esa-sceva/satcom-chunk-collection huggingface dataset:
Remote Sens._**2012**, \(4\), 404-423; doi:10.3390/rs4020404  
_Remote Sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
An Object-Based Image Analysis Method for Monitoring Land Conversion by Artificial Sprawl Use of RapidEye and IRS Data  
**[PERSON] 1,*, [PERSON] 2 and [PERSON] 2**  
1 CIRAD, UMR TETIS, 500 rue Francois Breton, F-34398 Montpellier, France  
2 IRSTEA, UMR TETIS, 500 rue Francois Breton, F-34093 Montpellier Cedex 5, France;  
E-Mails: [PERSON]RESS] (E.B.); [PERSON]] (M.B.)
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +33-567-548-727; Fax: +33-567-548-754.  
_Received: 21 December 2011; in revised form: 17 January 2012 / Accepted: 17 January 2012 / Published: 2 February 2012_  
**Abstract:** In France, in the peri-urban context, urban sprawl dynamics are particularly strong with huge population growth as well as a land crisis. The increase and spreading of built-up areas from the city centre towards the periphery takes place to the detriment of natural and agricultural spaces. The conversion of land with agricultural potential is all the more worrying as it is usually irreversible. The French Ministry of Agriculture therefore needs reliable and repeatable spatial-temporal methods to locate and quantify loss of land at both local and national scales. The main objective of this study was to design a repeatable method to monitor land conversion characterized by artificial sprawl: (i) We used an object-based image analysis to extract artificial areas from satellite images; (ii) We built an artificial patch that consists of aggregating all the peripheral areas that characterize artificial areas. The \"artificialized\" patch concept is an innovative extension of the urban patch concept, but differs in the nature of its components and in the continuity distance applied; (iii) The diachronic analysis of artificial patch maps enables characterization of artificial sprawl. The method was applied at the scale of four departments (similar to provinces) along the coast of Languedoc-Roussillon, in the South of France, based on two satellite datasets, one acquired in 1996-1997 (Indian Remote Sensing) and the other in 2009 (RapidEye). In the four departments, we measured an increase in artificial areas of from 113,000 ha in 1997 to 133,000 ha in 2009, _i.e._, an 18% increase in 12 years. The package comes in the form of a 1/15,000 valid cartography, usable at the scale of a commune (the smallest territorial division used for administrative purposes in France) that can be adapted to departmental and regional scales. The method is reproducible in homogenous spatial-temporal terms, so that it could be used periodically to assess changes in land conversion rates in France as a whole.  
Keywords:public policy; land use; artificial sprawl; artificialized patch; Object-Based Image Analysis (OBIA); France
# CoSimulating Communication Networks and Electrical System for Performance Evaluation in Smart Grid
## 5 Simulation Study
### Simulation Results
Figure 13 shows the CDF of the transmission delay of the simulations. As shown in Figure 13, WiMAX-Ethernet shows almost similar distribution of transmission delay which has about 0.1 s at 99% probability in the 13 node and the 34 node cases, while WLAN Mesh shows over 1 s at 70% probability in the 34 node system. However, 13 node WLAN Mesh system shows lower delay in the 13 node case. The evaluation shows that WLAN Mesh network system performs lower delay communication than WiMAX-ethernet network system in smaller network, and WiMAX-ethernet network system could be suitable choice for large-scale and delay-constrained smart grid application. Based on all the simulation results, we can confirm that the proposed OOCoSim can successfully conduct the simulation for the smart grid where the power grid and the communication infrastructure are tightly coupled with each other. Additionally, we can also realize that we can use OOCoSim to see how the underlying network affects the power grid. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Total Power Loss** & **Voltage Index** \\ \hline w/o DR & 136,180 kW & 0.4156 \\ DR (Ideal) & 94,545 kW & 0.2686 \\ DR (WLAN Mesh) & 119,340 kW & 0.6101 \\ DR (WiMax-Ethernet) & 95,412 kW & 0.2735 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The power loss and the voltage of 34 Node system. Figure 12: The power loss and the voltage of 34 Node system. (**a**) Total power loss; (**b**) Voltage of 852 bus.
