esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Datasets and Evaluation
### Evaluation Metrics  
In this study, five metrics, including overall accuracy (OA), precision, recall, F1 score and intersection over union (IoU), were adopted to evaluate the building extraction effects of different models. Among them, OA refers to the ratio of the number of correctly classified pixels to the total number of test pixels: precision reflects the ratio of true positives in all predicted positives, recall represents the ratio of true positives correctly identified, F1 score is the harmonic average of precision and recall, and IoU calculates the ratio of the intersection and union between the predicted and true targets, reflecting the segmentation accuracy. The definitions of the above metrics are as follows:  
\[\text{Overall Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP }+\text{FN}} \tag{1}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
\[\text{F1}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{4}\]  
\[\text{IoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{5}\]  
where TP, TN, FP and FN represent the number of pixels of true positives, true negatives, false positives and false negatives, respectively. These metrics reflect the building extraction performance of models from different perspectives. OA focuses on overall classification results, precision and recall focus on identification capabilities, F1 score combines precision and recall, and IoU directly examines segmentation accuracy. This study will use the above metrics to evaluate the building extraction results of different models and determine the pros and cons of models based on quantitative results.
## 3 Research Results and Discussion
### Tensile Test  
Figure 8 displays the tensile test curves of the AA6063 and AZ91 multilayer coated specimens. The tensile strength and strain (mm) of the multilayer coated specimens increased in comparison with the AZ91 base material. With the coating, the tensile strength of the AZ91 alloy increased from 137.89 MPa to 139.65 MPa. When the coated and uncoated AZ91 samples were compared, the coating increased the tensile strength from 137.89 MPa to 139.65 MPa (Table 3). The good adhesion, homogeneity, columnar, dense, and high-strength properties of the coating ensured that the coating maintained its integrity for a long time during the tensile test, resulting in an increase in tensile strength parallel to fatigue. The extended initiation time of cracks in the base material resulted in delayed plastic deformation, delayed crack initiation, nucleation, and propagation, allowing the tensile strength to increase. Consequently, it was evaluated that applying Cr/CrN/TiCrN/TiCrCN thin hard-coating film increased the tensile strength of the AZ91 Mg alloy and prolonged the crack initiation time. The above-mentioned findings show that coating applications represent an effective method to enhance the mechanical characteristics of materials such as the AZ91 Mg alloy. The said findings support similar research in the literature [34; 58].  
Upon reviewing the literature, in the study by [32; 33], the yield and tensile strength of the aluminum alloy 7075-T6 substrate coated with 3-\(\upmu\)m-thick TiN using PVD decreased significantly by 78% and 54%, respectively. It was thought that the high operating temperature during the deposition procedure led to a reduction in the tensile properties of coated Al 7075-T6 [32; 33]. The Cr/CrN/TiCrN/TiCrCN multilayer coating reduced the maximum tensile strength value of AA6063 Al alloy from 129.35 MPa to 118.16 MPa. The presence of Ti and Cr and the production of the multilayer coating increased the fatigue and tensile strength of AZ91 in parallel while reducing them in AA6063.
# Power Factor Correction Application Based on Independent Double-Boost Interleaved Converter (IDBIC)
###### Abstract  
In this paper, a Power Factor Correction (PFC) application, based on the novel power stage topology named Independent Double-Boost Interleaved Converter (IDBIC), has been analyzed. The novelty of the proposed PFC rectifier is based on the sum of capabilities, such as supplying three independent output voltage levels with interleaved operation at the input and high voltage gain. The hardware used within this application consists of an AC input L-C-L filter, a single-phase bridge rectifier, the IDBIC power stage, output capacitors group and a group of variable high-power rheostats (resistors) group as DC load. The main purpose of the carried study was to highlight the advantages and disadvantages of the novel power stage topology in the context of a green and modern AC to DC conversion solution. Nowadays, a high level of the efficiency and power factor have become a mandatory feature for the AC to DC conversion solutions to satisfy the international electrical standards. Thus, considering the modern electrical standards and recommendations, the current study tries to better depict the working steps and principles of the modern power stage topology within an AC to DC conversion application. The behavior of the considered power stage described in different detailed working steps (such as the Discontinuous Conduction Mode and Continuous Conduction Mode) may help understand how the energy conversions process of AC to DC becomes more efficient. The high output voltage gain of the considered power stage is the key feature in the Power Factor Correction process. With such a feature, the AC to DC conversion solution/application can also operate at lower input AC voltages (such as 90 [V] and 110 [V]). The proposed solution can be successfully used in the electric vehicle (automotive field) and high-power electrical traction (e.g., trains, high power electrical machines and drives). The same solution can also be used successfully in fast battery charging applications and chemical electrolysis processes.  
