esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Satellite Imagery and Pre-Processing  
For this study, imagery acquired by the WV2 and WV3 satellites was selected due its high spatial, spectral, temporal, and radiometric resolutions. WV2, launched by DigitalGlobe on 8 October, 2009, provides a spatial resolution of 1.84 m in the multispectral bands and 0.46 m in panchromatic band; Whilst WV3, launched on 13 August, 2014, provides a spatial resolution of 1.2 m in the multispectral bands and 0.3 m in panchromatic band. Both WV2 and WV3 have one panchromatic band (450-800 nm) and eight multispectral bands: coastal blue (407-448 nm), blue (455-509 nm), green (516-578 nm), yellow (585-623 nm), red (631-689 nm), red edge (703-742 nm), near infra-red 1 (NIR1) (774-874 nm), and near infra-red 2 (NIR2) (869-958 nm). Further details about the WV2 and WV3 sensor can be found on the website ([[https://www.digitalglobe.com/about/our-constellation](https://www.digitalglobe.com/about/our-constellation)]([https://www.digitalglobe.com/about/our-constellation](https://www.digitalglobe.com/about/our-constellation))) and in Kuester [41].  
For the 2014 harvest season a WV2 image was acquired on 29 th of May 2014, whilst additional WV3 images were acquired on 7 April 2016 and 16 May 2017 to correspond with the 2016 and 2017 harvest seasons (Processing Level: Ortho Ready Standard Level 2A, Scan Direction: reverse). All images were acquired under cloud-free conditions with the 'at-sensor' pixel digital numbers converted to 'Top of Atmosphere' (TOA) reflectance following the equation given in Kuester [41] and projected to WGS-84 UTM Zone 56S. The timing of image acquisition coincided with the 'fruit filling' growth period between final fruit set in late January and pre-harvesting in May.
## 3 Results  
### Alleplopathic Effects of Juglone in the Laboratory Bioassays  
When different levels of juglone were applied, the growth of two harmful algae, _S. hantzschii_ and _M. aeruginosa_, was inhibited by juglone, but different levels of juglone affected the two species differently (Figure 1). Growth of _S. hantzschii_ was significantly inhibited by juglone concentrations of 0.1 mg L\({}^{-1}\) and higher (F\({}_{(3,\,20)}\) = 10.240, \(p\) < 0.05) when compared to the control, while the lowest test concentration (0.01 mg L\({}^{-1}\)) exerted no inhibition (\(p\) = 0.995) (Figure 1a). The algicidal effect on the growth of _M. aeruginosa_ was only prominent at the highest concentration (1 mg L\({}^{-1}\)) (F\({}_{(3,\,20)}\) = 15.383, \(p\) < 0.001) (Figure 1b). Concentrations of 0.01-0.1 mg L\({}^{-1}\) did not significantly suppress cellular growth of _M. aeruginosa_ compared to the control throughout the experimental period (\(p\) > 0.05).  
Juglone concentrations of 0.1 and 1 mg L\({}^{-1}\) significantly reduced _S. hantzschii_ cell density by 66.2 \(\pm\) 8.6% and 75.1 \(\pm\) 10.1%, respectively, compared to that of the control (Figure 2a). A concentration of 1 mg L\({}^{-1}\) markedly inhibited the growth of _M. aeruginosa_ by 92.7 \(\pm\) 5.4%, whereas concentrations of 0.01 and 0.1 mg L\({}^{-1}\) only reduced its cell density by 35.2 \(\pm\) 6.4% and 37.7 \(\pm\) 6.9%, respectively, compared to that of the control (Figure 2e). Interestingly, 0.1 mg L\({}^{-1}\) of juglone showed more selective allelopathic effects among the examined diatoms than did 1 mg L\({}^{-1}\). Juglone at 0.1 mg L\({}^{-1}\) effectively inhibited the growth of _S. hantzschii_, while it stimulated the growth of three non-target diatoms (Figure 2a-d).  
Figure 1: Changes in cell density of _Stephanodiscus hantzschii_ (**A**) and _Microystis aeruginosa_ (**B**) at three juglone concentrations during the 7-day experimental period. Data are presented as averages with standard errors (bars). Lower case letters on the right side of the lines indicate significant differences (\(p\) < 0.05).  
