esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalized Difference Vegetation Index (NDVI). This study investigates the potential of a photochemical Reflectance Index (PRI), which has been linked to vegetation light use efficiency, to improve the accuracy of MODIS-based estimates of phenology in an evergreen conifer forest. Timings of the start and end of the growing season (SGS and EGS) were derived from a 13-year-long time series of PRI and NDVI based on a MALAC (multi-angle implementation of atmospheric correction) processed MODIS dataset and standard MODIS NDVI product data. The derived dates were validated with phenology estimates from ground-based flux tower measurements of ecosystem productivity. Significant correlations were found between the MAIAC time series and ground-estimated SGS (\(R^{2}\) = 0.36-0.8), which is remarkable since previous studies have found it difficult to observe inter-annual phenological variations in evergreen vegetation from satellite data. The considerably noisier NDVI product could not accurately predict SGS, and EGS could not be derived successfully from any of the time series. While the strongest relationship overall was found between SGS derived from the ground data and PRI, MAIAC NDVI exhibited high correlations with SGS more consistently (\(R^{2}\) > 0.6 in all cases). The results suggest that PRI can serve as an effective indicator of spring seasonal transitions, however, additional work is necessary to confirm the relationships observed and to further explore the usefulness of MODIS PRI for detecting phenology.  
p 2017 9 49; doi:10.3390/rs9010049
# Networked Unmanned Aerial Vehicles for Surveillance and Monitoring: A Survey
### Motivation for This Review  
Although the number of previous publications on surveillance and monitoring performed by UAVs is extensive, the existing surveys on this topic are not comprehensive. Some researchers reviewed works related to surveillance UAVs from different perspectives and for different applications, such as [PERSON] et al. [10] on crop monitoring, [PERSON] et al. [11] on harmful algal blooms, [PERSON] et al. [12] for search and rescue, and [PERSON] et al. [13] on railway surveillance. The existing surveys mostly focused on UAV hardware [10; 13], related policies [10], and computer vision (image processing) [11; 13]. In contrast, in this paper, we present a more comprehensive survey on this topic, with a particular focus on navigation and deployment optimization of UAVs. Furthermore, much attention has been paid to coordination among multiple UAVs.  
We selected some representative publications and classified them according to the target of interest. Networked unmanned aerial vehicles are used in numerous applications,  
Figure 1: Typical application scenarios for UAV surveillance, including border patrol, construction management, power grid inspection, and traffic and environmental monitoring.  
Figure 2: Number of publications on surveillance UAVs from the Web of Science Core Collection.  
and this research area is growing quickly. It is impossible to cover all of these applications in a relatively short survey article. Therefore, we concentrated only on four very important application areas. Specifically, we present a review on the use of UAVs for general target surveillance, traffic monitoring, wildlife monitoring, and radio surveillance, as shown in Figure 3. We also present the related research gaps and future directions. These four selected categories encompass the practical applications of UAVs that have been widely studied [14; 15].
## 3 Results and Discussion  
### High-Resolution Neutron Powder Diffraction  
#### 3.1.1 NCNCO01  
Table 1 contains the results of the structure refinement obtained for the NCNCO01 sample by TOF NPD. Refinement profiles are presented in Figures S1-S4. At 50 \({}^{\circ}\)C, all the samples under study before and after the temperature treatment had a structure with a _Bmab_ sp.gr. (\(N^{0}\) 64), which is common for nearly stoichiometric (regarding oxygen) R-P phases [28]. Atom coordinates calculated for the structure within this sp.gr. are: Nd/Ca (0, y, z); Ni/Cu(0,0,0); O1 (0.25, 0.25, z); O2 (0, y, z); O3 (0.25, 0.25, z) [63; 64]. The NCNCO01 sample, after the synthesis, possessed an oxygen composition close to the stoichiometric sample [49]. Thus, no interstitial oxygen was expected for this composition. This is because both Ca-doping at the rare-earth site and Cu-doping at the Ni site lead to a decrease in the overall amount of oxygen, and it is reasonable that the first candidate to leave the structure is weakly bound interstitial oxygen [33; 37; 49; 65]. Moreover, owing to the tendency of Cu cations to form structures with square-planar coordination [47], one can expect oxygen deficiency at the expense of the apical oxygen, particularly for the compositions with higher copper content.  
