esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points
## 2 Bibliometric Study and Typical Aerial Camera-to-Ground Target Localization Algorithm
### Active Positioning Algorithm Based on Laser Ranging Sensor
Therefore, the double-machine intersection positioning algorithm will not be affected by target movement, which shows better performance in real-time situations than that of the single-machine algorithm. In addition, since this method does not require the coordination of the ellipsoid model, it can also be applied to aerial targets such as airplanes or airships. When the laser rangefinder and SAR fail to achieve real-time fusion with the positioning algorithm, the intersection of single machine multi-point measurement is an effective means to improve the target positioning accuracy. With the rapid development of UAV, UAV formation further improves the positioning accuracy of this kind of algorithm, which is widely used in environmental monitoring, disaster rescue, and other fields. It can achieve large-scale search and high-precision positioning through small UAV formation. However, the cost of double-machine intersection positioning algorithms is high, and two aerial cameras perform photogrammetry on the same area, which reduces work efficiency. Moreover, the timing requirements for the two cameras are relatively high since the target is required to be photographed at the same time. The intersection algorithm only considers the theoretical angle intersection, ignoring the random errors generated by the working cameras, which will lead to the lines of sight failing to meet at one point, resulting in an inaccurate positioning model. Hence, it is necessary to use the data processing theory such as the least square method to process the positioning data subsequently to improve the positioning accuracy. Noticeably, the actual positioning accuracy will be lower than the theoretical positioning accuracy.
# Magnetic Localization Method for Vehicles Based on Transformer
## 4 Experiments and Discussions
### Experimental Setup  
**Dataset:** Considering that the existing publicly available magnetic datasets [32; 33; 34; 35; 36] are not suitable for our application, we collected a dataset ourselves to evaluate the proposed method. Magnetic field data were collected from a Huawei Mate 20 Pro smartphone fixed on a vehicle, as shown in Figure 9a, and mileages were integrated by the velocities obtained from the On-Board Diagnostic (OBD) system of the vehicle. A self-developed module accessed the interface of the OBD system and transmitted the velocity to the smartphone, as shown in Figure 9b. The statistical results showed that the error of the integrated mileage was approximately 1% after calibration. Additionally, a differential GNSS/INS system called SPAN-ISA-100C (Nov.Atel Inc., Calgary, Canada) with a horizontal positioning accuracy better than 0.04 m/60 s [37] was used to evaluate the positioning accuracy of the magnetic localization results, as shown in Figure 9c.  
The test environment was a parking garage at the Beijing New Technology Base of the Chinese Academy of Sciences, which has an area of approximately 80 \(\times\) 110 m\({}^{2}\), as shown in Figure 10. We collected 35 tracks, with a total length of 8094 m, as shown by the green lines in Figure 10; each track was divided into numerous sequences with diverse sequence lengths. In this section, we set the sequence length \(L\) from 5 m to 15 m, with a spatial interval \(d\) of 0.5 m, so \(W\) ranged from 10 to 30, as calculated by Equation (1). Taking a \(W\) of 14 as an example, the total number of sequences was 15,733, and 27 tracks containing 12,130 samples were selected for training (accounting for approximately 77%), while the remaining 8 tracks containing 3603 samples were used for evaluation purposes.  
**Model:** We set the feature dimensionality \(D\) to 256, and the numbers of encoder layers \(N_{1}\) and decoder layers \(N_{2}\) were both 4. In addition, the training batch size was set to 1, and the number of training epochs was set to 30. The weighted Adaptive Moment Estimation (AdamW) optimizer was used with parameters of [0.9, 0.999]. Our model was implemented with the PyTorch library, and the detailed configuration is shown in Table 1.  
Figure 9: Equipment used in the experiment: (**a**) smartphone, (**b**) OBD system interface and self-developed module, (**c**) SPAN-ISA-100C.
