esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain Solution for Remote Sensing Data Management Model
## 3 The Architecture of the System  
### Trusted Interoperability Service of Remote Sensing Data  
The remote sensing information from imaging to remote sensing product output roughly goes through the following processes: remote sensing data acquisition, data processing, image processing, information extraction/fusion/integration, and product output, in which various errors are introduced, due to various influences, such as the operation and environment, and these errors will eventually produce uncertainties in the remote sensing products, including uncertainties in the remote sensing imaging mechanism, uncertainties in the processing process, and uncertainty in the information product evaluation.  
The original intention of the remote sensing data trusted interoperability technology is to build a complete trust chain, from data acquisition to product production, in the entire remote sensing data processing system. Due to the non-objectivity of the actual operation, the remote sensing service itself may cause uncertainty and untrustworthiness in the results, due to different algorithm selections and process selections. Only through sorting out its scientific nature, can the origin and process of a remote sensing product image on the chain be explained, and then it can be mathematically deduced, and strictly and scientifically processed, to obtain credible results.  
After remote sensing images are acquired from satellite sensors, they are shared, collaborated, and fused between data storage centers. The products are dependent on various parameters in the production process. Facing the same target, the results obtained via different processing operations show different reliability. Therefore, remote sensing data products should be combined with the complete parameters on their own chains, to promote and ensure the scientificity, traceability, and high reliability of the production results, and to provide more reliable information for user decision-making.  
To address the above problems, in order to realize the requirements of traceability over the whole course of remote sensing data changes and information tamper-proofing, this paper will design and build an interoperable meta-information model. including the characteristics of the Earth observation data sources and the service records of the processing process, as shown in Figure 1.
## 3 Results and Discussion  
### Characterization of KOH/JS Catalyst  
Figure 2 shows XRD patterns of activated carbon derived from _Jatropha curcas_ fruit shell and KOH/JS catalyst. It is observed that pure activated carbon exhibits broad peaks with an absence of sharp peaks. This reveals an amorphous nature of material, which is advantageous for well-defined adsorbents. The occurrence of broad peaks around 26\({}^{\circ}\) and 43\({}^{\circ}\) indicates an alignment of carbon layer planes [21, 25]. For the KOH/JS catalyst, the existence of diffraction peaks at 20 = 30\({}^{\circ}\), 32\({}^{\circ}\), 34\({}^{\circ}\), 38\({}^{\circ}\), 39\({}^{\circ}\) and 41\({}^{\circ}\) refers to the potassium hydroxide phase. These results implied that the catalyst contained KOH as expected. It was hypothesized that KOH was coated within the highly porous structure of the activated carbon. The formation of the KOH phase contributes to higher basicity and catalytic activity for the transesterification reaction in comparison to activated carbon. The amount of adsorbed KOH was measured by a gravimetrical method. The total loading content of the potassium salts was 31.3 wt% based on the weight of activated carbon.  
The physical properties of activated carbon and KOH/JS catalyst are summarized in Table 2. The _Jatropha curcas_ fruit shell activated carbon had a BET surface area of 927.85 m\({}^{2}\)/g and a pore volume of 0.923 cm\({}^{3}\)/g. The KOH/JS catalyst, on the other hand, exhibits lower specific surface area (275.83 m\({}^{2}\)/g) and pore volume (0.209 cm\({}^{3}\)/g), compared to activated carbon. The significant reduction in BET surface area of 33.3 wt% KOH/JS catalyst indicates the filling of KOH molecules into the activated carbon pores, supporting the above hypothesis [15]. In addition, it can be seen that the catalyst resulted in a strong increase in the active sites. This assumption is supported by the SEM images.  
The porous nature of activated carbon and KOH/JS catalyst is illustrated by SEM images, as shown in Figure 3. The results showed that the metal loading affected the partial metal coverage on the activated carbon surface [26]. Figure 3(b) shows a good dispersion of potassium species on the entire surface of activated carbon. Based on previous research, good catalytic activity is enabled by preservation of the support material after catalyst preparation. In addition, the metal species must be highly distributed upon the surface of the support [22].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Material**} \\ \cline{2-3}
**Physical Property** & **Activated Carbon** & **KOH/JS Catalyst** \\ \hline BET surface area (m\({}^{2}\)/g) & 927.85 & 275.83 \\ Pore volume (m\({}^{3}\)/g) & 0.923 & 0.209 \\ Mean pore diameter (A\({}^{\circ}\)) & 44.86 & 93.71 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The physical properties of activated carbon and KOH/AC (AC = Activated Carbon) catalyst.  
