esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model
## 2 SCADA Data
### Predictive Fault Diagnosis  
Fault diagnosis aims at accurately identifying the fault types within a WT in a real-time application. However, it is insufficient to prevent damage caused by some severe failures only through online fault diagnosis. Then, fault prognosis is recommended by providing the predictive fault diagnosis prior to the fault occurrence, which decreases the maintenance fees and extends machinery life.  
The observed dataset for online fault diagnosis is expressed by \(\{X_{t},\ Y_{t}\}\), where \(X_{t}\) and \(Y_{t}\) are the given input data and the resultant diagnosed fault class. For example,  
Figure 5: Feature importance under RF classifier (the 33 features above the red line are reserved).  
as the SCADA data are collected at 10 min intervals, the 10 min, 20 min, and 30 min ahead fault predictions can be achieved with the modified datasets, \(\{X_{t-1},~{}Y_{t}\}\), \(\{X_{t-2},~{}Y_{t}\}\), and \(\{X_{t-3},~{}Y_{t}\}\), respectively, based on the original dataset, \(\{X_{t},~{}Y_{t}\}\). Thus, the predictive performances under \(n\)-step in advance will be determined using the modified dataset, \(\{X_{t-n},~{}Y_{t}\}\).
# Parameterized Modeling and Planning of Distributed Energy Storage in Active Distribution Networks
## 4 Mixed-Integer Second-Order Cone Programming (MISOCP) Model Conversion
### Scalability Verification  
The modified IEEE 123-node system is tested to verify the scalability of the proposed method on large-scale ADNs. The detailed parameters can refer to [36]. Three WTs and six PVs are integrated into the networks, of which the basic installation parameters are shown in Table 11. The test case is shown in Figure 5.  
The results of the economic benefits case study are shown in Tables 12 and 13. It can be seen from the results that a lead-acid battery is selected to allocate in ADN and the economic benefits are improved remarkably.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Location** & **Power Capacity (kVA)** & **Energy Capacity (kWh)** \\ \hline  & 32 & 70 & 300 \\ Only lead-acid battery & 47 & 110 & 1220 \\  & 58 & 80 & 760 \\  & 119 & 150 & 1720 \\ \hline Only Li-ion battery & — & — & — \\ \hline Only VRB & — & — & — \\ \hline \hline \end{tabular}
\end{table}
Table 12: Planning results of economic benefits improvement in IEEE 123-node system.  
Figure 5: Structure of the modified IEEE 123-node system.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameter** & \multicolumn{3}{c}{**Wind Turbines**} & \multicolumn{6}{c}{**Photovoltaic Generators**} \\ \hline Location & 28 & 92 & 108 & 33 & 42 & 86 & 97 & 111 & 116 \\ Capacity (kVA) & 800 & 1200 & 1000 & 300 & 300 & 200 & 200 & 400 & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Parameters of DGs.  
The results of the power fluctuation smooth by each DES type are shown in Tables 14 and 15. The coordinated planning results are the same as the planning of VRB in Table 14. It can be seen from the results that the power fluctuation cost is reduced $58,495 by the integration of VRB, a decrease of 49.73%. The Li-ion battery is also applied to power smoothing, but the effect is inferior to VRB.
# Blockchain-Empowered Fair Computational Resource Sharing System in the D2D Network +
## 5 Problem Formulation
### Connectivity Awareness  
To analyse the connectivity between requesters and helpers for more accurate and cost-effective computation assignment, we first define a \(p\times(h+1)\) probability matrix \(R_{t}\) at each time slot \(t\in\{0,1,...,\frac{T}{M}\}\). For a given \(t\), the element \(R_{i,j,t}\) is the probability of connection between \(p_{i}\) and \(h_{j}\) if \(j\in\{1,2,...,h\}\) and \(R_{i,(h+1),t}\) indicating the probability of connection between \(p_{i}\) and herself/himself. Obviously, \(R_{i,(h+1),t}=1\)\(\forall\)\(i,t\). At the start of each task period, i.e., \(t=0\), we randomly generate \(R_{i,j,0}\)\(\forall\)\(i\in\{1,2,...,p\},j\in\{1,2,...,h\}\) based on the pre-defined initial connection probability. Thereafter, according to Equation (6), we generate \(R_{i,j,t}\)\(\forall t\in\{1,2,...,\frac{T}{M}\}\) for each \(p_{i}\)-\(h_{j}\) pair.  