After the temperature treatment in air, oxygen stoichiometry in the sample remained the same; however, the structure refinement led to non-zero occupancies for the interstitial oxygen sites (O3) and, simultaneously, occupancy of the apical oxygen sites (O2) decreased. It has been established in a number of studies that oxygen migration in R-P phases occurs by the interstitialcy (push-pull) mechanism through the apical and interstitial oxygen sites [66; 67; 68]. That is why a rearrangement of oxygen between these two sites is expected, as it leads to a different canting of the oxygen octahedrons that may be favorable for relaxing the microstrains occurring in the R-P phases due to a difference in the thickness between the rock-salt and perovskites layers in the \(ab\) plane [28]. As follows from the structural refinement results, a change in the canting of the oxygen octahedrons leads to a change in the position of the Nd/Ca atom and to an increase in the U parameter of the Nd/Ca site, as well as to a decrease in the U parameter of the Ni/Cu site. Moreover, one can see a small increase in the \(c\) lattice parameter value, which correlates with the amount of interstitial oxygen in R-P phases [69].  
Figure 1 illustrates the above-mentioned structural changes for the NCNCO01 sample after the high-temperature treatment in static air.
# Energy Efficient Routing Protocol in Novel Schemes for Performance Evaluation
## 1 Introduction
### Review of Related Work of Energy Balancing

Table 2: Energy routing scheme with multi-objective CH selection. For increasing network lifetime, [PERSON] and [PERSON] developed the flow augmentation (FA) technique, a straight routing strategy, based on connection costs, that represents both the required residual energy and communication energy levels [40]. This algorithm ignores the traffic load of the node, while choosing a route. Furthermore, the efficacy of this strategy is greatly reliant on the empirical values of the parameters provided. DEBR is an abbreviation for distributed energy balanced routing [41]. This method, similar to FA, finds an optimal route path that achieves energy balance by combining the needed communication energy with the available energy. Each sensor calculates whether it is cheaper to send the desired traffic to one of the node's neighbors or BS directly. Research examines a specific circumstance, in which all sensors have direct access to the sink in a network. Liuetal, two cost function-based routing strategies that are energy conscious have been suggested [42]. A modest change in nodal energy is translated into a big change in cost value using the Exponential and Sine Cost Function-based Route (ESCFR). On the other hand, the Double Cost Function based Route (DCFR) considers both the rate of energy utilization and the remaining energy of nodes. This new cost function accounts for the high rates of energy consumption experienced by hotspot nodes, resulting in improved energy efficiency while attempting to balance performance for the protocol
## 4 Comparison Results
### Global Metrics of Real-World Datasets  
Figure 3 shows the global metrics for all combinations of decision-map techniques, classifiers, and datasets. Note that DV failed to run with SVM on the real-world datasets and thus is not included in the figure. From a dataset perspective, the results varied significantly based on the specific dataset being considered. On HAR, the SDBM and DV achieved comparable (high metric) results, whereas the DBM achieved slightly lower scores. On MNIST, the DBM achieved the best results in all aspects, even though the DBM's training did not use label information. On FashionMNIST, the SDBM achieved the highest scores, whereas DV achieved comparable results in the data-level metrics (\(ACC_{C}\), \(ACC_{M}\), \(Cons_{d}\)) but much lower results in the pixel-level metrics (\(Cons_{d}\), \(\bar{S}\)). Finally, on Reuters, DV achieved the best results. From a classifier perspective, the results were more stable. The accuracy of the classifiers themselves varied only slightly. The most noticeable difference was that random forest always achieved lower scores (in all global metrics, except for \(ACC_{C}\) and \(1-\bar{G}\)) compared to the other classifiers in all the considered metrics, particularly on the MNIST dataset. For \(1-\bar{G}\), almost all the results were close to 1. This was due to a large maximum value of DV, which influenced the normalization (see the definition of \(\bar{G}\) in Section 3.1). We explore this in more detail in Section 4.3.  
