esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The main challenges in achieving such networks are scaling network protocols for a large number of nodes, designing simple and efficient protocols for different network operations, and designing energy-saving protocols. The objective of this paper is to involve the sensors of a multi-level architecture of heterogeneous LS-WSNs in the process of maximizing network lifetime. This is done by proposing a model that should structure the functioning of the different sensors. A LS-WSN consists of several hundred thousand sensors with low battery and computing capacity and a very small number of base stations (mostly stationary) with unlimited battery and high computing capacity. The best way to take advantage of these devices is to allow them to operate autonomously as long as possible. In this paper, we present a unified multi-level LS-WSN architecture that encompasses the physical and logical modeling of the sensors, the data collection process, and the data routing strategy. Generally, improving lifetime is an important issue when deploying WSNs on a large scale. Indeed, these networks are composed of autonomous sensors that are mostly powered by batteries that are often difficult to recharge or replace. The challenge that animates us in this document is to ensure the operation of the LS-WSN for several years without any intervention related to the energy resources of the sensors. In order to maximize the lifetime, we rely on the structuring of the network according to a multi-level architecture. We proposed a two-stage solution. The first consists of a proposed deployment of the LS-WSN following a three-tier architecture as described in [16] and the second, based on a heuristic, which proposes a model of the communication of the different sensors of the network that guarantees the scaling and considerably reduces the consumption of the whole network. In simulations, we have used the network to collect big data in smart cities. The main objective of this research is to highlight the contribution of the LS-WSN communication system modeling in the optimization of the energy consumption of the sensors, which also amounts to the optimization of the network lifetime. In summary, our main contributions are:  
* Comparative study of LS-WSNs deployment architectures;
* Designing a three-tier architecture dedicated to LS-WSNs;
* Modeling of sensor communication according to the three-thirds LS-WSN architecture;
* Validation by simulation of our architecture on a scenario of big data collection in a smart city;
* Validation of the performance of the architecture by a comparative test with existing models;
* Analysis and interpretation of results  
The rest of the paper is organized as follows: Section 2 presents the related work that includes the problem of LS-WSNs for big data collection; Section 3 presents our three-tier architecture for a LS-WSN deployment; Section 4 presents the proposed model and its implementation at different levels of the network; Section 5 presents the results and discussions; and the conclusion and future research is presented in Section 6.
## 3 Results and Discussion
### The Relationship between Ecospatial Network Topology Index and NPP  
#### 3.3.1. Spatial Distribution Characteristics of NPP  
The distribution characteristics of NPP throughout Inner Mongolia are shown in Figure 13.  
Figure 12: (**a**) Node recovery robustness; (**b**) corridor recovery robustness.  
As shown in Figure 13, the average value of NPP in Inner Mongolia is 228.419 g:m\({}^{-2}\).a\({}^{-1}\), the maximum value is 821 g:m\({}^{-2}\).a\({}^{-1}\), and the standard deviation is 175.549 g:m\({}^{-2}\).a\({}^{-1}\). Spatially, the high NPP values are mainly distributed in the northern region, mostly in the 474 to 821 range; the NPP value in the western region is relatively low, mostly in the 0 to 66 range; the NPP value in the central region is at a medium level. The vegetation composition is the main reason for the spatial heterogeneity of NPP in the study area. In terms of the ecospatial network structure, the NPP values in the northern and western regions, where the ecospatial network is relatively sparse, are in the maximum and minimum value ranges, respectively; the NPP in the central region, where the ecospatial network is denser, is in the middle range. There are also differences in NPP among different land cover types. The highest average NPP is woodland, which is 467.852 g:m\({}^{-2}\).a\({}^{-1}\); followed by cultivated land at 300.225 g:m\({}^{-2}\).a\({}^{-1}\), residential land at 218.185 g:m\({}^{-2}\).a\({}^{-1}\), grassland at 214.279 g:m\({}^{-2}\).a\({}^{-1}\), water at 143.154 g:m\({}^{-2}\).a\({}^{-1}\), and unused land at 91.861 g:m\({}^{-2}\).a\({}^{-1}\).
