esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm
## 2 Method
### Multi-Strategy Genetic Algorithm
#### 2.3.3 Iterative Local Optimization  
We propose an iterative local optimization strategy using the GA and EM algorithms to determine the suitable model parameters to obtain a stable solution for a small population. This uses a maximum likelihood function to search efficiently for the globally optimal solution. The equations used for estimating the model parameters are as follows:  
\[\alpha_{k}=\frac{\sum\limits_{i=1}^{N}\gamma_{ik}}{N} \tag{12}\]  
\[u_{k}=\frac{\sum\limits_{i=1}^{N}\gamma_{ik}x_{i}}{\sum\limits_{i=1}^{N}\gamma _{ik}} \tag{13}\]  
\[\sigma_{k}^{2}=\frac{\sum\limits_{i=1}^{N}\gamma_{ik}(x_{i}-u_{k})^{2}}{\sum \limits_{i=1}^{N}\gamma_{ik}} \tag{14}\]  
After the EM algorithm has iteratively estimated the model parameters, the \(K\)-th component is ignored, and the scale factors of the first \(K\)-1 components are scaled equivalently to establish the MGMM model. This MGMM is used to calculate the detection threshold corresponding to the 95% probability, and the TLE data exceeding the detection threshold are obtained. In general, the TLE outliers represent maneuvers. However, the TLE data are also superimposed by noise; thus, an outlier may not represent a maneuver. Maneuver detection is not the focus of this paper. The threshold calculation and maneuver detection method are described in [25]. The flowchart of the maneuver detection and processing is shown in Figure 2.  
Figure 2: Workflow for maneuver detection.
## 2 Related Works  
The different proposals can be found divided into two areas: (1) The proposals to improve handover protocols and (2) The decision-making proposals.  
In the first section, you can find protocols that focus on reducing the scanning time such as [19; 20; 21]; on the other hand, there are proposals that focus on reducing the handover time such as [22; 23]. This work focuses on improving decision-making, that is, in point 2.  
Neural networks in the handover have been applied to solve decision-making changes in heterogeneous networks. In [24], the authors propose a back-propagation artificial neural network (ANN) using the RSS input parameters and the traffic intensity in the target networks, monitoring the training of the network. However, the delay caused by the training stage is a problem. In [25], a middleware based on an ANN is proposed to select the best network based on user preferences. Furthermore, it increases latency during handover execution due to the size of the signaling packets used and the time of training. Another proposal based on the use of RN is made in [26] where a neural network with RSSI input parameters and the speed of the MN is proposed, reducing the number of unnecessary handovers.  
Vertical handover algorithms involve several factors that can make difficult the quantification. Fuzzy logic can be applied to solve change decision problems like [7; 8; 9; 27] that use these policies to make decision changes, and at the same time they try to balance the load of the networks efficiently, using input parameters such as RSSI, latency, and data rate.  
The trend in reviewed literature shows that the networks will be more heterogeneous, and in this way, a vertical handover will be more normal. Some of the reviewed algorithms are classified based on the main criteria used to make the decision.  
Regarding the decision-making proposals, multiple algorithms have been proposed. These use schemes based on received signal strength (RSS), quality of service (QoS), decision functions based on multi-criteria, and algorithms based on artificial intelligence techniques. In general terms, the decision-making algorithm feeds on the data provided by the network. After processing the data, it decides which network to switch to. Some of the criteria commonly used to make this decision to change are illustrated in the following topic: network parameters.