At any given time slot \(t\in\{1,2,...,\frac{T}{M}\}\), we define a \(p\)-element vector \(\vec{l}_{t}\) with its element \(\vec{l}_{i,t}\) representing the amount of self-computing computational task assigned to \(p_{i}\) and a \(p\times h\) matrix \(J_{t}\) with element \(I_{i,j,t}\) representing the amount of assisting computational task assigned to \(h_{j}\) for \(p_{i}\). By concatenating \(J_{i,j,t}\) and \(\vec{l}_{i,t}\), we get a \(p\times(h+1)\) computation assignment matrix \(M_{t}=[J_{i,j,t}\)\(\vec{l}_{i,t}]\) at time slot \(t\). Joining all \(M_{t}\) where \(t\in\{1,2,...,\frac{T}{M}\}\), we get a \(p\times(h+1)\times\frac{T}{M}\) three-dimensional systemcomputation assignment matrix, \(M\), containing the computation assignment in a task period \(\tau\in[0,T]\). Consequently, \(M_{i,j,t}\) where \(j\in\{1,2,...h\}\) is the amount of computational task assigned to \(h_{j}\) for \(p_{i}\) and \(M_{i,j,h+1,t}\) is the amount of self-computing computational task assigned to \(p_{i}\) at time slot \(\tau=t\).  
Note that the element-wise product between \(R\) and \(M\),  
\[M^{exp}=M\odot R, \tag{8}\]  
is the matrix indicating the expected amount of computational task done and the result sent back to requesters. For example, \(M^{exp}_{i,j,t}=M_{i,j,t}\cdot R_{i,j,t}\) is the expected amount of assisting task done by \(h_{j}\) and sent back to \(p_{j}\) at time slot \(\tau=t\).
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 2 Designing DaaG
### Main Functions of DaaG
#### 2.3.2 Smart Data Processing  
The smartness that differentiates DaaG from conventional mobile sinks is as follows. First, DaaG collects the data from the sensors as:  
\[\left\langle id,pos,st,sv,timestamp\right\rangle\text{,}\]  
where \(id\) is the sensor's unique ID number, \(pos\) is the position of the sensor, \(st\) is the sensor type and \(sv\) is the value of the sensor. DaaG determines that there is a problem at some area or for some objects by using the collected sensor data \(sv_{t}\) and stored data \(sv_{t-1}\) with the following conditions:  
* the difference between \(sv_{t}\) and \(sv_{t-1}\) is larger than threshold value \(sv_{th}\),
* \(sv_{t}\) is lower than predefined lower bound \(sv_{lb}\),
* \(sv_{t}\) is higher than predefined upper bound \(sv_{ub}\),  
which can be described as follows,  
\[\left|sv_{t}-sv_{t-1}\right|\geq sv_{th}\quad\text{or}\quad sv_{t}\leq sv_{lb} \quad\text{or}\quad sv_{t}\geq sv_{ub}\text{.} \tag{1}\]  
Since we assumed the performance and the memory capacity of the sensor are low, we only consider \(sv_{t}\) and \(sv_{t-1}\). With this approach, the sensor could reduce the processing time, memory usage, and also the energy consumption. However, if the performance of the sensor is considerably higher for a complicated operation, the sensor could accumulate the data for a fixed period and analyze the accumulated value to detect the problematic sensor.  
Then, \(id\) of the sensor is added in a PSL. To manage the problem, DaaG calculates the correlation value between the problematic sensor and each actuator. The correlation value between the sensor and actuator is as follows,  
\[corr_{s_{i},a_{j}}=\frac{\lambda}{\text{dist}\left(s_{i},a_{j}\right)}+\left( 1-\lambda\right)\frac{av_{t}-av_{t-1}}{sv_{t}-sv_{t-1}}\text{,} \tag{2}\]  
where \(\text{dist}\left(s_{i},a_{j}\right)\) is the distance between the \(i\)-th sensor \(s_{i}\) and the \(j\)-th actuator \(a_{j}\), \(\lambda\) is the weight value, which can be determined according to the type of service, and \(av\) is the value from the actuator. Then, DaaG finds the actuator that has the highest correlation with the problematic sensor. This means that the chosen actuator is the best to monitor the problematic sensor out of all other actuators. This sensor-actuator matching can help to solve problems and issues by making the actuator focus on the sensors. In the paper, these correlation values are used to calculate the path-planning of DaaG.
