esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 4 Localization Algorithms
### Triangulation  
Triangulation is a localization algorithm for determining the position of a point by measuring the angles at the base of a triangle that has two points of known coordinates as its base vertices and a vertex of unknown coordinates as its top vertex. Trigonometric formulas are used to calculate the target coordinates.  
Assuming we know the coordinates of Anchor nodes \(A\) and \(B\), as represented in Figure 10, and that all nodes are at the same height, the distance \(c\) between the two reference nodes is known.  
Once the distances of the target node from the reference nodes are estimated, as \(a\) and \(b\) are also known, the coordinates of point \(C\) can be derived.  
Starting from the Cosine Theorem (or Carnot's Theorem) [15], \(\cos(\alpha)\) is calculated as follows:  
\[\cos\left(\alpha\right)=\frac{b^{2}+c^{2}-a^{2}}{2 bc}\]  
\[\sin^{2}\left(\alpha\right)+\cos^{2}(\alpha)=1\]  
\[\sin\left(\alpha\right)=\sqrt{1-\cos^{2}(\alpha)} \tag{9}\]  
Figure 10: Triangulation.  
Figure 9: A case where there are two intersection points ( (x1,y1) is closest to A\({}_{3}\)).  
If we set point \(A\) as the origin of the reference system, i.e., coordinates (0,0), then point \(C\) has the following coordinates:  
\[C=(b\text{cos}(\alpha),b\text{sin}(\alpha)) \tag{10}\]  
It should be specified that the triangulation method is not always successful. In particular, if the target node is on the segment joining the two Anchor nodes, the algorithm does not provide any position estimates because no triangle is formed, and therefore, the trigonometric formulas cannot be applied.  
In addition, unlike the previously described algorithms that were based on at least three Anchor nodes, for triangulation, only two nodes are needed, but this results in a lower accuracy in position estimation.
## 3 Experimental Setup
### Experimental Procedure  
Define the reduction ratio, \(\beta\), as  
\[\beta=\frac{R_{0}-R_{1}}{R_{0}}\times 100\% \tag{12}\]  
where \(R_{1}\) and \(R_{0}\) are the RMS acceleration responses of tower top ('tower top' is indicated in Figure 10), with and without ECTMD respectively. The measurements of the acceleration sensors at 1.34 m, namely 3(X) and 4(Y), are employed as the representative measurements for the aeroelastic model response in wind tunnel tests. That is to say, the results in the following Sections 4 and 5 are of the tower top.  
Since the first natural frequency predominates in the vibration of transmission towers under ABL wind, the frequency ratio, \(\lambda\), is defined as  
\[\lambda=\frac{f_{\text{ECTMD}}}{f_{1}} \tag{13}\]  
where \(f_{\text{ECTMD}}\) is the natural frequency of the ground-mounted ECTMD, and \(f_{1}\) is the first natural frequency of the tower in Y direction. Note that \(f_{1}\) = 21.2 Hz in this paper.  
Figure 13 shows the wind direction angles used in the test. The wind speed in this paper refers to the mean wind speed at the prototype height of 10 m.  
The experiment procedure can be divided into two phases. In the first phase, in the case of wind direction angle of \(0^{\circ}\) and wind speed of 24.7 m/s, the aeroelastic model is tested with various combinations of ECTMD frequency and damping ratio. By surveying the maximum amount of the reduction ratio, the favorable parameters of the ECTMD can be identified. In the second phase, using the favorable parameters of ECTMD determined, the aeroelastic model is tested with and without the  
Figure 12: Wind environment simulation. (a) Vertical profiles of horizontal wind speed and turbulence; (b) Wind power spectrum at height of 0.4 m.  
Figure 13: Wind direction angles in wind tunnel test.  
ECTMD, under wind speeds of 18.5, 22.6 and 24.7 m/s and wind direction angles in the range of 0\({}^{\circ}\) to 90\({}^{\circ}\).
