esa-sceva/satcom-chunk-collection huggingface dataset:
# An Image Edge Detection Algorithm Based on an Artificial Plant Community
## 4 Algorithm Design
### Algorithm Flow of APC  
The APC algorithm flow is shown in Figure 2, which simulates the evolution mechanism of the natural plant community and extends our knowledge about the learning behaviors of brainless living things. Based on the seeding, growing, and fruiting operations instructed by swarm learning, the APC algorithm can help us to efficiently extract the edges and delete the nonedges.  
The algorithm flow is composed of two main steps and a large circulation.  
The first main step is image preprocessing, including image format conversion, filtering, the calculation of habitable values, nonmaximal suppression, and double-threshold checking. These steps are performed to preprocess the image, reduce noise, identify the obvious edges, and provide candidate edges to reduce the amount of calculation required by the artificial plant community in the next stage.  
The next major step involves the initialization, seeding, growing, and fruiting of the APC, and the end judgment. The stage includes a large circulation and double-threshold checking, where the artificial plant community randomly searches for all possible edge pixels from the candidate edges by evolution computing, and then optimizes the edge image according to the fitness function.  
The APC algorithm successfully simulates the evolution process of a natural plant community through three main operations of seeding, growing, and fruiting. If the end conditions are not satisfied after the evaluation and comparison, the artificial plant community will return to the previous steps and repeatedly select the edge pixels.
## 2 Antenna Design  
### Antenna Specifications  
The antenna specifications are determined by operational requirements for a constellation of SmallSats tasked with monitoring Arctic snow and sea ice. The antenna specifications are presented in Table 1, which are based on a 550 km orbit. The proposed antenna is optimized at 17.2 GHz over a bandwidth of 200 MHz. To minimize the complexity of the deployment mechanism, an offset cylindrical reflector was selected. The feed offers linear polarization, providing the desired bandwidth with over 20 dB return loss at 17.2 GHz. With a simple deployable mechanism, it is possible to stow the reflector within 1U (10 \(\times\) 10 \(\times\) 10 cm\({}^{3}\)). Antenna production expenses need to account for only a small fraction of the total mission budget, similar to the scenario with composite antennas frequently employed in satellites and communication systems.
# Optimizing Support Locations in the Roof-Column Structural System
## 3 Controlling Optimization Tendency
### Filtering Scheme  
The third and final substrategy considers a filter in the optimization algorithm that can be used for producing a grid-independent solution and influencing optimization tendency [4]. This is illustrated using a 2D roof-column example in Figure 3. Figure 3a represents an intermediate state of an optimization process, with 5 columns being preserved from 9 candidate columns. The 5 remaining columns correspond to a different portion of a uniformly distributed load (\(P_{1}>P_{2}>P_{3}\)), where \(2\times P_{1}\) are supported by Columns s2 and s8, \(2\times P_{2}\) are supported by Columns s4 and s6, and \(1\times P_{3}\) is supported by column s5. As mentioned in Section 3.1, columns with a smaller loading area tend to be removed first. Therefore, the optimization algorithm obtains the solution by removing Column s5, as shown in Figure 3b. A filtering technique was introduced for blurring and smoothing the loading information of columns, which enabled the columns located on the symmetric axis (s5) with a small loading area to be preserved without losing solution accuracy, as shown in Figure 3c.  