## 2 Materials and Methods
### Flood Conditioning Factors  
The flood susceptibility map was generated utilizing ten environmental criteria, including elevation, slope, aspect, LULC, lithology, stream distance, stream density, TWI, surface runoff, and TRI. According to [PERSON] et al. [63], elevation has a significant impact on flood vulnerability because there is a negative correlation between elevation and floods. In other words, flash floods have less of an impact as elevation rises [64]. DEM elevations in the research region ranged from 61 to 190 m a.s.l (Figure 6a), with greater elevations found in the area's east and southeast. Flooding is a physiographic event that is closely tied to slope since water tends to build up in lower slope areas [63; 65]. ArcGIS 3-D analysis methods were used to prepare a slope map using ASTER data (Figure 6b). The slope aspect can have a significant impact on the direction of rainfall, and the significance of aspect is found in its relationship to the direction of prevalent meteorological phenomena [32]. The aspect thematic layer was also created in ArcMap using spatial analysis tools (Figure 6c). Landuse/Landcover (LU/LC) is a crucial factor impacting flood susceptibility [66; 67]. LULC affects infiltration, evapotranspiration, and surface runoff generation directly or indirectly. Supervised classification from Landsat-8 OLI imagery was employed to build the LULC map [68] (Figure 6d). Additionally, lithology is essential in causing floods because impermeable rock units increase runoff by decreasing infiltration rates. The lithological units of the study area were digitized at a scale of 1:500,000 from CONOCO [69] (Figure 6e). In addition, stream distance is the main pathway for flood discharge; thus, places near streams are more vulnerable to flooding [70]. Stream networks in the research area were identified using Arc Hydro tools that are part of ArcMap. Data from the stream network was used to create thematic layers that show the stream distance and stream density (Figure 6f,g). The topographic wetness index (TWI) and terrain ruggedness index (TRI) are determined to identify high topographic areas and wet areas at high risk of flash flooding. Spatial distribution maps of TWI and TRI were created using ArcGIS, with values ranging from 3.1 to 20 and from 0.11 to 0.89, respectively (Figure 6h,i). Due to a lack of infiltration surfaces, surface runoff is typically reinforced in urban regions, which leads to destructive urban flooding. Forecasting floods, which occur suddenly, flashily, and for a brief period of time, heavily depends on surface runoff caused by storm rainfall [71]. Based on a soil conservation service (SCS) model, a surface runoff database was created (Figure 6j).  
Figure 5: Testing and validating dataset used in the current work.
## 6 Conclusions  
The robustness of the innovative active control system for swing vibration control, namely the Active Rotary Inertia Driver (ARID) system, has been investigated in this study. In order to further verify the characteristics of the ARID system, numerical analyses and experimental investigations of five working conditions (free vibration, forced vibration, sweep excitation, earthquake excitation, and sea wave excitation) were carried out. The numerical analyses and experimental results of the ARID system, and numerical results of the TRID system, were compared to demonstrate the effectiveness and robustness of the proposed ARID system. According to experimental and numerical results, the following conclusions can be drawn:  
1. The ARID system is effective and feasible in structural swing vibration control and it exhibits a better control robustness than the TRID system;  
2. The feasibility of the ARID system for multi-type hazard excitation-induced structural vibration control has been successfully demonstrated;  
3. The closed-loop active control of the ARID system is effective. The ARID system is less sensitive to different external excitations and can deliver a superior control performance.  
**Author Contributions:** [PERSON] initiated the problem, proposed the idea, defined the research essentials, conceived the method, and revised the paper. [PERSON] carried out the theoretical research, parametric study, and experiments and prepared the draft.  
**Funding:** The research is financially supported by the Ministry of Science and Technology of China (Grant No. 2017 YFC0703603), the National Natural Science Foundation of China (Grant No. 51678322), the Taishan Scholar Priority Discipline Talent Group program funded by Shandong Province, and the first-class discipline project funded by the Education Department of Shandong Province.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 5 Conclusions  
In this study, the multiple features of multitemporal PlanetScope imagery were comprehensively utilized to construct object-oriented RF/SVM classification methods and pixel-based RF/SVM methods for monitoring rubber forests, and good results were achieved. The following conclusions can be drawn from this study.  
(1) The optimal time window for monitoring the rubber forests in the study area using remotely sensed imagery was determined to span from the 49 th day to the 65 th day of 2019 based on MODIS-NDVI time series change characteristics; thus, the PlanetScope images utilized in this study were confirmed based on this period.  
(2) The contribution rate of the dMSR feature was the largest among all considered features, regardless of whether pixel-based methods or object-oriented methods were used. Among the eight texture features, MEAN displayed the greatest contribution, with a weight of 1.10 in the pixel-based methods, and DIS exhibited the greatest contribution in the object-based methods, with a weight of 0.97.  