## 3 Digital Image Correlation Method  
At each stage of the procedure described in Section 2 full-field measurements of displacement vector **d** (u,v,w) of the investigated structures are required. The method which is best fitted to the measurement requirements is digital image correlation [18]. DIC is based on acquisition of a set of images of a tested object which is subjected to load. The surface of an object under investigation has to be covered with a random texture. The 2D DIC version uses a single camera. One of the acquired images is selected as a reference for the others. The reference image is divided into small regions (or subsets), a position of each subset is tracked in the remaining images, using the maximum zero-mean normalized sum of squared difference function as the criterion (or any other correlation metric). The image can be divided into hundreds or thousands of subsets, thus 2D DIC provides in-plane displacement maps over the selected area of interest (AOI). The 3D DIC is a technique that combines the 2D DIC with stereovision by using two cameras for observation of the same AOI. 3D DIC provides: the 3D shape of a surface, in-plane and out-of-plane displacement maps. The in-plane strain maps are calculated by differentiation of the in-plane displacement maps. According to [20], the minimum displacement measurement error can be less than 0.001 pixels, however, it must be noted that in real applications the accuracy of measurements strongly depends on factors such as image noise and stability of experimental conditions. Moreover, the out-of-plane measurement error is larger than the in-plane one, and strongly depends on a stereo angle between the cameras as set [20,21]. The accuracy of displacement measurements is scalable with the resolution of the cameras (larger camera's resolution and smaller FOV indicate higher displacement measurement accuracy). The displacements in the \"x\" direction are given as \"u\" in [pixels] and after scaling are expressed as \"U\" in [mm]; similarly, displacements in the \"y\" and \"z\" direction are given as \"v [pixels] \", \"V[mm] \", and \"w[pixels] \", \"W[mm] \", respectively.
# Aerospace
## 5 Single Small Satellite to Venus Orbit as Secondary Payload
### Small Satellite Approach Trajectory and Atmospheric Entry State  
An approach trajectory with \(r_{p}=(6051+104)\) km, \(\psi\) = \(\pi\) is selected to achieve a 90 deg. inclination orbit. This results in a planet-relative EFPA = \(-5.20\) deg. at the atmospheric interface (\(h\) = 150 km) and will be shown to fall within the aerodynamic entry corridor in Section 5.5. Table 7 lists the nominal atmospheric entry conditions for the aerodynamic vehicle. Figure 17 shows the approach trajectory of the vehicle until the atmospheric entry interface.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Entry altitude, km & 150 \\ Entry longitude, deg. & \(-6.22\) \\ Entry latitude, deg. & 23.28 \\ Atm. relative entry speed, km/s & 10.82 \\ Atm. relative heading angle, deg. & 89.99 \\ Atm. relative EFPA, deg. & \(-5.20\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Venus aerocapture vehicle atmospheric entry state.  
Figure 16: Venus drag-modulation aerocapture feasibility chart with \(\beta_{1}\) = 20 kg/m\({}^{2}\), \(R_{N}\) = 0.235 m, target apoapsis = 2000 km. The star indicates the selected baseline \(\beta_{2}/\beta_{1}\) = 7.5 and \(V_{\infty}\) = 3.51 km/s.  
Figure 17: Approach trajectory of the Venus small satellite aerocapture vehicle. The white ring and the solid line indicate the equatorial plane and the direction of the north pole, respectively.
# Approximating the Temporal Neighbourhood Function of Large Temporal Graphs
## 2 Definitions and Notations
### Temporal Reachability Cones and Neighborhood Functions  
Given a temporal graph \(G=(V,E)\), a node \(u\in V\), and a time interval \(\mathbb{I}=[t_{\alpha},t_{\omega}]\), in this paper, we are interested in efficiently computing the number of pairs of nodes \((u,v)\) such that \(v\) can be reached from \(u\) in the interval \(\mathbb{I}\). To this aim, we give the following definition.  
**Definition 2**.: _Given a temporal graph \(G=(V,E)\), a node \(u\), and a time interval \(\mathbb{I}=[t_{\alpha},t_{\omega}]\), the **temporal reachability cone** of \(u\) in the interval \(\mathbb{I}\) is defined as \(\mathcal{C}(u,\mathbb{I})=\{u\}\cup\{v\in V:\mathcal{P}(u,v,\mathbb{I})\
eq \emptyset\}\)._  
For example, by referring to the temporal graph of Figure 2, the left part of Figure 4 shows the temporal reachability cone of node \(1\) in the interval \([1,5]\): in this case, all nodes can be reached by node \(1\). The right part of figure, instead, shows the temporal reachability cone of node \(1\) in the interval \([9,12]\): in this case, all nodes can be reached by node \(1\) apart from node \(4\). It is also easy to verify that the temporal reachability cone of node \(1\) in the interval \([2,5]\) contains only node \(1\), since there are no temporal edges leaving it in this time interval.  