# Distributed Joint Source-Channel Coding Using Quasi-Uniform Systematic Polar Codes
## 3 QSPC and Punctured QSPC  
In this section, we briefly review QSPC and propose punctured QSPC. At first, two index sets and their properties are presented. Consider a binary sequence \(\mathbf{L}\) of length \(N=2^{n}\). The coordinates of the ones in \(\mathbf{L}\) are quasi-uniformly distributed and denoted by the index set \(\mathcal{Q}(N,K)\) or \(\bar{\mathcal{Q}}(N,K)\), which is defined as  
\[\begin{split}\mathcal{Q}(N,K)&=\left\{b=1+\sum_{i= 0}^{n-1}b_{i}2^{i}|1+\sum_{i=0}^{n-1}b_{i}2^{n-1-i}\in\{i:N-K+1\leq i\leq N\},b_ {i}\in\{0,1\}\right\},\\ \bar{\mathcal{Q}}(N,K)&=\left\{b=1+\sum_{i=0}^{n-1}b _{i}2^{i}|1+\sum_{i=0}^{n-1}b_{i}2^{n-1-i}\in\{i:1\leq i\leq K\},b_{i}\in\{0,1 \}\right\}.\end{split} \tag{6}\]  
The property of this sequence is given as Proposition 1.  
**Proposition 1**.: _The coordinates of the ones in \(\mathbf{L}_{M}=\underline{\mathbf{L}L...\mathbf{L}}_{M}\)\((M=2^{m})\) are \(\mathcal{Q}(MN,MK)\) and \(\bar{\mathcal{Q}}(MN,MK)\) if the coordinates of the ones in \(\mathbf{L}\) are \(\mathcal{Q}(N,K)\) and \(\bar{\mathcal{Q}}(N,K)\), respectively._  
An example for index sets is illustrated in Figures 3 and 4. The coordinates of the ones in \(\mathbf{L}=0001\) and \(\mathbf{L}=1000\) are \(\mathcal{Q}(4,1)=\{4\}\) and \(\bar{\mathcal{Q}}(4,1)=\{1\}\), respectively. For \(\mathbf{L}_{2}=00010001\) and \(\mathbf{L}_{2}=10001000\), the coordinates of ones are \(\mathcal{Q}(8,2)=\{4,8\}\) and \(\bar{\mathcal{Q}}(8,2)=\{1,5\}\), respectively.  
Figure 4: The codeword structure of punctured QSPCs with \(N=4,K=1,P=1\).  
Figure 3: The codeword structure of quasi-uniform systematic polar code (QSPCs) with \(N=4,K=1\).  
Figure 2: Distributed joint source-channel coding (DISCC) scheme.
# Fault Diagnosis Method Based on Time Series in Autonomous Unmanned System
## 1 Introduction
However, there are many types of equipment in autonomous unmanned systems, and their characteristics are also different. If each equipment adopts different fault diagnosis algorithms, it will cause great difficulties for the system's autonomous fault diagnosis and algorithm integration. Therefore, it is important to design a general fault diagnosis algorithm suitable for most equipment. The completion of autonomous unmanned systems missions is usually based on time, and the sampled equipment data have a strong dependence on time. Time series refers to a dataset in which the data are sorted according to the order of generation time. The recorded data are the monitoring data of the same indicator of the same device and object over time. These data have a trend with time change sequence. Time series is one of the most common forms of data recording, which widely exists in various fields such as underwater ships, aerospace, and so on. The main contributions of this paper are as follows. 1. This paper abandons the previous idea of designing a fault diagnosis algorithm based on the characteristics of special equipment and takes the time series characteristics of unmanned autonomous system data as the main research object. Based on the above assumptions, this paper proposes a fault diagnosis method suitable for most equipment. 2. To improve the applicability of the fault diagnosis algorithm, this paper proposes a fault diagnosis algorithm based on time series. This algorithm and clustering method are combined for fault detection so that autonomous unmanned systems can reveal fault data's inherent laws and properties by learning unlabeled training samples. 3. In order to reduce the computational complexity of clustering, this paper proposes a time series symbolization method based on Gaussian mixture clustering to express different types of sensor data uniformly. Through the above processing, the time series can effectively reduce the dimension and calculation. 4. In order to improve the clustering effect, a time series similarity measurement algorithm based on improved Markov chain is proposed. The algorithm can better extract features and make the discrimination between samples more obvious.
