esa-sceva/satcom-chunk-collection huggingface dataset:
# Full-Field Strain Determination for Additively Manufactured Parts Using Radial Basis Functions
## 2 Surface Strain Determination
### Strain Computation in Surfaces
\right|=\] \[=\mu^{2}-(\hat{C}^{1}_{\ 1}+\hat{C}^{2}_{\ 2})\mu+(\hat{C}^{1}_{\ 1} \hat{C}^{2}_{\ 2}-\hat{C}^{1}_{\ 2}\hat{C}^{2}_{\ 1})=0. \tag{6}\]  
If one introduces the principal invariants  
\[\mathrm{I}_{\hat{\mathbf{C}}} =\mathrm{tr}\,\hat{\mathbf{C}}=\hat{\mathbf{C}}\cdot\mathbf{I}= \hat{C}^{1}_{\ 1}+\hat{C}^{2}_{\ 2} \tag{7}\] \[\Pi_{\hat{\mathbf{C}}} =\frac{1}{2}\Big{(}(\mathrm{tr}\,\hat{\mathbf{C}})^{2}-\mathrm{tr }\,\hat{\mathbf{C}}^{2}\Big{)}=\det[\hat{C}^{\alpha}_{\ \beta}]=\hat{C}^{1}_{\ 1}\hat{C}^{2}_{\ 2}-\hat{C}^{1}_{\ 2}\hat{C}^{2}_{\ 1}, \tag{8}\]  
the eigenvalues read  
\[\mu_{1,2}=\frac{\mathrm{I}_{\hat{\mathbf{C}}}}{2}\pm\sqrt{\frac{\mathrm{I}_{ \hat{\mathbf{C}}}^{2}}{4}-\Pi_{\hat{\mathbf{C}}}}. \tag{9}\]  
Since the (in-plane) principal stretches are the eigenvalues of the right stretch tensor \(\hat{\mathbf{U}}\) of the polar decomposition \(\hat{\mathbf{F}}=\hat{\mathbf{R}}\hat{\mathbf{U}}\), the square root of (9)  
\[\lambda_{1,2}=\sqrt{\mu_{1,2}}, \tag{10}\]  
has to be calculated, see [4]. There are various principal strain measures  
\[\epsilon^{\mathrm{(m)}}_{\alpha}= \begin{cases}\frac{1}{m}(\lambda_{\alpha}^{m }-1)&\text{if }m\
eq 0\\ \ln\lambda_{\alpha}&\text{if }m=0\end{cases}  \tag{11}\]  
where the most common principal strain measures are given for \(m=0\) (Hencky strain), \(m=1\) (engineering strain), and \(m=2\) (Green-Lagrange strain). In conclusion, the spectral representation of the right stretch, the right Cauchy-Green tensor, and the Green strain tensor reads  
\[\hat{\mathbf{U}}=\sum_{\alpha=1}^{2}\lambda_{\alpha}\vec{n}_{\alpha}\otimes \vec{n}_{\alpha},\quad\hat{\mathbf{C}}=\sum_{\alpha=1}^{2}\lambda_{\alpha}^{2 }\vec{n}_{\alpha}\otimes\vec{n}_{\alpha},\quad\hat{\mathbf{E}}^{(2)}=\sum_{ \alpha=1}^{2}\epsilon^{(2)}_{\alpha}\vec{n}_{\alpha}\otimes\vec{n}_{\alpha}. \tag{12}\]  
Here, \(\vec{n}_{\alpha}=\vec{q}_{\alpha}/|\vec{q}_{\alpha}|\) with \(\vec{q}_{\alpha}=q^{\alpha}\vec{A}_{\alpha}\) are the normalized eigenvectors or principal directions.
## 4 Calculation of Structure Stress Fields  
### Structure Models and Boundary Conditions  
Previously, a great deal of research has investigated an overall impeller as a research object and merely kept the profile of the objects for study, which did not involve discussion or analysis about their structural details. In this paper, the blade angle is adjusted by the manual adjustment of the worm and gear, and the inner structure of the impeller is complex, as demonstrated in Figure 5. Different blade structures determine different solution domains of finite element calculation. Therefore, the overall impeller should not be simply regarded as a solid structure during finite element calculation, meaning that a single blade of the impeller is selected as the object for structural calculation, which ensures more accurate simulation results.  