The filter was formulated using the distance between columns as the weight factor to smooth the axial stress of columns.  
\[\hat{\sigma_{i}}=\frac{\sum_{j}{\omega_{ij}\sigma_{j}}}{\sum_{j}{\omega_{ij}}} \tag{13}\]  
\[\omega_{ij}=max\{0,(r_{min}-r_{ij})\} \tag{14}\]  
where \(\omega_{ij}\) is the linear weight factor, \(r_{ij}\) is the distance between column centers \(i\) and \(j\), and \(r_{min}\) is the filter radius, as shown in Figure 4. This paper only considered a \(3\times 3\) filter for the \(i\)-th column in the grid, which is the smallest filtering size, able to capture information of its 8 surrounding columns. The filter was only employed when a clear distribution of columns was obtained, as it is used for optimizing the locations of well-defined columns, as demonstrated in Figure 3. However, the definition of well-defined columns is mathematically vague in intermediate optimization states. The filtering condition was then set by introducing a parameter \(t_{s}\). This required the axial stress of columns to be ranked on the basis of their magnitude from high to low. The filter was employed if the summation of axial stress from the first \(T_{8}\)% of \(N\) columns exceeded 50% of total axial stress (\(0.5\sum_{i}{\sigma_{i}}\)),  
Figure 3: Controlling optimization tendency with filter utilization. (**a**) Intermediate state of optimization process. Optimization result generated (**b**) without a filter and (**c**) with a filter.  
where \(T_{s}=100\,t_{s}\). This paper utilizes the filtering technique in the following examples. The effect of different \(t_{s}\) is explored in the next section.
# Structural Health Monitoring of Walking Dragline Excavator Using Acoustic Emission
## 2 Materials and Methods
### Noise Parameters  
The main source of acoustic noise measured during the AE testing of walking dragline excavator is friction and vibration generated by the movement of the hoist ropes and dragropes that control the bucket position. The noise has a stochastic non-stationary nature, its intensity turned out to be different for AE sensors installed on various dragline constructive parts. The lower the noise intensity, the greater is the distance of the structural component from the moving ropes. Figure 8 shows the empirical probability function of the noise long-time realization measured with a different sensor located on the structural components above and below the boom, and on the side frame--back legs and a mast. The highest noise level was observed along the measuring channel installed in the lower part of the mast near the dragropes, while the lowest level was detected along the channels installed on the lower part of the boom. It can be explained by the fact that they are placed far away from the trajectory of the ropes.  
Figure 7: Dependence of AE parameters versus propagation distance \(L\): (**a**) rise angle (\(RA\)) vs. distance \(L\), (**b**) rise time (\(RT\)) vs. distance \(L\).  
It can be inferred from Figure 8 that there is a high level of noise with a standard deviation of 40-45 dB. The maximum values typical for a set of \(10^{8}\) samples (that correspond to 100 s with a sampling frequency equal to 1 MHz) are 50-65 dB. The noise is non-stationary because it is caused by the uneven movement of the ropes in different phases of the dragline operation cycle. For instance, when the bucket is loaded, the dragrope is pulled. When there is a stage of unloading, both the dragrope and hoist rope are weakened and moved. When the platform turns, no movement of the ropes is visible, and the intensity of noise is dramatically reduced. Figure 9 presents the longtime noise waveform; its peak corresponds to 68 dB, and in the low-intensity phase \(\sim\) 40-45 dB. A favorable factor is that the impulse noise components differ from the AE signal impulses by greater values of the \(RT\) and \(RA\) parameters. Figure 9 shows the empirical distribution of the \(RA\) parameter corresponding to noise impulse components. The distribution mode equaled the value \(RA\) = 900 ms/V, which is significantly higher than the values of the \(RA\) parameter for AE impulses emitted by a defect.  
Figure 8: Empirical probability distribution of acoustic noise for a various dragline component.  
Figure 9: Parameters of the noise process: (**a**) noise waveform longtime realization, (**b**) distribution of \(RA\) parameter for the noise impulse components.
