esa-sceva/satcom-chunk-collection huggingface dataset:
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 2 Optical Camera Communication
### Region of Interest  
Once the image converted and downsampled, the transmitted signal encoded through the dark and bright strips' pattern must be extracted from images. The operation represents a crucial step of the processing since a poor signal detection will inevitably lead to a poor signal demodulation, resulting in transmission errors. While this operation remains quite simple on images only composed of dark and bright strips with a high contrast, this becomes much more complex when the distance between the light source and the camera increases or when the surrounding environment interferes with the communication. Thus, different solutions have been proposed to address this issue, being derived from computer vision techniques to detect the light source position in the image using a Gaussian blur filter and a binary threshold [9], or using statistical methods, such as Principal Component Analysis (PCA). PCA is a dimension reduction method that defines a sequence of unit vectors, named principal components, where each vector represents the line that best fits the data by minimizing the average squared perpendicular distance from the points to the line while being orthogonal to the previous vector. By applying PCA directly to images, considered as data arrays, the transmitted signal can be extracted thanks to the high correlation between pixel rows, since the signal is recorded in the bright and dark strips' pattern, vertically aligned along the image [19]. The latter is the ROI method selected to perform the conducted experiments due to its performance and robustness.
## 4 Application Cases
### Results  
The resulting eigenvalues obtained for Scenario 1 (nominal loads) were 30; the system has 30 dynamic variables and 140 algebraic variables. In Figure 6 is shown the eigenvalues close to the origin considering Cases 1, 2, 3, and 4. The eigenvalues are determined from the Matlab script. Figure 5: Diagram of the methodology flow used in the case simulation. Figure 6: Eigenvalues obtained from the normal case modeling. The classic model is considered for Case 1, and Cases 2, 3, and 4 use the unbalanced model. It is possible to observe that the systems are stable since none of the eigenvalues has a positive real part. However, unbalanced models tend to be less stable because some real parts tend to move closer to the right of the complex plane. Analyzing the eigenvalues of only the unbalanced Cases 3 and 4, they have a further reduction of the stability due to the considered load distributions. To verify this situation, Table 5 presents the results of the damping percentages obtained for all the cases of Scenario 1. With respect to the damping percentages, when comparing all the modeled cases, there is a tendency in Cases 2, 3, and 4 to be less stable/steady. This is verified as the damping values are  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Case 1**} & \multicolumn{2}{c}{**Case 2**} \\ \hline
**Eigenvalues** & **Damping Ratio (\%)** & **Eigenvalues** & **Damping Ratio (\%)** \\ \hline \(-100.0009\pm 0.3054369i\) & 99.9995 & \(-100.0009\pm 0.3003607i\) & 99.9995 \\ \(-42.3732\pm 0.14653i\) & 42.3726 & \(  
The results obtained from Scenario 2 are presented in Figure 9, which shows the eigenvalues calculated with the Matlab software. As was expected, the system tends to instability in all the cases due to the load increase (see Table 6). This indeed occurs with the classical model. However, with such representation, it is not possible to model an asymmetrical load distribution in the three phases. Scenario 2 is a load situation that stresses the operation of the system. Case 5 is stable, and the analysis of the eigenvalues shows that the system is on the verge of instability. The cases that used the proposed approach, Cases 6, 7, and 8, are unstable because all of them have real positive eigenvalues. This clearly means that the proposed model has opposite results to the classical analysis. The different load conditions of Cases 7 and 8 generate different results and show the unstable eigenvalue to be closer to the origin compared to Case 6.
## 4 Testing and Result Discussion  
In this section, the testing operations that were performed to know the suitability of the developed system have been displayed. The UAV was deployed in an agrarian exploitation where a group of sheep had to be monitored, so that (a) their grazing habits and locations would become better known, (b) the farm staff were able to see the whole group of animals at the same time from an aerial perspective and (c) the UAV could, to an extent, guide the sheep towards a specific location. While this last objective was achieved, it must be noted that the reviewed literature has proven that after flying the UAV for several times, animals will get used to its presence and will not move away from it significantly unless they are frightened into doing so, which is to be avoided for animal welfare reasons.