The material of the impeller is ZG0 Cr13 Ni4 CuMo with a density of 7730 kg/m\({}^{3}\), a Young's modulus of 203 GPa, Poisson's ratio of 0.291, and yield strength of 550 MPa. As stress fields are concentrated on the roots of blades [3; 4; 30], the refinement of girds in this region is conducted. The sensibility analysis of grids is carried out by equivalent-stress finite element calculation of four schemes of impeller grids with different sizes (20 mm, 10 mm and two different refinements below 10 mm). It is found that results of two refinements below 10 mm were close, indicating a grid partition scheme of numerical convergence. A total of 126,136 units and 198,127 nodes are generated in the course of grid partition.  
Boundary conditions of the impeller blade model involve structural constraints and loads [25; 26; 27; 30; 32; 33]. Finite element calculation requires enough constraints to constrain the motion of the structure and prevent the rigid body displacement of the structure. Surface A is set as a fixed support, surface B and C are set as cylindrical supports, and surface D is set as the fluid-solid interface (Figure 6). Hence, a fixed constraint of cylindrical Surface A is conducted to constrain the motion of surface A in each direction. The cylinder constraint of cylindrical surfaces B and C is carried out to constrain the radial motion of blades. Blade surface D is defined as the interface of FSI to accomplish the information transferred between the flow fields and the solid fields. The time step and the total time obtained by finite element analysis are consistent with those of actual flow fields.  
Figure 5: Structure diagram of the impeller, (**a**) side view of middle section, (**b**) axial direction view of middle section, and (**c**) structural model of the blade.  
Figure 6: Boundary conditions of structure calculation.
_Applied_  
_sciences_  
Potential of Supercritical Carbon Dioxide Power Cycles to Reduce the Levelised Cost of Electricity of Contemporary Concentrated Solar Power Plants  
**[PERSON], [PERSON] *\({}^{\copyright}\)**, [PERSON] and [PERSON] and [PERSON]\({}^{\copyright}\)  
Department of Energy Engineering, University of Seville, Camino de los Descubrimientos s/n,  
41092 Seville, Spain; [EMAIL_ADDRESS] (F.C.); [EMAIL_ADDRESS] (G.S.M.); [EMAIL_ADDRESS] (T.S.-L.); [EMAIL_ADDRESS] (F.J-E.)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +34-954-486-488  
Received: 31 May 2020; Accepted: 15 July 2020; Published: 22 July 2020  
**Featured Application: Assessment of the potential of supercritical Carbon Dioxide power cycles to reduce the Levelised Cost of Electricity of contemporary Concentrated Solar Power plants, with the aim to benchmark the cost of electricity of the current and next generation Concentrated Solar Power (CSP) technology.**  
**Abstract: This paper provides an assessment of the expected Levelised Cost of Electricity enabled by Concentrated Solar Power plants based on Supercritical Carbon Dioxide (sCO\({}_{2}\)) technology. A global approach is presented, relying on previous results by the authors in order to ascertain whether these innovative power cycles have the potential to achieve the very low costs of electricity reported in the literature. From a previous thermodynamic analysis of sCO\({}_{2}\) cycles, three layouts are shortlisted and their installation costs are compared prior to assessing the corresponding cost of electricity. Amongst them, the _Transcritical_ layout is then discarded due to the virtually impossible implementation in locations with high ambient temperature. The remaining layouts, Allam and Partial Cooling are then modelled and their Levelised Cost of Electricity is calculated for a number of cases and two different locations in North America. Each case is characterised by a different dispatch control scheme and set of financial assumptions. A Concentrated Solar Power plant based on steam turbine technology is also added to the assessment for the sake of comparison. The analysis yields electricity costs varying in the range from 8 to over 11 \(\copyright\)/kWh, which is near but definitely not below the 6 \(\copyright\)/kWh target set forth by different administrations. Nevertheless, in spite of the results, a review of the conservative assumptions adopted in the analysis suggests that attaining costs substantially lower than this is very likely. In other words, the results presented in this paper can be taken as an upper limit of the economic performance attainable by Supercritical Carbon Dioxide in Concentrated Solar Power applications.**  
**Keywords:** LCoE; CSP; supercritical CO\({}_{2}\)
# AVSS: Airborne Video Surveillance System
## 1 Introduction
The installation of the proposed system can be carried out simply by sending UAVs to the surveillance area rather than visiting and installing CCTVs at the site. For maintenance, the proposed system can call in the UAVs, perform maintenance procedures, and resend them to the site. The proposed system allows the operator to easily modify the surveillance area by moving the deployed UAVs to the modified surveillance area. Since the UAVs are connected with a wireless network which tends to experience a dynamic network status, the system must have a means to successfully stream video over the network. The proposed system adopted a video stream rate changing scheme that allows the system to stream video with cognition of the network status and anomalous situation. The key contributions of the paper are as follows:  