## 3 Results and Discussion
### Characterization of the Tablet by Scanning Electron Microscopy  
Figure 6 shows the different microphotographs obtained by scanning electron microscopy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Formulation**} & \multicolumn{3}{c}{**Order 0**} & \multicolumn{3}{c}{**Order 1**} & \multicolumn{3}{c}{**Higuchi**} & \multicolumn{3}{c}{**Korsmeyer–Peppas**} \\ \cline{3-10}  & & \(\mathbf{R}^{2}\) & \(\mathbf{K_{0}}\) & \(\mathbf{R}^{2}\) & \(\mathbf{K_{1}}\) & \(\mathbf{R}^{2}\) & \(\mathbf{K_{H}}\) & \(\mathbf{R}^{2}\) & \(\mathbf{K_{KP}}\) & \(\mathbf{n}\) \\ \hline \multirow{4}{*}{Without digestive enzymes} & CP1 & 0.31 & 3.02 & 0.08 & 0.03 & **0.69** & 21.68 & 0.36 & 0.94 & 1.11 \\  & CP2 & 0.24 & 2.96 & 0.05 & 0.03 & **0.63** & 20.52 & 0.32 & 0.91 & 1.06 \\  & CP3 & 0.24 & 2.91 & 0.07 & 0.03 & **0.63** & 21.90 & 0.37 & 0.97 & 1.11 \\  & CP4 & 0.26 & 0.89 & 0.07 & 0.03 & **0.65** & 21.52 & 0.36 & 0.95 & 1.12 \\  & CP1 & 0.36 & 3.09 & 0.09 & 0.03 & **0.73** & 21.75 & 0.37 & 0.94 & 1.10 \\ \multirow{4}{*}{With digestive enzymes} & CP2 & 0.24 & 2.70 & 0.05 & 0.03 & **0.63** & 20.03 & 0.02 & 0.91 & 1.06 \\  & CP3 & 0.24 & 2.91 & 0.07 & 0.03 & **0.63** & 21.90 & 0.37 & 0.97 & 1.11 \\  & CP4 & 0.26 & 2.89 & 0.07 & 0.03 & **0.65** & 21.52 & 0.36 & 0.95 & 1.12 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Parameters of mathematical models and correlation coefficients.  
Figure 6A represents the microphotographs of the plant powder. It is composed of microparticles of practically homogeneous size with a large variation of morphology (spherical, cylindrical, and other particle shapes). The use of an ETD detector with a red filter allows us to see very clearly the cellulose fibers, which are the main components of the plant powder (Figure 6B).  
Figure 6C represents the tablet surface, and Figure 6D represents the tablet matrix; the surface is heterogeneous, and the interior is composed of compacted powder microparticles.  
Figure 6: Scanning electron microscope microphotography. Microphotographs of plant powder using an EDT detector (**A**) and using an EDT detector with red staining (**B**). Microphotographs of the tablet surface (**C**) and the tablet matrix (**D**). Microphotographs of a non-deformable microgranule on the surface of the tablet using an LFD detector (**E**) and using an BSED detector with red staining (**F**).  
There are two types of microparticles that are deformable (Figure 6D); those which are not deformable are shown in Figure 6E, and on this same sample, a red color with the ETD detector was applied (Figure 6F). It is observed that the plant powder microparticles may show cracks and sometimes even break. The crystalline structure of the cellulosic fiber lanyards is observed on non-deformable powder microparticles.  
Indeed, cellulose is a natural polymer that is not compressible [12, 45]. In the present study, it was demonstrated that when this polymer is mixed with other plant metabolites, it can undergo direct compression.