# The Resilience of Vegetation to the 2009/2010 Extreme Drought in Southwest China
## 2 Materials and Methods
### Datasets  
In our study, scPDSI was used to analyze the extent and duration of the extreme drought in SWC during 2009/2010. Proposed by [PERSON] et al. [25], scPDSI can automatically refit the parameters required for calculation according to the local climate, soil moisture, streamflow, potential evapotranspiration, and other related characteristics. The value range of scPDSI is close to \(-5\) to \(5\). Values below \(-4\) and above \(4\) represent extreme dry spells and extreme wet spells, respectively, and values between \(-1\) and \(1\) represent normal environments. The scPDSI used in our study was developed at the University of East Anglia ([[https://crudata.uea.ac.uk/cru/data/drought/](https://crudata.uea.ac.uk/cru/data/drought/)]([https://crudata.uea.ac.uk/cru/data/drought/](https://crudata.uea.ac.uk/cru/data/drought/)), accessed on 22 March 2021). The precipitation and mean air temperature were downloaded from National Tibetan Plateau Data Center ([[https://data.tpdc.ac.cn/en/data](https://data.tpdc.ac.cn/en/data)]([https://data.tpdc.ac.cn/en/data](https://data.tpdc.ac.cn/en/data)), accessed on 26 May 2021), which were posted by [PERSON] et al. [26]. Surface soil moisture and root-zone soil moisture were obtained from the Global Land Evaporation Amsterdam Model ([[https://www.gleam.eu/](https://www.gleam.eu/)]([https://www.gleam.eu/](https://www.gleam.eu/)), accessed on 29 August 2021).  
Figure 1: The spatial distribution of vegetation (**a**), geographical location (**b**), and percentages of vegetation (**c**) in SWC. EBF is evergreen broadleaf forest, DBF is deciduous broadleaf forest, ENF is evergreen needleleaf forest, DNF is deciduous needleleaf forest, MXF is broadleaf and needleleaf mixed forest, EBS is evergreen broadleaf shrubland, DBS is deciduous broadleaf shrubland, ENS is evergreen needleleaf shrubland, GRS is grassland, and CRP is cropland.  
Satellite data were derived from MODIS ([[https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)]([https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)), accessed on 23 April 2020). LST, which reflects the energy exchange between the land surface and the atmosphere, and which is a key index used to monitor extreme drought [27], was estimated based on brightness temperature using the generalized split-window algorithm [28]. NDVI, LAI, and GPP are widely used to assess vegetation greenness, cover, and carbon sequestration capacity, respectively [29]. NDVI is calculated using a combination of near-infrared band and red band data [30]. The three-dimensional radiative transfer model was applied to generate LAI [31]. GPP was estimated by using the light use efficiency approach [32]. Table 1 presents all the data indexes used in our study.
# FA-RCNet: A Fused Feature Attention Network for Relationship Classification
## 4 Experiments
### Experimental Results and Analysis
#### 4.3.1 Comparative Experimental Results and Analysis
This is the reason why the Bi-LSTM+LET model based on Bi-LSTM is used as the comparison model in this paper. The F1 values for both models, when considering directionality, are shown in Table 8.  