**Definition 3**.: _Given a temporal graph \(G=(V,E)\) and a time interval \(\mathbb{I}=[t_{\alpha},t_{\omega}]\), the **temporal neighborhood function** in the interval \(\mathbb{I}\) is defined as \(|\mathcal{N}(\mathbb{I})|\), where \(\mathcal{N}(\mathbb{I})=\{(u,v)\in V\times V:v\in\mathcal{C}(u,\mathbb{I})\}\)._  
For example, by referring to the temporal graph of Figure 2 and by choosing \(\mathbb{I}=[1,5]\), \(\mathcal{N}(\mathbb{I})\) contains the pairs \((1,1)\), \((1,2)\), \((1,3)\), \((1,4)\), and \((1,5)\), since all nodes belong to \(\mathcal{C}(1,\mathbb{I})\). \(\mathcal{N}(\mathbb{I})\) also  
Figure 4: Two temporal reachability cones of node \(1\): the first one (**left**) is in the interval \([1,5]\) and includes all nodes, while the second one (**right**) is in the interval \([9,12]\) and includes all nodes but node \(4\).  
Figure 3: The earliest arrival path from node \(1\) to node \(3\) in the temporal graph of Figure 2 in the time interval \([1,12]\) (**left**) and in the time interval \([2,12]\) (**right**).  
contains the pairs \((2,2)\), \((2,3)\), \((2,4)\), \((2,5)\) (since \(\mathcal{C}(2,\mathbb{I})=\{2,3,4,5\}\)), \((3,2)\), \((3,3)\), \((3,4)\), \((3,5)\) (since \(\mathcal{C}(3,\mathbb{I})=\{2,3,4,5\}\)), \((4,1)\), \((4,2)\), \((4,3)\), \((4,4)\), \((4,5)\) (since \(\mathcal{C}(4,\mathbb{I})=\{1,2,3,4,5\}\)), \((5,2)\), \((5,3)\), \((5,4)\), and \((5,5)\) (since \(\mathcal{C}(5,\mathbb{I})=\{2,3,4,5\}\)). Therefore, the temporal neighborhood function in \(\mathbb{I}\) is equal to \(22\).
# Micro-Satellite Systems Design, Integration, and Flight
## 4 Attitude Control System (ACS) Development
### PWM Offsets  
To achieve the pulse width modulation (PWM) offsets, data were gathered for how the magnetometer's magnetic field changed in the X, Y, and Z directions for each of the three coils. Each coil creates a three-dimensional offset vector because there is a displacement vector between the IMU reference frame and the coil's reference frame. With three coils, nine different offset values were obtained (three coils with three dimensions each). These were added together, giving an overall three-dimensional offset vector. The data collection script was sent to the CubeSat with an instruction to turn the magnet coils on for one second. Magnetometer readings were taken every 0.0015 s. Every ten readings were averaged and shown on the serial monitor. Using a serial monitor to.txt file converter called PuTTY, the data were saved and later copied into an Excel sheet. A marker was also shown to clearly indicate the interval when the coil was on. Once imported into Excel, these measurements were then averaged again, before and after the markers. The averages while the coil is on in the X, Y, and Z directions were compared to the same respective averages while the coil is off. The resulting vector represents a three-dimensional offset for the coil tested.  
During the experiment, one coil was turned on at a time, beginning with the X coil. The PWM value was increased by increments of five, starting from 255 to 0 in the forward and reverse direction. The offsets were measured as a three-dimensional vector, a function of the input PWM level. After making a matrix with offsets over the PWM range, the raw data were loaded into MATLAB(r). A script was written to help an operator find the order of the best-fit polynomial. The line of best fit was then plotted on top of the raw data. To assess the goodness of fit, a root mean squared error (RMS) was calculated and displayed in the legend. The second and third-degree polyfit models had the highest marginal return. See Figure 47 for a three-degree polyfit below.  
Figure 46: Effects of hard iron and soft iron offsets. (**a**) Skew matrix, and (**b**) displacement vector.  
Similarly, the same method was applied to increase the PWM values in the Y and the Z magnetic coils. Comparable graphs were created. See Figure 48.  
Figure 48: _Cont._  
Figure 47: X Coil 3-degree polyfit IMU offsets.  
The above nine polyfits show how each of the three magnet coils affects each of the three coordinate axes. By adding all three magnet coil effects, these equations could be combined into just three: total X offset, total Y offset, and total Z offset. Each of these three final equations would depend on all three PWM value inputs to each of the magnetic torque coils. To adjust the curves above properly, the CubeSat battery voltage was measured. Comparing it to the battery charge would give a reference for the voltage correction. The CubeSat had a full battery (4.2 V) when the PWM tests were performed.
