esa-sceva/satcom-chunk-collection huggingface dataset:
# Coarse-to-Fine Structure-Aware Artistic Style Transfer
## 3 Proposed Method  
### Framework Overview  
Inspired by the painting process of artists, in which the coarse structure and color distribution are first constructed and then fine details are added, our framework employs a coarse network and a fine network to simulate the coarse-to-fine process. As shown in Figure 1, given a content image \(x_{c}\in\mathbb{R}^{3\times h_{c}\times w_{c}}\) and a style image \(x_{s}\in\mathbb{R}^{3\times h_{s}\times w_{s}}\), our model eventually generates a stylized image \(x_{cs}\in\mathbb{R}^{3\times h_{cs}\times w_{cs}}\). In the first stage, the coarse network takes \(\overline{x}_{c}\) and \(\overline{x}_{s}\) as inputs, where \(\overline{x}_{c}\) and \(\overline{x}_{s}\) are the results of downsampling \(x_{c}\) and \(x_{s}\) by 2, respectively. Then three restructured coarse stylized features \(\overline{f}^{(i)}_{r}\in\mathbb{R}^{c^{(i)}\times h^{(i)}_{r}\times w^{(i)}_{ r}}\) (\(i=1,2\), 3) are generated by the coarse network, where \(c^{(i)}_{r}\), \(h^{(i)}_{r}\), and \(w^{(i)}_{r}\) are the number of channels, height, and width of the \(i\) restructured feature, respectively. In the second stage, the fine network takes \(x_{c}\) and \(\overline{f}^{(i)}_{r}\) as inputs and then generates the final stylized image \(x_{cs}\) by adopting SSF modules for feature fusion.  
As shown in Figure 2, different stylized images are generated by our method. In Figure 2b, we adopt the last restructured coarse stylized features only to directly restructure the coarse stylized image by the coarse network in the first stage. The coarse stylized image discards the unnecessary local structures of the content image and transfers the global color distribution of the style image. Then, the fine network is employed to encode the high-resolution content image to obtain the content features, and these content features and three coarse reconstructed features from the coarse network are decoded to generate the high-quality structure-aware stylized image in the second stage. As shown in Figure 2c, the final appealing stylized image is synthesized by adopting our full model with a coarse network and a fine network. Moreover, to more clearly show the local style structure of the final stylized image, we use the color control method [26] to keep the color of the final stylized image consistent with the color of the original content image. As illustrated in Figure 2d, although the color distribution of the stylized image remains the same as that of the content image, the local structure of the stylized image is similar to that of the style image.  
Figure 1: Overview of our framework.
## 3 Methodology
### Difference-Based Approach: Change-Enhanced Segmentation
#### 3.2.2 Direct 3-Class Segmentation  
A standard U-Net model, \(\mathcal{S}_{\theta}\) (identical in architecture to the one in 3.1 but trained differently), takes the 3-channel difference image \(\mathbf{X}_{\text{diff}}\) as its sole input. The network is trained end-to-end to directly output a 3-class segmentation map.  
\[\mathbf{Y}_{\text{pred}}=\text{argmax}(\mathcal{S}_{\theta}(\mathbf{X}_{\text {diff}})) \tag{5}\]  
By feeding the network with explicit change information, this method is highly sensitive to alterations between images. However, it risks losing context present in the original images and can be sensitive to noise or non-damage-related changes like illumination or seasonal variations.
# Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data
## 3 Results  
### Classification of Wetland Vegetation Habitats  
To estimate the relationship between microwave satellite data and soil-vegetation parameters precisely, there is a need to perform a classification of wetland habitats, as the relationships vary bythe vegetation type. For the classification procedure, the following Landsat 8 OLI channels acquired on 19 May 2013 were chosen: 2 (0.45-0.51), 3 (0.53-0.59 \(\upmu\)m), 4 (0.64-0.67 \(\upmu\)m), 5 (0.85-0.88 \(\upmu\)m), 6 (1.36-1.38 \(\upmu\)m), and 7 (1.57-1.65 \(\upmu\)m) along with three TerraSAR-X ScanSAR VV images acquired on 21 May 2013, 7 June 2013 and 23 June 2013. The validation and accuracy of the classification were performed using ground truth observations. As the results of wetland habitats classification, water and seven vegetation classes were distinguished: two forest habitats (coniferous and deciduous) and five representing most dominant non-forest wetland habitats: reeds, sedge-moss, sedges, grass-herbs, and grass. Table 2 presents the error matrix for classified data in %. The overall accuracy equals to 87.27% with 0.8333 of overall Kappa Statistics. Table 3 presents accuracy totals for each of the classes. Figure 2 presents a map of classified wetland habitat types.
## 4 Results  
In this section, we apply the Pyminisat module to study the constellation dynamics and power requirements for the system parameters summarized in Tables 1 and 2, which we refer to as Scenario A. It is interesting to point out that data rates assumed in Table 2 are in accordance with existing terminal technologies [19; 36]. Pointing error levels are indicative of values reported in the literature [37; 38]. A value of 20 dB is assumed for the extinction ratio, which is typical of commercially available lithium niobate electro-optic modulators. In addition, the choice of the receiver telescope diameter and wavelength is also compatible with state-of-the-art optical terminal specifications [1].  
We also consider two alternative scenarios: Scenario B, where the number of satellites \(N_{\text{sat}}\) per orbit and the number of orbits \(N_{\text{orb}}\) are reduced by half, resulting in a thinner constellation with \(\frac{1}{4}\) the total number of satellites \(N\) compared to Scenario A. In _Scenario C_, \(N_{\text{sat}}\) and \(N_{\text{orb}}\) are doubled compared to Scenario A, resulting in a quadrupled \(N\), and hence, we obtain a denser constellation. The constellations remain uniform in all scenarios. Note that the total number of satellites \(N\) in Scenario C remains well within the planning of commercial satellite internet constellation deployments [22; 26].  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Symbol** & **Units** & **Value** \\ \hline Laser wavelength & \(\lambda\) & nm & 1550 \\ Transmitter optical efficiency & \(n_{\text{T}}\) & & 0.8 \\ Receiver optical efficiency & \(n_{\text{R}}\) & & 0.8 \\ Data rate & \(R_{\text{b}}\) & Gbps & 1–100 \\ Receiver telescope diameter & \(D_{\text{R}}\) & mm & 80 \\ Transmitter pointing error & \(\sigma_{\text{T}}\) & urad & 1 \\ Receiver pointing error & \(\sigma_{\text{R}}\) & urad & 1 \\ Full transmitting divergence angle & \(\Theta_{\text{T}}\) & urad & 4 \\ Target BER & \(P_{\text{e}}\) & & \(10^{-12}\) \\ Extinction ratio & \(r_{\text{e}}\) & dB & 20 \\ Quantum efficiency & \(\eta\) & & 1 \\ Amplifier noise figure & \(F_{\text{n}}\) & dB & 3 \\ Load resistor & \(R_{\text{L}}\) & \(\Omega\) & 100 \\ Temperature in Kelvin & \(T_{\text{K}}\) & K & 300 \\ Link margin & \(L_{\text{LM}}\) & dB & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters.  
Figure 6: Receiver sensitivity with respect to the data rate obtained for the parameters of Table 2.  
Table 3 summarizes key properties of the constellations for the three scenarios considered, derived using the Pyminisat module, as referenced in Appendix A. For all scenarios, we assume an inclination angle \(i\) equal to \(53^{\circ}\) and an altitude of \(a=550\) Km. The table quotes the optimal \(F\) obtained for each constellation, the inter-satellite distance \(r_{\rm c}\) between two consecutive satellites in the same orbit, as well as the maximum and minimum link distances required (\(R_{\rm max}\) and \(R_{\rm min}\), respectively) to achieve network connectivity, considering the links to adjacent satellites as outlined in Section 2.3.