In the table, C-E1 is cause-effect (\(e_{1}\), \(e_{2}\)) and C-E2 is cause-effect (\(e_{2}\), \(e_{1}\)) in the opposite direction of C-E1. All other relations are abbreviated according to this rule. Since entity-destination (\(e_{2}\), \(e_{1}\)) has only one data item in the training set, a better training result was not obtained. The F1 values for both models are zero, a problem that is a data set defect and not sufficient to demonstrate the weak generalization of the model. Conversely, as can be seen from the rest of the table, the F1 values of the model in this paper are greater than or equal to those of the Bi-LSTM+LET model in different directions for each category. In particular, there was an 11.69% increase in the instrument-agency (\(e_{1}\), \(e_{2}\)) compared to the Bi-LSTM+LET model. The recognition accuracy in the direction of the instrument-agency (\(e_{1}\), \(e_{2}\)) is the bottleneck of other relational classification models, thus demonstrating that the model in this paper is sensitive to direction. As the directionality of the relationship is not defined in the KBP37 dataset, it is not possible to provide a comparative analysis of directional sensitivity on this dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **F1-Our** & **F1-BiLSTM+LET** & \(\Delta\) \\ \hline CE1 & **93.38\%** & 93.28\% & 0.1\% \\ CE2 & **92.35\%** & 89.72\% & 2.63\% \\ CW1 & **91.30\%** & 82.21\% & 9.09\% \\ CW2 & **84.59\%** & 77.44\% & 7.15\% \\ CC1 & **91.19\%** & 86.1  
To further investigate the directional sensitivity of the models in this paper, the experimental results of the two models on the SemEval-2010 Task8 dataset are visualized. The visualization is shown in Figure 5. It can be visualized from the figure that the model in this paper is higher than the Bi-LSTM+LET model in any of the directions of each relationship. As mentioned in the previous section, this paper overcomes the model shortcomings in the instrument-agency (\(e_{1}\), \(e_{2}\)) direction and improves the accuracy in this direction. At the same time, it can be seen from the \(\Delta\) values that this paper has not only improved significantly in this direction, but also other non-short areas. For example, with content-container (\(e_{2}\), \(e_{1}\)), member-collection (\(e_{1}\), \(e_{2}\)), component-whole (\(e_{1}\), \(e_{2}\)), and product-producer (\(e_{2}\), \(e_{1}\)), the F1 values improved by 16.89%, 11.42%, 9.09%, and 7.92%.
# Trimethoxycinnamates and Their Cholinesterase Inhibitory Activity
## 2 Materials and Methods
### Synthesis
5-Methyl-2-(propan-2-yl)phenyl (2E)-3-(3,4,5-trimethoxyphenyl)prop-2-enoate (**9**) [48]. Yield 74%; Mp 103-105 \({}^{\circ}\)C; IR (ATR, cm\({}^{-1}\)): 2973, 2936, 2838, 1724, 1638, 1580, 1502, 1461, 1432, 141, 1341, 1308, 1271, 1241, 1153, 1119, 1089, 1060, 934, 980, 922, 872, 828, 820, 785, 755, 740, 667, 654, 609, 586, 575, 545, 527, 517, 495; \({}^{1}\)H-NMR (DMSO-\(d_{6}\)), 8: 7.80 (d, \(J\) = 15.8 Hz, 1H), 7.27 (d, \(J\) = 8.2 Hz, 1H), 7.19 (s, 2H), 7.07 (dd, \(J\) = 8.2 Hz, \(J\) = 1.4 Hz, 1H), 6.95 (d, \(J\) = 15.8 Hz, 1H), 6.90 (d, \(J\) = 1.4 Hz, 1H), 3.84 (s, 6H), 3.71 (s, 3H), 2.96 (spt, \(J\) = 6.9 Hz, 1H), 2.28 (s, 3H), 1.14 (d, \(J\) = 1.14 Hz, 6H); \({}^{13}\)C-NMR (DMSO-\(d_{6}\)), 8: 165.4, 153.1, 147.7, 146.7, 139.8, 136.8, 136.2, 129.4, 126.9, 126.4, 122.8, 116.3, 106.3, 60.1, 56.1, 26.6, 22.9, 20.3; HR-MS: C\({}_{22}\)H\({}_{27}\)O\({}_{5}\) [M + H]\({}^{+}\) calculated 371.1853 \(m/z\), found 371.1850 \(m/z\). 