* We proposed a new surveillance system which consists of multiple UAVs. Contrary to the CCTV surveillance system, the proposed system can be easily deployed and the surveillance area can be modified at anytime. The experiment shows that the system allows the operator to view multiple images captured from UAVs at various locations simultaneously. * We present a fleet platform that allows the user to control multiple UAVs simultaneously. The fleet platform connects the UAVs through a multi-hop network and allows users to transmit data over the network. The experiment shows that the fleet platform is able to send mission data to the UAVs and deliver surveillance data from the UAV to the user. * We present a novel algorithm for deploying UAVs to a mission area. When a mission area is given, the algorithm intelligently selects the position of the UAVs that will cover the mission area as effectively as possible. * We devised an event detection algorithm that helps reducing the amount of required network bandwidth in video streaming. Based on the possible information a stream has, our algorithm controls the rate of the streaming video. The rest of this article is composed as follows. In Section 2, the background on current systems of UAV fleets and the UAV surveillance are discussed. In Section 3, a step by step guideline to build the fleet is given, and Section 4 explains the placement algorithm in detail. Then the features of our implementation are introduced in Section 5. The implementation details are not about general UAV fleets, but the features explained are essential to the surveillance. Evaluation including simulations and field experiments are provided in Section 6. Finally, we conclude the paper with Section 7.
# Anomaly Detection in Autonomous Deep-Space Navigation via Filter Bank Gating Networks
## 2 Related Work
The Magill gating network, as described, outputs weights that favor filters that produce measurement residuals that are small relative to the innovations covariance matrix (\(S_{i,k}\)) and that have a small innovations covariance matrix. Because it balances two costs, the estimator that it selects as best, does not necessarily have to be behaving nominally--it may just have a very small innovations uncertainty relative to other estimators in the filter bank. Furthermore, the way these weights are defined does not easily lend itself to supporting filter banks that have filters that only process a subset of the available measurement data types. These types of filters will likely have high innovations covariances for the data types that they do not process, which will lead to the estimator's weight dropping even if the filter is behaving nominally. For these reasons, we are motivated to design a new gating network that will focus on whether the filters in the filter bank are demonstrating nominal performance independent of the data that they are processing. The development of this new gating network is detailed in Section 3.
## 4 Experimental Study
### Quality Indicators  
The performance measurements of all tested systems were carried out using the minimum values of the three quality indicators (QI), including the Inverted General Distance (IGD), the Mean Inverted General Distance (MIGD) and the Hypervolume Difference (HVD), which are presented, respectively, in Equations (11)-(13) respectively. All these metrics are used to measure both the convergence and diversity of the tested DMOEAs.  
* The Inverted General Distance (IGD) [11] in Equation (11) measures a Euclidean distance _d(i,POF)_ between the ith points in the non-dominated solutions \(POF^{*}\) and the nearest approximated Pareto front (POF). \[IGD(POF^{*}_{t},POF_{t})=\frac{\sum_{i\in POF^{*}}d(i,POF)}{|POF|}\] (11)
* The Mean Inverted General Distance (MIGD) [11] is presented in Equation (12), presenting the average of the IGD values at each iteration \(t\in T\). \[MIGD((POF^{*}_{t},POF_{t})=1/T\times\sum_{t\in T}IGD(POF^{*}_{t},POF_{t})\] (12)
* The Hypervolume Difference (HVD) [11] detailed in Equation (13) aims to compute the difference between the Hypervolume (HV) of the true \(POF^{*}\) and the approximated \(POF\) \[HVD=HV(POF^{*}_{t})-HV(POF_{t})\] (13)
# Scaling of Rain Attenuation Models: A Survey
## 3 Existing Scaling Techniques
### Frequency Scaling
#### 3.3.3 Composite Frequency Scaling Model  
In the composite frequency scaling model, the effect of an element-by-element technique can be tracked to determine related coefficients at a lower frequency to yield the total attenuation of a radio link at a higher frequency. The scaling techniques of different environmental elements (oxygen, fog, cloud) can be different. These elements are oxygen, LWC, or fog, cloud, and DSD-based rain attenuation as reported in [37].
# Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography
## 2 Literature Review
These comparative studies consistently demonstrate the scale-dependent nature of thermal monitoring, with UAVs occupying a critical middle ground between in situ and satellite measurements. [PERSON] and [PERSON] [36] provided important context by comparing diurnal LST measurements across platforms, finding that satellite-based approaches performed adequately for homogeneous surfaces like barren lands but demonstrated reduced accuracy in vegetated areas. This finding underscores the context-dependent performance of different measurement approaches rather than suggesting universal superiority of any single platform. A critical assessment of temporal frameworks reveals significant variation in approaches to capturing diurnal thermal patterns. [PERSON] et al. [37] implemented three-dimensional thermal monitoring from 1.5 m to 200 m at the morning, afternoon, and night periods, providing valuable insights into vertical thermal structure but offering limited resolution of diurnal transitions. [PERSON] et al. [8] employed ground-based radiometry complemented by UAV thermal imaging in Tel Aviv, recommending early-morning and midday measurements as critical points for capturing maximum thermal contrast--though this two-point approach necessarily simplifies complex diurnal patterns. More comprehensive temporal frameworks have emerged in limited studies. [PERSON] et al. [38] documented approximately 20 \({}^{\circ}\)C daytime temperature swings in Saudi Arabian deserts compared to 12 \({}^{\circ}\)C in grass fields--highlighting the substantial influence of surface materials on diurnal amplitude. [PERSON] et al. [39] demonstrated significant improvements in urban thermal characterization through integration of UAV thermal imagery with near-ground meteorological data, suggesting the importance of contextualizing thermal measurements within broader atmospheric conditions. Although numerous studies have contributed valuable insights into UAV-based thermal imaging, most research has focused on single time points or limited daytime measurements. However, evening and nighttime measurements are crucial as they provide insights into heat retention, cooling rates, and thermal inertia effects across different land cover types, which are essential for understanding long-term microclimatic variations. Consequently, the complete diurnal cycle, particularly during evening and nighttime periods, remains under-investigated. Given that understanding these day-to-night transitions is essential for comprehending how different land cover types store and release heat, there is a clear need for a comprehensive analysis. This study aims to bridge that gap by systematically examining the full diurnal thermal behavior of various surfaces using high-resolution UAV-derived data.
# Underground Mine Road Detection Using Deep Learning Technique
###### Abstract
However, due to the lack of information exchange between parallel branches, it cannot achieve good results in the road identification of underground mine slopes, and it mainly studies the straight roads of mines, and does not consider special roads features such as bends and forks. [PERSON] et al. studied a method for rapid detection of rockfall using laser radar, which can reduce the wear of rockfall on mine car tires and reduce mining costs [26]. [PERSON] et al. derived the vehicle position and attitude information using ultra-wideband positioning technology to provide support for unmanned positioning technology in underground coal mines [27]. [PERSON] et al. improved the RRT * algorithm to solve the path planning problem of underground unmanned vehicles using articulated structures and drift environmental conditions. This provides a new method for underground mine vehicle path planning [28]. [PERSON] et al. proposes a real-time traffic identification method based on a low-power embedded device, NVIDIA Jetson AGX Xavier [29]. This low-power device embedded into the unmanned mine car can judge the road conditions of the underground mine road in time and ensure the safe driving of the unmanned mine car. In summary, there are many problems in the application of driverless technology to mine roads, such as the complex environment of mine roads, the fuzzy and discontinuous road boundaries, the uneven road surfaces, and the flying sand. It can be concluded that the special challenges of underground mine roads are as follows:  
* Underground mine roads have a certain slope, and misidentification may occur when identifying roads through images. * The boundary of underground mine road is not obvious and the light is not sufficient, which makes it difficult to identify the exercise area of underground mine road. To address the aforementioned challenging issues in the mine environment, this paper studies an underground mine road recognition method using a camera to collect images for real-time semantic segmentation of underground mine slopes, effectively segmenting the drivable area in the mine slope scene, and realizing real-time and accurate recognition of underground mine slopes. The main contributions of this study are as follows:1. Aiming at the characteristics of the underground mine slope road environment, we improved the BiSeNet semantic segmentation network. 2. A dataset of underground mine roads is constructed, and the dataset is augmented by data augmentation. 3. The model is verified by experiments and compared with other classical models.