## 5 Performance Evaluation  
### Simulation Environments and Parameters  
In this section, we evaluate the performance of the proposed routing protocol, DLSMR. To illustrate our proposed protocol, we deploy 30, 50, and 100 nodes within an urban area of 1000 m \(\times\) 1000 m and a transmission range of 250 m. In addition to that, we deploy a wormhole pair consisting of two wormhole nodes. These wormhole nodes are placed randomly within the network for each simulation run. We establish a 'tunnel' between these randomly positioned wormhole nodes to simulate the wormhole attack, allowing them to shortcut the normal network routing. In this scenario, we simulate our proposed protocol under two different mobility models, namely random waypoint mobility (RWP) [39] and reference point group mobility (RPGM) [40], to evaluate its performance in FANETs. Every result in this simulation is an average from 200 sections, with pausing and moving times set at 3 s and 5 s, respectively. Furthermore, the mobile nodes are initially randomly distributed around the simulated area and move at different speeds (15 km/h, 30 km/h, 45 km/h, 60 km/h, and 75 km/h). The MAC protocol is modeled using the IEEE 802.11a standard and uses a receiver signal strength indicator (RSSI) threshold of \(-80\) dbm for communication range to make it more practical. The main reason for considering RSSI is that the value of RSSI fluctuations obtained has taken into consideration its effect on changes in channel conditions, including multi-path fading [41].  
The simulation experiments are conducted using the NS3 simulator. We summarize the simulation environment and parameters in Table 7. Additionally, we measured the accuracy between the predicted secure next node and the output data of the test set by calculating the root mean square error (RMSE) of the proposed deep learning framework. The RMSE can be written as [42]  
\[\mathsf{RMSE}=\sqrt{\frac{1}{n}\sum_{t=1}^{n}(p_{(t)}-\widehat{p}_{(t)})^{2}} \tag{15}\]  
where \(n\) denotes the number of samples in the test set, \(p_{(t)}\) denotes the predicted value of the \(t\)-th observation in the dataset, and \(\widehat{p}_{(t)}\) denotes the observed value for the \(t\)-th observation in the dataset. It is more likely that the predicted secure next node and observation are closely matched when the RMSE is smaller.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Size** & **Activation Function** \\ \hline Input & 5 & - \\ Layer 1 & \(150\) & ELU \\ Layer 2 & \(100\) & ELU \\ Layer 3 & \(200\) & ELU \\ Layer 4 & \(150\) & ELU \\ Layer 5 & \(100\) & ELU \\ Output & 4 & LINEAR \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model of the deep neural network to predict the secure next node.  
Figure 7: The structure of the deep learning framework for predicting the secure and unsecured next node.
# Composition and Basic Physical Properties of the Phobos Surface: A Comprehensive Review
## 2 Phobos  
Phobos is a small and irregularly shaped celestial body (Figure 1) with dimensions of 13.0 \(\times\) 11.4 \(\times\) 9.1 km [5; 6], a volume of 5742 \(\pm\) 32 km\({}^{3}\)[7], and a gravitational mass of GM = 7.11 \(\pm\) 0.09 \(\times\) 10\({}^{-4}\) km\({}^{3}\cdot\)s\({}^{-2}\) (GM determined along with the uncertainty range, considering most solutions from flybys, gravitational field extractions, and \"distant\" encounters) [8].  
Asteroids are divided into different spectral types, including asteroids of C, D, and T types, with the following definitions:  
* C-type asteroids--a very dark and non-reflective type of asteroid, gray in color, with a composition believed to be similar to that of carbonaceous chondrites.
* D-type asteroids--a very dark and non-reflective asteroid, reddish in color, probably due to the surface presence of organic materials.
* T-type asteroids--a rare type of asteroid with a fairly low albedo and a moderate absorption feature at wavelengths shorter than 0.85 micron.  
