esa-sceva/satcom-chunk-collection huggingface dataset:
# Animal Pose Estimation Based on 3D Priors
## 4 Proposed Method
### Pose Dictionary Learning  
We used dictionary learning to find a good set of bases for 3D poses, and the dataset established in Section 3.1 was used as training data. We believe that a reasonable 3D  
Figure 4: Overview: (**a**) we constructed a 3D pose dataset (as described in Section 3.1) to learn a 3D pose dictionary \(B\), which was used to provide 3D prior constraints; (**b**) in the pose estimation stage, the 2D pose \(p\) corresponding to the image was initially estimated using existing algorithms; (**c**) refinement was performed by combining the initial pose \(p\) and the 3D constraints provided by the 3D pose dictionary \(B\), resulting in a more accurate 2D pose \(p^{\prime}\). In (**b**,**c**), the blue, red and green lines indicate the connections between the joints on the trunk, left limb and right limb, respectively.  
configuration space of 3D poses can be generated on these bases, so as to make the sparse representation of any 3D pose possible. Our task of dictionary learning can be formulated as follows:  
\[\min_{B_{1},\ldots,B_{K},C} \sum_{n=1}^{N}\frac{1}{2}\left\|P_{n}-\sum_{k=1}^{K}C_{k,n}B_{k} \right\|_{F}^{2}+\lambda\left\|C\right\|_{1}\] \[s.t. C_{k,n}\geq 0,\quad\left\|B_{k}\right\|_{F}\leq 1\] \[k\in[1,K],\quad n\in[1,N] \tag{1}\]  
where \(\lambda\) is a non-negative parameter, \(K\) is the size of the dictionary, \(N\) represents the number of training samples, \(P_{n}\) denotes a 3D pose in the collected dataset, \(B_{k}\) is the base pose to be learned, and \(C_{k,n}\) represents the \(n\)-th coefficient of the representation of \(P_{n}\). The two terms in the cost function corresponding to the reconstruction error and the sparsity of representation, respectively. Examples of learned dictionary atoms are shown in Figure 4.  
The problem in Equation (1) is locally solved by alternately updating \(C\) and \(B_{k}\)s via the projected gradient descent, an algorithm widely used in dictionary learning that converges to a local optimum [53]. The cost function can be rewritten as  
\[f(\widetilde{B},C)=\frac{1}{2}\left\|P_{n}-\sum_{k=1}^{K}C_{k,n}B_{k}\right\|_ {F}^{2}+\lambda\sum_{k,n}C_{k,n} \tag{2}\]  
where \(\widetilde{B}\) is the concatenation of \(B_{k}\)s. These steps are summarized in Algorithm 1.  
```
0:\(P_{1},\cdots,P_{N}\)
0:\(B_{1},\cdots,B_{K}\)
1: initialize \(\widetilde{B},C\), step size \(\delta_{1}\) and \(\delta_{2}\);
2:while not converged do
3:while not converged do
4:\(C\gets C-\delta_{1}\
abla_{c}f(\widetilde{B},C)\)
5:for\(k=1\) to \(K\)do
6:for\(n=1\) to \(N\)do
7:if\(C_{k,n}<0\)then
8:\(C_{k,n}\gets 0\)
9:\(\widetilde{B}\leftarrow\widetilde{B}-\delta_{2}\
abla_{\widetilde{B}}f( \widetilde{B},C)\)
10:for\(k=1\) to \(K\)do
11:if\(\left\|B_{k}\right\|_{F}>1\)then
12:\(B_{k}\gets B_{k}/\left\|B_{k}\right\|_{F}\)
```  
**Algorithm 1** Pose Dictionary Learning
## 4 Choice of Optimal Bottoming Cycle Configuration
### Performance Comparison of Different Configurations  
The total power obtainable by combining the propulsion ICE and the ORC system is shown in Figure 10, for different engine loads and the considered configurations of the bottom cycle.  