## 2 Materials and Methods
### Methods  
#### 2.3.1 Framework of NPP Assessment  
A process-based Carnegie-Ames-Stanford Approach (CASA) model was used to simulate net primary productivity (NPP) for the carbon sequestration function. NPP at a grid cell (x) in month t is a product of intercepted photosynthetically active radiation (IPAR) and a light utilization efficiency (\(\varepsilon\)) that is modified by temperature and soil moisture. Neither IPAR nor \(\varepsilon\) is dependent on ecosystem type [31].  
\[\text{NPP(x,t)}=\text{IPAR(x,t)}\times\varepsilon(\text{x,t)} \tag{1}\]  
\[\text{IPAR(x,t)}=\text{SOL(x,t)}\times\text{FPAR(x,t)}\times 0.5 \tag{2}\]SOL is the total solar radiation incident on grid cell x in month t (MJ/m\({}^{2}\)) [32], FPAR is the fraction of the incoming photosynthetically active radiation (PAR) intercepted by green vegetation, and the factor of 0.5 accounts for the fact that approximately half of the incoming solar radiation is in the PAR waveband (0.4-0.7 \(\mu\)m) [33].  
FPAR is calculated based on the AVHRR simple ratio (SR), where  
\[\mathrm{SR(x,t)}\ =\ \ \frac{1\ +\ \mathrm{NDVI(x,t)}}{1\ -\ \mathrm{NDVI(x,t)}} \tag{3}\]  
\[\mathrm{FPAR(x,\ t)}\ =\ [\frac{\mathrm{SR(x,t)}-\mathrm{SRmin}}{\mathrm{SRmax \ -\ \mathrm{SRmin}}},\ 0.95] \tag{4}\]  
SRmin represents SR for unvegetated land areas and is set to 1.08 for all grid cells [31]. SRmax approximates the value at which all downwelling solar radiation is intercepted and corrects for effects of canopy architecture and residual cloud contamination. \(\varepsilon\) is calculated based on temperature and water condition [31]:  
\[\varepsilon\mathrm{(x,t)}=\mathrm{T_{\varepsilon 1}(x,t)}\times\mathrm{T_{ \varepsilon 2}(x,t)}\times\mathrm{W\varepsilon(x,t)}\times\varepsilon^{*} \tag{5}\]  
where \(\mathrm{T_{\varepsilon 1}}\) and \(\mathrm{T_{\varepsilon 2}}\) account for the effects of temperature stress, \(\mathrm{W\varepsilon}\) accounts for effects of water stress, and \(\varepsilon^{*}\) is the maximum possible efficiency and is set to 0.56 g C/MJ [34].
## 2 Background  
### Basis Pursuit (BP)  
BP is a convex optimization method used for sparse signal recovery and feature selection [15]. The main objective of this method is to find the most \"sparse\" solution (with as many coefficients as possible that are equal to zero) for an underdetermined system of linear equations by minimizing the L1 norm of the coefficients. This is a popular approach in feature selection [4], as it helps to obtain more interpretable models and reduce overfitting by selecting only significant features.  
Given the feature matrix \(X\in R^{nxp}\) and observations \(y\in R^{n}\), the optimization problem for basis pursuit is formulated as follows:  
\[\underset{\beta}{\text{min}}\frac{1}{2}\ \left\|y_{i}-X_{i}\beta\right\|_{2}^{2} \ \ \text{subject to}\ \left\|\beta\right\|_{1}\leq t\]  
where \(\beta\) represents the vector of the coefficients for the selected features, \(\frac{1}{2}\ \left\|y_{i}-X_{i}\beta\right\|_{2}^{2}\) is the prediction error, \(\left\|\beta\right\|_{1}\) is the L1 norm which promotes sparsity, and \(t\) is a parameter that controls the sparsity of the solution [16]. This formulation ensures that the selected solution has a minimal number of non-zero coefficients, meaning that only a small subset of features is chosen.  
Classic BP is often solved using standard convex optimization methods, such as: (i) the simplex method--used for small linear problems; (ii) interior-point methods--popular for large-scale problems but computationally intensive [17]; (iii) gradient descent--an iterative solution that may have slow convergence.  
The minimization of the L1 norm ensured by BP guarantees sparse solutions when the data satisfy the restricted isometry property (RIP) conditions [18], making it a benchmark for other feature selection methods. By directly minimizing the L1 norm, classic BP provides rigorous solutions that are globally optimal for well-conditioned problems. Thus, the classic BP method is ideal for scenarios where the number of selectable features does not exceed the data dimensions, and its performance is strong in low-noise environments. Classic BP is often used as a baseline method for comparing the performance of other algorithms, serving as a reference standard in theoretical and applied research related to sparsity and feature selection [15; 16; 19].  
In addition to all these advantages, there are still some disadvantages that need to be mentioned regarding classic BP, namely that the classical solution method is computationally expensive and does not scale well for large problems; the method requires the direct solution of large systems of equations, which is prohibitive for large-scale applications; and last but not least, the implementation of classical BP is more rigid and does not allow the decomposition of the problem into independent sub-problems.