# An Improved Predicted Model for BDS Ultra-Rapid Satellite Clock Offsets
###### Abstract  
The satellite clocks used in the BeiDou-2 satellite navigation System (BDS) are Chinese self-developed Rb atomic clocks, and their performances and stabilities are worse than GPS and Galileo satellite clocks. Due to special periodic noises and nonlinear system errors existing in the BDS clock offset series, the GPS ultra-rapid clock model, which uses a simple quadratic polynomial plus one periodic is not suitable for BDS. Therefore, an improved prediction model for BDS satellite clocks is proposed in order to enhance the precision of ultra-rapid predicted clock offsets. First, a basic quadratic polynomial model which is fit for the rubidium (Rb) clock is constructed for BDS. Second, the main cyclic terms are detected and identified by the Fast Fourier Transform (FFT) method according to every satellite clock offset series. The detected results show that most BDS clocks have special cyclic terms which are different from the orbit periods. Therefore, two main cyclic terms are added to absorb the periodic effects. Third, after the quadratic polynomial plus two periodic fitting, some evident nonlinear system errors also exist in the model residual, and the Back Propagation (BP) neural network model is chosen to compensate for these nonlinear system errors. The simulation results show that the performance and precision using the improved model are better than that of China iGMAS ultra-rapid prediction (ISU-P) products and the Deutsches GeoForschungsZentrum GFZ BDS ultra-rapid prediction (GBU-P) products. Comparing to ISU-P products, the average improvements using the proposed model in 3 h, 6 h, 12 h and 24 h are 23.1%, 21.3%, 20.2%, and 19.8%, respectively. Meanwhile the accuracy improvements of the proposed model are 9.9%, 13.9%, 17.3%, and 21.2% compared to GBU-P products. In addition, the kinematic Precise Point Positioning (PPP) example using 8 Multi-GNSS Experiment MGEX stations shows that the precision based on the proposed clock model has improved about 16%, 14%, and 38% in the North (N), East (E) and Height (H) components.  
BDS; prediction model; real-time; ultra-rapid; satellite clocks 14 November 2017; Accepted: 3 January 2018; Published: 4 January 2018
## 3 The Improved Symmetric Zero-Area Conversion Method
### Background Deduction and Intensity Screening  
Background refers to other interference factors that cause count in the spectral data except the measured radioactive sources, and it is necessary to conduct deduction processing before peak search [20]. Background deduction methods mainly include Fourier conversion technology, digital filtering technology, statistically sensitive nonlinear iterative peaking algorithm (SNIP), etc. [21,22]. SNIP is simple in principle and fast in calculation. In this article, an improved Simpson-SNIP algorithm is used for background deduction.  
(I) In order to reduce the wide variation of each address count [23], the original spectral data is processed according to Expression (9).  
\[V(i)=\ln[\ln(\sqrt{y(i)+1}+1)+1] \tag{9}\]  
where \(y(i)\) represents the count of \(i\) address, and \(V(i)\) represents the conversion result.  
(II) Obtain the full spectrum background iteratively. One study found that if the Simpson formula is used to replace the rectangular formula in the original SNIP algorithm, the calculation results will have higher accuracy [24].  
\[V_{p}(i)=\min\biggl{\{}V_{p-1}(i),\frac{1}{6}[V_{p-1}(i-p)+4V_{p-1}(i)+V_{p-1} (i+p)]\biggr{\}} \tag{10}\]  
where \(p\) represents the number of iterations, \(V_{p}(i)\) represents the iteration result of the \(p\) th time, and \(V_{0}(i)=V(i)\).  