# Inspection of Semi-Elliptical Defects in a Steel Pipe Using the Metal Magnetic Memory Method
## 4 Conclusions  
An MMM-based analysis of the magnetic field variations around five semi-elliptical defects with different sizes on an ASTM A36 steel pipe was reported. A measuring system not requiring high-cost equipment, highly skilled operators, and additional pipe treatment was implemented for monitoring MMM signals on the surface of ferromagnetic pipes. The characterized MMM normal and tangential component signals due to semi-elliptical defect dimensions were achieved by the measuring system.  
The defect locations were discovered by using the measured MMM normal and tangential components in the experimental stage. Although the defects were completely located, further experimental work involving signal analysis techniques is required to quantitatively characterize the defect severity level, which is a promising future application of the measuring system. Based on the results of the MMM component signals, the maximum and mean tangential component values as well as the distance between the  
Figure 8: Normal MMM signals around the semi-elliptical defects (**a**) S1, (**b**) S2, (**c**) S3, (**d**) S4, and (**e**) S5 of the ASTM A36 steel pipe.  
location of the maximum and minimum normal component values were found to be related to the defect dimension. These results can be used in future research for a quantitative assessment of the defect severity level.  
The described MMM signals measuring system is a helpful tool in researching NDT magnetic methods focused on analyzing defects or SCZs on ferromagnetic tubes. The effect analysis of measured MMM signal components due to defined defect shapes, stress concentration in specific pipe zones, and damage severity level is left as future work.  
Conceptualization, [PERSON] and [PERSON]-M.; methodology, [PERSON]-C. and M.S.L.-C.; validation, [PERSON]-S.; investigation, [PERSON]-H., J.P.-O. and C.A.C.-A.; writing--review and editing, [PERSON] and [PERSON]-C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Tecnologico Nacional de Mexico grant number 16960.23-P and project title: \"Diseno, construccion de un sistema embebido y una estructura de inspecion para identifier la variacion del campo magnetico debido a discontinuiudades geometricas en superficies de materiales ferromagneticos por el metodo de memoria magnetica de los metales (MMM)\".  
Not applicable.  
Data available statement: Data is contained within the article.  
This research was developed at \"Universidad Veracruzana\" and \"Tecnologico Nacional de Mexico Campus Celaya\".  
The authors declare no conflicts of interest.
## 4 Results
### Qualitative Evaluation of the SSDA Smoke Detections
#### 4.1.3 Qualitative Evaluation Using Published Results  
Through comparison with published smoke detection results, we further evaluated the performance of the SSDA. Figure 8 shows the comparison of the SSDA smoke detection results with two smoke datasets derived from Aqua MODIS TOA reflectance based on existing algorithms [15; 58] over southeastern North America. These datasets were selected because both papers [15; 58] focused on smoke detection over southeastern North America and were supposed to perform better than the results from other regional smoke detections. A comparison with smoke on the MODIS true-color image (Figure 8c) shows substantially higher omission errors for smoke detection results from [PERSON] et al. (2007) (Figure 8d) and correspondingly higher commission error from [PERSON] et al. (2007) (Figure 8e). In contrast, the smoke detected from the SSDA (Figure 8b) matches well with smoke plume extents on the VIIRS true-color image (Figure 8a). The number of smoke pixels detected by the  
Figure 7: Comparison of (**b**) the SSDA detection result with (**c**) VIIRS aerosol detection product (ADP) and (**d**) TROPOMI smoke over the southeastern North America (\(-\)99.15\({}^{\circ}\) E—84.95\({}^{\circ}\) E and 30.45\({}^{\circ}\) N–44.75\({}^{\circ}\) N) on 22 March 2019. Insets (**a’-d’**) are zoom-in images of the blue square area in (**a–d**), respectively.  
SSDA is about five times (4052/797) of that from [PERSON] et al. (2007) but only about one fifth (4052/21808) of that from [PERSON] et al. (2007). Comparison with smoke plumes visible in true color imagery shows that the SSDA algorithm's smoke detections better represent the smoke conditions than methods used in the two references.
# Data Conversion Strategies for Effective Aviation Technical Support as a Service
## 3 Results
### Extended Decision-Making Model for Optimal ATSaaS Architecture Selection
#### Extended Decision-Making Model with Dynamic Indicators for the System Life Cycle  
To account for the possibility of changing indicators during the system life cycle, the proposed multi-criteria decision-making model can be extended to include time-dependent criteria and weights. This dynamic approach allows the model to reflect variations in costs, performance, reliability, security, scalability, control, and sustainability over time, ensuring a more realistic evaluation of centralized and decentralized architectures.  