## 3 Direction-of-Arrival Estimators with Known Waveforms
### Interferometer-Based Estimator with Known Waveforms
 \tag{14}\]  
According to (11), we have a phase-difference equation set corresponding to the sensor interval \(\Delta\) as follows:  
\[\mathbf{\eta}_{k}(\Delta)=\mathbf{C}(\Delta)\mathbf{g}_{k} \tag{15}\]  
Consider the combination of all phase-difference equation sets corresponding to different sensor intervals \(\Delta=1,2,\cdots,S\), where \(S=M/2\) if the sensor number \(M\) is even,or \(S=(M-1)/2\) if \(M\) is odd. The examples of baselines configuration at \(M=5\), 6, 7 and 8 are shown in Figure 2. The rest of the cases are similar. Define:  
\[\overline{\eta}_{k}\triangleq[\eta_{k}^{T}(1),\eta_{k}^{T}(2),\cdots,\eta_{k}^{T }(S)]^{T}\in\mathbb{R}^{MS\times 1} \tag{16}\]  
\[\overline{C}\triangleq[C^{T}(1),C^{T}(2),\cdots,C^{T}(S)]^{T}\in\mathbb{R}^{MS \times 2} \tag{17}\]  
Equation (15) will be further extended to:  
\[\overline{\eta}_{k}=\overline{C}g_{k} \tag{18}\]  
Substitute the unambiguous phase-difference estimate \(\hat{\eta}_{k}^{P,q}=\arg\Big{\{}\hat{\mathbf{b}}_{k}(p)\hat{\mathbf{b}}_{k}^{*}(q) \Big{\}}\) into (18), the LS solution of \(g_{k}\) is given by:  
\[\hat{g}_{k}=[\hat{g}_{k}(1),\hat{g}_{k}(2)]^{T}=\Big{(}\overline{C}^{T} \overline{C}\Big{)}^{-1}\overline{C}^{T}\hat{\mathbf{\eta}}_{k} \tag{19}\]  
Finally, the estimates of \((\theta_{k},\varphi_{k})\) can be obtained as:  
\[ \begin{cases}\hat{\theta}_{k}=\sin^{-1}\Big{(}\frac{\lambda}{2\pi r}\sqrt{ \hat{\mathbf{g}}_{k}^{2}(1)+\hat{g}_{k}^{2}(2)}\Big{)}\\ \hat{\varphi}_{k}=\arg\{\hat{g}_{k}(1)+j\hat{g}_{k}(2)\}\end{cases}  \tag{20}\]  
The procedure of the proposed 2D DOA estimator with known waveforms just requires a few multiplications and additions of complex numbers, where computational complexity is \(MNS+O(4M)\) in the case of \(M\) sensors and \(N\) snapshots for each source. Moreover, 2D  
Figure 2: Baselines configuration of UCA. (**a**) \(M=5\), (**b**) \(M=6\), (**c**) \(M=7\), (**d**) \(M=8\). DOA estimates of \(K\) sources can be acquired separately and in parallel at the same time; hence, it is computationally efficient and easy to implement in practice.
## 8 Conclusions  
(1) Shading is predictably significant at low altitudes, earlier in the morning, and later in the afternoon, but the azimuth also plays a role and is more subtle and difficult to predict.  
(2) Blocking occurs at midday and is more significant with square than with rectangular heliostats, because the first are taller and can be slightly more closely packed.  
(3) Front-row heliostats suffer no blocking and only lateral shading, while back-row heliostats are much more representative of the whole heliostat field.  
(4) The geometric efficiency of a heliostat field can be described by a single number, the average heliostat efficiency (AHE).  
(5) The overall efficiency of a heliostat field can also be described by a single number, the average total efficiency (ATE), which depends on the instantaneous sun position. These figures of merit allow easy ranking and comparison of the performance of very different heliostat fields.  
(6) To improve ATE, increasing the tower height is more effective (decreasing blocking, when the sun is high) than increasing the distance between heliostats or raising the ground slope to 20\({}^{\circ}\) (decreasing shading, when the sun is low).  
J.C.G.P., conceptualization, methodology, software, data curation, validation, and writing--original draft preparation. L.G.R., formal analysis, validation, writing--review and editing, project administration, and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work benefited from financial support from the European Union through the SFERA-III project, Grant Agreement No. 823802 (proposal SURPF2101310018-CAHO \"Development of Computer Algorithms to Optimize Heliostat Orientation\"). The authors would like to thank the Fundacao para a Ciencia e a Tecnologia (FCT) for its financial support through the LAETA Base Funding (DOI: 10.54499/UIDB/50022/2020).  