(III) Using the inverse conversion of Expression (9), the processed data is converted into the original form.  
\[y_{SNIP}=[e^{V_{p}(i)-1}-1]^{2}-1 \tag{11}\]  
where \(y_{SNIP}\) is the final result of background deduction using the improved Simpson-SNIP algorithm.  
Intensity screening of spectral data after background deduction can reduce the calculation amount and improve the precision of peak searching [25,26]. Intensity values based on statistical indicators of spectral data are set, and only data larger than this value are selected for conversion and peak searching [27].
## 4 Mode Scrambling for Few-Mode Fibers
### Combined Statistical CSI and Deterministic MS  
To further enhance the FMFs capacity, we combine the deterministic MS with the statistical CSI. In Figure 7a, we plotted the average capacity with different techniques for the 10-mode fiber and a misalignment std \(\sigma_{x,y}=4\%r_{c}\). From the figure, we notice that the statistical CSI provides an important capacity gain for low SNRs, however this gain decreases as the SNR increases. The deterministic MS increases the capacity as the SNR increases. The combination of both techniques enhances the average capacity for all levels of SNR. In Figure 6b, we simulate the outage capacities for the same previous techniques and an outage probability of \(10^{-3}\) and SNR = 20 dB. From the figure, we notice that the capacity with deterministic MS is better than the capacity with statistical CSI, this is due the high SNR level of 20 dB. Furthermore, the outage capacity with the combination of both techniques is the highest one.  
Figure 5: Average capacities vs. SNR for a misalignment std \(\sigma_{x,y}=4\%r_{c}\). (a) 6-mode fiber; (b) 10-mode fiber.  
Figure 6: Outage capacities vs. misalignment std \(\sigma_{x,y}\) for different scrambling strategies for SNR = 20 dB and an outage probability \(p_{\text{out}}=10^{-3}\). (a) 6-mode fiber; (b) 10-mode fiber.
## 10 Change Detection  
To determine land-cover change that may be attributed to mining over the years, land classification over three decades was performed. A classified image of 1990 served as the baseline image for the change analysis throughout the various study years. In this study, the method of image differencing was adopted, where the baseline image was deducted from the subsequent years. Figure 5 represents the changes in land cover between the years, 1990 and 2020.  
Figure 4: Area performance for each class over three decades.
## 4 Time Domain Response Solution of Mechanical Model  
Similar to the finite element method, combining the above theory, the element stiffness matrix formed in the given frequency-wavenumber domain in the spectral element method analysis can be determined by the boundary conditions and interlayer continuity conditions of the pavement structure. Subsequently, the stiffness matrix and boundary moment matrix of each spectral element are assembled in the same manner as the finite element stiffness matrix. Following this, the global stiffness matrix, the global boundary force matrix, and the global node displacement matrix are combined into a system of linear equations. Finally, by solving the system of linear equations, the response solution in the frequency domain is obtained, and the response solution in the time domain is obtained through the Fourier inverse transform. Equation (23) provides the equations for the overall stiffness matrix, overall displacement matrix, and overall nodal force matrix.  
\[\widetilde{F}=\begin{bmatrix}\widetilde{T}_{mm1}\\ \widetilde{T}_{mm1}\\ \widetilde{T}_{mm1}\\ \widetilde{T}_{mmi}\end{bmatrix}=[K]\begin{bmatrix}\widetilde{u}_{mm1}\\ \widetilde{w}_{mm1}\\ \widetilde{w}_{mmi}\end{bmatrix}=\widetilde{K}_{mm}\widetilde{X}_{mm} \tag{23}\]  
where \(i\) represents the nodes of the spectral element, \(\widetilde{K}_{mm}\) is the overall stiffness matrix, \(\widetilde{F}\) is the overall nodal force matrix, and \(\widetilde{X}_{mm}\) represents the overall displacement matrix.  
