recorded by the GBPT under test and then compared to the reference trajectory, perfectly known from the mathematical model adopted by the RFCS. This method requires a sensitive amount of resources, especially if dynamic tests have to be performed.  
An overview of this approach for automotive testing is given in [11], where the authors presented a methodology to evaluate the position availability of automotive-grade GPS receivers utilizing a multichannel satellite signal simulator in a controlled laboratory environment.
## 6 V-DPAP Solution Methods
### Greedy Allocation (greedy)  
For very fast decisions, approximate leximin might still be too slow. In such cases, a greedy approach can quickly provide valid allocations. The main idea of greedy path allocation is to iterate over the set of graphs. At each step, one graph \(g^{*}\) that has the best utility path is selected and this path is chosen as \(\pi(g^{*})\). Moreover, given the nodes already selected and the new ones in \(\pi(g^{*})\), all the nodes in the other graphs that are in conflict are deactivated. Graph \(g^{*}\) is then removed, and the process continues until there is no more graphs to consider. This process ensures that constraints (1)-(6) are met. Determining the best path in a DAG \(g\) has a linear time complexity \(\mathcal{O}(|E_{g}|+|V_{g}|)\)[24]. Obviously, greedy is equivalent to utilitarian when there is no conflict between graphs. Indeed, greedy will return the best path for each graph, which is the best utilitarian solution in such settings. Moreover, if there are no ties when selecting the best path for each graph, then this greedy approach leads to a Nash equilibrium, where no agent can improve its utility without a negative impact on other agents. This is equivalent to the _Nashify_ procedure from [13] in the context of congestion games, with only one turn. We will see in the experiments that this equilibrium is far from being fair.  
**Example 10**.: _For the example in Figure 3, there is a path of value \(1\) in the two graphs \(g_{\mathsf{a}}\) and \(g_{\mathsf{b}}\). If the best path in \(g_{\mathsf{a}}\) is chosen first, then the allocation obtained in the end is \(\pi_{\mathsf{greedy}}=\{g_{\mathsf{a}}\mapsto\{s_{\mathsf{a}},a_{\mathsf{3}},a_{ \mathsf{2}},t_{\mathsf{a}}\},g_{\mathsf{b}}\mapsto\{s_{\mathsf{b}},b_{\mathsf{ b}},b_{\mathsf{1}},b_{\mathsf{d}},t_{\mathsf{b}}\}\}\), with global utility \(u(\pi_{\mathsf{greedy}})=u_{\mathsf{A}}(\pi_{\mathsf{greedy}})+u_{\mathsf{B}}( \pi_{\mathsf{greedy}})=1.0+0.6=1.6\) and utility vector \((1.0,0.6)\)._
# An Improved Product Code-Based Data Hiding Scheme
## 2 Matrix Embedding  
Given a 1-D linear code (\(n\), \(k\)), the secret message \(\mathbf{m}=(m_{1}\), \(m_{2}\),..., \(m_{n\cdot k})\) of \(n\)-\(k\) bits is hidden into the least significant bits (LSBs), denoted as \(\mathbf{x}=(x_{1},x_{2},\)..., \(x_{n})\) of the original \(n\) image pixels. Due to the characteristic of the strong algebraic structure, the resultant fact is at most only one LSB needs to be modified among those \(n\) LSBs. Taking a (7, 4) binary systematic Hamming code with the generator polynomial \(1+x+x_{3}\) as the example, as shown in Figure 1. Table 1 is the standard array of the (7, 4) Hamming code.  
\[\mathbf{G}=\begin{bmatrix}1&1&0&1&0&0&0\\ 0&1&1&0&1&0&0\\ 1&1&1&0&0&1&0\\ 1&0&1&0&0&0&1\end{bmatrix}\quad\mathbf{H}=\begin{bmatrix}1&0&0&1&0&1&1\\ 0&1&0&1&1&1&0\\ 0&0&1&0&1&1&1\end{bmatrix}\] (a) (b)  
Figure 1: (a) Generator matrix \(\mathbf{G}\); and, (b) Parity check matrix \(\mathbf{H}\) of a (7, 4) Hamming code.  
First, calculate the 1-D syndrome **s** (= **x**\(\times\)**H**\({}^{\rm T}\)) and the 1-D toggle syndrome **ts** (= **s**\(\oplus\)**m**), where \(\oplus\) denotes the component-wise EXCLUSIVE OR (XOR) operation between **s** and **m**. Then, according to Table 1, find the coset leader **e** with the minimum Hamming weight of the coset to which **ts** belongs, that is **e**\(\times\)**H**\({}^{\rm T}\) = **ts**, and change **x** to **y** (= **x**\(\oplus\)**e**) to complete the data embedding. In the receiving end, the receiver can easily extract the message **m** by **y**\(\times\)**H**\({}^{\rm T}\). Usually, the coset leader **e** for a 1-D linear code is called the optimal toggle sequence **t**, which is the optimal vector matrix that we are looking for to make the image pixels change as little as possible.