# An Improved Deviation Coupling Control Method for Speed Synchronization of Multi-Motor Systems
###### Abstract  
In order to enhance the synchronization of welding robot arms and improve welding quality, this study proposes a fuzzy PID-based improved deviation coupling multi-axis synchronous control method. Firstly, in response to the intricacies inherent in the compensation mechanism of the deviation coupling control structure and the substantial volume of system computation, the integration of average speed and sub-average speed is proposed to optimize the speed compensator. This integration aims to mitigate speed synchronization errors, minimize synchronization adjustment time, and elevate overall system synchronization performance. Moreover, the fuzzy PID algorithm is employed to design the controller to realize the single-motor adaptive control, leading to improvement in both system stability and dynamic response performance. Finally, a simulation model for six-axis synchronization control and an experimental platform were developed. Both simulation and experimental results demonstrate that the improved deviation coupling control method exhibits superior synchronization performance. The proposed multi-axis synchronous control method effectively heightens the synchronous performance of the six-degrees-of-freedom robotic arm.  
robot arm; multi-axis synchronization; deviation coupling; fuzzy PID +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Broad Band Optical Sensors and Wireless Sensor Network (WSN)  
A wireless sensor network (WSN), composing of 36 nodes, was used to measure the fraction of absorbed photosynthetically active radiation (_f_APAR). The WSN sensor nodes were arranged in a hexagonal pattern, spaced every 20 m over a one-hectare plot located within the flux footprint. The spatial extent of the WSN footprint accounted for approximately 55% of measured flux measurements. Additional details on the WSN deployment and technical capabilities can be found in [PERSON] et al. [53]. Individual nodes were outfitted with an upward and downward facing quantum sensors (SQ-110, Apogee Instruments Inc.), providing transmitted PPFD and soil reflected PPFD measurements, respectively. Additional nodes were placed on the flux tower, positioned above the canopy. Upward and downward looking quantum sensor pairs provided measurements of incident and reflected PPFD, respectively. The sampling interval of WSN nodes was 15 min. _f_APAR was calculated using the equation:  
\[f\mathrm{APAR}=1-t-r+(t\times\mathrm{r_{s}}) \tag{7}\]  
where \(t\) is the fraction of transmitted radiation, \(r\) is the reflected radiation from the canopy, and \(r_{s}\) is the soil reflectance component.  
A broadband normalized difference vegetation index (NDVI) was derived from upward and downward looking quantum and pyranometer pairs installed on the flux tower, positioned above the canopy. Spot radiometers had a sampling interval of 10 min. Broadband NDVI was calculated using the equation:  
\[\mathrm{NDVI}=(\rho\mathrm{PYR}-\rho\mathrm{PPFD})/(\rho\mathrm{PYR}+\rho \mathrm{PPFD}) \tag{8}\]  
where \(\rho\mathrm{PYR}\) is the solar radiation reflectance calculated from the upwelling-downwelling radiation from pyranometer pairs; and \(\rho\mathrm{PPFD}\) is the total reflectance of PPFD derived from upwelling: downwelling PPFD sensor pairs. Broadband NDVI values have been shown as adequate proxies of narrowband NDVI measurements [54]. Seasonal NDVI values were used to identify phenolic stages. A second derivative function was used to identify the transitions from maturity to leaf senescence (21 August 2015).
## 5 Use Case: Collaborative Issues Detection in an Intelligent Vehicle Surveillance System
### The Operational Flow of Messages and Commands Illustration  
As shown in Figure 2, when an autonomous vehicle, \(V_{i}\), detects an issue on road 1 leading to the stadium, it creates a message stating that this road has certain issues, attaches the time and a captured image, and then \(V_{i}\) sends this information to LEC. Upon receiving the information, LEC verifies the integrity of the message and the validity of its signature. If we want to add a layer of security in applications that do not require real-time processing, LEC also extracts the issue from the captured image using artificial intelligence tools to ensure the actual existence of the issue and that \(V_{i}\) made the correct decision.  
Figure 1: Hierarchical levels in the use case.  