The overall displacement in the time domain can be obtained by summing and applying the Fourier inverse transformation, as shown in Equations (24) and (25):  
\[u=\sum_{n=1}^{N}\sum_{m=1}^{M}\widetilde{u}_{mm}\widetilde{F}_{n}F_{m}I_{0}(k_ {m}r)e^{i\omega t} \tag{24}\]  
\[w=\sum_{n=1}^{N}\sum_{m=1}^{M}\widetilde{w}_{mm}\widetilde{F}_{n}F_{m}J_{0}(k_ {m}r)e^{i\omega t} \tag{25}\]
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 3 Proposed Classifier
### Gabor Filter Network (GFN)  
The Gabor filter network (GFN) is a sub-optimal feature-based pattern recognition technique that operates on a one-dimensional filter network. The GFN is very dependable in optimally detecting and differentiating the modulated signal and effectively identifying complicated signal parameters at the optimal lower and upper limits.  
The Gabor filter network (GFN) parameters are calculated in [44], and the performance of these parameters is assessed in [17; 34], which shows the enhanced performance for these bounds. The authors chose the lower and upper bounds in [17; 34] for the GFN and evaluated the performance with these parameters. A detailed discussion on the calculation of these parameters can be found in [44], whereas the discussion on the chosen bounds' performance is given in [17; 34].  
The input to the GFN is defined in Equation (3), which is the received signal. It is the first serial-to-parallel conversion to extract the distinct features from \(\mathbf{y}\). The \(\ell\) number of Gabor atom nodes is selected and can be represented as \(\Omega=[\Omega_{0},\Omega_{1},...,\Omega_{\ell-1}]\). The \(v\)th Gabor atom node is defined as:  
\[\Omega_{v}(t)=\frac{1}{\sqrt{\sigma_{v}}}e^{-\pi(\frac{t-c_{v}}{\sigma_{v}})^ {2}}\cos(f_{v}t)\]  
```
1 Initialize Upper and Lower Bounds
2 Gabor parameters: \(\mathbf{c}\), \(\sigma\), \(\mathbf{f}\), \(\wp\)
3 while\(\forall\to v\)do
4 evaluate Gabor Atom Node \(\Omega_{v}\) using (5)
5 evaluate Weighted Output \(\chi_{v}\) using (6)
6 applyDesired Response \(\vartheta(\varrho)\)
7 calculateCost Function \(\mathsf{J}(\varrho)\) using (8)
8
9 end for return \(\mathbf{c}\), \(\sigma\), \(\mathbf{f}\), \(\wp\)
```  
**Algorithm 1**Proposed Gabor filter network
## 2 System and Channel Models
### Phase-1 (\(P_{1}\)): BS\(\rightarrow\) (\(D_{1}\), Nul) Data Transmission  
During \(P_{1}\), the BS transmits to NU and D\({}_{1}\) by invoking SM. The information bit stream of BS are mapped into two data carrying units: the \(M_{1}\)-PSK/QAM modulation order bits and the antenna index. In S\({}_{1}\), the BS maps NU data bits as antenna indices and FU data bits as \(M_{1}\)-PSK/QAM modulation symbols. The mapping is shuffled in S\({}_{2}\); NU data bits are mapped as \(M_{1}\)-PSK/QAM modulation symbols and FU data bits are mapped as antenna indices. Hence, in each transmission, the total number of bits that can be transmitted by BS in S\({}_{1}\) and S\({}_{2}\) are respectively given by [12; 38]  
\[\begin{array}{ll}X_{1,\text{S}_{1}}&=Q_{1}+N_{1}\\ &=\log_{2}(A_{\text{BS}})+\log_{2}(M_{1}),\end{array} \tag{1}\]  
Figure 3: Proposed system model: S\({}_{2}\).  