The datasets in Figure 3 are sorted (from top to bottom) by increasing dimensionality (see also Table 1). The higher the dimensions, the more difficult for the models to learn. So, it is not surprising that the DBM, which is a fully unsupervised method, experienced trouble when aiming to depict classifiers for higher-dimensional datasets like Reuters (5000 dimensions). In contrast, on this dataset, the SDBM achieved higher quality metrics, whereas DV achieved the highest values. This suggests that DV is the decision-map  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Type** & \(|\mathbf{D_{t}}|\) & \(|\mathbf{D_{T}}|\) & **Dimensionality** & **No. of Classes** \\ \hline Synthetic Blobs & Synthetic & 1000 & 500 & 100 & 5 \\ HAR & Time Series & 5000 & 2352 & 561 & 6 \\ MNIST & Image & 5000 & 5000 & 784 & 10 \\ FashionMNIST & Image & 5000 & 5000 & 784 & 10 \\ Reuters Newswire & Text & 5000 & 2432 & 5000 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets used in our decision-map evaluations and their properties.  
method of choice--from the perspective of the global quality metrics--for high-dimensional datasets. However, as we will demonstrate, there are other factors that influence the three decision-map methods in different ways.
# GIS-Based Evaluation Method for Accessibility of Social Infrastructure Facilities
## 3 Methodology
### Data
#### 3.3.1 Spatial Data  
Namdong-gu (Namdong District), Incheon Metropolitan City, South Korea, was selected as the case study area. Namdong-gu (Namdong District) has 22 administrative dongs, the smallest urban-area division in South Korea with its own office and staff. The walking or vehicle travel time between the RBs and social infrastructure facilities was required to compute the AI. The RBs and social infrastructure facilities in Namdong-gu were created as spatial features in the GIS environment (Figures 2 and 3). The population data obtained from National Spatial Data Infrastructure Portal were combined with the corresponding spatial feature [28].  
We obtained the location information of the infrastructure facilities from the administrative office [29]. Information about the childcare centers and kindergartens was collected from Child School Info [30]. Then, we created spatial features of the social infrastructure facilities as polygons for parks and as points for the others. Table 1 lists the data sources and data types by facility.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Source** & **Spatial Data Type** \\ \hline Childcare centers & Child School Info [30] & Point \\ Kindergartens & Child School Info [30] & Point \\ Elementary Schools & Administrative Office [29] & Point \\ Parks & Administrative Office [29] & Polygon \\ Libraries & Administrative Office [29] & Point \\ Sports facilities & Administrative Office [29] & Point \\ Residential blocks & Census Shapes [28] & Point, Polygon \\ Population & Census of Population Data [28] & Text \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data sources and types.  
Figure 2: RBs in Namdong-gu, Incheon: (**a**) residential blocks in polygon features; (**b**) residential blocks in point features.
# Torque Characteristic Analysis of a Transverse Flux Motor Using a Combined-Type Stator Core
###### Abstract  
An external rotor transverse flux motor using a combined-type stator core is proposed for a direct drive application in this paper. The stator core is combined by two kinds of components that can both be manufactured conveniently by generic laminated silicon steel used in traditional motors. The motor benefits from the predominance of low manufacturing cost and low iron loss by using a silicon-steel sheet. Firstly, the basic structure and operation principles of the proposed motor are introduced. Secondly, the expressions of the electromagnetic torque and the cogging torque are deduced by theoretical analysis. Thirdly, the basic characteristics such as permanent magnet flux linkage, no-load back electromotive force, cogging torque and electromagnetic torque are analyzed by a three-dimensional finite element method (3D FEM). Then, the influence of structure parameters on the torque density is investigated, which provides a useful foundation for optimum design of the novel motor. Finally, the torque density of the proposed motor is calculated and discussed, and the result shows that the proposed motor in this paper can provide considerable torque density by using few permanent magnets.  