## 3 Results and Discussion  
### Solubility of ETB in Formulation Components  
The solubility of ETB in eucalyptus oil, Tween 20, and PEG 200 was found to be 78.9 \(\pm\) 3.20, 62.10 \(\pm\) 3.5, and 58.5 \(\pm\) 2.0 mg/mL, respectively. The solubility of ETB in eucalyptus was much higher compared to its solubility reported in the archis, castor, mustard, olive, sesame, and sunflower oils [27]. Thus, eucalyptus oil was chosen as the oil phase for the formulation of ETB-loaded nanoemulsion. In the case of Tween 20, it was already successfully used as a surfactant for nano emulsification of eucalyptus oil [28]. Further, the solubility of ETB in Tween 20 was good. The solubility of ETB in PEG 400 was around 2.2 mg/mL in a previous report; however, the study was carried out at 25 \(\pm\) 1.0 \({}^{\circ}\)C for 24 h. In addition, this reported study suggested that hydrophobic interactions were responsible for higher solubility in PEG [29]. The higher solubility of ETB in PEG in the present study could be attributed to the higher temperature (37 \(\pm\) 1.0 \({}^{\circ}\)C) and time (72 h) employed for the solubility determination. Further, the present study used PEG 200 instead of the reported PEG 400. It is established that PEGs with higher molecular weights have higher hydrophilicity and vice versa [30]. Conversely, low molecular weight PEGs have more hydrophobicity. Thus, PEG 200 with a lower molecular weight than PEG 400 has better hydrophobic interactions with ETB, resulting in higher solubility. Interestingly, this argument can be easily justified by the reported higher solubility of ETB in PEG 400 than in PEG 600 [31].
## 5 Experimental Results and Discussion
### Simulation and Technical Development Framework  
Based on the comparison presented in Table 5, which summarizes the most widely used simulators to address the SPP, we observe that iFogSim is a more recent tool, designed  
Figure 10: Activity diagram of the proposed solution.  
specifically for simulating Fog environments. It fulfills our requirements by enabling the modeling and simulation of Fog systems. In addition, it allows for the performance evaluation of Fog architectures, considering various aspects such as latency, power consumption, and resource management. The work [40] defines iFogSim as \"an open-source Java simulator developed by the Cloud computing and Distributed Systems (CloudS) Laboratory at the University of Melbourne\". iFogSim is an extension of a popular simulator called CloudSim. So, to implement the features of the iFogSim architecture, we are relying on CloudSim's core event simulation functionality [41]. The physical topology of this simulation tool is presented in the work [42] in the form of Java classes. It consists of a set of sensors, a set of actuators, and a set of Fog devices. Instances of the \"Sensor\" class are entities that act as IoT sensors. The \"Actuator\" class models an actuator and defines a method for acting on the arrival of a tuple or task from an application module. The last class specifies the hardware characteristics of Fog devices and their connections to other Fog devices, sensors, and actuators.  
To overcome the current limitations of the iFogSim simulator in supporting application migration, the logical grouping of Fog nodes, and the orchestration of loosely coupled application services, three new components, mobility, clustering, and microservices, have been implemented into iFogSim2 [43]. This simulator is well equipped with APIs and built-in policies to illustrate use cases related to mobility, microservices, and node clustering in Edge/Fog Computing environments. Additionally, the functions of its various components can be adjusted according to specific case studies to create variations in the simulations [43].  
In the Table 6, iFogSim2 is compared with other existing simulators designed to model Edge/Fog environments. As shown in the table, iFogSim2 simultaneously supports the integration of a real-world dataset to evaluate the performance of different service management policies in Edge/Fog Computing environments. It provides default techniques for mobility management, node clustering, and microservice orchestration, which can be adopted as benchmarks when comparing performance. Finally, based on the analyses summarized in Tables 5 and 6, we decided to conduct our simulations using iFogSim2, as it best meets the requirements of our study.