However, 1 mg L\({}^{-1}\) of juglone induced 89.7 \(\pm\) 5.7%, 96.8 \(\pm\) 5.1%, and 94.9 \(\pm\) 14.4% cell density reductions in _F. crotonensis, A. formosa_, and _S. acus_, respectively (Figure 2b-d). These reduction rates were higher than that of _S. hantzschii_. Conversely, juglone produced marked stimulatory effects on the growth of the two green algae, _S. quadicauda_ and _C. vulgaris_, at all three concentrations (Figure 2f,g). Moreover, there were no significant differences among the three concentrations (p > 0.05). Thus, the juglone concentration required for algicidal effects on _S. hantzschii_ (0.1 mg L\({}^{-1}\)) was lower than that on _M. aeruginosa_ (1 mg L\({}^{-1}\)), and this lower concentration also exerted hormetic effects on some diatoms and green algae by stimulating their growth.
# Dynamic Programming Ring for Point Target Detection
## 2 Model of Point Target  
In this study, it is assumed that point targets are moving relative to the radar or infrared target detection system, and the detection system obtains an observation sequence for each full scan, which is called an image, and observes a total of \(N\) images.  
At time \(1\leq t\leq N\), the observation data with coordinates of \(\mathbf{p}\) on an image plane \(\mathbf{\Omega}\) are denoted by \(X_{\mathbf{p}}^{(t)}\) and expressed as [15]:  
\[X_{\mathbf{p}}^{(t)}=\left\{\begin{array}{l}A^{(t)}+n_{\mathbf{p}}^{(t)},\text{ target at coordinates of }\mathbf{p}\\ n_{\mathbf{p}}^{(t)},\quad\quad\text{no target at coordinates of }\mathbf{p}\end{array}\right., \tag{1}\]  
where \(A^{(t)}\) denotes the target amplitude, which is assumed to be a positive constant for simplicity, i.e., \(A^{(t)}=A>0\); \(n_{\mathbf{p}}^{(t)}\) represents additive noise and obeys the zero-mean Gaussian distribution, i.e., \(n_{\mathbf{p}}^{(t)}\sim N\big{(}0,\sigma_{n}^{2}\big{)}\); SNR is defined as \(A/\sigma_{n}\). For more details about processing a complex background, please refer to the image preprocessing presented in ref. [26].
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 4 Proposed Fast CU Size Selection Algorithm
### Level I(L-1) SVM Models: Features and Optimal Weight Calculation
#### 4.1.2 Feature Selection  
Let \(F_{j}^{i}\) be the set of features extracted for CU depth \(i\) in the \(j\)-th level. Therefore, at L-1, \(F_{j=1}^{i}\) is defined as  
\[F_{j=1}^{i}:=\{\alpha^{i},\beta^{i},\gamma^{i}\}, \tag{7}\]  
where, \(\alpha^{i}\), \(\beta^{i}\), and \(\gamma^{i}\) correspond to the texture complexity, RD cost pre-analysis of current CU, and context information, respectively, for the \(i\)th CU depth level [10]. In this case, the texture complexity (\(\alpha^{i}\)) is calculated using the mean absolute difference (\(\alpha_{1}\)) of the current CU and the texture difference between the current CU and its four sub CUs (\(\alpha_{2}\)). RD cost pre-analysis includes division between the RD cost and QP (\(\beta_{1}\)) and division between RD cost and distortion (\(\beta_{2}\)). In this case, RD cost is calculated by encoding the CU with planar intra mode. The RD cost (\(\gamma_{1}\)), CU depth (\(\gamma_{2}\)), and PU depths (\(\gamma_{3}\)) of the spatially neighbouring above and left CTUs are extracted as context information features for the \(F_{j=1}^{i}\)[10]. Different combinations of these features are utilised at different depth levels, as depicted in Table 1.
# A Kalman Filter-Based Method for Reconstructing GMS-5 Land Surface Temperature Time Series
###### Abstract  
Satellite-derived environmental parameters play important roles in environmental research on global changes and regional resources. Atmosphere effects and sensor limitations often lead to data products that vary in quality. The main goal of time series data reconstruction is to use various statistical and numerical analysis methods and to stimulate changing seasonal or annual parameters, providing more complete data sets for correlational research. This paper aims to develop a time series reconstruction algorithm for LST based on data assimilation according to the current problems of unstable precision and unsatisfactory results, and the simplistic effects of evaluation methods while using remote sensing-derived LST data as the basic parameters and the daily LST data derived from the static meteorological satellite GMS-5 as the input data. The data assimilation system used the Kalman filter as the assimilation algorithm. A complete set of global refined LST time series data sets were obtained by constantly correcting the LST values according to the regional ground-based observations. This method was implemented using MATLAB software (version R2017a), and was applied and validated through partitioning using the principal elevation in the Beijing, Tianjin, and Hebei regions. The results show that the accuracy of the reconstructed LST data series improved significantly in terms of the mean and standard deviation. Better consistency was achieved between the variables obtained over a year from the reconstructed LST data and the ground observations from the LST data set.  