Figure 2: XRD patterns of activated carbon and _Jatropha curcas_ fruit shell activated carbon (KOH/JS) catalyst.
# Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network
## 3 Waypoint-Based Formation-Switching Method
### Consensus-Based Design for UAV Swarm Formation Control Protocol  
The algebraic graph theory was used to describe the UAV swarm system and its behavior. Assume that the ad hoc network UAV swarm system has \(n\) UAVs, and each UAV is regarded as a node, then the communication relationship is seen as an edge. An undirected graph \(G=(V,E,\mathbf{A})\) represents the UAV swarm system, where \(V=\left\{s_{1},s_{2},\cdots,s_{n}\right\}\) is a collection of nodes, \(E=\left\{(s_{i},s_{j})\in s\times s,i\
eq j\right\}\) is a collection of edges, and \(\mathbf{A}=\left[a_{ij}\right]_{n\times n}\) represents an adjacency matrix with weights. The edge of the graph is indicated by \(e_{ij}=(s_{i},s_{j})\). For an undirected graph, UAV\({}_{i}\) and UAV\({}_{j}\) can receive the information sent by each other, namely \((s_{i},s_{j})\in E\Leftrightarrow(s_{j},s_{i})\in E\). The adjacency matrix is defined as \(a_{ii}=0\), and \(a_{ij}=a_{ji}\geq 0\), when \(e_{ij}\in E\), \(a_{ij}>0\). The neighbor set of the node \(s_{i}\) is defined as \(N_{i}=\left\{s_{i}\in V\big{|}(s_{i},s_{j})\in E\right\}\).  
An undirected graph \(G_{n}\) was used to describe the communication topology relationship in the UAV swarm. At each moment \(t\), the communication connection between the ad hoc network and UAV swarm forms a communication topology. For the vertex \(i\) of graph \(G\), let \(x_{i}(t)\in R^{q}\) and \(u_{i}(t)\in R^{q}\) denote the state variable and state information input variable of the UAV swarm, respectively, at the time \(t\). The classic first-order continuous-time consistency protocol [26] is:  
\[\dot{x}_{i}(t)=u_{i}(t)\quad i=1,2,\cdots,n \tag{2}\]  
\[u_{i}(t)=-\sum\limits_{j=1}^{n}a_{ij}(t)\big{(}x_{i}(t)-x_{j}(t)\big{)} \tag{3}\]  
For any UAV\({}_{i}\), the initial state is \(x(0)\in R^{P}\), when \(t\rightarrow\infty\), there is \(\big{|}x_{i}(t)-x_{j}(t)\big{|}\to 0\), which is called the state of UAV swarm system reaching consensus.  
In this paper, the consensus algorithm needed to be applied to the formation switching of the UAV swarm. The distance between UAVs needed to reach the expected value eventually. Therefore, to improve the classic first-order consistency protocol, the first-order consistency protocol with reference location information was proposed, and the specific form was as follows:  
\[\dot{x}_{i}(t)=u_{i}(t)=\frac{\sum\limits_{j=1}^{m}\big{(}a_{ij}(t)[(\mathbf{X }_{j}(t)-\mathbf{X}_{i}(t))-\mathbf{X}_{jinf}]\big{)}}{\sum\limits_{j=1}^{m}a_ {ij}(t)}i=1,2,\cdots,n \tag{4}\]  
where \(\mathbf{X}_{jinf}\) is the expected formation relative distance between UAV\({}_{j}\) and UAV\({}_{i}\).
## 3 Results
### Attenuated Backscatter Profiles  
One objective of the Colibri micro-lidar is to monitor the evolution and dispersion of the aerosol plumes, here fog-oil smoke particles, in the short-range with high range-resolution and temporal-resolution. Attenuated backscatter profiles \(\mathrm{U}(\mathrm{r})\) are the first-level micro-lidar products. They can be used to give a direct indication of the spatio-temporal distribution and structure of the aerosol plumes.  