Not applicable.  
The software source code and data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to thank the Cyprus Institute for providing access to its installations and the support of its scientific and technical staff in 2022, as part of the European Union SFERA-III project (Grant Agreement No. 823802).  
The authors declare no conflicts of interest.
# Laser Observations of GALILEO Satellites at the CBK PAN Astroegodynamic Observatory in Borowiec
## 1 Introduction
During this campaign, 30 ILRS stations provided laser measurements, yielding a total of 11,720 normal points [14]. The primary objective of both campaigns was to enhance the measurement coverage, specifically improving the temporal and spatial coverage of selected satellites from each constellation.
## 4 Filter Parameters Analysis
When \(1\leq 1/k_{Br}\leq 1.5\), the trends of SNRG and DCRG change oppositely, and DCR decreases in PTM and SPT when \(1/k_{Br}>4\). Therefore, the suitable \(1/k_{Br}\) should be chosen as the intersection of SNRG and DCRG curves; that is, \(1/k_{Br}\) should be 1.25, and \(B_{r}=\)1.25\(B_{t}\). The optimized synthesized UGW signals processed by FBR-RC-SSP are shown in Figure 10. It can be observed that only defect and end-reflected echoes are retained, of which the amplitudes of defect echoes are the same as that of the synthesized UGW signal. In addition, PT and PTM show an excellent coherent noise elimination effect: their SNRGs are 10.18 dB and 12.2 dB, and DCRGs are 10.18 dB and 9.91 dB, while the coherent noise of SPT still can be found clearly in the time-frequency domain. Figure 9: Effects of filter parameters: (**a**) \(B_{r}\), (**b**) \(B_{t}\), (**c**) \(\Delta f\) and (**d**) \(B_{r}\) on SNRG and DCRG of the synthesized UGW signals processed by FBR-RC-SSP.
## 2 Materials and Methods
### Statistical Analysis  
Normal distribution of the data was tested using Kolmogorov-Smirnov test and sphericity was verified using a Mauchly test. When the assumption of sphericity was not met, the significance of \(F\) ratios was adjusted according to the Greenhouse-Geisser correction. Analysis of variance on repeated measures in two factors (3 groups \(\times\) time points) was used for all dependent variables (anthropometric characteristics, performance time in the 4 \(\times\) 50 m, 100 m, SR, SL, SI, and 1 RM). A Tukey honest significant difference as a post hoc test was used to compare the means when significant F ratios were found. In addition, analysis of variance in two factors (3 groups \(\times\) repeated measures) was used for all dependent variables as well as training volume and training load during the 6-week training period. The \(\Delta\) values were estimated from post- to pre-measurements and from week 6 to week 1 for the performance time, the SR, SL, and SI. Furthermore, one-way analysis of variance between groups was used for percentage differences (%\(\Delta\)). To estimate the size of the main effects and interaction, the partial eta-squared (\(\eta_{\text{p}}\)\({}^{2}\)) values from the analysis of variance were used. The \(\eta_{\text{p}}\)\({}^{2}\) was considered small if the value was \(\leq\)0.01, medium if it was \(\leq\)0.06, and large if it was \(\geq\)0.14. The \(\eta_{\text{p}}\)\({}^{2}\) for the sample size in the present study (n = 24) separated by three equal groups with sample (n = 8) resulted in a power of analysis corresponding to 0.71 [26]. Pearson correlation was used to examine relationships between variables and was qualitatively interpretated as small (\(r\) = 0.1-0.3), moderate (\(r\) = 0.3-0.5), large (\(r\) = 0.5-0.7), very large (\(r\) = 0.7-0.9), and nearly perfect (\(r\) > 0.9) [27]. The ICC using 1-way random effects was used to test the reliability. Data are presented as mean \(\pm\) SD. Statistical significance was set at \(p\)\(\leq\) 0.05.