After extracting \(V_{i}\)'s signature embedded in the message, LEC signs this signature, i.e., an implicit signature of the original message, then sends the new ciphertext to NTCC. Upon receiving the ciphertext, NTCC extracts the information from it, which is the original message and signature, then verifies the integrity of the message and the signature validity, and signs it. Finally, NTCC sends the ciphertext which contains the original message of the creator and the aggregated signature (hierarchical signature); and also sends an order to all vehicles in the area surrounding the stadium to avoid road 1 and take other road options.
# Improved Brain Storm Optimization Algorithm Based on Flock Decision Mutation Strategy
## 1 Introduction
Slow convergence hampers the overall efficiency of the algorithm; specifically, achieving a predetermined level of accuracy requires more time when the convergence rate is lower, which diminishes the algorithm's practical utility. Optimization accuracy is a critical indicator of an algorithm's efficacy, and a lack of precision indicates substandard performance. Moreover, there is a risk that the algorithm could get caught in a local optimum, resulting in considerable time lost during iterative processes and affecting the ultimate optimization accuracy. Hence, refining the BSO algorithm in this study aims to enhance the rate of convergence and the precision of optimization beyond the current improvements and to augment the algorithm's capacity to escape local optima in multi-model problems. Furthermore, an improved clustering technique is required to address the high computational demands and low clustering accuracy caused by the K-means clustering method in the conventional BSO algorithm. Ultimately, the objective to enhance the precision of weight calculation in AUV performance evaluation can be realized. Overall, this work proposes the flock decision mutation strategy and introduces the good point set and spectral clustering. The paper's primary innovations include the algorithms' exceptional capability to escape local optima during complex optimization tasks involving multiple models and high dimensions coupled with their enhanced rate of convergence and greater precision in optimization. Subsequently, a refined brain storm optimization algorithm that incorporates the flock decision mutation strategy has been introduced. This work: (1) designs the flock decision mutation strategy to improve the optimization accuracy; (2) introduces the good point set designed to establish the initial population to enhance the diversity of the population at the beginning of the iteration process; (3) replaces K-means clustering with spectral clustering to improve the clustering accuracy of the algorithm; (4) extensive experimentation and data analysis are conducted utilizing a benchmark test suite from the CEC2018 [21]; (5) multiple simulations based on the combined weight model in AUV intelligence evaluation further confirm the efficacy of the suggested algorithm.
## 5 Discussion and Recommendations
### Recommendations  
In this section, we present our recommendations based on our results, as well as the implications of our experiments. Generally, the deployment of big data platforms in fog computing layers leads to better performance and is appropriate in most cases.  
According to our results, as reported in in Section 4.2.1, we specify the following recommendations for the **batch processing paradigm** :  
* Deploying Spark in the edge computing layer can be a reasonable option if (a) the dataset size for training is small, (b) a long training time is tolerable for the business objective, and (c) only machine learning algorithms included in the Spark MLlib library are used. Our experiments reported in Section 4.2.1 were conducted under such a scenario and confirmed that training can be performed in the edge layer, achieving reasonable performance.
* When training large and complex models, it is necessary to use a distributed deep learning library on Spark, which is still not available or compatible with edge resources. Fog-layer Spark deployment is recommended for model training, where we can utilize the collaborative power of nodes in the cluster and a distributed deep learning library for speedup, as the cloud EMR cluster incurs high costs when sending large amounts of data to the cloud.
* Cloud EMR clusters are more suitable for the processing of extremely large datasets and when the throughput is important.  
Based on our experiments, as presented in Figure 6 and Table 2 in Section 4.2.2, we list the following recommendations for the **stream processing paradigm**:  
* Fog-layer-based stream processing achieves the best performance in terms of both the prediction time and detection rate. Although we can appropriately deploy the Flink streaming platform in the edge layer, the prediction time and detection rate are significantly low due to the size of the object detection model, which is hosted in resource-constrained devices.
* The advantage is pronounced only when multiple video streams are distributed to several nodes in the cluster and when real-time data analytics tasks that do not involve large computations in each individual task are distributed and parallelized among the nodes in the cluster.  
