esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dataset Description
### Satellite Data and in situ Measurements  
#### 2.2.1 SAR Images  
Twenty-five X-band SAR images were acquired by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) sensors between April and October 2013, with incidence angles between 28.3\({}^{\circ}\) and 32.5\({}^{\circ}\); both the HH and HV polarizations were analyzed (Tables 1 and 2). The nine TSX images were acquired in \"Stripmap mode\", with a ground pixel spacing of 3 m. Sixteen CSK images were obtained from the four satellites in the CSK constellation (six images from CSK1, four images from CSK2, one image from CSK3, and five images from CSK4) in \"Stripmap Pingpong mode\", with a pixel size of 15 m.  
Radiometric calibration of the SAR images was carried out using algorithms developed by the German Aerospace Center (DLR) and the Italian Space Agency (ASI). For TSX MGD (Multi Look Ground Range Detected) products, radiometric calibration was carried out using the following Equation (1):  
Figure 2: Daily air temperature recorded over the study area.  
\[\sigma^{\circ}=\mathrm{Ks}\times\mathrm{DN}^{2}\times\mathrm{sin}(0)-\mathrm{NESZ} \tag{1}\]  
This equation transforms the digital number of each pixel DN (the amplitude of the backscattered signal) into a backscattering coefficient (\(\sigma^{\circ}\)) corrected for background sensor noise known as _NESZ_ (Noise Equivalent Sigma Zero) on a linear scale. This calibration takes into account the radar incidence angle (0) and the calibration constant (Ks) provided in the image data. The _NESZ_ must be lower than the term \(\mathrm{Ks}\times\mathrm{DN}^{2}\times\mathrm{sin}(0)\) to ensure a high signal-to-noise ratio. For our TSX images, the _NESZ_ varies from \(-25.2\) dB to \(-22.6\) dB for both HH and HV polarizations [1].  
The calibration of the CSK images is given by the following formula:  
\[\sigma^{\circ}=DN^{2}\times\frac{1}{K\cdot F^{2}}\times\mathrm{sin}\left( \theta\right)R_{ref}^{2\cdot R_{\mathrm{exp}}} \tag{2}\]  
where 0 is the reference incidence angle, \(\mathrm{R_{ref}}\) is the reference slant range, \(\mathrm{R_{exp}}\) is the reference slant range exponent, K is the calibration constant and F is the rescaling factor. For CSK satellites in Pingpong mode with an incidence angle of approximately \(30^{\circ}\), [PERSON] _et al._[72] reported a noise equivalent sigma zero between \(-22\) dB and \(-29\) dB (depending on the antenna pattern).  
The backscattering coefficients are then calculated in decibels using the following formula:  
\[\sigma^{\circ}_{\mathrm{dB}}=10\times\mathrm{log}_{10}\left(\sigma^{\circ}\right) \tag{3}\]  
\begin{table}
\begin{tabular}{c|
# A Fitting Recognition Approach Combining Depth-Attention YOLOv5 and Prior Synthetic Dataset
## 5 Experiments and Results
### Line Field Experiment
#### 5.2.6 Comparison with the Recognition Results Using Different Attentional Mechanisms  
We introduce the ECA, CA, and SE attention mechanisms into the last layer and C3 module of the YOLOv5 backbone, respectively, where the better test results with the C3 module in two ways are shown in Figure 24 and Table 5. All results below 10% of the standard YOLOv5 are not displayed in Figure 24 and Table 5. \(D_{\alpha}\) is 2, and \(D_{\beta}\) is \(-1\) in depth-attention.  
Figure 21: Loss and mAP5095 during the training process of the YOLOv5 model in the line field experiments.  
Figure 22: Comparison of recognition accuracy. (**a**) YOLOv5; (**b**) depth-attention YOLOv5.  
The mAP5095 of ECA, CA, and SE attention mechanisms have no overall improvement in Figure 24, but the mAP5095 of a single class is improved. Our proposed approach could improve the recognition validity for all classes. Except for the PS class, the improvement  
Figure 23: Confusion matrix. (**a**) YOLOv5; (**b**) depth-attention YOLOv5.  