Figure 10: Power of the combined propulsion system, according to the load, for the different cycle configurations studied.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Engine Load [\%]**} & \\  & **100** & **85** & **75** & **50** & **25** \\ \hline \multicolumn{5}{c}{**Simple cycle**} \\ Power [kW] & 592 & 541 & 523 & 410 & 227 \\ \(\eta\) [\%] & 23.2 & 22.8 & 22.9 & 23 & 22.9 \\ \hline \multicolumn{5}{c}{**Pre-heated cycle**} \\ Power [kW] & 671 & 637 & 620 & 484 & 280 \\ \(\eta\) [\%] & 23.7 & 23.7 & 23.9 & 23.9 & 21.9 \\ \hline \multicolumn{5}{c}{**Regenerating cycle**} \\ Power [kW] & 684 & 639 & 622 & 488 & 288 \\ \(\eta\) [\%] & 26.7 & 26.7 & 26.8 & 26.8 & 26.7 \\ \hline \multicolumn{5}{c}{**Cascade cycle**} \\ Power [kW] & 706 & 652 & 608 & 459 & 275 \\ \(\eta\) (toluene) [\%] & 20.8 & 20.4 & 19.6 & 19.2 & 19 \\ \(\eta\) (isobutene) [\%] & 10 & 10.2 & 10 & 10.2 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of different configurations of the ORC bottom cycle.  
A significant power gain (about 10%) is already achieved with the simple cycle. For the other configurations the increase is slightly higher, at least with reference to the full load of the engine. The corresponding efficiencies are reported in Figure 11, showing that they grow (up to 8 points) with decreasing engine load. In fact, if the engine efficiency reduces, a bigger fraction of the energy of the fuel input is available as waste heat for the bottom cycle.  
The regenerated and the two combined cycles show the bigger efficiency increase, but the latter is affected by the temperature decrease in the HT cooling circuit at low engine loads. Therefore, it can be inferred that the regenerated cycle gives the best performance, without excessive plant complexity. Then, the following design analysis has been carried out on this plant configuration and the sizing and the realistic off-design performance of the required heat exchangers have been obtained.
# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
###### Abstract
[PERSON] et al. [18] aimed to develop the modified normalized difference water index (MNDW), a normalized index of surface ground water, using Sentinel-2A satellite images. To do this, they improved the spatial resolution of the shortwave infrared (SWIR) bands by utilizing the near-infrared (NIR) bands of Sentinel-2A along with panchromatic images. [PERSON] et al. [19] sharpened the visible and near-infrared (VNIR) bands of Worldview-3 satellite images using various pansharpening techniques and compared them. [PERSON] et al. [20] proposed three pansharpening methods that utilized a hypersharpening technique to improve the spatial resolution of Worldview-3 satellite images. However, few studies have been conducted regarding improvements to the spatial resolution of Worldview-3 satellite images. In particular, very few studies have been conducted on the SWIR bands. Thus, this study aimed to develop a technique that could fuse 1.24 m spatial resolution VNIR images with 7.5 m spatial resolution SWIR images from Worldview-3 satellite images of various spatial and spectral resolutions into images with 0.31 m spatial resolution. In this manuscript, we propose a pansharpening technique for enhancing the spatial resolution of VNIR and SWIR bands while minimizing the spectral distortion of sharpened images. First, optimal panchromatic images were generated through a combination of high-spatial-resolution images and then applied in the sharpening process of SWIR bands based on the hypersharpening algorithm using the selected or synthesized scheme of [PERSON] et al. [15] and [PERSON] et al. [21]. Then, to minimize the spectral distortion of the initial sharpened image, a postprocessing methodology for spectral distortion reduction was developed. To prove the performance of the proposed pansharpening technique, its results were compared with the results of existing applicable pansharpening techniques. Then, to confirm the usability of the proposed technique, accuracy was evaluated by applying the supervised classification. The remainder of this paper is organized as follows. Section 2 summarizes the theories of the selected band scheme and the synthesized band scheme, which are pansharpening techniques based on the CS and MRA algorithms, evaluation indexes for quantitative evaluation, and theories about the spectral distortion reduction technique. Section 3 provides an analysis and discussion of the experimental results. Conclusions are presented in Section 4.