Power Factor Correction (PFC); Independent Double-Boost Interleaved Converter (IDBIC); high voltage gain; low input voltage; electrical traction; electric vehicle; fast battery charging +
Footnote †: journal: Applied Sciences
# Realizing Efficient Security and Privacy in IoT Networks
## 5 Numerical Analysis and Results
### Joint Power Assignment for Multiple Destinations  
For a multiple transmission destinations scenario, we considered \(C_{n}=\chi C_{n}^{*}\), representing \(C_{n}^{*}\) as the optimal data rate attainable from _Source_ to _Destination_ when power \(\frac{\text{P}_{s}}{N}\) is assigned to _Source_ for data broadcast to the \(n\)th destination. Considering \(\chi=0.8\) and \(N=4\), Figures 5 and 6, respectively, represent the maximum attained privacy capacities and data rates from the _source-to-destination_. Likewise, the equivalent outputs with no rate constraint are represented in the two figures. It is observed that if rate constraints are applied, a small measure of privacy capacities is lost, however, significant data rates are gained and maintained. For both Figures 5 and 6, under rate constraint, both the achieved and constrained rates are separable.  
Figure 5: Maximum realized privacy capacity for multiple destinations transmission.
## 2 Drivers of Photovoltaic Technologies
### PV Module Efficiency  
In addition, soft costs can be divided into parts: a part proportional to the PV system area, a part proportional to the system power, and a part that is fixed. In this way, the non-module costs can be divided into \"area-related\" costs, \(A\), and \"others,\" \(O\). Then, the investment costs, \(IC\), as a function of the module efficiency, \(\eta\), can be expressed as:  
\[IC=M(\eta)+O+A(\eta), \tag{3}\]  
where \(M\) is the module costs, \(A\) is the area-related costs, and \(O\) represents the others.  
If \(D\) is a coefficient of proportionality, the area-related costs can be expressed as:  
\[A(\eta)=\frac{D}{\eta}. \tag{4}\]  
With a change in the module efficiency from \(\eta_{1}\) to \(\eta_{2}\), the relative change of the area-related costs is  
\[\frac{A(\eta_{2})-A(\eta_{1})}{A(\eta_{1})}=\frac{\eta_{1}-\eta_{2}}{\eta_{2}}. \tag{5}\]  
However, as the efficiency of the modules increases, the relative change in the area-related costs decreases, as shown in Figure 3, where the relative changes of the area-related costs caused by a 1% increase in module efficiency are plotted as a function of module efficiency. For example, an increase in efficiency from 10% to 11% results in a relative reduction in the area-related costs of 9%; an increase in efficiency from 20% to 21% leads to a relative reduction in the area-related costs of 4.8%. The overall reduction in total investment costs due to the increase in module efficiency will be proportional to the share of the area-related costs in the total investment costs. In the latter case, in a system with  
Figure 2: Typical division of costs for 500 kW\({}_{\text{P}}\) commercial roof system within the Czech Republic installed on own roof.  
approximately 33% area-related costs, there will be a relative decrease in total investment costs of 1.6%.  
Besides improving module efficiency, the use of higher system voltages (e.g., 1500 V), sun-tracking constructions, or bifacial modules can result in system-level changes in the expenditure structure [7]. Increasing module power can also help reduce BOS costs [8] by reducing the spending on other components, including racking, foundations, and cabling.