Figure 2: Proposed system model: S\({}_{1}\).  
\[\begin{array}{ll}X_{1,S_{2}}&=Q_{1}+N_{1}\\ &=\log_{2}(M_{1})+\log_{2}(A_{\rm BS}).\end{array} \tag{2}\]  
where \(Q_{1}\), \(N_{1}\), and \(M_{1}\) represent number of bits per NU symbol, number of bits per FU symbol, and modulation order used by BS, respectively. Various order of modulation can be used, e.g., BPSK, QPSK/4-QAM, 8-PSK/8-QAM, 16-PSK/16-QAM, 32-PSK/32-QAM and 64-PSK/64-QAM which represent corresponding values of \(M_{1}\) = [2, 4, 8, 16, 32 and 64], respectively. Exploiting SM allows BS to activate only one of its antennas for a particular transmission. As a result, ICI is avoided and synchronization among the antennas is not required anymore. The data blocks transmitted by the BS is then de-mapped using iterative-maximum ratio combining (\(i\)-MRC) [12] technique by NU and D\({}_{1}\) to retrieve their desired information.  
From (1) and (2), it is apparent that there is a trade-off between the modulation order and the number of transmit antennas for any number of transmitted information bits. For example, three data bits per symbol can be sent with four transmit antennas using BPSK modulation. As a trade-off, the same three bits can be sent from two antennas using 4-QAM modulation.
## 3 Proposed Method
### System Architecture
#### 3.1.3 Accumulated Global Learnings  
In order to obtain the efficiency of centralized ML methods, which somewhat contain global data learning, the detection models are exchanged with the server-based aggregation platform. The aggregation server is in charge of aggregating local learning and transforming it into global learning. The distributed clients receive the optimized model through the communication platform, which facilitates knowledge sharing. A client can detect intrusions using comparable behaviors obtained from several participating devices thanks to this sharing mechanism, which gradually improves learning.  
Figure 2: Proposed FL architecture for IIoT intrusion detection.
## 3 Datasets and Methodology
### Methods  
#### 3.2.1 Calculation of the Extreme Climate Indices  
We selected 12 extreme climate indices, recommended by the Climate Change Detection and Index Expert Group (ETCCDMI), that exhibited strong associations with vegetation, flood disasters, high temperatures, and low temperatures in the Tarim Basin, as shown in Table 1. The raw data were first screened with the Rclimdex model to exclude outliers. Missing values were recorded as \(-\)99 to ensure that no more than 1% of the values were missing.  
Figure 1: Study area location distribution maps: (**a**) distribution map of the geographical location and meteorological stations of the Tarim Basin; (**b**) distribution map of vegetation types in the Tarim Basin.
# Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond
## 3 Results and Discussion
### Reported Application of AI to RT
#### 3.2.1 Patient Care Coordination and Optimization  
AI has been applied to patient care coordination and optimization to create a data management system for clinical and research processes [9]. The implemented system was trained to extract relevant information by direct connection with structured data and text mining technology, which allowed an objective evaluation of key performance indicators to improve patient care management. ML was also used as a tool for clinical decision support in a multicentric context [10]. In particular, [PERSON] et al. [10] developed a platform to coordinate data analysis across RT centers using distributed or federated learning methods after a harmonization phase. Moreover, other examples of complex jobs with custom constraints that AI could optimize include scheduling RT treatment appointments, checking machines, and patient-specific quality assurance (QA).  
Moreover, the possibility to analyze OIS data and set an alert in terms of the monitor units, i.e., the number of delivered fractions (i.e., ad hoc modified to account for the treatment gap), might also help assess adherence to guidelines [11], especially during pandemic periods. As described in [12], AI methods were also applied to patient face recognition before RT treatment. These solutions reduce the risk of the patient misidentification that may occur in a busy RT department, which could lead to delivering an inappropriate RT treatment plan.  
Figure 4: The numbers of identified papers according to the AI subgroups (IO, ML, and DL) and RT workflow steps (highlighted in Figure 1).