esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapid Tracking Satellite Servo Control for Three-Axis Satcom-on-the-Move Antenna
###### Abstract  
To overcome the possible gimbal lock problem of the dual-axis satcom-on-the-move (SOTM) antenna, a three-axis tracking satellite SOTM antenna structure appears. The three-axis SOTM antenna is realized by adding a roll axis to the azimuth axis and pitch axis in the dual-axis SOTM structure. There is coupling among the azimuth axis, pitch axis and roll axis in the mechanical structure of the three-axis SOTM antenna, which makes the kinematic modeling of the antenna difficult. This paper introduces a three-axis SOTM antenna kinematic modeling method based on the modified Denavit-Hartenberg (MDH) method, named the new modified Denavit-Hartenberg (NMDH) method. In order to meet the modeling requirements of the MDH method, the NMDH method adds virtual coordinate systems and auxiliary coordinate systems to the three-axis SOTM antenna and obtains the kinematic model of the three-axis SOTM antenna. During the motion of the carrier, the SOTM antenna needs to adjust the antenna pointing in real time according to the changes of the location and attitude of the moving carrier. Therefore, this paper designs a servo control system based on the active disturbance rejection controller (ADRC), introducing a smooth and continuous ADRC fal function to enhance the tracking speed of the servo control system and reduce the overshoot of the output response. Finally, system experiments were carried out with a 60 cm caliber three-axis SOTM antenna. The experiment results show that the proposed servo control method achieves higher antenna tracking satellite accuracy and better communication effects.  
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Footnote †: journal: Aerospace
## 1 Introduction
In this context, in Reference [15], the performance evaluation of BER for an M-MIMO system with M-ary PSK (phase shift keying) scheme over a three-dimensional (3-D) correlated fading model is presented, which involves correlation phenomena between angles of arrival and departure. In Reference [16], the effects of spatial correlation are highlighted in BER calculations in M-MIMO systems. In all the previously mentioned studies, however, either a limited number of antennas/active users or non-frequency selective fading environments were considered. In this article, a computationally efficient BER calculation method is proposed and evaluated, for various MIMO orientations. To this end, BER can be calculated for a varying number of transmit/receive antennas, as well as resolvable multi-path components. Therefore, the maximum number of active users for a specific quality of service (equivalently express via the maximum BER) can be directly calculated. Although the CDMA physical layer protocol has been considered, the same formula can be applied in OFDMA networks as well. The rest of this article is organized as follows. The MIMO SM transceiver model is described in Section 2, while BER calculations along with the proposed approach for SINR estimation are presented in Section 3. Results are presented and discussed in Section 4, while concluding remarks along with proposals for future work are described in Section 5.
## 3 WSAN System Models
### WSAN Simulation Model  
The WSAN simulation model is shown in Figure 2. Node ID 0 is the only sensing source in our case. This source node detects the temperature in the room and transmits its sensing data to the next node every sampling period. All nodes' locations are fixed and the communication range of each node is not farther than one hop. All radio parameters assigned for sensor nodes are configured according to the IEEE 802.15.4 standard at the 2.4 GHz ISM band. Node ID 1 is the sink node connected to the PID controller/the actuator explained previously. We assume that one sensor node is placed in one room, and the actuator heats the inflow air into the room via the air duct.  
In addition, another source-destination pair (node ID 7 and node ID 8) is set for a CBR traffic flow. The aim of this background traffic is to interrupt the data transmissions from the source node ID 0. In WSANs, several nodes may send their routing messages or their sensing data to the sink simultaneously and randomly. This generated traffic can cause more load in the networks and interrupt the data transmissions from any source nodes. The extra CBR traffic load between node ID 7 and node ID 8 is only the illustration of this phenomenon. For this scenario, if temperature data from the source are delivered through the path with traffic interruption, how the AODV routing handles this situation and how the effects of routing procedure influence the control objective are investigated. The CBR traffic has a fixed packet size of 1,000 bytes. We vary the CBR rate from 0.25 Mbps to 3.25 Mbps with a step size of 0.25 Mbps. Node ID 7 initiates to send CBR traffic to node ID 8 when the simulation  
Figure 2: WSAN simulation model.  
time is greater than 500 s for all scenarios. To investigate effects of CBR traffic to the communication and the control performances along the simulation time, sensor nodes should not die due to their power constraints. Consequently, we define that all nodes have enough energy to carry on transmissions and receptions during the whole simulation time. All simulation parameters are provided in Table 2.