Figure 4 shows an example of the Colibri micro-lidar measurements carried out on 13 th February at ONERA center. The glycol fog-oil emission event was remotely and horizontally characterized from 0 to 60 m during 60 s. It was possible to study the structural properties and spatio-temporal variability of the aerosol layers in the short-range with high range and temporal resolution. Note that all range levels given in this section are meant as the horizontal distance from the lidar system. The range-corrected signal RCS measurements were converted to attenuated backscatter profiles by applying the geometric and radiometric calibration procedure, fully explained in the previous Section 2.3.  
A straightforward pre-processing procedure was employed to improve the quality of the lidar signals because of the high signal-to-noise ratio in the short-range and the fact that all measurements were done in an indoor environment. First, a 10 min measurement of dark current (DC) was done at the end measurement for each day during the campaign, covering the telescope (obtaining up to 100,000 profiles). DC measurements were first subtracted to the lidar signals. The background (BG) contribution was measured in the long-range (over 80 m). The BG presented a Gaussian distribution, therefore the mean value was used for suppressing the noise to each profile. Lastly, a 50 ms temporal-averaging filter was used to smooth the micro-lidar signals for the particle inversion method.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{4}{c}{**Microphysical and Optical Properties**} \\ \hline \hline  & & Value & Units \\ \hline Type of aerosol & & fog-oil & \\ Aerosol size distribution & & log-normal & \\ Modal radius & \(R_{p}\) & \(0.18\pm 0.01\) & \(\upmu\)m \\ Geometric standard deviation & \(\sigma_{g}\) & \(1.15\) & \\ Complex refractive index & \(m\) & \(1.508+i10^{-5}\) & \\ \hline \multicolumn{4}{c}{**Averaged Lidar-Relevant Properties**} \\ \hline Differential backscattering cross-section & \(\langle\mathrm{dC}^{\mathrm{bac}}\rangle\) & \(3.16\times 10^{-3}\) & \(\upmu\)m\({}^{2}\;sr^{-1}\) \\ Lidar extinction-to-backscatter ratio & \(\langle\mathrm{LR}\rangle\) & \(73.1\) & sr \\ \hline \hline \end{tabular}
\end{table}
Table 2: Averaged lidar-relavant parameters for fog-oil particles from their microphysical properties.
## 2 Theoretical Framework  
### RUL: Reliability and Predictive Maintenance  
[PERSON] et al. [1] stated that easier access to data resulting from the technical advances brought about by Industry 4.0 increased the use of tools to predict failures and reduce their effects through failure prognostic information. Most studies focus on estimating the remaining useful life (RUL).  
There are three possible maintenance strategies [2]. In reactive maintenance, a machine or component is used until its failure. In preventive maintenance, failures are avoided through maintenance carried out at predefined time intervals. In predictive maintenance, the time to failure of a machine is estimated, which helps in maintenance scheduling and reduces the probability of unexpected failures. Figure 1 presents each maintenance strategy in a graph of machine health as a function of time and the effect of performing maintenance at different points in time.  
According to [PERSON], [PERSON] et al. [3] and [PERSON], [PERSON] et al. [4], the prognosis of machinery is one of the main functions of condition-based maintenance to predict its remaining useful life. This research area employs techniques that provide early warnings of failures, which are important in managing the health of systems and machines. [PERSON], [PERSON] et al. [5] defined the remaining useful life of a piece of equipment or component as the time until it reaches the end of its useful life.  
[PERSON], [PERSON] et al. [6] established that the term is widely used in analyses in industries and presented a study estimating the remaining useful life of power generation machines in the nuclear industry. [PERSON], [PERSON] et al. [4] presented a general application for rotating machinery.  
There are three ways to estimate the remaining useful life of equipment [2], as presented in Figure 2.  
One of these forms, reliability analysis, involves a probabilistic function whose main distributions are the normal, exponential, lognormal and Weibull distributions [7; 8; 9].  
Many authors [7; 8; 9; 10; 11; 12] have presented applications and developments of reliability studies using the exponential model. These studies highlight the low complexity of the model as the main advantage, even though the lack of memory is an important limitation that must be considered.  