## 4 Simulation and Result Analysis
### System Performance Validation
#### 4.3.4 System Complexity  
For the SC-FDE, OFDM, and OSDM systems, system complexity is mainly determined by IFFT and FFT points. The modulation complexity of SC-FDE, OFDM, and OSDM are compared in Table 2. At the transmitter, D-point IFFT which complexity is \(O(D\text{log}D)\) is used to modulate the OFDM signal, and M times N-point IFFT which complexity is \(O(MN\text{log}N)\) is used for the OSDM modulation. Given that the product of M and N is a fixed value D, the OSDM and OFDM modulation complexity is merely influenced by logarithmic term. With the increase of vector length M (the decrease of subcarriers N), the modulation complexity of the OSDM system decreases with fewer subcarriers. Generally speaking, the transmitter complexity is sorted from low to high as  
\[\text{SC-FDE}<\text{OSDM}<\text{OFDM} \tag{21}\]  
At the receiver, block equalization is considered in this paper. OFDM equalization and demodulation can be implement at the same time, which complexity is \(O(D\text{log}D+DLK)\). But both OSDM and SC-FDE are needed to be transformed by IFFT and FFT for equalization which complexity is \(O(2D\text{log}D+DLK)\). Besides that, M times N-point FFT which complexity is \(O(MN\text{log}N)\) is needed for OSDM demodulation. The receiver complexity is sorted from low to high as  
\[\text{OFDM}<\text{SC-FDE}<\text{OSDM} \tag{22}\]  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Modulation Scheme** & **Transmitter Complexity** & **Receiver Complexity** \\ \hline SC-FDE & - & \(O(2D\log D+DLK)\) \\ OSDM & \(O(MN\log N)\) & \(O(2D\log D+MN\log N+DLK)\) \\ OFDM & \(O(D\log D)\) & \(O(D\log D+DLK)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The system complexity of single-carrier frequency-domain equalization (SC-FDE), orthogonal frequency division multiplexing (OFDM) and OSDM.  
Figure 11: The system performance comparison: (**a**) comparison of zero forcing (ZF) and minimum mean square error (MMSE) equalization methods with different vector lengths; (**b**) comparison of least squares (LS), discrete Fourier transform-based least squares (LS-DFT), and orthogonal matching pursuit (OMP) estimation methods with MMSE equalization method and different vector lengths.
# Visual Tracking of Small Unmanned Aerial Vehicles Based on Object Proposal Voting
## 6 Experiment  
To evaluate the proposed method (**OPV**: object proposal voting), we used our six UAV datasets and a standard benchmark dataset (OTB) [40]. We compared the OPV method with state-of-the-art deep learning-based visual trackers, SiamDW [29], SiamRPN++ [30], SIN-Top [31], DAT [32], TADT [33], ECO-HC [34], and COT [35]. In addition, we also compared the OPV method with non-learning-based visual trackers, including SCM [19], Struck [20], TLD [21], ASLA [22], CXT [23], VTS [24], VTD [25], CSK [26], LSK [27], and LOT [28].  
We adopted three evaluation metrics, which are precision plot, success plot, and area under the curve (AUC) [40]. To illustrate the precision plot, we first compute the distance between center locations of predicted and ground truth bounding boxes. Subsequently, we determine the number of frames, in which the distance is less than a specific threshold. To obtain the success plot, we first compute the overlap ratio of the predicted and ground truth bounding boxes. Subsequently, we determine the number of frames, in which the overlap ratio is greater than a specific threshold. The AUC metric indicates the entire area under the success plot.
## 3 Image Feature Extraction and Feature Matching Based on SIFT
### Feature Matching  
After extracting the image features, it is necessary to perform feature matching with the pre-stored feature point set of the charging port to complete the identification of the charging port. Fast Library for Approximate Nearest Neighbors (FLANN) is a collection of nearest neighbor search algorithms for high-dimensional features in large data sets, optimizing the nearest neighbor search algorithms and high-dimensional features in large data sets. The FLANN matching algorithm records the feature points of the target image and the image to be matched, according to which a feature vector is constructed. By comparing and filtering the feature vectors, a mapping set of matching points is obtained. When using the FLANN matching algorithm, it is necessary to select an appropriate nearest neighbor search algorithm, such as random k-d tree algorithm, priority search k-means tree algorithm, hierarchical clustering tree, etc., as well as the number of recursive traversals. The more traversal times, the more accurate the results, but the longer the corresponding search time. Therefore, it is necessary to optimize and reasonably select the parameters [19].