## 2 Conventional Digital Image Correlation (DIC) Applications
### Bending Behavior of Thick-Walled Composite Tubes  
#### 2.3.1 Synopsis  
In this case study, the bending behavior of thick-walled composite tubes was investigated with focus on the bending stiffness property of tubes. Both theoretical and experimental approaches were considered. The theoretical formulation was developed based on a 3D elasticity theory, and the bending stiffness calculation was validated by pure bending tests performed on thick-walled composite tubes. The pure bending test was shown to be a better alternative test, compared to the conventional three-point and four-point bending tests, to compare the theoretical result with the experiment for validation purposes. This was mainly because the effect of stress concentration at the loading point was less compared to three-point and four-point bending tests, and also the effect of shear was not present in the pure bending test. A 3D DIC system was used during the test of composite tubes to capture deformations and strains of the tubes until failure occurred [16].
# A Novel Radio Network Information Service (RNIS) to MEC Framework in B5G Networks
## 5 Conclusions  
This work proposed a new RNIS as a service to the MEC framework in B5G networks which defines the requirements for the implementation of the RNIS. Furthermore, our implementation of the RNIS resulted in detailed information about its performance in the environment evaluated.  
Analyzing the RNIS metrics in different load scenarios highlights its efficiency and challenges. The proposed service is able to meet these demands, being viable for up to four simultaneous applications in our scenario; however, the growing demand may require improvements in specifications for optimal performance. The current implementation of RNIS as a service covers different use cases considering possible limitations in mission-critical cases with very restricted time requirements and high device density. In this way,the application satisfies the use cases of our test context and can satisfy other use cases depending on the need. It is possible to improve the application for new cases.  
As future work, we are collaborating with the EALTEdge development group to implement the service within the framework and expanding the proposed architecture to include a real test environment. A real implementation between MEC 5G and 6G will be developed to allow the RNIS to reach its potential. Advances in RNIS will include improvements in implementation, development of new components such as AI and the enhancement of information capture to optimize message delivery. The inclusion of an AI focused on analyzing contextual information from UE can enhance the RNIS, allowing for better ways of delivering and optimizing the service.  
Conceptualization, K.M.R.C., S.C., F.S., M.R., W.M., R.G., L.A.F., A.O.-J.; methodology, K.M.R.C., R.G., W.M., L.A.F., A.O.-J.; software, K.M.R.C.; validation, K.M.R.C., R.G., W.M., L.A.F., A.O.-J.; investigation, K.M.R.C., A.O.-J.; resources, S.C., F.S., M.R., W.M., A.O.-J.; writing--original draft preparation, K.M.R.C.; writing--review and editing, K.M.R.C., S.C., F.S., M.R., W.M., R.G., L.A.F., A.O.-J.; visualization, S.C., F.S., M.R., A.O.-J.; supervision, A.O.-J., L.A.F., W.M.; project administration, A.O.-J., W.M.; funding acquisition, A.O.-J., W.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fundacao Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) - Financing Code 001, and the Fundacao de Amparo a Pesquisa do Estado de Goias (FAPEG) - Empresa Brasileira de Pesquisa Agropecuaria (Embrapa). This work was partly supported by the MCTIC/CGI.br/Sao Paulo Research Foundation (FAPESP) through the Project Smart 5 GC And MUliRAn Integration (SAMURAI) under Grant 2020/05127-2.  
Not applicable.  
The authors declare no conflicts of interest.
# Near-Real-Time Detection of Insect Outbreaks in Urban Trees Using a PlanetScope Time Series
## 2 Materials and Methods
### Vegetation Index Calculation  
From the weekly composite PlanetScope images, we extracted the values of the four spectral bands corresponding to each georeferenced point. Then, the Renormalized Difference Vegetation Index (RDVI) (Equation (1)) was calculated based on previous studies [30; 40; 41].  
\[\text{RDVI}=\frac{\left(\text{NIR}-\text{R}\right)}{\left(\text{NIR}+\text{R} \right)^{0.5}} \tag{1}\]  
where R is the red band, and NIR is the near-infrared band. Although the Normalized Difference Vegetation Index (NDVI) is the most commonly used index in vegetation studies [41; 42], the RDVI is derived from the NDVI, reducing its sensitivity to soil effects ([[https://www.nv5](https://www.nv5) geospatialsoftware.com/docs/BroadbandGreenness.html#Renormal]([https://www.nv5](https://www.nv5) geospatialsoftware.com/docs/BroadbandGreenness.html#Renormal) accessed on 18 February 2024) and minimizing saturation effects [43]. Furthermore, the RDVI has been demonstrated to be efficient in terms of capturing subtle changes in forest conditions over time [44; 45].