## 4 Research Methodology
### Principal Component Assessment  
Table 5 displays two measurements, which demonstrate the consistency of the data for structural analysis.  
The KMO Test of Sampling Adequacy is a statistic that indicates the proportions of variation in the indicators which can be affected by the factors involved. Maximum levels (closer to 1) usually mean that a factor analysis can be beneficial for your results. When its minimum is more than 0.50, the findings of the factor analysis are unlikely to be effective. A Bartlett test of the hypothesis that your matrix for correlation is an identity matrix suggests that your variables are unrelated and, thus, unsuitable for structural identification. Less than 0.05 of the level of significance reveals that a factor analysis may be appropriate for certain results. Therefore, the results of study, i.e., Kaiser-Meyer-Olkin 0.934, \(\chi^{2}\) value 4519.32, df value 324, which is significant at 0.000, are supported as per the threshold criteria.  
In continuing to follow the Principal Component Assessment method, the next process is to rotate the Principal Component Analysis matrix used in study to determine the influence of 8 variables. The results summarized the grade of 8 variables, via high to low, which are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Constructs** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline UC1 & 0.09 & 0.261 & 0.273 & 0.355 & **0.515** & 0133 & 0.032 & 0.237 \\ UC2 & 0.084 & 0.127 & 0.023 & 0.123 & **0.712** & 0.211 & 0.133 & 0.141 \\ UC3 & 0.157 & 0.117 & 0.378 & 0.125 & **0.665** & \(-\)0.011 & 0.112 & 0.17 \\ SI1 & 0.110 & 0.329 & **0.587** & 0.143 & 0.342 & 0.089 & 0.048 & 0.158 \\ SI2 & 0.236 & 0.146 & **0.724** & 0.269 & 0.08 & 0.159 & 0.198 & 0.076 \\ SI3 & 0.18 & 0.176 & **0.776** & 0.189 & 0.145 & 0.10 & 0.074 & 0.09 \\ PEU1 & 0.109 & 0.039 & 0.078 & 0.235 & 0.233 & 0.139 & 0.049 & **0.87** \\ PEU2 & \(-\)0.043 & 0.421 & 0.18 & 0.08 & 0.178 & \(-\)0.033 & 0.224 & **0.643** \\ PU1 & **0.776** & 0.138 & 0.232 & 0.098 & \(-\)0.009 & 0.244 & 0.312 & \(-\)0.011 \\ PU2 & **0.787** & 0.236 & 0.128 & 0.310 & 0.154 & 0.53 & \(-\)0.010 & \(-\)0.009 \\ PU3 & **0.837** & 0.119 & 0.187 & 0.156 & 0.183 & 0.112 & 0.0037 & 0.176 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Rotation matrix measurement.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Latent Variable** & **UC** & **SI** & **PEU** & **PU** & **PE** & **ATT** & **FL** & **BI** \\ \hline UC & \(1\) & & & & & & & & \\ SI & 0.622 & \(1\) & & & & & & & \\ PEU & 0.646 & 0.493 & \(1\) & & & & & \\ PU & 0.526 & 0.48 & 0.416 & \(1\) & & & & & \\ PE & 0.54 & 0.488 & 0.563 & 0.293 & \(1\) & & & \\ ATT & 0.652 & 0.518 & 0.632 & 0.623 & 0.483 & \(1\) & & \\ FL & 0.562 & 0.453 & 0.476 & 0.612 & 0.373 & 0.596 & \(1\) & \\ BI & 0.656 & 0.518 & 0.617 & 0.563 & 0.512 & 0.688 & 0.637 & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Discriminant validity measurement.