Keywords:land surface temperature; time series; reconstruction; Kalman filter; GMS-5 +
Footnote †: journal: _Sciences_
# Surrogate Based Optimization of Aerodynamic Noise for Streamlined Shape of High Speed Trains
## 7 Conclusions  
An aerodynamic noise optimization strategy for high-speed trains has been proposed in the present paper. In order to take all the acoustic probes into consideration, the equivalent continuous sound pressure level (SPL) has been taken as the objective for noise optimization. In order to improve the optimization efficiency, the local shape function method has been utilized for streamlined shape parameterization, which owns stronger generality and could set design variables directly on the focused region. Meanwhile, the surrogate based optimization approach is adopted, taking the Kriging model on the base of cross-validation method as the surrogate model. Once the Kriging model is built up, multi-objective optimization by genetic algorithm could be performed on this model to avoid the computational cost of CFD/CAA for each design point, which is the key for efficiency improvement.  
Taking the drag of the whole train and the equivalent continuous SPL as the objectives, a multi-objective optimization of the streamlined shape of HSTs is performed. Results reveal that the aerodynamic performance has been improved apparently compared to the prototype. The drag of the whole train has been reduced by 7.1%, and the equivalent continuous SPL has been reduced by 1.26 dB. The OASPLAs of the probes for the optimal shape are mostly reduced except for very few of them, indicating that the acoustic circumstance in the far field has been effectively improved and it is feasible to perform aerodynamic noise optimization for high-speed trains with the use of the strategy in the present paper.  
This work was supported by National Natural Science Foundation of China under 11302233, and Computing Facility for Computational Mechanics Institute of Mechanics, Chinese Academy of Sciences is gratefully acknowledged.  
[PERSON] and [PERSON] conceived the whole numerical simulations; [PERSON] contributed the optimization tools; and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# An Intelligent Real-Time Object Detection System on Drones
## 4 Results and Discussion
### Power  
Figure 7 illustrates the power consumption of the FPGA. It is shown that the total on-chip power is 3.529 W, where dynamic power is the dominant one with a portion of 93%. The on-chip power is not large and can be supplied with portal DC power sources. Thus, we adopt a battery bank to supply a 5 V voltage for the FPGA.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Resource** & **Available** & **Utilization** & **Utilization \%** \\ \hline LUT & 254,200 & 185,046 & 72.80 \\ LUTRAM & 90,600 & 1297 & 1.43 \\ FF & 508,400 & 136,483 & 26.85 \\ BRAM & 795 & 304 & 38.24 \\ DSP & 1540 & 1256 & 81.56 \\ IO & 400 & 119 & 29.75 \\ BUFG & 32 & 5 & 15.63 \\ MMCM & 10 & 2 & 20.00 \\ PLL & 10 & 1 & 10.00 \\ \hline \end{tabular}
\end{table}
Table 2: NN engine resource consumption of the FPGA.  
Figure 6: The illustration of intelligent object detection by providing more pixels for the target of interest, which effectively increases the object detection confidence score.  
There are two power sources for the intelligent system, i.e., a battery for the flying of the drone and a battery for the FPGA. Table 3 lists the battery-life duration of the drone and the FPGA. We can see that although the FPGA exhibits low-power characteristics that can work for 2 hours, the system's duration is determined by the battery duration of the drone, which is limited to the duration of 11 minutes.