## 3 Full-Scale Testing
### Elliptical Tube Geometry  
A total of 24 elliptical tubes have been printed in either PLA or ABS with cross-sectional aspect ratios \(a/b=1.5\), 2.0 and 3.0, and nominal tube wall thickness \(t\) ranging between 1.0 mm and 3.0 mm; the full schedule of specimens and their cross-sectional properties are shown in Table 7. The specimens are labelled thus: EHS[specimen identification number]-[material batch]-[nominal maximum outer diameter \(2a\)]-[nominal minimum outer diameter \(2b\)]-[nominal \(t\)]. Specimens EHS01 to EHS08 [41] were printed in PLA at the lower printing temperature mentioned in Section 2.2; specimens EHS09 to EHS17 have been printed in PLA at the higher printing temperature. EHS18 to EHS24 have been printed in ABS. Specifically, the dimensions of these specimens have been chosen so that failure is encouraged to occur via local modes in the tube walls; the slenderness of the cross-sections is defined and discussed in Section 4.1. The cross-sectional area of an elliptical tube \(A=P_{\mathrm{m}}\times t\), where the mean tube wall perimeter \(P_{\mathrm{m}}\) is given by the approximation of Ramanujan [8] thus:  
\[P_{\mathrm{m}}=\pi(a_{\mathrm{m}}+b_{\mathrm{m}})\bigg{(}1+\frac{3t_{\mathrm{m }}}{10+\sqrt{4-3t_{\mathrm{m}}}}\bigg{)} \tag{1}\]  
where \(a_{\mathrm{m}}=(a-t/2)\), \(b_{\mathrm{m}}=(b-t/2)\) and \(h_{\mathrm{m}}=(a_{\mathrm{m}}-b_{\mathrm{m}})^{2}/(a_{\mathrm{m}}+b_{\mathrm{ m}})^{2}\). The maximum and minimum outer diameters \(2a\) and \(2b\) of the sections have been chosen so that the mean perimeter \(P_{\mathrm{m}}\) is approximately constant for all specimens; the values of \(P_{\mathrm{m}}\) and \(A\) calculated using the measured properties of the specimens are shown in Table 7 (additional parameters are defined in Section 4). The nominal length \(L\) is 280 mm for all specimens, reflecting the maximum height achievable by the printer.
# Research on Lazy Theta+
## 2 Octree Meshing Modeling
### Octree Meshing  
Octree [23] is a tree-like data structure that describes a three-dimensional space. Figure 1 illustrates an Octree in which each node represents a cubical volume element and has eight child nodes. The sum of the eight child node volumes equals the parent node volume.  
Octrees recursively divide space into eight squares, organized and stored in memory as octrees [24]. Figure 2 depicts the data organization of the environment illustrated in Figure 1. Specifically, the ball P in Figure 1 is stored within a child node, continuously divided into octaves to obtain a cube storing the ball P.  
In the octree data storage setup, the idle nodes can be pruned, and only the parent and the target nodes are retained [25], saving memory and improving search efficiency. When planning a route, the octree grid divides the global environment, significantly improving planning accuracy, safety, and efficiency.  
Compared with the standard grid, the octree grid is more convenient to identify and query objects. Figure 3 shows that the calculation methods of the standard grid and the octree grid subdivision model are different. The grid occupied by the red numbers (letters) in the figure indicates that it is also occupied by the model.  
Figure 2(a) illustrates that the standard grid subdivision model is queried by traversing all grids to find the grids occupied by the model; this method is slow in computing large-scale models.  
Figure 2(b) shows the query method of the octree grid subdivision model. It searches the grid occupied by the model through the continuous octree division of space, which has a higher search efficiency. At the same time, due to the characteristics of the octree structure, it is convenient to encode the grid. In addition, octree grids can be pruned to remove unoccupied grids, reducing the number of grids while further improving search efficiency.  
Figure 1: Schematic diagram of the octree partitioning structure.  
Figure 2: Schematic diagram of data organization of octree.
## 3 Method of Fault Diagnosis Based on GWO-gcForest for HVCB
### Diagnosis Process  
The gcForest model is based on tree-based ensemble learning. By integrating the forest composed of trees and connecting them in series, the classifier can perform representation learning, thereby improving the classification effect. Its internal structure can mine fault feature information and identify HVCB faults. However, when employing the gcForest model for fault identification, the determination and control of crucial model parameters rely heavily on human experience. As a result, the diagnostic outcomes are significantly influenced by human manipulation.  