# Green Small Cell Operation of Ultra-Dense Networks Using Device Assistance +
## 1 Introduction
The main difference is that we further consider the case of ultra-dense networks and investigate the impact of the proposed algorithms under the extremely dense scenario. For example, we consider the case of 100 APs with 50 UEs for dense network, as well as 10,000 APs with 1000 UEs for ultra-dense networks. In doing this, we have modified the conventional BP algorithm to the approximated BP and also verify that the proposed online algorithms of DANCE perform well even under extreme densification. Furthermore, in Section 4.3, we provide an operational procedure and a framework to practically implement the online algorithms of DANCE. The main contributions of this paper are summarized as follows. We apply the BP optimization framework to the AP on/off operation. To target the small cell environment, a model exploiting UE feedbacks is proposed. Then, by constructing a factor graph representing APs and UEs, the optimization problem is solved by the BP optimization framework. Since BP is an offline algorithm, however, we also propose algorithms that can run in an online manner where these algorithms are named DANCE (Device-Assisted Networking for Cellular grEening) that is a collection of low complexity algorithms inspired by BP. As the complexity of the BP approach can increase with the number of BSs and UEs, DANCE algorithms can be used in dense networks. Then, we evaluate our algorithms by performing numerical simulations. Our extensive simulation results show that BP and DANCE increase energy efficiency significantly. For instance, in a small-sized network, BP increases energy efficiency 129% compared to the baseline where all APs are always on. Furthermore, in ultra-dense networks, DANCE algorithms increase energy efficiency from 25-times up to 44-times compared to the baseline in the case of ultra-dense networks. The remainder of this paper is organized as follows. In Section 2, the assumptions of the system model and the problem formulation are presented. Section 3 is devoted to the graphical modeling of our framework using the BP algorithm. In Section 4, we propose a family of online algorithms. In Section 5, the performances of the proposed algorithms are demonstrated with extensive simulations. Finally, we conclude the paper in Section 6.
# Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement
## 2 Methods
### Factor Graph Model
#### 2.2.3 Graph Optimization Equations  
The factor graph theory calculates the navigation state estimation based on the maximum a posteriori probability estimation. The specific derivation process is as follows.  
The factor \(f_{i}\) represents an error function of the states \(\mathbf{X}_{k}^{i}\)  
\[f_{i}(\mathbf{X}_{k}^{i})=d(\text{err}_{i}(\mathbf{X}_{k^{\prime}}^{i},\mathbf{z}_{i})) \tag{5}\]  
where the operator \(d(\cdot)\) represents the specific cost function. \((\mathbf{X}_{k^{\prime}}^{i},\mathbf{z}_{i})\) represents the error function of the factor.  
For Gaussian noise distributions, the above formula can be changed into  
\[f_{i}(\mathbf{X}_{k}^{i})\propto\exp(-\frac{1}{2}\left\|h_{i}(\mathbf{X}_{k}^{i})-\mathbf{ z}_{i})\right\|_{\mathbf{z}_{i}}^{2}) \tag{6}\]  
where \(h_{i}()\) is the nonlinear measurement function, and \(\mathbf{z}_{i}\) is the actual measurement.  
\(X(t)^{\text{MAP}}\) means the corresponding state value when the maximum posterior probability of the state under the condition of measurement \(Z(t)\) occurrence, as  
\[\begin{array}{ll}X(t)^{\text{MAP}}&=\text{argmax}P(X(t)|Z(t))\\ &=\text{argmax}\underset{i}{\prod}f_{i}(V_{k}^{i})\end{array} \tag{7}\]  
If the negative logarithm is taken on both sides of Equation (6), then the observation equation is introduced and transformed into the form of 2-norm.  
\[\Delta^{*}=\underset{\Delta}{\text{argmin}}\underset{i}{\sum}||\mathbf{\Sigma}_{i }^{-1/2}\mathbf{H}_{i}\Delta_{i}-\mathbf{\Sigma}_{i}^{-1/2}(\mathbf{z}_{i}-h_{i}(\mathbf{X}_{ i}^{0}))||_{2}^{2} \tag{8}\]  
where \(\mathbf{H}_{i}\) is the the Jacobian matrix of the observation equation. \(\mathbf{H}_{i}\) is calculated by \(\mathbf{H}_{i}\triangleq\frac{\partial h_{i}(\mathbf{X}_{i})}{\partial\mathbf{X}_{i}}|_{ \mathbf{X}_{i}^{0}}\), and it means a partial differential of \(h_{i}()\) at point \(\mathbf{X}_{i}^{0}\)[25].  
After calculating the updated value \(\Delta\), the states' value can be obtained by accumulating and updating variables \(\mathbf{\hat{X}}_{k}+\Delta\). Therefore, in the factor graph method, the state estimation problem is solved by the least square method, and the updated estimated state is based on the least square solution.  
Through the above formula derivation, the factor graph is updated by the least square method. Federated filtering needs to be filtered at the lowest common frequency of the sensor, while the factor graph method only needs to add factor nodes and delete fault factor nodes to realize the overall update.