## 4 Communication Networks and Technologies in NMGs
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Connection Type** & **Coverage** & **Data Rate** & **Applicability** & **Advantages** & **Disadvantages** & **Application** \\ \hline
**Wired Technologies** & & & & & \\ \hline Ethernet & Up to 100 m & Up to 100 Gbps & WAN, FAN, HAN & Widely available, suitable for low coverage & Limited coverage distance & Home automation \\ \hline Fiber-optic & Up to 60 km & Up to several Tbps & WAN, FAN & High data rate, fast transmission & More expensive than copper cables and some wireless technologies & Communication channel in generation site \\ \hline Narrowband PLC & Up to 3 km & Up to 500 kbps & WAN, FAN, HAN & Affordable, widely available & Greater electromagnetic noise, susceptible to disruptions, low data rate & Transmission of electricity \\ \hline Broadband PLC & Up to 1.5 km & Up to 300 Mbps & WAN, FAN, HAN & Affordable, widely available & Greater electromagnetic noise, susceptible to disruptions & Transmission of electricity \\ \hline HomePlug & Up to 200 m & Up to 10 Mbps & HAN & Affordable, energy efficient & Limited coverage distance & Home automation \\ \hline Asymmetric Digital Subscriber Line (ADSL) & Up to 5 km & Up to 8 Mbps & HAN & Affordable, energy efficient & Limited coverage distance, low data rate & Home automation \\ \hline
3G & Up to 75 km & Up to 7.2 Mbps & WAN, FAN, HAN, HAN, infrastructure & Widely available, infrastructure, risk of congestion & Shared network infrastructure, data collection \\ \hline
4G & Up to 12 km & Up to 100 Mbps & WAN, FAN, HAN, HAN & Wide coverage distance, fast data transmission & Shared network infrastructure, risk of congestion & Two-way communication between smart device and control centre \\ \hline \end{tabular}
\end{table} 
Table 2: Wired and wireless communication technologies used in NMGs [19; 24; 31; 32; 33; 34; 35]. Figure 6: Communication network types in NMGs. When coverage distance is extended, a higher data rate is required [30]. WANs are the highest tier of communication networks in NMGs, covering large geographical areas where substantial electricity generation occurs. They also cover the transmission of power generated from generation sites to electricity substations. Wide-area monitoring, wide-area protection, and wide-area control are examples of WANs in NMGs [29]. The coverage distance of WANs ranges from 10 km to 100 km. A large number of data points are needed to transmit control data for system stability and decision-making in NMGs at a fast rate, ranging from 10 Mbps to 1 Gbps. Given that wide area coverage is needed in WANs, communication technologies providing broad geographical coverage are employed in NMGs. These include cellular, satellite communication, LoRaWAN, NB-IoT, WiMAX, cloud solutions, and fiber-optic cables [30; 53]. FANs are the intermediate stage, and they facilitate information flow between WANs and HANs. For example, they can be situated between power stations and industrial business parks. FANs are a critical layer of communication networks in NMGs, receiving
# Anthraquinone Rhein Exhibits Antibacterialativity against _Staphylococcus aureus_
###### Abstract  
Staphylococcus aureus (S. aureus) represents an important pathogen of clinical relevance, causing a wide variety of symptoms. The broad distribution of multidrug-resistant strains necessarily demands new antibacterial agents for the treatment of _S. aureus_ infections. The aim of this study was to assess the antibacterial activity of plant-derived compounds, pure 4,5\({}^{\prime\prime}\)-dihydroxy-anthraquinone-2-carboxylic acid (Rhein), against standard and clinical isolated _S. aureus_ strains. The hemolysis and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assays were used to determine the cytotoxicity on human erythrocytes and bronchial epithelial cells after treatment with Rhein. The antibacterial effect was assessed via disk diffusion test, broth microdilution methods, time-killing assays and live-dead evaluation (50-0.39 ug/mL). Rhein effect on the hemolytic activity of \(\alpha\)-toxin and catalase were estimated. Moreover, crystal violet (CV) assay evaluated its impact on biofilm biomass. The compound exhibited 50% cytotoxic concentration (CC\({}_{50}\)) and 50% hemolysis concentration (EC\({}_{50}\)) of 43.6 and >50 ug/mL, respectively. The minimum inhibitory concentration (MIC) of Rhein was 12.5 ug/mL for all tested strains, exerting bacteriostatic action. MIC and sub-MIC concentrations of Rhein significantly reduced hemolytic and catalase activities, impairing the major virulence factors of _S. aureus_ strains. Rhein also reduced biofilm biomass in a dose-dependent manner, reaching rates of about 50% eradication at a dose of 50 ug/mL. These findings suggest that Rhein could represent a promising therapeutic option for the treatment of _S. aureus_ infections.  