In reference to the obtained results reported in Section 4.2.3, we present the following recommendations for the **function processing paradigm**:  
* The fog computing layer is recommended for function processing, in which we observe the lowest latency for function invocations compared to the edge and cloud layers.
* Function processing is best-suited for applications with uniform and regular arrival patterns. Specifically, for the applications, the time between function invocations is not too long (i.e., function inactivity time), which can lead to cold-start latency, and the time between function invocations is not too short, which can lead to increased latency when serving multiple synchronous requests at the same time.
# Application of Absolute Nodal Coordinate Formulation in Calculation of Space Elevator System
## 3 Numerical Simulation
### Finite Element Method  
The FEM is realized by ANSYS software; the element is BEAM188 with a rigid cross-section. Since ANSYS cannot handle the loading of acceleration with coordinate changes, some extra calculation steps should be taken. The logic diagram of the calculation is shown in Figure 5.  
Figure 4: Forces acting on the lumped-mass.  
Figure 3: The lumped mass tether model.  
The mass matrix is derived by the established structure of the rope. With this mass matrix, together with the nodal position, the gravitational and centrifugal force can be calculated. In addition, the force can be applied as a nodal force back to the structure. When the new nodal position is calculated, if it is close to the last result, the result will be output and calculation ends, otherwise the nodal position is updated and calcultation continues.
## 3 Methodology
### Methods
#### 3.2.2 Constructing Time Series Data with a Pre-Defined Time Scale \(t_{s}\)
In addition, statistical feature (\(\mathcal{P}\)) is extracted as \(\chi(w_{o_{i}},o_{i})\), where \(\chi(\cdot)\) indicates the ANOVA test, \(w_{o_{i}}\) represents the detailed coefficients of \(o_{i}\), and \(o_{i}\) denotes the \(i\)th vector of the original sequences \(\mathcal{O}\) at \(\
abla t_{i}\). This feature represents a \(p\)-value, indicating if there is any statistical difference between the original sequences and the wavelet coefficients. We also compute an additional feature using the first moment (\(\mathcal{E}\)) as \(\frac{1}{|o_{i}|}\sum o_{i}\) in V\(t_{i}\). Algorithm 1 presents a pseudo-code that converts network traffic series to a targeted time scale (\(t_{s}\)). ```
Input: network traffic observations (\(O\)) Output: converted time series data (\(F\))
1 for\(i=0\)to\(\frac{m_{i}}{t_{s}}\)do
2 for\(\forall j,v_{j}\in O_{i}\)do /* \(O_{i}\): \(i^{th}\) observations, \(v_{j}\): \(j\)th variable */
3 if\(v_{j}\in\mathcal{C}_{N}\)then
4 Determine \(|\phi(v_{j})|\) /* \(\phi(\cdot)\): a mapping to determine unique attributes */
5 Generate one-hot encoded nominal variables, \(n_{1},n_{2},\cdots,n_{|\phi(\cdot)|}\)
6 for\(k=1,2,\cdots,|\phi(\cdot)|\)do
7\(\mathcal{N}_{j}[k]=\Sigma I(n_{k}\in v_{j})\) /* \(\mathcal{N}\): nominal variable features */
8
9 elseif\(v_{j}\in\mathcal{D}_{Y}\)then