# Automatic Bridge Crack Detection Using a Convolutional Neural Network
###### Abstract  
Concrete bridge crack detection is critical to guaranteeing transportation safety. The introduction of deep learning technology makes it possible to automatically and accurately detect cracks in bridges. We proposed an end-to-end crack detection model based on the convolutional neural network (CNN), taking the advantage of atrous convolution, Atrous Spatial Pyramid Pooling (ASPP) module and depthwise separable convolution. The atrous convolution obtains a larger receptive field without reducing the resolution. The ASPP module enables the network to extract multi-scale context information, while the depthwise separable convolution reduces computational complexity. The proposed model achieved a detection accuracy of 96.37% without pre-training. Experiments showed that, compared with traditional classification models, the proposed model has a better performance. Besides, the proposed model can be embedded in any convolutional network as an effective feature extraction structure.  
deep learning; image classification; bridge crack detection Article
## 6 Conclusions and Future Work  
Owing to the massive volume, high number of dimensions, and spatial-temporal correlations involved, spatial-temporal big data faces many visualization challenges, including large memory consumption, high rendering delay, and poor visual effect. Accordingly, in order to solve these problems, this paper proposes a novel spatio-temporal big data organization model, namely, the multi-dimensional aggregation pyramid (MAP), which integrates the merits of both the tile pyramid model and key-value pair model. The MAP model supports quick aggregation on the space, time, and attribute dimensions, as well as efficient distributed storage with key-value conversion. Based on the proposed MAP model, an open-source visualization framework, MAP-Vis, is implemented on a Linux cluster. The MAP-Vis system uses Spark as a pre-processing tool and HBase as a distributed storage platform. Several key features, namely, distributed storage and distributed computation, enable our solution to scale to large datasets.  
The MAP-Vis realizes millisecond-level multidimensional data querying and achieves good interactive visualization. Experimental results validate the efficiency of both the MAP model and the MAP-Vis framework, both of which can provide high scalability for processing capability and online visualization.  
Future work can be conducted to explore the following aspects:  
Figure 12: The MAP generation time with different numbers of Spark worker nodes.  
Our research work currently concentrates on massive point data; however, polyline or polygon types of spatio-temporal big data (e.g., vehicle trajectory) are not well-addressed. Thus, we will extend our proposed framework to accommodate these more complex data types.  
Other acceleration mechanisms, e.g., GPU, will be considered for quicker pre-processing. In addition, parallel generation of query ranges will be also implemented to improve the query efficiency.  
Direct GPU-based rendering methods such as WebGL will be further used to increase the data visualization speed.  
Current functions provided by the MAP-Vis framework are still limited. More analysis functions (including point distribution pattern, time-series prediction, etc.) will be added in the near future.  
Conceptualization, X.G.; methodology, X.G. and C.X.; software, C.X., L.H. and D.S.; validation, X.G., C.X. and L.H.; formal analysis, X.G. and C.X.; resources, L.H. and W.X.; data curation, Y.Z. and D.S.; writing--original draft preparation, X.G. and C.X.; writing--review and editing, X.G. and L.H.; visualization, L.H. and Y.Z.; project administration, X.G.; funding acquisition, X.G. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 41971348 and National Key Research and Development Program of China, grant number 2017 YFB0503802. The authors are grateful to the editor and reviewers for their careful and valuable suggestions. The authors declare no conflict of interest.
###### Abstract  
The use of UAV-based laser scanning systems is increasing due to the rapid development in sensor technology, especially in applications such as topographic surveys or forestry. One advantage of these multi-sensor systems is the possibility of direct georeferencing of the derived 3D point clouds in a global reference frame without additional information from Ground Control Points (GCPs). This paper addresses the quality analysis of direct georeferencing of a UAV-based laser scanning system focusing on the absolute accuracy and precision of the system. The system investigated is based on the RIEGL miniVUX-SYS and the evaluation uses the estimated point clouds compared to a reference point cloud from Terrestrial Laser Scanning (TLS) for two different study areas. The precision is estimated by multiple repetitions of the same measurement and the use of artificial objects, such as targets and tables, resulting in a standard deviation of \(<\)1.2 cm for the horizontal and vertical directions. The absolute accuracy is determined using a point-based evaluation, which results in the RMSE being \(<\)2 cm for the horizontal direction and \(<\)4 cm for the vertical direction, compared to the TLS reference. The results are consistent for the two different study areas with similar evaluation approaches but different flight planning and processing. In addition, the influence of different Global Navigation Satellite System (GNSS) master stations is investigated and no significant difference was found between Virtual Reference Stations (VRS) and a dedicated master station. Furthermore, to control the orientation of the point cloud, a parameter-based analysis using planes in object space was performed, which showed a good agreement with the reference within the noise level of the point cloud. The calculated quality parameters are all smaller than the manufacturer's specifications and can be transferred to other multi-sensor systems.  