Staphylococcus aureus; Rhein; natural product; antibacterial activity; antibiofilm activity +
Footnote †: journal:  
_Applied_  
_sciences_
###### Abstract
Finally, comparison experiments with Leica T-MAC were conducted to verify the accuracy and dynamic performance of the method. The method verifies that the attitude accuracy of the integrated system is better than \(0.15^{\circ}\) while the three-coordinate accuracy can reach 0.3 mm under dynamic conditions. In summary, the laser tracking attitude measurement system utilizes the visual measurement method, which is recognized for its compact size, low cost, and flexible measurement capabilities, yet is not without its drawbacks. The accuracy of the measurement is observed to diminish as the distance increases, with limited potential for enhanced precision in long-distance attitude measurements. Additionally, visual measurement, being quasi-static, is susceptible to frame loss when tracking rapidly moving objects, thereby constraining its dynamic measurement capabilities. The attitude measurement method based on a pinhole prism, photoelectric position sensor, and inclinometer solves the problem of the low sensitivity of yaw and pitch angles, but it is still not suitable for dynamic measurement due to the problems of low measurement update rate, slow response, and limited incident angle. The multi-sensor data fusion based on inertial navigation with good complementarity and autonomy is a new research hotspot and an important development direction in the field of industrial measurement. Leveraging the laser tracking equipment as the central station, this paper introduces a dynamic attitude measurement method that integrates visual and inertial measurement units and conducts an in-depth study with the data fusion methods derived from both visual and inertial data sources. Ultimately, the effectiveness of the methods proposed in this paper has been substantiated through experimental validation. The key highlights of this study are as follows:  
1. A laser tracking attitude dynamic measurement method combining CCD and IMU is proposed. By training neural network models, high-precision dynamic measurements have been achieved. 2. Applying machine learning algorithms to solve attitude measurement problems. Machine learning techniques are used to optimize the process of determining attitude angles from the fused sensor data, improving the accuracy and robustness of the measurement system. 3. An innovative method based on neighborhood search was proposed to determine the true values of training. This approach optimizes the training process by selecting the most relevant and accurate data points for training the machine learning model, leading to an optimized design of the training model.
# Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm
## 4 The Spatial Optimized Dynamic Path Planning Algorithm
### Control the Scale of the Road Network
#### 4.1.1 Road Sub-Net Partition Considering Connectivity  
According to graph theory, the urban road network can be regarded as a directed graph (\(G\)), and the sub-net of the urban road network is a sub-graph of the directed graph (\(G\)). Because the urban road network system itself is a connected system, there must be a path between every two nodes in the road network. According to the definition of the strongly connected graph, we know that urban road network is a strongly connected graph. For a sub-net of a network, if there is a path between any two nodes in the sub-net, the sub-net is a connected sub-net. The problem of dividing a complete network into a connected sub-net can be transformed into solving the strongly connected components in a directed graph. The digraph is the complete network, and the strong connected component is the connected sub-net of the network.  
The Kosaraju algorithm or Tarjan algorithm is generally used to solve the strongly connected components of digraphs. The principle of the Kosaraju algorithm is simple. In order to obtain all strong connected components of graph \(G\), we first need a Depth First Search (DFS) of graph \(G\). Then, the inverse graph (\(G^{T}\)) of graph \(G\) is calculated, and then another DFS is carried out for the inverse graph (\(G^{T}\)); thus, the connected component of graph \(G\) is obtained. Although the Kosaraju algorithm has a simple process, it has a high time complexity due to the need for two DFS searches.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Time Efficiency** & **Complexity** & **Convergence** & **Robustness** & **Optimal Solution** \\ \hline Dijkstra & Wose & Easy & No & Good & Yes \\ A* & Good & Easy & Yes & Good & Yes \\ ACA & Worst & Complex & Yes & Good & Not \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the three algorithms.  
The Tarjan algorithm is a DFS algorithm implemented by recursion. The theoretical basis of the Tarjan algorithm is to perform a DFS search for a directed graph (\(G\)) from any node. Every strongly connected component in a directed graph is a sub-tree in a search tree. The core idea of the algorithm is to add the unprocessed node in the current search tree to the stack when DFS and to determine whether the node is a strongly connected component when backtracking. Compared with the Kosaraju algorithm, the DFS is performed only once in the Tarjan algorithm, and the inverse graph is not needed, so the time complexity of Tarjan algorithm is lower.  
Therefore, in this paper, the Tarjan algorithm was selected to solve the connected sub-net of road network, and the appropriate sub-net was selected to be used as the search area.