The surface of Phobos is very dark, with an albedo not exceeding a few percent, and its spectra are similar to the spectra of D-type asteroids, which are often found in the outer main asteroid belt and the outer Solar System [10].  
Figure 1: Phobos from 6800 Kilometers (Color). Image taken by the High Resolution Imaging Science Experiment (HiRISE) camera on NASA’s Mars Reconnaissance spacecraft [9].  
The gravitational field of Phobos is small due to its small size and is highly non-uniform due to its irregular shape (g = 5.7 \(\times\) 10\({}^{-3}\) ms\({}^{-2}\), [11]). Consequently, the escape velocity from Phobos' surface is highly variable [12] and ranges from \(\sim\)4 to 10 ms\({}^{-1}\) (depending on the latitude and longitude, as well as the local time of the launch site on Phobos) [13].  
The orbit of Phobos is nearly circular and almost equatorial, with a distance to the center of Mars of approximately 2.76 R\({}_{\rm M}\), where R\({}_{\rm M}\) represents the radius of Mars (3396 km) [1,14] Additionally, the orbit gradually decays as it approaches Mars due to the tidal forces dissipated within the planet. Consequently, in approximately tens of millions of years, Phobos will be torn apart by tidal forces and disintegrate in the Martian atmosphere [14].  
The general orbit data are summarized in Table 1.
# CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC
## 4 Proposed Scheme
### Improved PUSCH Allocation Scheme  
We propose an improved PUSCH allocation scheme to mitigate the degradation of PUSCH resources. To improve the utilization of PUSCH resources, we extend the waiting message proposed in [31] to the PUSCH waiting message as shown in Figure 5. The traditional RA method drops devices when PUSCH resources are insufficient, even though a successful preamble transmission has already been achieved. In Figure 5, the legacy PUSCH allocation drops the second device when the number of deduced preamble codeword sets is larger than the allocable PUSCH resources in one RA cycle. By contrast, the improved PUSCH allocation procedure first transmits a PUSCH waiting message to devices that have successfully transmitted the preamble, allowing devices to wait for a certain waiting window time without dropping. Because the access intensity is different for each RAO, the remaining PUSCH resources at low access intensity may be allocated to devices waiting for the RAR message. This means that the utilization of PUSCH resources is increased, thereby alleviating the PUSCH resource utilization degradation problem caused by large-scale preamble codewords.  
In the proposed PUSCH allocation scheme, the BS temporarily records a successfully transmitted preamble codeword. Because the BS cannot obtain specific information from  
Figure 5: PUSCH allocation scheme comparison. (**a**) Legacy PUSCH allocation scheme. (**b**) Improved PUSCH allocation scheme.  
the device that transmits the preamble codeword, PUSCH resources are allocated to the preamble codeword based on the first-come first-out method [16; 24; 32]. If the RAR message is received within the PUSCH waiting time, the device may proceed to the connection-request process. Otherwise, the device performs an RA reattempt because the PUSCH waiting time has expired.