transverse flux; torque density; three-dimensional finite element method; external rotor
# Concept of Virtual Reality Training of Facility Management Employees Using Eye Tracking
###### Abstract  
In facility management, employees' unique skills and high technical knowledge are necessary to operate buildings efficiently and safely. Employees acquiring professional skills requires the use of an effective training procedure. Traditional facility management training is often challenging because it requires using real devices in a work environment. It is only sometimes possible to stop these devices. Additionally, there is a risk of equipment damage or injury to employees. Therefore, organizing staff training in virtual reality (VR) seems interesting. Properly conducted training of employees in virtual reality allows for the reconstruction of identical test conditions for all participants, thanks to which high comparability of training results is achieved. In addition, virtual training allows for an in-depth analysis of each participant's results. We can carefully analyze their movement, how they perform a task, and even their attention directed to critical elements. To be able to perform such an in-depth analysis, it was proposed to use eye-tracking technology in the VR glasses used during training and evaluation in the VR environment. Eye movement tracking allows for an accurate analysis of the focus of each participant on the critical elements of the devices in the decision-making process. It enables obtaining much more comprehensive information on the concentration and correctness of the employee's actions. The result of the conducted research is the development of the concept of a VR environment for training and evaluating facility management staff using eye-tracking technology, enabling employees to improve and verify their qualifications in a very comfortable way without the need to access the existing infrastructure. The developed training procedure also allows the classification of training participants as fast and slow learners, which is very important for the employer, as it identifies employees who require additional training support. The developed approach was verified during the realization of selected training conducted with 100 employees working in facility management and was implemented as a commercially offered training service.  
virtual reality; virtual training; training effectiveness evaluation; facility management 2023
###### Abstract
While this publication focuses on the sensor itself, including the calculation and calibration methods required for its application, we will also give a brief overview about its capability of being embedded into a wireless sensor node system. Equipped with solar cells providing the needed energy for data acquisition and wireless transmission, it can operate energy-autonomously. Furthermore, the overall size is comparable to a matchbox. This means, the system can be easily attached to small branches simplifying the deployment process and enabling the user to deploy the sensor nodes in a wide range of vertical and horizontal space, making it possible to obtain the GPP. These sensor nodes can be equipped with additional sensors to collect valuable parameters affecting plant growth beside the light intensity and quality. While our sensor node sticks to temperature and humidity data, other sensor combinations to quantify, for example, CO\({}_{2}\)-concentrations or stem diameters are possible and could be integrated in separate sensor nodes and be connected via wireless communication to the overall-system, as well.
## 2 Materials and Methods
### Mechanical Analysis  
#### 2.2.1 Resilient Modulus  
After the storage period described in item 2, the compacted CRAM was subjected to the RM test. The test was conducted according to the standard test method of the DNIT 134 brazilian standard [65], using a dynamic triaxial testing machine (Owntec-MS-151). During the RM test, eighteen pairs of confining stresses (\(\sigma_{3}\)) and deviatoric stress (\(\sigma_{d}\)) were applied after the conditioning phase of the specimen. The load cycle lasted 1 s, with 0.1 s of load application and a frequency of 1 Hz. In the conditioning stage, the specimens were exposed to three sets of stresses and subjected to 500 load cycles for each set. Subsequently, they  
Figure 3: CRAM sample in curing environment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Water Content (\%)** & **1** & **3** & **5** & **7** & **9** \\ \hline Wet Density (g/cm\({}^{3}\)) & 1.59 & 1.97 & 2.05 & 2.08 & 2.06 \\ Converted Density (g/cm\({}^{3}\)) & 1.84 & 1.91 & 1.95 & 1.94 & 1.89 \\ Optimum Water Content (\%) & 5.6 & & & & \\ Maximum Dry Density (g/cm\({}^{3}\)) & 1.95 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Proctor compaction test results.  
were subjected to 18 additional sets of stresses, with 100 load cycles for each set, totaling 3300 cycles per test. The applied stress values are presented in Table 4. Figure 4 shows the specimen molded inside the tripartite mold and in the dynamic triaxial equipment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{Conditioning Phase} & \\ Par & \(\sigma_{3}\) (KPa) & \(\sigma_{d}\) (KPa) & \(\sigma_{3}/\sigma_{1}\) \\ \hline  
420 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Stress pairs for the resilient modulus test.  
Figure 4: RM test. (**a**) The specimen molded inside the tripartite mold. (**b**) dynamic triaxial equipment.  