10 Determine \(|\phi(v_{j})|\)
11 Generate one-hot encoded dependent variables, \(y_{1},y_{2},\cdots,y_{|\phi(\cdot)|}\)
12 for\(k=1,2,\cdots,|\phi(\cdot)|\)do
13\(\mathcal{Y}_{j}[k]=\Sigma I(y_{k}\in v_{j})\) /* \(\mathcal{Y}\): dependent variable features */
14
15 else /* \(\chi(\cdot)\): ANOVA test, \(\varphi_{w}(\cdot)\): wavelet transform, \(\psi(\cdot)\): permutation entropy */
16\(\mathcal{W}_{i}[j]=\psi(\varphi_{w}(v_{j}))\) /* \(\mathcal{W}\): wavelet features */
17\(\mathcal{P}_{i}[j]=\chi(\varphi_{w}(v_{j}),X_{j})\) /* \(\mathcal{P}\): statistical features */
18\(\mathcal{E}_{i}[j]=\frac{1}{|v_{j}|}\sum v_{j}\) /* \(\mathcal{E}\): first-moment features */
19
20
```  
**Algorithm 1**Conversion of network traffic data to time series format with a pre-defined time scale \(t_{s}\)
# Optimization on Linkage System for Vehicle Wipers by the Method of Differential Evolution
## 2 Materials and Methods
### Method of Differential Evolution
#### 2.2.3 Crossover  
The crossover operation is a method for producing an extra point \(\mathbf{Z}_{i,G}\) using the coordinates of \(\mathbf{v}_{i,G}\) and of \(\mathbf{x}_{i,G}\) with randomness. The _crossover ratio_\(CR\) is a real number in the interval \([0,1]\). For each \(i\) of the population set \(\{1,2,3,\cdots,M\}\), we choose \(d\) real numbers \(w_{i,G,1},\cdots,w_{i,G,d}\) in \([0,1]\) as _\"weights\"_. Besides we choose, for this \(i\), an integer \(s_{i,G}\) from the dimension set \(\{1,\cdots,d\}\). We define the point  
\[\mathbf{Z}_{i,G}=(Z_{i,G,1},\cdots,Z_{i,G,d})\]  
As follows:  
\[Z_{i,G,j}=\left\{\begin{aligned} & v_{i,G,j},\text{ if }j=s_{i,G}\text{ or }w_{i,G,j}\leq CR;\\ & x_{i,G,j},\text{ if }j\
eq s_{i,G}\text{ and }CR<w_{i,G,j}.\end{aligned}\right. \tag{28}\]  
The definition (28) implies the \(s_{i,G}\)-th coordinate of the point \(\mathbf{Z}_{i,G}\) is definitely taken from that of \(\mathbf{v}_{i,G}\). However, for \(j\
eq s_{i,G}\), the \(j\)-th coordinate of \(\mathbf{Z}_{i,G}\) is _dependent on CR_. When \(CR\) is small, more of the the coordinates of \(\mathbf{Z}_{i,G}\) are taken from that of \(\mathbf{x}_{i,G}\). When \(CR\) is large, more of the the coordinates of \(\mathbf{Z}_{i,G}\) are taken from that of \(\mathbf{v}_{i,G}\). Thus the parameter \(CR\) is important for controlling the deviation of the population \(\{\mathbf{Z}_{1,G},\cdots,\mathbf{Z}_{M,G}\}\) from the population \(\{\mathbf{x}_{1,G},\cdots,\mathbf{x}_{M,G}\}\).  
It could happen that the some of the points \(\{\mathbf{Z}_{1,G},\cdots,\mathbf{Z}_{M,G}\}\) are _not_ in the bounded domain  
\[D=[x_{1}^{(L)},x_{1}^{(U)}]\times\cdots[x_{d}^{(L)},x_{d}^{(U)}]\]  
If \(Z_{i,G,j}<x_{j}^{(L)}\) or \(Z_{i,G,j}>x_{j}^{(U)}\) happens, we will _redefine_\(Z_{i,G,j}<x_{j}^{(L)}\) as follows:  
\[Z_{i,G,j}=x_{j}^{(L)}+(randZ_{i,G,j}[0,1])\cdot(x_{j}^{(U)}-x_{j}^{(L)}) \tag{29}\]  
in which \(randZ_{i,G,j}[0,1]\) is a randomly chosen real number in the interval \([0,1]\). This _modification of crossover_ ensures that all the points \(\{\mathbf{Z}_{1,G},\cdots,\mathbf{Z}_{M,G}\}\) are included in the bounded domain \(D\).