Figure 24: Variation trend of training metrics in the experiment. (**a**) mAP5095; (**b**) recall.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Add Location** & **Best Result Category** & **P** & **R** & **mAP 5095(\%)** & **Inference Speed (ms)** \\ \hline \multirow{4}{*}{YOLOv5} & \multirow{4}{*}{-} & All & 0.863 & 0.87 & 61.4\% & \multirow{4}{*}{9.1} \\  & & XP & 0.92 & 0.78 & 70.4\% & \\  & & PS & 0.59 & 0.99 & 57.6\% & \\  & & FXBW & 1 & 0.907 & 69\% & \\  & & FPQ & 0.941 & 0.804 & 48.6\% & \\ \hline \multirow{4}{*}{ECA} & \multirow{4}{*}{C3} & PS & 0.943 & 1 & 68.2\% & \multirow{4}{*}{27.3} \\  & & FXBW & 1 & 1 & 59.4\% & \\ \hline \multirow{4}{*}{CA} & \multirow{4}{*}{C3} & PS & 0.82 & 1 & 67\% & \multirow{4}{*}{22.4} \\  & & FXBW & 1 & 0.863 & 62.6\% & \\ \hline \multirow{4}{*}{SE} & \multirow{4}{*}{C3} & PS & 0.98 & 1 & 69.2\% * & \multirow{4}{*}{27.1} \\  & & FXBW & 1 & 1 & 59.7\% & \\ \hline \multirow{4}{*}{Depth-attention YOLOv5} & \multirow{4}{*}{Head last} & All & 0.987 & 0.858 & 64.6\% * & \multirow{4}{*}{12.6} \\  & & XP & 0.987 & 0.749 & 72.6\% * & \\ \cline{1-1}  & & PS & 0.962 & 1 & 64.4\% & \\ \cline{1-1}  & & FXBW & 1 & 0.958 & 72.4\% * & \\ \cline{1-1}  & & FPQ & 1 & 0.726 & 49.0\% * & \\ \hline \hline \end{tabular}  
* Best results for improvement.  
\end{table}
Table 5: Performance comparison with the YOLOv5 model and the depth-attention YOLOv5 model.  
validity is better than other attention mechanisms. The inference speed is 3 ms slower than the standard YOLOv5 model and 10 ms to 15 ms faster than other attention mechanisms.
## 2 Antennas Design and Working Principle Analysis
### Stable Radiation Patterns and Titled Beams
To introduce the metal plate, the tilted beams are formed with stable gain from 12.1 dBi to 13.8 dBi over the band of 5.7-7.7 GHz. Overall, as the metal plate is introduced, the beams of the proposed antenna are tilted, the beam-width is narrower, and the gain increases by 3 dB. Figure 12: Theory of tilted-beam formation (\(f\) = 5.7 GHz, \(h_{g}\) = 8 mm). Figure 9.0 Reflective coefficients and gain of the proposed with and without plate.
## 5 Experimental Section
### Experimental Results
#### 5.2.2 Detailed Results on Different Types of Sentences  
To further examine the capability of ERHGA in extracting overlapping relational triples, detailed experiments were carried out on different types of sentences and the experimental results were compared with prior works. Table 5 shows the experimental results of our model and the baseline models on distinct types of overlapping subsets of the dataset. The experimental results highlight that our model achieves the best F1 score on all subsets. The above experimental results demonstrate the ability of our method to recognize overlapping relational triples and the superiority of our method in the identification of relational triples under complex scenarios.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**WebNLG**} \\ \cline{2-4}
**Method** & **Prec.** & **Rec.** & **F1** \\ \hline NovelTagging & 52.5 & 19.3 & 28.3 \\ CopyRE & 37.7 & 36.4 & 37.1 \\ GraphRel & 44.7 & 41.1 & 42.9 \\ CopyMTL & 58.0 & 54.9 & 56.4 \\ Relation-Aware & 66.4 & 62.7 & 64.5 \\ CasRel & 93.4 & 90.1 & 91.8 \\ TPLinker & 91.8 & 92.0 & 91.9 \\ RIFRE & 93.3 & 92.0 & 92.6 \\ PRGC & 94.0 & 92.1 & 93.0 \\ RMAN & 83.6 & 85.3 & 84.5 \\ SGNet & 91.8 & 91.9 & 91.9 \\ \hline ERHGA\({}_{\O}\) & 93.1 & 90.5 & 91.8 \\ ERHGA & **94.3** & **92.3** & **93.3** \\ \hline \hline \end{tabular} Bold marks the highest score, while underline marks the second best score. The method marked with Ø means unused entity and relation heterogeneous graph attention networks.  
\end{table}
Table 4: Results (%) of the proposed ERHGA method with the prior works.