(3) Compared to the pixel-based approach, the object-oriented RF/SVM classification approach achieved better classification results, with an overall accuracy of 93.87% and a KIA of 0.92. The highest producer's accuracy and user's accuracy of plantation classification with this method reached 95.18%. The object-based SVM classifier exhibited better performance than the object-based RF classifier to some extent.  
(4) This study showed that it is feasible to use PlanetScope data to perform rubber tree monitoring. PlanetScope sensors are thus acceptable data sources that can effectively solve problems associated with missing images in the optimal monitoring period, which includes the rubber defoliation and leaf-extraction phases; finally, these images can be used in other practical applications.  
Conceptualization, B.C. and W.H.; funding acquisition, W.H., B.C. and H.Y.; methodology, B.C.; investigation, B.C. and Q.C.; writing--original draft preparation, B.C.; writing--review and editing, B.C. and H.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA19080304), the National Natural Science Foundation of China (No. 41801352), and the Hainan Provincial Major Science and Technology Program of China (No. ZDKJ2019006).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Covert Surveillance Video Transmission with QAM Constellations Modulation
## 3 Performance Analysis of Cover Surveillance Video Transmission
### Analysis of Covertness Performance
(b) Case 2: If we have \(\sum_{l=1}^{N}\sum_{k=1}^{N}p_{j,k}\mathbf{d}[j]=0\) and \(\sum_{l=1}^{N}\sum_{k=1}^{N}p_{j,k}\mathbf{d}[k]=0\), we can obtain  
\[=\iint\sum_{t=1}^{N}\sum_{s=1}^{N}p_{t,s}q_{t,s,w}(x,y)\log\frac{ \sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}q_{j,k,w}(x,y)}{q_{0}(x,y)}\mathrm{d}x \mathrm{d}y, \tag{30}\]3 \[=\iint\sum_{t=1}^{N}\sum_{s=1}^{N}p_{t,s}q_{t,s,w}(x,y)\log\frac{ \sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}q_{j,k,w}(x,y)}{q_{0}(x,y)}\mathrm{d}x \mathrm{d}y, \tag{30}\]4 \[=\iint\sum_{t=1}^{N}\sum_{s=1}^{N}p_{t,s}q_{t,s,w}(x,y)\log\frac{ \sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}q_{j,k,w}(x,y)}{q_{0}(x,y)}\mathrm{d}x \mathrm{d}y, \tag{30}\]5 (46) \[=\iint\sum_{t=1}^{N}\sum_{s=1}^{N}p_{t,s}q_{t,s,w}(x,y)\log\frac{ \sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}q_{j,k,w}(x,y)}{q_{0}(x,y)}\mathrm{d}x \mathrm{d}y, \tag{30}\]6 (47) \[=\iint\sum_{t=1}^{N}\sum_{s=1}^{N}p_{t,s}q_{t,s,w}(x,y)\log\frac{ \sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}q_{j,k,w}(x,y)}{q_{0}(x,y)}\mathrm{d}x \mathrm{d}y, \tag{30}\]7 \[=\iint\sum_{t=1}^{N}\sum_{s=1}^{N}p_{t,s}q_{t,s,w}(x,y)\log\frac{ \sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}q_{j,k,w}(x,y)}{q_{0}(x,y)}\mathrm{d}x \mathrm{d}y, \tag{30}\]8  
Combining (39) with (45)-(48), we have  
\[=\iint\sum_{t=1}^{N}\sum_{s=1}^{N}p_{t,s}q_{t,s,w}(x,y)\log\frac{ \sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}q_{j,k,w}(x,y)}{q_{0}(x,y)}\mathrm{d}x \mathrm{d}y, \tag{30}\]9 \[q_{j,k,w}(x,y) =\frac{1}{2\pi\sigma_{w}^{2}}\Bigg{[}\exp\bigg{(}-\frac{(x+g_{w} \mathbf{d}[j]\beta)^{2}}{2\sigma_{w}^{2}}-\frac{(y+g_{w}\mathbf{d}[k]\beta)^{ 2}}{2\sigma_{w}^{2}}\bigg{)}\Bigg{]}, \tag{31}\]0 \[q_{j,k,w}(x,y) =\frac{1}{2\pi\sigma_{w}^{2}}\Bigg{[}\exp\bigg{(}-\frac{(x+g_{w} \mathbf{d}[j]\beta)^{2}}{2\sigma_{w}^{2}}-\frac{(y+g_{w}\mathbf{d}[k]\beta)^{ 2}}{2\sigma_{w}^{2}}\bigg{)}\Bigg{]}, \tag{31}\]1  
and the transmission rate is given by  