[PERSON] [13] established the probability density function of a random variable \(X\) with an exponential distribution, its mean and its variance as follows:  
\[f(x)=\lambda e^{-\lambda x}\ for\ 0\ \leq x<\infty\]  
\[\mu=E(X)=\frac{1}{\lambda}\]  
\[\sigma^{2}\ =V(X)=\frac{1}{\lambda^{2}}\]
## 1 Introduction
To summarize, the aim of this study is to optimize RF classifiers and compare against other machine learning methods for urban land cover classification (five classes including building, tree, road, water, and crop field) by using Sentinel-2A/B remote sensing satellite images, where the study area is located in Beijing, China. The optimized RF classifier by Bayesian optimization is compared against the SVM and the random forest with default hyperparameters. In addition, both Red-Green-Blue (RGB) features and full band features are selected for training and testing in different methods so that their effects on classification performance can be assessed. It is expected that a better classification result can be achieved by the optimized RF over the conventional SVM and RF with default hyperparameters. To be more exact, the main contributions of this study are summarized:  
1. State-of-the-art earth observation satellite Sentinel-2A/B with the best spatial/spectral/temporal resolution among freely available satellites are evaluated for urban land cover classification;
2. Bayesian optimization is drawn to automatically tune the hyperparameters of random forest classifiers for satellite remote sensing image classification. 3. Both RGB band and full multispectral bands available on Sentinel-2A/B of an urban scenario with five classes are adopted to evaluate the classification performance of the optimized RF against the SVM and the RF with default hyperparameters. The remainder of this paper is organized as follows: Section 2 introduces some related work; Section 3 introduces related materials in this case study; Section 4 proposes the methodology of the optimized random forest classifier; Section 5 demonstrates the comparative results by using various methods; Finally, discussion and conclusions along with future work are drawn in Sections 6 and 7, respectively.
# Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method
## 5 Experiment and Discussion  
### \(Gtc-\)Facet Algorithm Positioning Accuracy  
Select master gears with an accuracy of JIS B1751\(-\)M00 from Japan Technomax Inc, Osaka, Japan. As shown in Figure 12a, the number of teeth is 38, the modulus is 1.0 mm, the pressure angle is 20 degrees, and the diameter of the center hole is labeled 12.0 mm. The positioning accuracy is compared by measuring the center hole of the gear. Figure 12b shows a comparison of positioning errors between the two algorithms, and the errors at each point represent the difference between the distance between the extracted center hole contour points and the nominal radius.  
The experiment counted the difference between the distance from the contour point of 2200 holes to the center of the circle and the nominal radius, and the pixel equivalent of the gear image is 7.71 \(\upmu\)m/pixel. The positioning error using a Canny pixel-level algorithm is relatively large, with a standard deviation of 28.77 \(\upmu\)m. There is a significant issue of eccentricity. The standard deviation of positioning error obtained using the GTC\(-\)Facet algorithm is 3.58 \(\upmu\)m. Using the standard deviation as the evaluation standard for positioning accuracy, its positioning accuracy is 3.58/7.71 = 0.46 pixel.
# An NDN Cache Management for MEC
## 3 Cache Decision Factors  
To solve the problems mentioned in the previous section, this paper proposes two decision factors as follows:  
* The first factor is the popularity of the _Data_. As existing CMSs, it proposes to gradually increase the number of nodes caching Data proportionally to the generation frequency of the _Interests_ requesting the _Data_.
* The second factor is the cache usage rate of each node. For that, we first analyze the cache hit rate of the nodes. Figure 3 shows the evaluation result of the cache usages of NDN. To evaluate the cache usages of NDN, we utilize a network topology and configuration as described in Section 5. This assumes that the average popularity of the content is 0.001. It means that a new _Interest_ for the content is published per each 1000 s. Then it measures the cache hit rate of each node according to the different cache lifetimes (1 h, 1 day, and 1 week). Specifically, if a custodian based-NDN is applied [19], the cache hit rate of the custodian nodes of a publisher-side domain is relatively higher than those of RsENs. Figure 3 shows that the cache usages rate of the nodes of the domain of a publisher is relatively high.  
Figure 3 could cause misunderstanding that caching _Data_ in RsEN is useless. However, the result of Figure 3 has some limitations because the average popularity of the content is fixed. Nevertheless, it is sufficiently meaningful since it shows the necessity of consideration of the cache usage rate of a node.