## 5 Performance Analysis and Simulation
### Security Performance Analysis
#### 5.1.2 Security Performance Analysis of our Proposed Schemes
\[\rm T_{1}\leq M*T_{NRSPSpp}\leq M*T_{NRSPSpp}=M*(\rm Hop_{ps,sink})*T \tag{17}\]  
\[\rm T_{1}\leq M*T_{NRDPSpp}\leq M*T_{NRDPSp}=M*(\rm Hop_{ps,pr}+\rm Hop_{pr,sink })*T \tag{18}\]  
From Equations (17) and (18), we can see that when \(\rm\frac{T_{1}}{T_{PP}}\leq M\), the attacker cannot capture the SoN, where \(\rm T_{PP}\) in the NRSPS scheme is shown in Equation (15), and \(\rm T_{PP}\) in the NRDPS scheme is shown in Equation (16). (3) Routing Path \(\rm Hop_{Sum}\)  
According to the assumption of \(\rm 0\), the hops of routing path \(\rm Hop_{sum}\) of the NRSPS scheme and NRDPS scheme can be derived by Equations (19) and (20), respectively:  
\[\rm Hop_{NRSPSsum}=\rm Hop_{s,ps}+\rm Hop_{ps,sink} \tag{19}\]  
\[\rm Hop_{NRDPSsum}=\rm Hop_{s,ps}+\rm Hop_{ps,pr}+\rm Hop_{pr,sink} \tag{20}\]  
Since the attacker can only hop-by-hop backtrack, it takes time \(\rm T_{s}=T*\rm Hop_{sum}\) to capture the SoN. According to Equations (15)-(20), the usage time of the PN \(\rm T_{PP}\leq\) the time for the attacker to trace to the PN \(\rm T_{ps}<\) the time for the attacker to trace to the SoN \(\rm T_{s}\). Therefore, before the attacker traces to the PN, the SoN has updated the PN and the attacker cannot continue backtracking.
## 3 ML-Based Routing Algorithms in WSNs
### Reinforcement-Learning-Based Routing Methods
The powerful learning ability and generalization ability of artificial intelligence combined with a software-defined network (SDN) and programmable routing equipment make reinforcement-learning-based intelligent routing feasible. A deep reinforcement-learning-based single-optimality routing (DRLSOR) algorithm was implemented in both centralized and distributed modes, and the convergence and re-convergence time were analyzed. A multi-optimal routing scheme was also implemented by the model fusion, which improves the route efficiency and reduces energy consumption. Experimental results show that the centralized routing has faster convergence speed, while the distributed routing has better scalability.
## 3 Identifier-Related Challenges  
Resource description framework identifier (rdfID) is specified in IEC 61970-552 to ensure that every object in the CIM XML message file has a unique identifier so that all the objects realize their relationships to other objects, such as association and aggregation in the message file [17; 23]. The concept of mRID is recommended to be used for identifying a unique name for a resource, and the mRID is mapped to rdfID for CIM XML data files in RDF syntax. In this section, two main problems regarding identifiers are described in detail.
## 2 Materials and Methods
### Experimental Investigation  
#### 2.3.1 Material Used and Test Setup  
1. Soil: The soft clay soil obtained from Kalathur lake situated near Ocheri in the Vellore district of Tamil Nadu, India, is used for the present study. The properties of the soft clay are: liquid limit (LL) = 52%, plastic limit (PL) = 27%, plasticity (\(I_{p}\)) = 25%, and undrained shear strength (\(c_{u}\)) at the consistency (\(I_{c}\)) of 0.5 = 7.5 kN/m\({}^{2}\). The soil is classified as clay with high compressibility (CH) type as per the Indian standard classification system.  
2. Helical pile: Model helical pile material and dimensions are chosen in accordance with similitude regulations. The size of model helical piles should be carefully scaled down in comparison to the size of the prototype. Nevertheless, owing to the difficulties in fabricating a scaled-down size of model helical pile for a given size of prototype, the size of the model pile in the current work is set based on the sizes that are available on the market. The model is made of the steel solid circular rod having a shaft diameter of 14 mm and a helical plate diameter of 40 mm with a 2 mm plate thickness and a pitch of 8 mm, cut and welded to the shaft as mentioned earlier. It has been decided to use the scaling law given by [PERSON] et al. [39] in this investigation: \[\frac{E_{p}I_{p}}{E_{m}I_{m}}=n^{4.5}\] (6) where \(n\) = scale factor, \(E_{m}\) = elasticity modulus of model pile, \(E_{p}\) = elasticity modulus of prototype pile, \(I_{m}\) = moment of inertia of model pile, and \(I_{p}\) = moment of inertia of prototype pile. For an assumed prototype the hollow helical pile was made of steel having an outer shaft diameter (\(d\)) of 100 mm, a thickness of 10 mm, and helical plate diameter (\(D\)) of 285 mm, assuming \(d\)/\(D\) ratio = 2.85, which is the same as the model pile, the scaling factor is estimated as 4.53. All other parameter considerations are discussed earlier.