With the aim of achieving the utmost diagnostic precision, GWO iteratively searches for optimal model parameters. This iterative process leads to an improvement in the diagnostic accuracy of the model. The process of fault diagnosis on the basis of the GWO-gcForest model comprises three primary phases: data preprocessing, parameter optimization, and fault identification using the optimized model. These steps are illustrated in Figure 4, which shows the overall process.  
The diagnostic process steps are as follows:  
1. Obtain vibration signal data. Normalize the features extracted and create a set of training data and a set of test data.
2. Initialize the population and determine its size, maximum iteration number, dimensions, and boundary range.
3. Construct the gcForest model using the initialized wolves. Train and test the model using the training and testing sets, respectively. Further divide the training set into a verification set. Use the diagnostic accuracy value as the fitness value to assess the model's performance.
4. Calculate the fitness of each wolf, sort each wolf according to the fitness function value, and continually update the wolf pack's position.
5. Halt the GWO process when the fitness value becomes stable or the specified number of iterations is achieved, thereby obtaining the optimal parameters. If neither condition is met, return to the previous step and continue with parameter optimization.
6. Build the GWO-gcForest fault diagnosis model using the optimum parameters acquired through the GWO algorithm. Comprehensively analyze the outcomes of the diagnosis, taking into account various evaluation indicators for the assessment of the model's performance.
# A Simplified Algorithm for Dealing with Inconsistencies Using the Analytic Hierarchy Process
## 1 Introduction
These approaches assume that the inconsistencies arise due to incorrect preference choice. That is, the decisions maker is aiming to be consistent, but chooses the wrong value when making the comparisons. The use of the geometric mean method (GMM) [37] for deriving attribute weights is often applied instead of the eigenvalue method, and hence an eigenvector is not derived making some of these approaches impractical. Further, for large sets of comparisons and potentially many respondents, such as might be realized through an online survey, complicated approaches or computationally intensive methods (e.g., evolutionary methods) may be impractical due to the large number of adjustments that may be necessary. Hence, an alternative simple algorithm is required to adjust the pairwise comparison matrix. In this study, a simplified iterative approach is developed for correcting for inconsistencies that is applicable when using the GMM to derive weights and applicable for large data sets, such as those derived from an online survey. What separates this approach from the previous approaches is that it is readily implementable in freely available software (in this case, R [38]), and can be integrated into the general analysis and derivation of weights. The approach is applied to several example cases, as well as to a large set of bivariate comparisons derived from an online survey to elicit preferences for protection of different types of coastal habitats. The focus of this paper is to describe the simplified adjustment process and its impact on preferences rather than focus on the results of the AHP analysis of preferences for coastal assets per se.
# A Data Processing Framework for Polar Performance Diagrams
## 2 Problem Formulations and Theoretical Backgrounds
### Weather Routing -- An Application of Polar Diagrams  
The task of _weather routing_ comes naturally when steering a sailing vessel for a longer period of time. We assume that wind (and other factors such as geographical information, current, waves, etc.) are dependent on time and position, and the velocity of our vessel can be predicted at any given time and position. Of course, in reality a weather forecast relies on statistics and interpolation and is never 100% reliable. A simple question is: what is the best course to take from one position to another? The term \"best course\", in its simplestform, refers to the fastest path. However, more convoluted cost functions, considering more factors (such as safety), can also be used (see [6,14]).  
We continue to discuss two standard approaches to solve a weather-routing problem and some related problems. For a more detailed overview we refer to [15,16].
## 6 Experiments
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Detection Method** & **Number of Sample Images** & **Recognition Rate \%** & **Average Time Consumption (ms)** \\ \hline IRANSAC & \(100\) & \(50\%\) & \(131.5\) \\ LS & \(100\) & \(20\%\) & \(66.3\) \\ IPSO & \(100\) & \(100\%\) & \(61\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance comparison of three methods. Figure 15: The vision-guided AGV prototype. In order to further illustrate the advantages of the IPSO method used for the model estimation of the straight path, the variation curves of the model parameters \(\beta_{0}\) and \(\beta_{1}\), and the fitness value \(f^{S}\) of the global extremum of the IPSO method are depicted in the 100 iteration evolution, as shown in Figure 17.  