LIDAR; airborne laser scanning; mobile mapping system; georeferencing; pose estimation; GNSS; UAV; quality assessment Article  
* 2021
## 4 Applications
### Identification of Modal Information of a Steel Beam
It should be noted that the considered steel beam has a very low inherent damping, making it difficult to measure accurately. The mode shapes obtained for the first five modes are shown in Figure 15 alongside those computed analytically. Note that the analytical mode shapes are only an approximation of the real behavior, as they disregard sensor masses as well as potential imperfections in the system due to variations of weight and stiffness. Both systems show good agreement especially in the first three modes. The general shape of the fourth and fifth mode are still determined well from RPi measurements; however, the amplitude at the sensor locations shows deviations from the analytical shape. The reference measurement system shows agreement with the analytical shape that is broadly similar to that of the RPi system. Still, the fourth mode shows a significantly better performance, as indicated by the modal assurance criterion (MAC) given in Table 6. The MAC value for the highest modes is still greater than 0.98 for the reference system, while it drops to 0.91 for the modes identified from synchronous RPi measurements. However, the reference mode shapes lack supporting points to properly reflect the higher sine waves due to the smaller number of sensors used. It is worth considering this aspect in the context of the cost of the measurement system. The results clearly show that the RPi system obtains modal information, specifically mode shapes, not available from a reference system of at least 20 times the cost. Of course there are SHM applications where such a highly accurate system may be strictly required. Furthermore, it is possible to overcome the above-highlighted shortcomings of a limited sensor count by performing multiple measurements at different sensor arrangements. Yet, where the presented WSN solution is sufficiently accurate for the task at hand, it represents an attractive alternative to traditional technologies, including proprietary WSN systems, at a fraction of their cost. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{3}{c}{**Natural Frequency**} & \multicolumn{3}{c}{**Critical Damping Ratio**} & \multicolumn{3}{c}{**MAC**} \\ \cline{2-10}  & **Ref. [Hz]** & **RPi [Hz]** & \(\delta\) [\%]** & **Ref. [\%]** & **RPi [\%]** & \(\delta\) [\%]** & **Ref. [-]** & **RPi [-]** & \(\delta\) [\%]** \\ \hline
1 & 5.857 & 5.859 & 0.22 & 0.032 & 0.023 & \(-\)28.8 & 1.000 & 1.000 & 0.0 \\
2 & 22.286 & 22.252 & \(-\)1.51 & 0.243 & 0.150 & \(-\)38.3 & 1.000 & 0.996 & 0.4 \\
3 & 50.601 & 50.611 & 0.21 & 0.078 & 0.078 & \(-\)0.7 & 1.000 & 0.998 & 0.1 \\
4 & 91.361 & 91.362 & 0.01 & 0.262 & 0.233 & \(-\)11.0 & 0.999 & 0.910 & 8.9 \\
5 & 131.330 & 131.350 & 0.16 & 0.144 & 0.133 & \(-\)7.7 & 0.984 & 0.969 & 1.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the identified dynamic properties of the steel beam, where \(\delta\) is the relative difference \(\delta\) between the reference and the RPi-based measurement system.