###### Abstract  
The European Space Agency (ESA) defines Earth observation (EO) Level 2 information product the stack of: (i) a single-date multi-spectral (MS) image, radiometrically corrected for atmospheric, adjacency and topographic effects, with (ii) its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud-shadow. Never accomplished to date in an operating mode by any EO data provider at the ground segment, systematic ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem (_chicken-and-egg_ dilemma) in the multi-disciplinary domain of cognitive science, encompassing CV as _subset-of_ artificial general intelligence (AI). In such a broad context, the goal of our work is the research and technological development (RTD) of a \"universal\" AutoCloud+ software system in operating mode, capable of systematic cloud and cloud-shadow quality layers detection in multi-sensor, multi-temporal and multi-angular EO _big data_ cubes characterized by the five Vs, namely, volume, variety, veracity, velocity and value. For the sake of readability, this paper is divided in two. Part 1 highlights why AutoCloud+ is important in a broad context of systematic ESA EO Level 2 product generation at the ground segment. The main conclusions of Part 1 are both conceptual and pragmatic in the definition of remote sensing best practices, which is the focus of efforts made by intergovernmental organizations such as the Group on Earth Observations (GEO) and the Committee on Earth Observation Satellites (CEOS). First, the ESA EO Level 2 product definition is recommended for consideration as state-of-the-art EO Analysis Ready Data (ARD) format. Second, systematic multi-sensor ESA EO Level 2 information product generation is regarded as: (a) necessary-but-not-sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO _big data cubes_, where SCBIR and SEIKD are _part-of_ the GEO-CEOS visionary goal of a yet-unaccomplished Global EO System of Systems (GEOSS). (b) Horizontal policy, the goal of which is background developments, in a \"seamless chain of innovation\" needed for a new era of Space Economy 4.0. In the subsequent Part 2 (proposed as Supplementary Materials), the AutoCloud+ software system requirements specification, information/knowledge representation, system design, algorithm, implementation and preliminary experimental results are presented and discussed.  
**Keywords:** artificial intelligence; color naming; color constancy; cognitive science; computer vision; object-based image analysis (OBIA); physical and statistical data models; radiometric calibration; semantic content-based image retrieval; spatial topological and spatial non-topological information components
## 4 Results and Discussion
### Comparison of the Main Effects
#### 4.1.4 Main Effects of Factors on Routing Overhead  
Figure 6 summarizes the main effects of factors on AODV routing overhead derived from the three methods. It appears that the trends of the main effects produced by the FFM and the TM are equivalent for the four factors. Compared to those obtained by applying the PBM, the plots indicate that the only difference observed consists of the effect of the NC factor. This latter presents a more important negative effect on the routing overhead.  
Regarding the overall magnitude of the effects, it is clearly indicated that the factor ND has the biggest influence on the routing overhead. This observation is due to the fact that AODV routing protocol generates enormous quantities of control packets in scaled-up scenarios. This is merely due to the large number of RREQ messages produced by AODV and the specific features of VANETs, such as a highly dynamic environment and the frequent connections and disconnections of vehicles. Further, it is revealed that the routing overhead is not very sensitive to black hole attacks. This observation can be supported by the fact that the malicious node broadcasts a fake and fast route reply (RREP) to make other nodes believe that it owns the shortest path. Accordingly, the overhead is cut down because the route request (RREQ) packets are lesser.  
These findings suggest that the Taguchi method gives closer results to the FFM, unlike the PBM that show quite different values. However, the three methods, including the PBM, are able to give an efficient estimate of the most influential factors within the defined ranges.
## 3 Results and Discussion
### Impact on Seed Germination  
#### 3.1.1 Shoot and Root Length  
The effect of different concentrations of SPs and Cu NPs on the root and shoot lengths of the germinated wheat seeds is depicted in Table 2.  