4-(2,2,4,4-Butyl)phenyl (2E)-3-(3,4,5-trimethoxphenyl)prop-2-enoate (**10**). Yield 70%; Mp 150-152 \({}^{\circ}\)C; IR (ATR, cm\({}^{-1}\)): 2983, 2957, 2905, 2839, 2824, 1716, 1632, 1584, 1504, 1455, 14419, 1395, 1365, 1341, 1326, 1272, 1240, 1200, 1166, 1157, 1123, 1093, 1018, 1009, 9902-Propoxyethyl (2E)-3-(3,4,5-trimethoxyphenyl)prop-2-enoate (**12**). Yield 48%; Mp 67-69 \({}^{\circ}\)C; IR (ATR, cm\({}^{-1}\)): 3003, 2952, 2870, 2843, 1701, 1639, 1583, 1506, 1464, 1450, 1417, 1389, 1346, 1318, 1306, 1272, 1246, 1176, 1152, 1124, 1100, 1064, 1049, 1024, 996, 980, 901, 887, 847, 817, 791, 766, 671, 621, 611, 593, 567, 527, 497; \({}^{1}\)H-NMR (DMSO-\(d_{6}\)), 8: 7.60 (d, \(J\) = 15.8 Hz, 1H), 7.09 (s, 2H), 6.70 (d, \(J\) = 15.8 Hz, 1H), 4.26 (m, 2H), 3.82 (s, 6H), 3.69 (s, 3H), 3.63-3.61 (m, 2H), 3.38 (t \(J\) = 6.5 Hz, 2H), 1.51 (sxt, \(J\) = 6.9 Hz, 2H), 0.86 (t, \(J\) = 7.6 Hz, 3H); \({}^{13}\)C-NMR (DMSO-\(d_{6}\)) \(\delta\): 166.8, 153.3, 145.2, 139.7, 129.9, 117.5, 106.1, 72.1, 68.2, 63.6, 60.4, 56.3, 22.6, 10.7; HR-MS: C\({}_{17}\)H\({}_{25}\)O\({}_{6}\) [M + H]\({}^{+}\) calculated 325.1646 \(m/\)z, found 325.1639 \(m/\)z.
# Code Similarity and Location-Awareness Automatic Program Repair
## 4 Empirical Study
### Experimental Result  
4.5.1. RQ1: Does SLARepair Really Work Better Than Genprog's Automatic Program Repair? Table 2 summarizes the GenProg and SLARepair repair performance on Defects4J. GenProg has successfully repaired 25 bugs on Defects4J, with a success rate of 11.2%. SLARepair can repair 43 bugs with a success rate of 19.2%. SLARepair improves repair accuracy by 72% compared with GenProg, mainly because code similarity information in SLARepair can better guide the whole search process. Due to the lack of code similarity guidance information, GenProg failed to generate a valid patch for the remaining 18 bugs within a limited time or iteration. In order to further compare the repair efficiency of SLARepair and GenProg, Table 3 displays the specific running time and the number of candidate patches generated for the 25 bugs that can be repaired by both SLARepair and GenProg. It takes at least 3 min and at most 57.6 min for SLARepair to successfully repair a bug, with a total time of 491.1 min and an average time of 19.6 min. GenProg needed at least 7.8 min and at most 85.9 min to successfully repair a bug, which took a total of 1114.8 min and an average of 44.6 min. Compared with GenProg, SLARepair reduced the total repair time by 55.9% and the average repair time per bug by 56.1%. As above, from the point of view of patch generation, SLARepair reduces the total number of patch generations by 51%, and the average number of patch generations per bug can be reduced by 50.8%. Therefore, in the case that both repair methods can successfully repair, SLARepair improves the repair efficiency by at least 50% compared with GenProg. On the other hand, the time spans of SLARepair and GenProg on the 25 bugs are 54.6 min and 78.1 min, respectively, while the \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Subject Id**} & \multirow{2}{*}{**Bugs**} & \multicolumn{2}{c}{**Bug Id**} \\ \cline{3-4}  & & **GenProg** & **SLARepair** \\ \hline \multirow{2}{*}{Chart} & \multirow{2}{*}{26} & C1,C3,C5,C7, & C1,C3,C5,C7,C12,C13, \\  & & C13,C15,C25 & C15,C19,C25,C26 \\ \hline Lang & 65 & - & - \\ \hline \multirow{4}{*}{Math} & \multirow{4}{*}{106} & M2,M5,M8,M28,M40,M49,M50, & M2,M5,M8,M20,M28, \\  & & M53,M70,M73, & M32,M39,M40,M49,M50, \\  & & M80,M81,M82, & M53,M56,M60,M64,M70,M71, \\  & & M84,M85,M95 & M73,M47,M85,M80,M81,M82, \\ \hline Time & 27 & T4,T11 & T4,T11 \\ \hline total & 224 & 25 & 43 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of GenProg and SLARepair repair performance on Defects4J. time spans of the number of candidate patches are 92 and 131 respectively. This shows that the difficulty of repair can vary greatly for different bugs.