Let each criterion \(CR_{k}\) depend on time \(t\), such that \(CR_{k}=CR_{k}(t)\). Correspondingly, the score \(S_{k}(U)\) for each architecture \(U\) and criterion \(CR_{k}\) also becomes time dependent:  
\[S_{k}(U)=S_{k}(U,t)\]  
where \(S_{k}(U,t)\) is the evaluation function for criterion \(CR_{k}\) at time \(t\).  
For example, cost may increase with operational and scalability costs over time, reliability may degrade due to aging infrastructure or increased complexity.  
Weights \(w_{k}\) can also vary over time to reflect changing priorities. Define \(w_{k}(t)\) as the weight of criterion. \(CR_{k}\) at time \(t\):  
\[\sum_{k=1}^{7}w_{k}(t)=1\]  
For example, early in the system life cycle, cost \((w_{1})\) may dominate as initial investments are critical, later, scalability \((w_{5})\) and sustainability \((w_{7})\) may become more important as the system expands and environmental concerns grow.  
The time-dependent overall performance score for architecture \(U\) becomes:  
\[P(U,t)=\sum_{k=1}^{7}w_{k}(t)\cdot S_{k}(U,t)\]  
The architecture is selected based on the time-averaged performance over the system's life cycle \([0,T]\):  
\[\overline{P}(U)=\int_{0}^{T}P(u,t)dt\]  
The optimal architecture is:  
\[U_{opt}=arg\max_{U\in\{U_{1},U_{2}\}}\overline{P}(U)\]  
For each criterion \(CR_{k}\) the score \(S_{k}(U,t)\) is modeled to reflect changes over time. Examples include in the Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria** & **Centralized System** & **Decentralized System** \\ \hline \multirow{3}{*}{Cost \(CR_{1}(t)\)} & \(S_{1}(U_{1},t)=\dfrac{1}{1+\dfrac{\tilde{C}_{1}(U_{1},t)}{C_{max}}}\) & \(S_{1}(U_{2},t)=\dfrac{1}{1+\dfrac{\tilde{C}_{2}(U_{2},t)}{C_{max}}}\) \\  &  
The decision-making algorithms includes the next steps:  
Define life cycle horizon \(T\).  
Input time-dependent parameters \(w_{k}(t)\) and \(S_{k}(U,t)\).  
Calculate time-dependent scores \(P(U,t)\).  
Calculate the average performance score for each architecture \(\overline{P}(U)\).  
Choose the architecture with the higher \(\overline{P}(U)\).  
This extended model captures dynamic changes in costs, reliability, scalability, and other criteria throughout the system's life cycle. By incorporating time-dependent weights and indicators, the model allows for a more realistic and flexible decision-making process, ensuring that the selected architecture aligns with evolving priorities and conditions.
# ICN-Based Enhanced Content Delivery for CDN
## 3 Proposed Design
### Details  
#### 3.4.1 ID-Based ICN Protocol Stack  
In our design, an ID-based ICN communication paradigm is utilized to decouple the name and location. In that sense, the content is named by a unique ID but retrieved with a late-binding network address as presented in the above section. In order to process IDs while avoiding an overhaul in the current IP network, it is favorable to deploy the ICN paradigm over the existing IP infrastructure. Figure 3 shows the whole picture of this ICN protocol stack. The identifier protocol (IDP) [41] is extended over the IP protocol, and it regulates ID-based NA operations, such as ID-to-NA register and resolution. Above IDP, an ID-to-ID ICN transmission protocol [42] is running, and it provides a pull-based, chunk-oriented transmission service with efficiency and reliability mechanisms, such as retransmission and congestion control.