RM is obtained from the results of the repeated load triaxial tests. It is defined as the ratio between the deviatoric stress (\(\sigma_{d}\)) and the resilient axial strain (\(\Delta r\)), as presented in Equation (1).  
\[MR=\frac{\sigma_{d}}{\Delta r} \tag{1}\]  
where:  
* \(\Delta r\): is the ratio of \(\Delta h\) to \(h_{0}\). \(\Delta h\) is the maximum vertical displacement, and \(h_{0}\) is the initial reference length of the cylindrical specimen.  
For the results, the average of two specimens for each curing time was considered and analyzed using the MRCalc tool of the Systrain software. The aim was to determine the most appropriate model for the mixture's behavior during the triaxial test. The evaluation was based on the coefficient of determination to identify the model with the best fit to the experimental data.
# A BIM Based Framework for Damage Segmentation, Modeling, and Visualization Using IFC
## 3 Methodology  
Figure 3 shows the framework with the three pillars Damage Input Data in blue, Damage Data Processing in yellow, and the resultant Model States in red. Possible subsequent processes are depicted in green on the right side. The bridge inspection delivers the required damage data, such as photos, measurements, and textual descriptions. To segment defects and generate the related geometry, the damage segmentation and geometry generation processes take the visual data. Subsequently, the damage alignment defines the position of the defects at their related components using the defect geometry and the BIM Model of the bridge. Finally, semantic damage data is added manually to the geometric as-damaged BIM model. The resulting model is used for later planning, analysis and assessment. Planning is necessary for further surveys, for example, ultrasonic or impact-echo. Given the 3D as-damaged BIM model, 3D Finite Element Analyses (FEA) may be set up easier because the geometry of damaged components may be directly imported into the simulation environment. Furthermore, probabilistic simulations may utilize semantic and geometric data. Last, the assessment of the bridge is based on all data incorporated in the as-damaged BIM model. The proposed method covers the entire framework with no in-depth information about damage segmentation, geometry generation, or damage alignment.
## 2 Design Parameters of the Metamaterial and Simulation Setup  
Figure 1a shows the front view of the unit cell, which is labeled with symbols. It is a new combination of three different octagonal rings surrounded by a split-ring resonator (SRR). A popularly used dielectric Rogers RT 6002 with dimensions 11 \(\times\) 11 mm\({}^{2}\) and a thickness of 1.524 mm is used as a substrate. The dielectric constant, thermal conductivity and tangent loss of RT6002 are 2.94, 0.6 W/m/K and 0.0012, respectively. Copper (annealed) with an electrical conductivity of 5.96 \(\times\) 10\({}^{7}\) Sm\({}^{-1}\) is used for all resonators of the upper layer. The outer and inner radii of the first octagon are R\({}_{1}\) = 4.3 mm and R\({}_{2}\) = 3.8 mm, respectively, whereas the radii of the second octagon are r\({}_{1}\) = 3.3 mm and r\({}_{2}\) = 2.8 mm, respectively. Each split gap (g) of the octagon is 0.40 mm. The outer and inner radii of the smallest octagon are r\({}_{3}\) = 1.5 mm and r\({}_{4}\) = 0.75 mm, respectively. These three octagons (OSRRs) are placed at the center an SRR with dimensions of 10.40 \(\times\) 10.40 mm\({}^{2}\) and a split gap (G) of 0.50 mm. The three octagons are attached to each other by four metal strips with a length of 3 mm and a width of 0.40 mm.  
It is noteworthy that the width of the SRR (t), as well as that of the first two octagons, is 0.50 mm, whereas the width of the smallest octagon (e) is 0.75 mm. The perspective view and the simulation setup of the proposed MM are depicted at Figure 1b,c respectively. The symbolic presentations of the design parametric values of the projected unit cell are given in the Table 1.  
Proper boundary conditions are applied to attain the expected results from the proposed metamaterial design. The electromagnetic radiation propagates along the z coordinate, whereas the perfect electric conductor (PEC) and the perfect magnetic conductor (PMC) propagate along the x coordinate and y coordinate, respectively.