# DisA Limits RecG Activities at Stalled or Reversed Replication Forks
## 3 Results
### DisA Does Not Form a Stable Complex with RecG  
In unperturbed exponentially growing cells, both DisA (-75 octamers/colony forming unit [CFU]) and RecG (-40 RecG monomers/CFU) are low abundant proteins [27; 55]. Thus, to gain insight into a putative DisA interaction with RecG, a bacterial two-hybrid assay was used (see Materials and Methods). The 5'- or 3'-end of the genes was fused to the coding sequence of either the T18 or T25 domain of the _Bordetella_ adenylate cyclase. If the fused pair interacts, it allows the interaction of these domains, and the enzyme is reconstituted. Then, cAMP is synthesized, and the cAMP-bound catabolic activator protein becomes activated. This induces the expression of the _lacZ_ gene and \(\beta\)-galactosidase activity. Unexpectedly, full-length RecG interacted with itself rendering blue colored colonies. RecG failed to render blue colonies when combined with DisA (and _vice versa_), suggesting that no stable interaction between these proteins exist (Supplementary Figure S2A).  
To confirm this result, we used a His-tagged DisA (1 \(\upmu\)g) variant and native RecG (0.5 \(\upmu\)g). DisA (predicted mass of 40.7 kDa) is contaminated with traces of His-DisA-bound to c-di-AMP (a complex that runs as a 41 kDa protein). His-DisA was retained in the 50-\(\upmu\) Ni\({}^{2+}\) matrix through coordination with its C-terminal histidine tag, and eluted (E) with 400 mM imidazole (Supplementary Figure S2B, lane 6). However, apo RecG (predicted mass 78.1 kDa) was mainly present in the flow-through (FT), and the traces of the protein entrapped in the Ni\({}^{2+}\) matrix were released after the first wash (W1) (Supplementary Figure S2B, lanes 7-8). When both His-tagged DisA and RecG were loaded into the Ni\({}^{2+}\) column, we observed that Apo RecG was present in the FT (Supplementary Figure S2C, lane 3) while DisA was observed in the E fraction in the presence of 400 mM imidazole (Supplementary Figure S1C, lane 6). This suggests that DisA does not form a stable complex with RecG, at least in a way that can be detected using these assays (Supplementary Figure S2A,C).
## 3 Results
### Verification of Functional Similarities  
The functional similarity of both candidate and non-candidate drug-disease pairs was investigated by comparing them with the functional modules of all known drugs for each disease. The steps for the verification of functional similarity are illustrated in Figure 4. First, enrichment analysis was conducted for the candidate drugs, non-candidate drugs, and known drugs based on functional modules or pathways for each disease using the KEGG database [26] (Figure 4a). Second, we compared the similarity of the functional modules for each candidate and non-candidate drug with that of a known drug for each disease (Figure 4b). The similarity score for the functional modules was computed using Jaccard similarity as follows:  
\[Jaccard(M_{d},M_{kd})=\frac{|M_{d}\cap M_{kd}|}{|M_{d}\cup M_{kd}|}, \tag{10}\]  
where \(M_{d},M_{kd}\) are the set of functional modules for a drug and a known drug, respectively.  
We then computed the Jaccard similarity score for the functional modules of each candidate and non-candidate drug in comparison to all known drugs. The maximum Jaccard score was employed to represent the functional similarity score for each candidate and non-candidate drug in comparison with all known drugs for a disease (Figure 4c). Subsequently, the functional similarity scores for all candidate and non-candidate drugs were computed. Finally, we validated the functional similarity scores for the candidate and non-candidate drugs in comparison with known drugs using the Wilcoxon rank-sum test.  
The null hypothesis was that there was no difference in the medians functional similarity scores between the candidate drugs and the non-candidate drugs when compared with the known drugs. In other words, the alternative hypothesis was that the median functional similarity scores for the candidate drugs was higher than that of the non-candidate drugs when compared with known drugs. This was applied to all diseases.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**N = 629,526 Pairs** & **Found in the Database under Clinical Trial Data** & **Not Found in the Database** \\ \hline Candidate drug-disease pairs & 5900 & 14,975 \\ \hline Non-candidate & 21,734 & 586,917 \\ drug–disease pairs & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion table for validating the predictions with known drugs in clinical trial data.  
The results showed that there were 478 diseases with corresponding \(p\)-values that were lower than 0.05 in the Wilcoxon rank-sum test. Hence, the functional similarity scores of the candidate drugs were higher than the scores for non-candidate drugs when comparing them with known drugs for all 478 diseases. This indicates that the candidate drugs associated with each disease exhibited stronger functional similarity with known drugs than with non-candidate drugs.