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 2 System Model
### Coverage Rate and Energy Harvesting  
For multi-tier heterogeneous WPCNs, the thermal noise is usually negligible as compared to the interference. Hence, we consider the signal-to-interference ratio (\(\mathit{SIR}\)) instead of SINR in this work. When a randomly chosen node (termed the typical node) located at the origin \(O\) associates with its serving BS (termed the typical BS) at cell \(C_{i}\) in tier \(k\), the \(\mathit{SIR}\) of typical UE expression can be obtained based on the channel path loss model as  
\[\mathit{SIR}_{k} = \frac{P_{t,k}h_{k,x}L^{-1}_{k,x}}{\sum_{j=1}^{K}\sum_{y\in\Phi_{ j,x}}P_{t,j}h_{j,y}L^{-1}_{j,y}}, \tag{2}\]  
where \(L_{k,x}\) is the distance between the typical node and its serving BS in tier \(k\), \(L_{j,y}\) is the distance between the typical node and interference BS in tier \(j\).  
In this paper, we consider the case when a node associated with cell \(j\) in tier \(k\). Then, the coverage probability of the node is defined as [29]  
\[C_{k} = P\left(\mathit{SIR}_{k}>\theta_{k}\right), \tag{3}\]  
where \(\theta_{k}\) is the \(\mathit{SIR}\) threshold of nodes associated to the tier \(k\) in the network. By adopting the coverage probability, the node cannot transmit information when \(\mathit{SIR}_{k}\leq\theta_{k}\). It is expected that when nodes with a better transmit condition or less stringent QoS requirement will enable information transmission.  
Moreover, the \(SIR\) and receive threshold play important roles in the analysis of EE proportionally fairness since they can affect the coverage probability.  
On each spectrum resource block, we assume that if the \(SIR\) is less than \(\theta\), the node does not allocate any rate and it just receives the wireless power transfer from all the BSs in the network. Otherwise, it will be served with a constant rate to obtain the fairness of information transmission. The downlink transmission rate of the node associated with tier \(k\) under this model is given by  
\[R_{k} = \frac{W}{N}C_{k}\log_{2}(1+\theta), \tag{4}\]  
where \(N\) is the total number of active nodes sharing the downlink spectrum resource, and \(W\) is the total bandwidth allocated to the nodes in the WPCNs.  
When the nodes cannot transmit information, they can harvest energy from the BSs in whole plane to save the energy consumption of networks, the received power of the nodes associated to tier \(k\) is  
\[\begin{array}{l}P_{EH,k}=\eta(1-C_{k})P_{EH,t,k}\\ =\eta(1-C_{k})\sum_{j=1}^{K}\sum_{y\in\Phi_{j}}P_{t,j}h_{j,y}L_{j,y}^{-1},\end{array} \tag{5}\]  
where \(\eta\) is the energy harvesting efficiency of the nodes [30].
###### Abstract  
Periodic disturbance may cause serious effects on the attitude of meteorological satellites, and the attenuation of periodic disturbance is required. In this paper, a fundamental study on the optimal design of constant compensations against known-law periodic disturbance for meteorological satellites is investigated. An analytical solution for the relationship between the frequency and amplitude ratios and the response of a typical second-order vibration system is firstly derived. The compensation and disturbance torques are determined according to practical engineering. The criteria for designing the optimal compensations are based on the analytical and simulation results. Then, the criteria are applied to a flexible spacecraft actuated by constant control torque in the presence of sustained periodic disturbance. The optimal compensation torque parameters for spacecraft are acquired based on former criteria. The compensate effectiveness of the optimal compensation torque is provided and compared with results of other selections in the frequency and amplitude ratio domain. Numerical simulation results and experimental results clearly demonstrate the good performance of proposed criteria. This work provides a significant reference for the vibration attenuation of meteorological satellites in the present of periodic disturbance.  
flexible spacecraft; periodic disturbance compensation; compensate torque design; vibration attenuation; reaction wheel. Article
# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
## 4 Discussion  
### Ground Measurement Analysis and Uncertainty Characterisation  
We quantified the uncertainty and bias of non-destructive measurements of LAI and LCC by comparisons to data derived from the destructive sub-sample analysis (Figure 2). The linear relationship established between the SunScan LAI and destructive measurements was biased (NRMSE = 62%), which was likely to have been due to the SunScan measurements over-estimating the LAI by including the contribution of stems and branches [49]. A non-linear relationship was observed between the LCC (SPAD) with increasing N content. This result is in agreement with research by [PERSON] et al. [50], where a quadratic plateau was observed between SPAD data with higher levels of N content in maize. In comparison to the LAI measurements, the LCC measurements made at each plot and sampling date were less consistent, which could be attributed to a high variability in the distribution of chlorophyll at both the leaf and canopy level [11; 51].