## 2 Materials and Methods
### Alpha Shape  
Each 3D point cloud collected using a UAV was composed of simply scattered points and did not constitute lines, surfaces, or shapes. Therefore, a reconstruction process was necessary to reconstruct the shape of the desired object. Among them, notable methods included using a convex hull, alpha shape, and voxel [22; 23; 30; 44; 45; 46; 47; 48]. While convex hull and alpha shapes connected edge points, the convex hull method connected the exterior points with straight lines, creating limitations in representing rugged objects. In contrast, the alpha shape is a method for reconstructing points composed in 2D or 3D from a discrete space point set. First introduced by [PERSON] et al. [44; 47], the alpha shape has since been expanded to higher dimensions as a geometric tool for inferring the shape of a point set [41; 44]. An alpha shape forms on the boundary of the alpha-complex, which is a sub-complex of the Delaunay triangulation for a given point set [47]. Essentially, the alpha shape composes the boundary region or volume that surrounds a 2D or 3D point set, as  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Raw Data** & **Sampling by Space** & **Filled Point Data** \\  & **(Surface Only)** & **(Surface Only)** & **Filled Point Data** \\ \hline Raw data & 27,004,538 & - & - \\ \hline SOR 1 st & 23,882,816 & 97,361 & 446,050 \\ \hline SOR 2 nd & 20,599,446 & 90,762 & 414,791 \\ \hline SOR 3 rd & 18,255,112 & 85,151 & 386,791 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial point information of the 3D point cloud and point information after editing.  
shown in Figure 4. For a given set of points in space, a very rough boundary surface to a very refined optimal range around that point could be defined by the parameter alpha (Figure 4). To conduct this, a circle with a radius alpha that connects the contours of two randomly selected points was drawn, forming a 2D or 3D object surrounded by multiple circles. This generated a larger alpha result with a rougher fit, whereas a smaller alpha produces a finer fit (in [46], Figure 4). The level of fine-tuning required for the volume of the 3D alpha shape to match the volume of that alpha shape can be considered a measure of shape complexity. The more complex the object, the more optimal alpha shape adjustment is required.  
The result of applying the alpha shape can either represent the optimal shape based on alpha or, if an alpha is set too low, a smaller volume can be created [45]. That is, if the alpha used to construct the alpha shape for an irregular and convex object is too large, it will occupy a volume larger than the actual size. Conversely, an excessively refined alpha shape could have a volume smaller than the original structure. Ultimately, the volume of the alpha shape at the optimal level of refinement and the sample volume could be the same, and finding the optimal alpha was to be prioritized.
# Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis
## 2 ITFRSNP System
### Iftrsnp System-Based General Fault Diagnosis Process  
#### 3.3.1 Suspected Faulty Component Search Method  
As mentioned in the introduction, the missing alarm message from the CB at the boundary of the outage area (referred to as the boundary CB) significantly affects the diagnosis efficiency. A missing boundary CB alarm can result in outage areas not being recognized. All components in the power system are diagnosed to identify faulty components. In practical applications, it is difficult to traverse the entire power system topology and construct fault diagnosis models for all the components, and the workload is large. Therefore, this study proposes a new search method for suspected faulty components. According to the relay protection configuration, when a fault occurs in a component, the main PR trips the CBs to isolate the faulty component; if the CB refuses to trip, the backup protection at the remote end of the neighboring component trips the corresponding CB. It illustrates that the faults of the components connected and adjacent to the CB cause the CB to trip. Therefore, this study searches for components connected and adjacent to the tripped CBs as their isolation ranges; the isolation ranges of different CBs take the intersection of two to determine the suspected faulty components, thus narrowing down the scope of fault diagnosis.  
The specific steps to search for suspected faulty components are as follows.  
1. Alarm messages are received from the W CBs. These CBs form the CB set (\(CBS\)). Initialize \(w=1\) and begin the search.
2. Select the \(w\)-th CB (CB\({}_{w}\)) in \(CBS\).
3. Buses, lines, and transformers connected to CB\({}_{w}\) are searched to form the \(w\)-th subset of suspected faulty components (\(COM_{w}\)).
4. Adjacent buses, lines and transformers are searched in the direction of the line or transformer connected to the CB\({}_{w}\) and placed into \(COM_{w}\).
5. If \(w=W\), proceed to the next step; otherwise, \(w=w+1\) and return to Step 3.
6. The suspected faulty component subsets intersect in pairs. If the intersection of two suspected faulty components is not empty, then the intersection results are combined into a total set of suspected faulty components (\(COMS\)). If the intersection of a certain suspected faulty component subset with other subsets is empty, all the components in this subset are input into \(COMS\). The components in \(COMS\) are suspected to be faulty components.