## 3 Results
### Color Measurement and Total Carotene Content  
The changes in total carotenoids content and color values (L* a* b*) in the investigated pumpkin flesh, pumpkin pulps and probiotic frozen desserts are shown in Tables 6 and 7. Figure 2: **(a)** Antioxidant characteristics of investigated samples. DPPH Method; **(b)** Antioxidant characteristics of investigated samples. ABTS Method. Explanatory notes: The presented samples are coded according to Section 2; Table 1. Values denoted by different upper-case letters in the cultivar “_Melon Yellow_” of the _Cucurbita maxima_ species raw material, pumpkin fermented pulp and frozen desserts, differ significantly (\(p<0.05\)). Values denoted by different lower-case letters in the “_Miranda_” of the _Cucurbita moschata_ species raw material, pumpkin fermented pulp and frozen desserts, differ significantly (\(p<0.05\)). The results are expressed as the mean \(\pm\) standard deviation (\(n=3\)). The pumpkin flesh presented the highest total carotenoids contents. It ranged from 1.75 \(\mathrm{\SIUnitSymbolMicro g}\)/g FW (the cultivar _\"Miranda\"_ of the _Cucurbita moschata_ species) to 4.30 \(\mathrm{\SIUnitSymbolMicro g}\)/g FW (the cultivar _\"Melon Yellow\"_ of the _Cucurbita maxima_ species) (Tables 6 and 7). As a result of the heat treatment during the production of the pumpkin pulps, the total carotenoids content has decreased slightly compared to the raw material content, irrespective of the variety of the used plant (\(p<0.05\)). Further technological processes, such as the fermentation of pures and the production of sorbet, also resulted in a significant reduction in total carotenoids content for both pumpkin cultivars (\(p<0.05\)) (Tables 6 and 7). Depending on the pumpkin cultivar, total carotenoid content was recorded in frozen sorbets at a level of 0.65 to 0.95 \(\mathrm{\SIUnitSymbolMicro g}\)/g FW (cultivar _\"Miranda\"_ of the _Cucurbita moschata_ species) and 1.80 to 2.60 \(\mathrm{\SIUnitSymbolMicro g}\)/g FW, respectively. The color characteristics of pumpkin flesh cultivars were closer to each other, as yellowness (b*) was dominant in comparison to redness (a*) (Tables 6 and 7). During fermentation of pumpkin pulps with selected bacterial strains (Tables 6 and 7), a significant reduction in value L* was observed, which indicates the lightness of samples relative to the raw material, irrespective of the used plant cultivar. The samples were slightly dark. However, in the final products, a significant increase in L* values regarding fermented pumpkin pulps was observed. The values of b* for the investigated sorbets are in the positive range, indicating the presence of yellow in the products, originating from the base components of the frozen desserts. The b* values decreased during the lactic acid fermentation of pumpkin pulps and the manufacturing of potentially functional sorbets.
## 2 Method Introduction and Data Processing
### Denoising Processing  
The obtained data were denoised to reduce the interference of the original data on the prediction. The process included the following:  
* Outlier processing: Due to the interference of the sensors in the underground working face, some data in the sample data clearly deviate from the rest. In this paper, the 3\(\sigma\) principle is used to identify outliers in the sample data; that is, the data other than the three standard deviations in the sample data can be regarded as wrong and thus be removed.
* Missing value processing: Due to power outages, coal mining operation stoppage, etc., sensors and mine pressure monitoring systems shut down, resulting in data not being collected. This situation is often solved by using averages, medians to fill in or delete those data, etc. In this paper, the method of deleting missing data is adopted to reduce the impact of missing values on the prediction results.
* Repeat value processing: Due to system reasons, a piece of data appears many times. It is necessary to delete the remaining duplicate pieces of data and retain only one of each datum.
* Convert qualitative variables to numerical variables: The form of hydraulic support in the sample data of the mine pressure is a categorical variable, and the values of this variable are [\"support-type\", \"shield-type\", \"support-shield-type\"]; they cannot be directly used for logistic regression prediction so it is necessary to convert them to numeric-type variables. In this paper, the value of \"support-type\" is converted to a value of 0, the value of \"shield-type\" is converted to a value of 1, and the value of \"support-shield-type\" is converted to a value of 2. These values are all discrete values because they are class variables converted to numerical variables. In the subsequent calculation process, since these values also exist in the calculation, the remaining continuous values are approximated as discrete values.
# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
## 3 Methodology
### Analysis of Influencing Factors of PIM in Huainan Mining Area  
Parameters of PIM are mainly affected by geological and mining conditions such as the structure and lithology of the overlying rock stratum, stratum dip angle, coal mining method and roof management method. These specific factors include average mining depth (\(H_{0}\)), the thickness of the loose layer (\(h_{\text{s}}\)), coal seam dip angle (\(\alpha\)), etc. If the relationship between the geological mining conditions and parameters of PIM can be established according to the existing observation data, parameters of PIM can be obtained with unknown geological mining conditions. Then, the range of surface mining can be obtained before mining. The Huainan mining area is taken as an example for the following research. It was found that under the corresponding geological and mining conditions in the Huainan mining area, the offset of the inflection point generally fluctuated within a certain range without obvious rules. Relevant data show that its value is generally maintained at 0.1 times the mining depth to meet the requirements. Therefore, this paper ignores it.
## 2 Materials and Methods
### System Architecture  
The system's architecture is presented in Figure 1. The proposed architecture consists of eight main modules, which are the: (a) user interface, (b) user manager, (c) breaking news detector, (d) data fusion (e) database manager (f) image processing, (g) journalist profile extractor, and (h) EarthBot module. The architecture of the platform includes two parts, the front-end, and the back-end. The front-end concerns the design of the platform's user interface (UI), while the back-end includes the platform's database, the implemented AI methods for image processing and text synthesis, as well as, algorithms for data collection and data filtering. A detailed description for each sub-module is provided in the following sub-sections, while a description of the user interface is available in Section 3.
## 3 Results
### Biomass  
The assessment of the effect of GLY on the biomass of adult _Eisenia fetida_, in the absence of biochar, revealed that consistent biomass loss occurred in the treatments, with significant loss (\(p<0.05\)) in the 8 mg/kg treatment (Figure 2).  
In the biochar-amended treatments, only in the 2 mg/kg treatment amended with 5% biochar was a slight non-significant biomass loss observed (\(p>0.05\)). In all the other treatments and for all biochar amendment rates, the worms experience non-significant biomass gains (\(p>0.05\)). Despite clear variations in earthworm biomass, within the GLY concentration range studied, no significant gains or losses in biomass were observed in the presence of biochar (Figure 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**LC\({}_{5}\) (mg/kg)** & **0\% Biochar** & **5\% Biochar** & **10\% Biochar** & **15\% Biochar** \\ \hline LC\({}_{10}\) & 5.54 (2.889–7.003) & \(>\)8 & \(>\)8 & \(>\)8 \\ LC\({}_{20}\) & 7.067 (5.292–10.517) & \(>\)8 & \(>\)8 & \(>\)8 \\ LC\({}_{50}\) & \(>\)8 & \(>\)8 & \(>\)8 & \(>\)8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lethal concentrations (LC\({}_{5}\)) calculated after 96-h exposure of _Eisenia fetida_ to glyphosate in biochar-amended (5, 10 and 15%) and non-amended OECD soil.  
Figure 1: Number of surviving _Eisenia fetida_ adults after 96-h exposure to glyphosate in biochar-amended (5, 10 and 15%) and 0% biochar-amended OECD artificial soil. Data represented as mean of 3 replicates (\(n=30\) per treatment). Error bars represent standard deviations and different letters represent significant differences.  
Based on biomass variation data in _E. fetida_, the effective concentrations (EC\({}_{10}\), EC\({}_{20}\) and EC\({}_{50}\)) of GLY in the absence of biochar were 5.874, 6.973, and \(>\)8 mg/kg, respectively (Table 2). In worms exposed to 5, 10, and 15% biochar-amended, the reduction in biomass was not high enough to allow the computation of any effective concentrations within the GLY concentration range investigated.
## 3 Results
### Proactive Balancing
#### 3.1.2 Path Length  
Unlike the path time, a significant interaction between target distance and direction (\(p=0.02\)) was observed in the path length results. This suggested that the response of path length to target distance was not identical in different target directions. The path lengths were significantly (\(p<0.01\)) shorter in shorter target distances only at 90, 135, and 180 degrees although the trend was seen in all directions (Figure 5b).