## 5 Discussion
### Feasibility Evaluation of PSInSAR Result Stitching Based on Overlap Constraints
Notably, the connectivity analysis module introduced in this method automatically identifies independent connected components within sub-blocks (such as isolated building groups or scattered villages) and applies adaptive adjustment, thus avoiding the loss of isolated PS point signals and achieving the dual goals of \"global coverage without omissions\" and \"high-fidelity local deformation monitoring\". However, this method is currently primarily suitable for large-scale partitioning and the stitching of single-scene SAR imagery or cropped single-scene images. Its applicability to multi-scene SAR imagery for wide-area coverage still has limitations. Multi-scene images may suffer from mismatches at the stitching boundaries due to geometric registration errors, radiometric differences, and deformation model parameter transfer across scenes. Further research into cross-scene overlap region constraint mechanisms or adaptive registration algorithms is needed to improve the overall consistency of the deformation field. Additionally, fixed-size grid partitioning may interfere with the identification of connected components in complex terrain areas (such as mountainous-plain transition zones). Future work will explore dynamic grid-partitioning strategies or machine learning-assisted connected domain optimization methods. Potential local parameter estimation biases due to independent sub-block adjustment also need to be addressed by global iterative optimization or by introducing external observation data constraints. In this study, the manual selection of stable ground reference points within connected components ensured absolute calibration but introduced subjectivity. For instance, in Section 4.1, 19 ground points were manually selected across nine components to unify the reference frames. Future work will integrate GNSS data or automated algorithms to mitigate systematic error accumulation and improve the robustness of large-area deformation field inversion.
# 3D Skeletal Joints-Based Hand Gesture Spotting and Classification
###### Abstract
In paper [3], they segmented the videos into sets of images and used them to predict the fusion score, which means they simultaneously did the gesture spotting and gesture classification. The authors in [5] utilized the Connectionist temporal classification to detect the nucleus of the gesture and the no-gesture class to assist the gesture classification without requiring explicit pre-segmentation. In [4; 6], the continuous gestures are often spotted into isolation based on the assumption that hands will always be put down at the end of each gesture which turned out to be inconvenient. It does not work well for all situations, such as in \"zoom in\", \"zoom out\" gestures, i.e., when only the fingers move while the hand stands still. In this paper, we propose a spotting-classification algorithm for continuous dynamic hand gestures which we separate the two tasks like [4; 6] but we avoid the existing problems of those methods. In the spotting module, as shown in Figure 1, the continuous gestures from the unsegmented and unbounded input stream are firstly segmented into individually isolated gestures based on 3D key joints extracted from each frame by 3D human pose and hand pose extraction algorithm. The time series of 3D key poses are fed into the Bidirectional Long Short-Term Memory (Bi-LSTM) network with connectionist temporal classification (CTC) [12] for gesture spotting. The isolated gestures segmented using the gesture spotting module are classified in the gesture classification module with a multi-modal M-3D network. As indicated in Figure 1, in the gesture classification module, the M-3D network is built by combining multi-modal data inputs which comprise RGB, Optical Flow, Depth, and 3D pose information data channels. Three residual 3D Convolution Neural Network based on ResNet architectures (3D_ResNet) [13] stream networks of RGB, Optical Flow and Depth channel along with an LSTM network of 3D pose channel are effectively combined using a fusion layer for gesture classification. The preliminary version of this paper has appeared in [14]. In this paper, depth information has been considered together with 3D skeleton joints information with extensive experiments, resulting in upgraded performance. The remainder of this paper is organized as follows. In Section 2, we review the related works. The proposed continuous dynamic hand gesture recognition algorithm is intently discussed in Section 3. In Section 4, the experiments with proposed algorithms conducted on three published datasets--Chalearn LAP ConGD dataset, 20 BN-Jester, and NVIDIA Dynamic Hand Gesture Dataset are presented with discussions. Finally, we conclude the paper in Section 5. Figure 1: Gesture Spotting-Classification Module.