\[q_{j,k,w}(x,y) =\frac{1}{2\pi\sigma_{w}^{2}}\Bigg{[}\exp\bigg{(}-\frac{(x+g_{w} \mathbf{d}[j]\beta)^{2}}{2\sigma_{w}^{2}}-\frac{(y+g_{w}\mathbf{d}[k]\beta)^{ 2}}{2\sigma_{w}^{2}}\bigg{)}\Bigg{]}, \tag{31}\]2 \[q_{j,k,w}(x,y) =\frac{1}{2\pi\sigma_{w}^{2}}\Bigg{[}\exp\bigg{(}-\frac{(x+g_{w} \mathbf{d}[j]\beta)^{2}}{2\sigma_{w}^{2}}-\frac{(y+g_{w}\mathbf{d}[k]\beta)^{ 2}}{2\sigma_{w}^{2}}\bigg{)}\Bigg{]}, \tag{31}\]3 \[q_{j,k,w}(x,y) =\frac{1}{2\pi\sigma_{w}^{2}}\Bigg{[}\exp\bigg{(}-\frac{(x+g_{w} \mathbf{d}[j]\beta)^{2}}{2\sigma_{w}^{2}}-\frac{(y+g_{w}\mathbf{d}[k]\beta)^{ 2}}{2\sigma_{w}^{2}}\bigg{)}\Bigg{]}, \tag{31}\]4  
As discussed in Case 1, the typical rate for covert communication is characterized by \(\mathcal{O}(\sqrt{n})\). Building upon the conditions outlined in Case 2, we can ascertain a transmission rate of \(\mathcal{O}(\sqrt{n})\). This implies that the conditions specified in Case 2 are prerequisite for achieving covert communication.
# Three-Way Alignment Improves Multiple Sequence Alignment of Highly Diverged Sequences
## 3 Results  
### Three-Way Alignment Tends to Generate Guide Trees Closer to the True Tree than Other Approaches  
We first evaluated the two guide trees, one generated from the MAFFT L-INS-i option and the other using our three-way alignment (3-WAY in Table 1) by comparing them with the true tree (i.e., the tree used for sequence simulation). With the symmetric tree, both approaches recovered some true trees, but the three-way alignment approach recovered slightly more true trees (Table 1). Similarly, the RFd is greater for L-NS-i than it is for the three-way alignment approach. These results are consistent with our hypothesis that the three-way alignment approach would produce better guide trees. However, these differences are small and not statistically significant, given our sample size of 50 sets of simulated sequences (two-tailed paired-sample \(t\)-test, t = 1.1881, DF = 49 and \(p\) = 0.2405, Table 1). Given the effect size, a sample of 140 would be needed get a \(p\) value below 0.05.  
With the asymmetric tree, neither the L-NS-i approach nor the three-way alignment results in a guide tree that is identical to the true tree (Table 1). However, the difference in the RFd, similar to the results with symmetric trees, is in the expected direction, i.e., being smaller for the three-way alignment than for the L-INS-i approach. However, the difference between the two groups is not statistically significant given the sample size of 50 for each group (t = 1.1586, DF = 49, \(p\) = 0.2522).  
Table 2 presents the result of the comparisons of guide trees for 16-taxa trees. There are four different 16-taxa trees, including half-symmetric, symmetric, half-asymmetric, and asymmetric trees, which are represented as H-S tree, S tree, H-AS tree and AS tree, respectively, in Table 2. We compared the guide trees from four different approaches, including three-way alignment (3-WAY in Table 2) and the three MAFFT options (FFT-NS-1, FFT-NS-2, and L-INS-i), based on the simulated sequences. The guide trees reconstructed from k-tuple similarities (FFT-NS-1 and FFT-NS-2, with k = 6) are apparently much worse than those reconstructed from pairwise alignment (L-INS-i) or three-way alignment (Table 2). However, just as in Table 1, there is no significant difference between the last two approaches. The L-INS-i approach actually performed slightly better than the three-way alignment approach with the H-AS tree, recovering more true trees (41 versus 39) and having a smaller mean RFd (0.36 versus 0.52) than the three-way alignment approach (Table 2), although the difference is not statistically significant. The only difference reaching borderline significance involves the asymmetric tree (Table 2). The three-way alignment appears to produce a better guide tree, with an RFd nearly significantly smaller than that of the L-INS-i option (paired sample \(t\)-test, t = 1.8448, DF = 49, \(p\) = 0.0711).