## 3 Wind Speed Prediction Model Based on CEEMDAN-RIME-MHSA-BiLSTM
### Projected Evaluation Indicators  
Prediction evaluation indicators are decisive for assessing the predictive ability of a model. The selection of the appropriate evaluation indicators is essential to ensure the accuracy of the prediction results. In this study, five indicators, the root-mean-square error (\(K_{RMSE}\)), mean absolute percentage error (\(K_{MAPE}\)), mean absolute error (\(K_{MAE}\)), coefficient of determination (\(R^{2}\)), and Nash efficiency coefficient (\(NSE\)), were chosen to objectively and comprehensively evaluate the accuracy of the wind speed prediction model. The first three indicators were mainly used to measure the degree of error between the predicted and actual values of the model. When the predicted value deviates from the actual value, the model will have a relatively large value [46]:  
\[K_{RMSE}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{{{\left({{y_{i}}-{f_{i}}}\right) }^{2}}}} \tag{27}\]  
\[K_{MAE}=\frac{1}{n}\sum\limits_{i=1}^{n}{{{\left|{{y_{i}}-{f_{i}}}\right|}}} \tag{28}\]  
\[K_{MAPE}=\frac{1}{n}\sum\limits_{i=1}^{n}{{{\left|{{\frac{{{y_{i}}-{f_{i}}}}{{ y_{i}}}}}\right|}}}\times 100\% \tag{29}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{{{\left({{y_{i}}-{f_{i}}}\right)}^{2}}}}{ \sum\limits_{i=1}^{n}{{{\left({{y_{i}}-{\frac{{\sum\limits_{i=1}^{n}{{y_{i}}}} }{{n}}}}\right)}^{2}}}} \tag{30}\]  
\[NSE\ =1-\frac{\sum\limits_{i=1}^{n}{{{\left({{y_{i}}-{f_{i}}}\right)}^{2}}}}{ \sum\limits_{i=1}^{n}{{{\left({{y_{i}}-{\overline{y}_{i}}}\right)}^{2}}}} \tag{31}\]where \(y_{i}\) is the real value of the wind speed, \(f_{i}\) is the predicted value of the wind speed obtained by the prediction model, and \(n\) is the number of samples. A decrease in the \(K_{RMSE}\), \(K_{MAPE}\), and \(K_{MAE}\) values alongside an increase in the \(R^{2}\) and \(NSE\) values shows an improvement in the model's prediction accuracy. \(R^{2}\) and \(NSE\) values close to 1 indicate that the model prediction ability is better.
# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 2 Materials and Methods
### Feature Extraction
#### 2.3.1 Root Mean Square (RMS) Features  
The first group of spatial features was derived from the RMS values of the signals. They were included in the study in order to check whether such simple metrics would be useful to differentiate between the spatial scenes.  
For each timeframe, a ratio between the RMS values of the \(x\) and \(y\) signals was computed (expressed in dB). It was referred to as \(x\)-to-\(y\) RMS ratio. This descriptor constituted a crude approximation of the interaural level differences (ILDs). Similarly, for every timeframe, a ratio between \(m\) and \(s\) signals was also calculated. This metric was termed \(m\)-to-\(s\) RMS ratio. It was assumed by the authors that this ratio could also be considered to be a simple descriptor of spatial characteristics.  
All the metrics were calculated on a frame-by-frame basis. Then, they were summarized using the absolute mean values, yielding two metrics (one for the \(x\)-to-\(y\) RMS ratio another one for the \(m\)-to-\(s\) RMS ratio). In addition, they were also summarized by calculating the standard deviations, producing the two extra metrics. In order to account for temporal fluctuations of the RMS ratio across the timeframes, the standard delta metrics [46] were also computed in a similar way, as explained above, adding four more descriptors. The number of frames over which the delta features were computed (so-called delta width) was equal to two. Hence, the above procedure produced eight RMS-based features in total.