The parameter estimation process of straight path 1 in Figure 3a achieves convergence after 20 iterations, as shown in Figure 17. The fitness value \(f^{S}\) of the global extremum is 1663, as shown in Figure 17c, with the corresponding particle position at point (309.40, 0.0943), as shown in Figure 17a,b. The parameters of straight path 2 in Figure 3b are estimated as (295.30, 0.1382) after 25 iterations, with a 2046 fitness value. Similarly, the straight path 3 in Figure 3c is recognized after 20 iterations, the fitness value equaling to 1460 and the model parameters being (340.20, 0.0414). The experimental results show that no matter whether the path images are interfered by the non-uniform illumination, sight-line occlusion or stripe damage, or no matter whether the path pixels  
Figure 16: The fitting results of a straight path using three methods: (**a**) a straight path, (**b**) a straight path with highlights, and (**c**) a straight path with coverings. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image** & **Detection Method** & \(\beta_{0}\) & \(\beta_{1}\) \\ \hline \multirow{3}{*}{Figure 16a} & IPSO & 309.40 & 0.0943 \\  & IRANSAC & 309.22 & 0.0908 \\  & LS & 310.36 & 0.0914 \\ \hline \multirow{3}{*}{Figure 16b} & IPSO & 295.30 & 0.1382 \\  & IRANSAC & 328.24 & \(-\)0.1257 \\  & LS & 346.39 & \(-\)0.1475 \\ \hline \multirow{3}{*}{Figure 16c} & IPSO & 340.20 & 0.0414 \\  & IRANSAC & 380.45 & \(-\)0.1391 \\ \cline{1-1}  & LS & 362.26 & \(-\)0.1421 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The parameters of straight paths. are surrounded by the under- or over-segmentation error points, the IPSO method we propose is able to estimate an accurate linear model for the straight path by recognizing the actual path pixels from their image artifacts.
# Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
## 2 Data and Methods  
Throughout this study, we employ three different sensors to measure surface ice velocity. One is optical, i.e., works in visible part of spectrum, and two others are synthetic aperture radars working at C-band, 5.405 GHz or 56 mm wavelength. For each sensor, we describe its characteristics and the methodology used to obtain displacement maps. We then explain how these displacement maps are filtered, calibrated, and mosaicked together using a common workflow for all sensors to generate vector velocity products. A large amount of the SAR data employed in this study have been acquired under the overarching umbrella of the Polar Space Task Group (PSTG, [[http://www.wmo.int/pages/prog/sat/pstg_en.php](http://www.wmo.int/pages/prog/sat/pstg_en.php)]([http://www.wmo.int/pages/prog/sat/pstg_en.php](http://www.wmo.int/pages/prog/sat/pstg_en.php))), which was established under the auspices of the World Meteorological Organization (WMO) Executive Council Panel of Experts on Polar Observations Research and Services (EC-PORS). The group mandate is to provide coordination across International Space Agencies to facilitate acquisition and distribution of fundamental satellite datasets in support of polar science. With input from the science community, a set of requirements for ice sheet observations was generated to serve space agencies as a guidance for planning data acquisitions. In addition, the Landsat Science team independently provided the Landsat project at the United States Geological Survey (USGS) with specific recommendations for ice sheet wide acquisitions for Landsat-8.
# Pre-Composting and Vermicomposting of Pineapple (_Ananas Comosus_) and Vegetable Waste
## 3 Results and Discussion
### Pre-Composting Process
#### 3.1.3 Humidity  
Figure 3 shows humidity behavior during the pre-composting period, results initially showed a value of 80%; humidity was maintained between 79 and 82.8% during the process, which coincides with values reported by literature [25,27]. Humidity is an important control parameter in the process due to the fact that values outside of the reported range may generate anaerbiosis if it is excessive, whereas a lack of humidity inhibits the microorganisms involved in the pre-composting process [8].