# Effective Optical Scattering Range Determination Based on Ray Tracing
###### Abstract  
Surface imperfections or contamination on an optical smooth surface is usually inevitable and causes scattering. The directional information of the scattered ray can be related to the spatial frequency through the grating equation. On the other hand, the layout of the optical system determines whether a scattered ray will finally reach the detector according to the positions and directions of the scattered rays. Therefore, the scattering propagation in an optical system is usually band-limited and the effective optical range differs for different systems. In this paper, a method based on ray tracing is described that can statistically determine the optical scattering and the band of roughness. The results can be an essential reference for optical surface polishing and contamination control.  
optical scattering; surface roughness; stray light analysis Article
###### Abstract  
Regular inspection and monitoring of buildings and infrastructure, that is collectively called the built environment in this paper, is critical. The built environment includes commercial and residential buildings, roads, bridges, tunnels, and pipelines. Automation and robotics can aid in reducing errors and increasing the efficiency of inspection tasks. As a result, robotic inspection and monitoring of the built environment has become a significant research topic in recent years. This review paper presents an in-depth qualitative content analysis of 269 papers on the use of robots for the inspection and monitoring of buildings and infrastructure. The review found nine different types of robotic systems, with unmanned aerial vehicles (UAVs) being the most common, followed by unmanned ground vehicles (UGVs). The study also found five different applications of robots in inspection and monitoring, namely, maintenance inspection, construction quality inspection, construction progress monitoring, as-built modeling, and safety inspection. Common research areas investigated by researchers include autonomous navigation, knowledge extraction, motion control systems, sensing, multi-robot collaboration, safety implications, and data transmission. The findings of this study provide insight into the recent research and developments in the field of robotic inspection and monitoring of the built environment and will benefit researchers, and construction and facility managers, in developing and implementing new robotic solutions.  
construction robots; project monitoring; robotic inspection; construction automation; autonomous robots; mobile robots +
Footnote †: journal: Applied Sciences  
14 January 2023  
Revised 8 February 2023  
Accepted 8 February 2023  
Published: 10 February 2023
## 2 Materials and Methods
### Dataset  
Landsat 8 OLI and GOCI images were used in this study. The Landsat 8 OLI images were downloaded from the United States Geological Survey (USGS) Earth Explorer website at [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 2 September 2023). The image code is Path-119, Row-33, and the spatial resolution is 30 m, which covers the entire study area. GOCI images with a spatial resolution of 500 m were downloaded from the Korea Ocean Satellite Center website at [[http://kosc.kiost.ac.kr/](http://kosc.kiost.ac.kr/)]([http://kosc.kiost.ac.kr/](http://kosc.kiost.ac.kr/)) (accessed on 2 September 2023). Among the eight pieces of GOCI data obtained for a day, the third one (acquisition time: 10:30 GTM+8:00) was used as it had the same acquisition time as the Landsat 8 OLI image. The GOCI product deleted values where pixels had cloud cover and where retrieval Chl-a was present in the tidal zone. In this study, the same mask was performed on both of the datasets, which retained only sea areas. The detailed data list is shown in Table 1.
# Review of Fast Computation Methods for Finite-State Predictive Control of Multi-Phase Drives
## 2 MPC for Current Control of MPS
### Basic VV and Virtual VV  
As discussed in the previous sections, each VSI configuration generates a specific voltage in the \(\alpha\)-\(\beta\)-\(x\)-\(y\) axes. This voltage is referred to as the basic Voltage Vector (VV). It is useful to describe the distribution of these basic VVs according to their magnitude in the \(\alpha\)-\(\beta\) subspace:  
1. Large VV (LVV): These are VVs that have the largest magnitude in the \(\alpha\)-\(\beta\) subspace, as shown in Figure 2.
2. Medium VV (MVV): These VVs are placed in the second largest corona.
3. Small VV (SVV): These VVs are placed in the smallest corona.
4. Null Voltage (ZVV): This refers to the VVs in the origin.  
FSMPC can select any of these basic VVs to be applied at each sampling period. Approaches that follow this strategy are known as single-vector FSMPC. However, it is also possible to release a sequence of basic VVs during a single sampling period. This results in a form of explicit inter-sample modulation. The average voltage generated over the sampling period can be interpreted as a Virtual VV or Synthesized VV [19].  
While there are several variants of the Virtual Voltage Vector (VVV) technique, it is particularly useful to focus on VVV for \(x\)-\(y\) elimination. In this method, VVs are chosen such that their average \(x\)-\(y\) excitation is zero. For example, a Large VV applied during a subperiod \(T^{L}\) followed by a Medium VV during \(T^{M}\) is selected. The time durations \(T^{L}\) and \(T^{M}\) are chosen so that they add up to the sampling period \(T^{L}+T^{M}=T_{s}\). Additionally, the average voltage in the \(\alpha\)-\(\beta\) subspace over the sampling period is set to zero, \(\overline{V}_{xy}=0\). This cancellation is achieved by pairing an LVV with an MVV positioned in the opposite part of the \(x\)-\(y\) subspace.