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 6 Experimental and Result Evaluations
### Beam Training Performance Evaluation  
Figure 3 shows the received SNR of three algorithms based on 100 independent experiments. The three algorithms are classical RA, RA based on SA (\(RA+SA\)), and enhanced RA based on TS (\(RA+SA+TS\)), respectively. The performance of the proposed algorithm is evaluated by the achievable values of receiving SNR. The upper bound values are the optimum SNR by exhausted search. According to statistical calculations of the simulation results, the search success probability of the classical RA is 36%; the RA based on SA is 60%, and the enhanced RA based on TS is 92%. As shown in Figure 3, the searched values are the most consistent with the target when using the enhanced RA during beam training. Although the tabu table introduces some complexities due to the storage of solutions, the increase in the complexity of the enhanced algorithm is not great compared with the existing algorithms, which is of great significance for practical applications. Therefore, the enhanced algorithm proposed in this paper is more conducive to the actual optimization problem.
# On Hybrid Nanogrids Energy Management Systems--An Insight into Embedded Systems
###### Abstract  
In recent years, the growing demand for efficient and sustainable energy management has led to the development of innovative solutions for embedded systems. One such solution is the integration of hybrid nanogrid energy management systems into various applications. There are currently many energy management systems in different domains, such as buildings, electric vehicles, or even naval transport. However, an embedded nanogrid management system is subject to several constraints that are not sufficiently studied in the literature. Indeed, such a system often has a limited energy reserve and is isolated from any energy supply for a long time. This paper aims to provide a comprehensive overview of the current state of research, advancements, and challenges in the field of hybrid nanogrid energy management systems. Furthermore, it offers a comparative analysis between hybrid nanogrids and microgrids and the implications of their integration in embedded systems. This paper also discusses the key components, operation principles, optimization strategies, real-world implementations, challenges, and future prospects of hybrid nanogrid energy management systems. Moreover, it highlights the significance of such systems in enhancing energy efficiency, reducing carbon footprints, and ensuring reliable power supply.  
nanogrid; energy management system; power management system; optimization; hybrid system; embedded system; isolated nanogrids; renewable energy sources +
Footnote †: journal: Applied sciences
# Input Shaping Control of a Flexible Structure for Rest-to-Rest and Non-Rest-to-Rest Maneuvers
## 2 Dynamic System Model
The physical dimensions of the system are shown in Table 1. Such a parametrization gives \(c_{1}=0.8243\)\(\mathrm{Hz}^{2}\), \(c_{3}^{2}=1.6684\)\(\mathrm{Hz}^{2}\), \(b_{2}=6.2419\) rad \(\mathrm{kg}^{-1}\)\(\mathrm{m}^{-2}\), and \(b_{4}=-7.4937\) rad \(\mathrm{kg}^{-1}\)\(\mathrm{m}^{-2}\). Two problem cases are considered for this system:  
1. **Rest-to-Rest Example:** Starting from rest, the system performs a \(180^{\circ}\) rotation. \(\theta(t_{0})=[180^{\circ},0,0,0]^{T}\) \(\theta(t_{f})=[0,0,0,0]^{T}\)
2. **Non-Rest-to-Rest Example:** Starting with the initial nonzero values of all states, the system performs a \(180^{\circ}\) rotation and reaches a final rest state. \(\theta(t_{0})=[180^{\circ},0.5^{\circ}/s,10^{\circ},0.5^{\circ}/s]^{T}\) \(\theta(t_{f})=[0,0,0,0]^{T}\)  
Note that in this article, for all examples, we assume that \(\mid\) u(t)\(\mid\) = 0.005 N.m.