# Performance Evaluation of a Compound Power-Split CVT for Hybrid Powertrains
## 3 Power Flows
### Efficiency
With this assumption, the global efficiency can be calculated as follows  
\[\eta_{\text{gl}}=\frac{\text{P}_{\text{i}}+\text{P}_{\text{Loss}}}{\text{P}_{ \text{i}}} \tag{20}\]  
The conservation of energy applied to the node connecting the input to shafts 1 and 3 leads to:  
\[\text{P}_{\text{i}}-\text{P}_{1}-\text{P}_{3}=0 \tag{21}\]  
If the power in the eCVT flows from left to right, that is when \(\text{P}_{5}<0\) and \(\text{P}_{6}>0\) (power flows (a), (e), (f), (i), (l) and (m)), the efficiency of the eCVT is given by:  
\[\eta_{\text{eCVT}}=-\frac{\text{P}_{6}}{\text{P}_{5}} \tag{22}\]  
where the signs of the powers refer to Figure 2. From the conservation of energy across the eCVT, the power loss in the eCVT is given by:  
\[\text{P}_{\text{Loss}}=\text{P}_{5}+\text{P}_{6}=(1-\eta_{\text{eCVT}})\text{ P}_{5} \tag{23}\]  
Combining Equations (15), (20)-(23) the global efficiency can be derived as a function of the characteristic transmission ratios and of the efficiency of the eCVT:  
\[\eta_{\text{gl}}=\tau_{\text{gl}}\frac{\tau_{\text{gl}}-\tau_{\text{W2}}- \eta_{\text{eCVT}}\Big{(}\tau_{\text{gl}}-\tau_{\text{W1}}\Big{)}}{\tau_{\text {W1}}\Big{(}\tau_{\text{gl}}-\tau_{\text{W2}}\Big{)}-\eta_{\text{eCVT}}\tau_{ \text{W2}}\Big{(}\tau_{\text{gl}}-\tau_{\text{W1}}\Big{)}} \tag{24}\]  
If the power in the eCVT flows from right to left (\(\text{P}_{5}>0\) and \(\text{P}_{6}<0\), power flows (b), (c), (d), (g), (h) and (n)), the efficiency of the eCVT is given by:  
\[\eta_{\text{eCVT}}=-\frac{\text{P}_{5}}{\text{P}_{6}} \tag{25}\]  
From the conservation of energy across the eCVT, the power loss in the eCVT is given by:  
\[\text{P}_{\text{Loss}}=\text{P}_{5}+\text{P}_{6}=-\frac{(1-\eta_{\text{eCVT} })}{\eta_{\text{eCVT}}}\text{P}_{5} \tag{26}\]  
Combining Equations (15), (22), (23), (25) and (26) the global efficiency is given by:  
\[\eta_{\text{gl}}=\tau_{\text{gl}}\frac{\tau_{\text{gl}}-\tau_{\text{W1}}-\eta _{\text{eCVT}}\Big{(}\tau_{\text{gl}}-\tau_{\text{W2}}\Big{)}}{\tau_{\text{W2 }}\Big{(}\tau_{\text{gl}}-\tau_{\text{W1}}\Big{)}-\eta_{\text{eCVT}}\tau_{ \text{W1}}\Big{(}\tau_{\text{gl}}-\tau_{\text{W2}}\Big{)}} \tag{27}\]  
In conclusion, the purpose of this approach is to develop a tool to study the advantages and disadvantages of the type C compound power-split device and to evaluate its performance and the power flow obtained within the system. In the design stage, knowing the input design parameters (namely the ranges of global and eCVT speed ratios), the parameters of the planetary gears are obtained with Equations (10) and (11) (assuming direct proportionality between global and eCVT speed ratios). Once the components of the transmission are designed, the Tables 3 and 5 show the type of power flow according to the kinematic parameters of the transmission. Equations (19), (24), and (27) can be used to estimate the power flowing through the eCVT and the efficiency of the whole system.
## 5 Performance Evaluation
### Discussion on Scalability  
The scalability of RBMM is analyzed from the following perspectives:  
* Scalability with respect to network size: We design experimental scenarios with both simple and complex network topologies, as well as varying path parameters. The results demonstrate that RBMM can operate effectively across networks of different scales and complexities, achieving optimized transmission performance with relatively low resource consumption.
* Scalability with respect to computational complexity: As discussed in Section 4.4, RBMM is designed with careful consideration of algorithmic complexity. By efficiently utilizing data structures and carefully designing computational processes, it avoids significant computational overhead as data scales increase.  
Figure 13: Impact of buffer size on throughput and transmission completion time under varying bandwidth distributions.  
* Scalability with respect to service support: RBMM is designed to handle diverse network traffic, extending beyond the optimization of a single scenario or network function. In the experiments, we simulate realistic internet traffic characteristics using a Pareto distribution and evaluate its functionality and performance under dynamically changing traffic conditions. The results demonstrate that RBMM exhibits strong adaptability when handling mixed traffic types. By optimizing resource allocation and improving the fairness of buffer resource utilization across different traffic types, RBMM ensures the quality of various network services.
* Scalability with respect to deployment: We modularize the RBMM-based reordering functionality, making it a flexible optional service that can be easily deployed at the required gateway nodes. In complex network environments, this module can be deployed across multiple network nodes to collaboratively manage large-scale traffic demands.