Upon the exposure of wheat seeds to either SPs or Cu NPs, a significant reduction can be observed in their shoot and root lengths as compared to the untreated control at all tested concentrations. For SPs exposure, the highest reduction in the shoot and root length was observed at the concentration of 0.06 mg/mL where the shoot and root length were recorded to be \(0.37\pm 0.22\) and \(1.58\pm 0.74\) cm, respectively. Exposure of wheat seeds to Cu NPs revealed a clear insignificant dose-dependent effect with respective shoot and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Concentration (mg/mL)**} & \multicolumn{2}{c}{**Wheat**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Average Shoot**} & \multicolumn{2}{c}{**Average Root**} \\  & \multicolumn{2}{c}{**Length (cm)**} & \multicolumn{2}{c}{**Length (cm)**} \\ \hline Untreated & 0.00 & 2.67 \({}^{\text{a}}\pm 0.72\) & 3.78 \({}^{\text{a}}\pm 1.32\) \\ \hline \multirow{4}{*}{SPs} & 0.002 & 0.99 \({}^{\text{b}}\pm 0.86\) & 2.65 \({}^{\text{b}}\pm 1.20\) \\  & 0.004 & 0.88 \({}^{\text{bc}}\pm 0.79\) & 2.47 \({}^{\text{b}}\pm 1.11\) \\  & 0.03 & 0.45 \({}^{\text{d}}\pm 0.21\) & 2.41 \({}^{\text{b}}\pm 0.83\) \\  & 0.06 & 0.37 \({}^{\text{d}}\pm 0.22\) & 1.58 \({}^{\text{c}}\pm 0.74\) \\ \hline \multicolumn{4}{c}{Mean of treatment} & 0.67 & 2.27 \\ \hline \multirow{4}{*}{Cu NPs} & 0.002 & 0.55 \({}^{\text{cd}}\pm 0.43\) & 2.34 \({}^{\text{b}}\pm 0.91\) \\  & 0.004 & 0.52 \({}^{\text{cd}}\pm 0.50\) & 2.10 \({}^{\text{bc}}\pm 1.31\) \\ \cline{1-1}  & 0.03 & 0.46 \({}^{\text{d}}\pm 0.24\) & 2.02 \({}^{\text{bc}}\pm 0.44\) \\ \cline{1-1}  & 0.06 & 0.35 \({}^{\text{d}}\pm 0.26\) & 1.39 \({}^{\text{c}}\pm 0.70\) \\ \hline \multicolumn{4}{c}{Mean of treatment} & 0.47 & 1.96 \\ \hline LSD & 0.47 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of SPs and Cu NPs on root and shoot lengths (cm) of wheat seedlings.  
Figure 3: XRD pattern (**a**) of Cu NPs and FTIR spectra (**b**) of SPs and Cu NPs.  
root lengths of \(0.35\pm 0.26\) and \(1.39\pm 0.70\) cm obtained at 0.06 mg/mL. These results indicate that SPs and Cu NPs have comparable inhibitory effects on the shoot and root lengths of wheat seeds.
# Seismic Periodic Noise Attenuation Based on Sparse Representation Using a Noise Dictionary
###### Abstract  
Periodic noise is a well-known problem in seismic exploration, caused by power lines, pump jacks, engine operation, or other interferences. It contaminates seismic data and affects subsequent processing and interpretation. The conventional methods to attenuate periodic noise are notch filtering and some model-based methods. However, these methods either simultaneously attenuate noise and seismic events around the same frequencies, or need expensive computation time. In this work, a new method is proposed to attenuate periodic noise based on sparse representation. We use a noise dictionary to sparsely represent periodic noise. The noise dictionary is constructed based on ambient noise. An advantage of our method is that it can automatically suppress monochromatic periodic noise, multitoned periodic noise and even periodic noise with complex waveforms without pre-known noise frequencies. In addition, the method does not result in any notches in the spectrum. Synthetic and field examples demonstrate that our method can effectively subtract periodic noise from raw seismic data without damaging the useful seismic signal.  
periodic noise; notch; dictionary; sparse representation +
Footnote †: journal: Applied Sciences  
Methods based on sparsity representation, such as S-transform, singular spectrum analysis and empirical mode decomposition, extend realization of automatic denoising [15; 16; 17]. [PERSON] et al. [4] proposed a method based on morphological diversity of monochromatic noise and seismic waves, which assumed that the raw data are only composed of two kinds of signals, the monochromatic noise and seismic waves. It is not applicable to seismic data with strong white Gaussian noise or multitoned noise.  
In this paper, a new method is proposed to attenuate periodic noise. The proposed method is based on sparse representation using a noise dictionary. The novelty is the construction of a noise dictionary, which can represent periodic noise sparsely. First, the noise dictionary is constructed using ambient noise. Next, the noise dictionary is used to sparsely represent periodic noise. Then, the de-noised data are obtained by subtracting the periodic noise from the raw seismic data. The method is applied to synthetic and field seismic data. The effectiveness of the proposed method is that it can subtract periodic noise from raw seismic data without any notches in the spectrum compared with the notch filtering method.