## 2 Materials and Methods
### Geografically Weighted Regression  
The geographically weighted regression (GWR) method was developed in 1996 [23; 24]. The aim of the model is to be able to describe spatial non-stationarity. It makes it possible to identify different dependencies in data at different locations using the multiple regression model. There are numerous examples of GWR model applications in the fields of geology and geotechnics in the literature. One of the most common uses of GWR is for mapping soil organic carbon [25; 26; 27]. In [25], a model developed using environmental variables such as rainfall, land cover, and terrain type was used to estimate carbon stock. The method was tested on data from Ireland, and the authors described the results as promising. A similar problem was analyzed by the authors of [26] in the case of the Heihe River Basin in China, who compared the performance of GWR and its extended versions (such as geographically weighted ridge regression (GWRR), simple kriging with GWR-derived local means (GWRSK)) with multiple linear regression (MLR). The authors of [27] also applied the extended version of GWRK (i.e., geographically weighted regression kriging) to this problem and found that GWRK increased the precision of the model compared to standard regression kriging. The authors of [28] investigated a similar problem concerning heavy metals in soil, using spatial interpolation, GWR, and self-organizing map methods to identify the relationship between changes in the spatial distribution of heavy metals in soil and human activity. Another topic for the application of GWR is the study of the effect of groundwater on land subsidence [29]. The authors presented results that describe the patterns of spatial influence of aquifers on subsidence zones. Interesting results were also presented for bathymetry data from satellites [30]. The water depth was estimated using an adaptive-geographically weighted regression (A-GWR) model, which gave a determination coefficient above 0.98. The high accuracy of the results and the numerous applications of the GWR model and its modifications demonstrate their usefulness in describing the problem of spatial instability.
# Post-FEC Performance of Pilot-Aided Carrier Phase Estimation over Cycle Slip
###### Abstract  
The POST-forward error correction (FEC) bit error rate (BER) performance and the cycle-slip (CS) probability of the carrier phase estimation (CPE) scheme based on Viterbi-Viterbi phase estimation (VVPE) algorithm and the VV cascaded by pilot-aided-phase-unwrap (PAPU) algorithm have been experimentally investigated in a 56 Gbit/s quadrature phase-shift keying (QPSK) coherent communication system. Experimental results show that, with 0.78% pilot overhead, the VVPE + PAPU scheme greatly improves the POST-FEC performance degraded by continuous CS, maintaining a low CS probability with less influence of filter length. Comparing with the VVPE scheme, the VVPE + PAPU scheme can respectively obtain about 3.1 dB, 1.3 dB, 0.6 dB PRE-FEC optical signal noise ratio (OSNR) gains at PRE-BER of \(1.8\times 10^{-2}\). Meanwhile, the VVPE + PAPU scheme respectively achieves about 3 dB, 1 dB, and 0.5 dB POST-FEC OSNR gain and improves the FEC limit from \(2.5\times 10^{-3}\) to \(1.4\times 10^{-2}\), from \(8.9\times 10^{-3}\) to \(1.8\times 10^{-2}\), and from \(1.6\times 10^{-2}\) to \(1.9\times 10^{-2}\) under the CPE filter length of 8, 16, and 20.  
Keywords:coherent communication; quadrature phase-shift keying; carrier phase estimation; cycle-slip; pilot-aided-phase-unwrap; low-density parity-check (LDPC) +
Footnote †: journal: Applied
###### Abstract  
Modulation bandwidth enhancement of directly modulated semiconductor lasers (DMLs) has attracted broad interest to accommodate the tremendously growing demand for network traffic. In this paper, a monolithically integrated mutually coupled (IMC) laser for the O-band is demonstrated both numerically and experimentally. The direct modulation bandwidth was enhanced utilizing a photon-photon resonance (PPR) effect based on the mutual injection-locking technique. The IMC laser consisted of two distributed feedback (DFB) laser sections with a semiconductor optical amplifier (SOA) section in between. The relationship between the PPR frequency and SOA length was analyzed numerically to achieve a flat modulation response by optimizing the SOA length. Then, an enhanced 3-dB bandwidth of 38.7 GHz was realized experimentally, a nearly threefold enhancement over the modulation bandwidth of a solitary DFB laser at the same bias. Moreover, clear open eyes up to 40 Gb/s transmission over a 25-km single-mode fiber were achieved. Although the dynamic extinction ratio of the eye diagram was 1.1 dB, it can be further improved by increasing the mutual injection locking range of the IMC laser.  
mutually coupled laser; modulation bandwidth; mutual injection locking; photon-photon resonance +
Footnote †: journal: Applied Physics Letters