###### Abstract  
Monitoring vegetation cover is an essential parameter for assessing various natural and anthropogenic hazards that occur at the vicinity of archaeological sites and landscapes. In this study, we used free and open access to Copernicus Earth Observation datasets. In particular, the proportion of vegetation cover is estimated from the analysis of Sentinel-1 radar and Sentinel-2 optical images, upon their radiometric and geometric corrections. Here, the proportion of vegetation based on the Radar Vegetation Index and the Normalized Difference Vegetation Index is estimated. Due to the medium resolution of these datasets (10 m resolution), the crowdsourced OpenStreetMap service was used to identify fully and non-vegetated pixels. The case study is focused on the western part of Cyprus, whereas various open-air archaeological sites exist, such as the archaeological site of \"Nea Paphos\" and the \"Tombs of the Kings\". A cross-comparison of the results between the optical and the radar images is presented, as well as a comparison with ready products derived from the Sentinel Hub service such as the Sentinel-1 Synthetic Aperture Radar Urban and Sentinel-2 Scene classification data. Moreover, the proportion of vegetation cover was evaluated with Google Earth red-green-blue free high-resolution optical images, indicating that a good correlation between the RVI and NDVI can be generated only over vegetated areas. The overall findings indicate that Sentinel-1 and -2 indices can provide a similar pattern only over vegetated areas, which can be further elaborated to estimate temporal changes using integrated optical and radar Sentinel data. This study can support future investigations related to hazard analysis based on the combined use of optical and radar sensors, especially in areas with high cloud-coverage.  
vegetation proportion cover; vegetation indices; Sentinel-1; Sentinel-2; OpenStreetMap; Cyprus; archaeological sites; radar vegetation index; normalized difference vegetation index; crowdsourced data +
Footnote †: journal: Applied Sciences  
15 June 2020; 7 July 2020; Published: 10 July 2020
# Cross-Layer Methods for Ad Hoc Networks--Review and Classification
## 3 Cross-Layer Method Publications Review
### Multiple Layers
#### 3.2.2 Independent Layers and Information Sharing
[111] propose using a cross-layer approach for joint routing, dynamic spectrum allocation, choice of package transmission moment, and power control with the minimization of user interference and the guarantee of the signal-to-noise ratio for ad hoc networks with cognitive radio. [PERSON] et al. [112] offer joint multipath routing and congestion control to maximize bandwidth and reduce queuing delay. A global optimization algorithm is proposed, in which all nodes take into account the data of the entire network, and a distributed optimization algorithm is proposed, in which nodes take into account only the data from neighboring nodes.
## 2 Materials and Methods
### Achievable Tolerance Interval  
The calculation of machine performance indexes implies that a \(T\!I\) was established during the design stage. Using their experience and the product requirements, designers should be capable of defining the required \(T\!I\) that will make a particular characteristic acceptable. In the case of AM processes, as it was discussed in Section 1, there is a lack of technical knowledge regarding what would be the proper quality specification for a particular problem. In this work, we propose using the machine performance analysis to obtain an estimation of the \(T\!I\) that could be achieved for a given characteristic. This estimation of the achievable tolerance interval (\(\hat{T}\!I\)) is obtained by setting a target value for the performance indexes (\(P_{m}\) and \(P_{mk}\)) and then calculating the minimum \(\hat{T}\!I\) that will assess such performance level.  
When at least 125 samples are available, 1.33 is the acceptable target for the estimated capability or performance indexes since, given the correspondent confidence intervals, it assesses that the real values of the indexes would lie between 1.08 and 1.58 with a 99.73% probability. Nevertheless, when machine performance studies are conducted with smaller sample sizes, the target must be increased to compensate the reduction on the reliability associated to larger confidence intervals [41]. The performance and capability indexes depend on the standard deviation of the statistical distribution model, and its variation can be described by a \(\chi^{2}\) distribution [41]. Accordingly, for small sample sizes, the \(\chi^{2}\) distribution can be used to calculate the confidence intervals for the performance index estimation and the target value shall be increased in accordance with [41]. In the case of 50 samples or more, the adequate target value for performance indexes shall be 1.67 [38,41], and the potential \(\hat{T}\!I\) shall be calculated as in Equation (4).  
\[\hat{T}\!I\ =\ 1.67\!\cdot\!(X_{99.865\%}-X_{0.135\%}) \tag{4}\]  
This calculation shall be adapted to the alternative formulations of the performance indexes described in Section 2.1 and discussed in ISO 22514-3 [34] and ISO 22514-8 [40].