# Compressive Sensing-Based IoT Applications: A Review
## 3 Compressive Sensing Overview
While the latter exhibit a two stages framework, first, the signal is sampled with respect to its bandwidth rate following the Nyquist sampling frequency high \(f_{s}\) to acquire an \(N\)-length signal. Secondly, data compression is performed by means of filtering, coding and extracting the most salient features in the signal to reduce the signal dimension from \(N\) to \(M\) such that (\(M\ll N\)). CS, on the other hand, is a one single stage framework that aims to perform both acquisition and compression simultaneously to acquire directly a compressed form of the signal. To this end, CS leverages the fact that most real-world signals exhibit a sparse behavior when represented by the appropriate basis (this basis can be either a fixed transform such DCT and DWT or it can be formed by means of dictionary learning techniques). A sparse behavior indicates that the signal information rate is much smaller than its bandwidth rate. Thus, rather than sampling the signal at a Nyquist rate and then performing compression, CS takes random measures of the signal in a sub-Nyquist rate corresponding to the signal information rate. This process is performed by taking inner products between the signal of interest and a well-designed tall sensing matrix \(\mathbf{\Phi}\in\mathbb{R}^{M\times N}\). Therefore, to reduce sampling rate and to achieve the objective of joint sensing and compressing, CS is applied prior to sampling and quantization adopting two approaches, Analog CS and Digital CS [37]. Therefore, Applying CS will result in the sensing/transmission of fewer samples. Thus, such paradigm is well suited for WSN applications which require a continuous data.
## 2 Materials and Methods
### Methods
#### 2.3.1 Land Use-Land Cover (LULC) Classification and Change Detection Analysis  
In the present study, the land use-land cover (LULC) dynamic was assessed based on the classification of multi-temporal Landsat images using the Random Forest (RF) classification method. RF is a powerful and robust machine-learning method widely used for classifying LULC from remote-sensing satellite datasets. RF yields higher classification accuracy than some well-known classifiers, such as support vector machine (SVM), k-nearest neighbors (kNN), and maximum likelihood classification (MLC), among others [55; 56; 57; 58]. The random forest algorithm is the non-parametric supervised classification algorithm that uses sets of decision trees to obtain the utmost classification accuracy. It studies the training sets of each class and forms a classified raster for identical reflectance as output by averaging multiple deep decision trees.  
As the present study focused on vegetation loss due to mining activity, the LULC was broadly classified into four classes, namely mining, vegetation, water body, and others. The mining class includes coal and stone mining, while vegetation comprises forests, shrublands, and grasslands. The water body encompasses ponds, river streams, and dams. Whereas, Other class embraces bare land, sands, and fallow land. The training (150 points) and validation (50 points) samples for each class were collected based on the false color composite (FCC) of satellite data and the Google Earth experience. The RF classification on the Landsat dataset was performed using Sentinel Processing Application (SNAP) software (version 8.0), developed by European Space Agency (ESA). After performing the LULC classification, the accuracy assessment was done using the confusion matrixwherein the overall accuracy, user's accuracy, producer's accuracy, and kappa coefficient were estimated.  
Furthermore, the land cover dynamics were evaluated using the cross-tabulation change detection approach between two study years (e.g., 1990-2000, 2000-2010, 2010-2020, and 1990-2020). The LULC transformation between different LULC classes (conversion of one class into another) was also estimated by preparing the LULC change matrix. A change matrix makes it possible to identify the primary sorts of changes or directions among the LULC classes in the study region. Later, the annual rate of change in LULC class was estimated using Equation (1), as suggested by [PERSON] et al. [59].  
\[r=\left(\frac{1}{t_{2}-t_{1}}\right)\times\ln\left(\frac{A_{2}}{A_{1}}\right) \tag{1}\]  
where \(r\) = the rate of change for each class per year, \(A_{2}\) and \(A_{1}\) = the class areas at the end and the beginning, respectively, for the period being evaluated, and \(t\) is the number of years spanning that period.
# A Survey on Bug Deduplication and Triage Methods from Multiple Points of View
## 7 Findings and Future Direction
### Future Directions
* Stack trace is a primary source of information for works based on information retrieval. However, existing research has shown that this information may not be sufficient to accurately locate bug characteristics. Therefore, future work can consider studying methods to enrich and strengthen stack traces.
* Works based on bug reports heavily rely on bug descriptions, and the accuracy of these descriptions has a significant impact on the results. Currently, most works lack an evaluation analysis of the availability of bug reports. In future work, it would be beneficial to construct models to evaluate the usability of bug reports.
* Current works using DNN models mainly focus on CNN and LSTM. In future work, consideration of the use of updated models such as transformers can be explored to evaluate their effectiveness.
* Exploring ways to improve the efficiency of collecting runtime information and reducing the complexity of processing is a hopeful research point.