## 3 Methodology
### Input Layer  
We use two methods to obtain embedding vector and contextualized representation.  
The first method is pre-trained GloVe static embedding and BiLSTM. GloVe is a popular embedding method, and we use it to embed each word token into a low-dimensional real-valued vector space. Through the pre-trained GloVe embedding matrix \(L\textsubscript{le}\mathbb{R}^{d_{m}\times|V|}\), each word is matched to the corresponding embedding vector \(\epsilon_{i}\in\mathbb{R}^{d_{m}\times 1}\), where \(d_{m}\) represents the embedding dimension of the word vector, and \(|\,\forall\,|\) represents the size of the vocabulary. Then, we send the word embedding matrix to the BiLSTM to obtain the hidden state output of the input layer. BiLSTM is an extension of RNN. It solves the gradients vanishing or exploding problem in standard RNN, makes the hidden state output of any time step contain previous and subsequent timing information.  
The second method is pre-trained BERT. It is worth noting that we are using the Whole Word Masking variant of BERT-Large. The reason is that the tokenizer used by BERT is Wordpiece Tokenizer, which cuts certain words into a collection of several subwords, and then randomly selects subwords to mask for prediction training. In order to match our dependency tree, we need to add these subwords first. Therefore, compared with the word  
Figure 1: Overview of the proposed model for aspect-based sentiment classification.  
vector obtained by the random sub-word masking method, the word vector obtained by the whole word masking method is more consistent with our model.  
We use the output \(H^{c}=\left\{h_{1}^{c},h_{2}^{c},\cdots,h_{n}^{c}\right\}\in\mathbb{R}^{d_{hid} \times n}\) of BERT or BiLSTM as the contextual representation of the input text.
# A Distributed Indoor Mapping Method Based on Control-Network-Aided SLAM: Scheme and Analysis
## 3 Field Tests and Results
### Field Test Overview  
A series of field tests were designed and carried out in a large indoor parking lot (about 10,000 square meters) to evaluate the performance of the distributed indoor mapping scheme based on control-network-aided SLAM. The experimental environment is shown in Figure 4. In the parking lot, we designed and laid out a control network that contained 35 control points, and the coordinates of the control points were measured using a total station. The parking lot was divided into four independent mapping blocks, as shown in Figure 5. The LiDAR/IMU integrated hardware platform was mounted on a mobile platform. During the data acquisition, when the mobile hardware platform moved near a control point, we carefully adjusted the movement of the mobile hardware platform to align the LiDAR scanning center with the control point, which means that the origin of the platform coordinate system coincided with the control point. Then, the aligned scanning frame data were marked with a special number by the data collection software. To fully assess the accuracy and effectiveness of the proposed algorithm, we performed corresponding field tests and compared the mapping result with a high-precision reference map produced using a ground laser scanner to evaluate the relative accuracy and absolute accuracy of the mapping result, and we evaluated the impact of different numbers of control points on the accuracy of the mapping result. Accuracy assessment of the algorithm was achieved by evaluating the accuracy of selected main feature points (such as corners or some obvious fixed object). The high-precision reference map produced by the FARO Focus3D X130 HDR ground laser scanner and the map produced by our algorithm could both be imported to ArcGIS software, where we could manually extract the coordinates of the common feature points for accuracy assessment.  
Figure 3: The LiDAR/IMU integrated system.