###### Abstract  
With the global advancement of maritime autonomous surface ships (MASS), the critical task of verifying their key technologies, particularly in challenging conditions, becomes paramount. This study introduces a synthetic maritime traffic generation system (S-MTGS) designed for the efficient and safe verification of these technologies. The S-MTGS encompasses a maritime traffic generator integrating a generator based on absolute position (GAP) and a generator based on relative position (GRP). This innovative system leverages historical maritime data to create various scenarios or generate virtual ships based on their interactions with and proximity to other ships. The virtual ships adeptly navigate and perform collision avoidance maneuvers with nearby vessels enabled by the integrated collision avoidance algorithm. The S-MTGS's ability to generate a wide range of maritime traffic information mirroring actual maritime conditions is pivotal for thoroughly verifying the performance of MASS technology under both standard and extreme situations. The development of the S-MTGS represents a significant advancement in maritime safety and technology. It can evaluate collision avoidance and navigation systems in MASS, featuring a virtual environment for realistic scenario testing and an intelligent navigation system focused on route tracking and collision avoidance.  
maritime autonomous surface ship; synthetic maritime traffic generation system; collision avoidance algorithm; maritime traffic scenario; autonomous ship performance verification
###### Abstract  
Self-localization has become one of the major areas of research in drifted underwater acoustic networks (DUANs) since many applications are based on the knowledge of nodes' positions. However, self-localization for DUANs faces two main challenges: the insufficient anchors and the varying network topology. Both affect the localization performance seriously. In this paper, we focus on these two challenges and propose a dynamic reference selection-based self-localization algorithm for DUANs (DRSL) to improve the localization performance. First, an optimal reference selection scheme is presented to solve the insufficient anchors' problem. The selected optimal reference node can not only assist the insufficient anchors in accomplishing the localization procedure, but also obviously increase the localization accuracy. Based on the proposed optimal reference selection scheme, a dynamic reference selection-based self-localization algorithm is proposed to solve the topology changing problem. The proposed algorithm can improve the localization performance for DUANs significantly by selecting the reference node dynamically according to the predicted network topology, which is more suitable for DUANs with mobile sensor nodes. Simulation results show that the proposed DRSL algorithm can increase the localization accuracy greatly with insufficient anchor nodes and varying network topology. In addition, DRSL algorithm also has a lower communication cost than other anchor-free approaches, which distinctly demonstrates the advantages of the proposed DRSL algorithm.  
self-localization; drifted underwater acoustic networks; reference selection +
Footnote †: journal:
## 3 Results and Discussion
### TiO\({}_{2}\) Nanoparticles Adsorption Capability  
The analysis of the concentration of aerosol titanium dioxide nanoparticles adsorbed by samples was necessary to evaluate the adsorption capacity of each material. This evaluation was performed comparing pristine and exposed materials. Figure 5 reports the results of the titanium dioxide concentrations (expressed in mg/kg) presented on samples before (pristine material) and after (exposed material) the exposure to TiO\({}_{2}\) in logarithmic scale.  
SUNSPACE BA had the best adsorption capacity, probably due to its branched morphology that may favor the PM entrapment.  
Even the leaf showed high concentrations of titanium dioxide deposited onto its surface; these values were probably due to the fact that the titanium dioxide suspension was deposited on the leaf surface without actually being adsorbed. SUNSPACE SF seemed to show lower adsorption capacity than SUNSPACE BA. As reported in [21], this material was characterized not only by macro pores but also ink bottle shaped pores in order to nm. Especially, the hysteresis loops observed in the N\({}_{2}\) physisorption isotherms revealed pores with narrowing sizes of about 15-30 nm and swelling more than 150 nm [21]. This shape probably hindered the penetration of titanium dioxide nanoparticles, with sizes of about 300 nm, in the pores. In fact, the ability of SUNSPACE SF to trap titanium dioxide nanoparticles with lower dimensions (about 25 nm) was demonstrated in a previous paper [20]. Cement had no adsorption capacity because it was clearly characterized by few pores, as shown in Figure 3c.  
Figure 4: SEM images of SUNSPACE BA at different magnifications (**a,b**), SUNSPACE SF (**c**), and cement material (**d**).