esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain
## 3 Results
### Effects of AOD Data Sources on Full-Expression Blue-Sky Albedo  
Blue-sky albedo generated for comparison using three sources of Aerosol Optical Depth (AOD) information representing different levels of data precision and availability. AERONET aerosol optical depth is a coincident, real-time measure of atmospheric properties and provides the best indication of local aerosol conditions at the time of satellite overpass. Level-2 aerosol optical depth AERONET data coincident to Landsat overpass date and time were limited to 5 sites (BARR, HARV, NYA, SXF, and TOOL) in the study domain. The coarser resolution MODIS AOD from the Terra and Aqua Aerosol Cloud Water Vapor Products (MOD08v06 and MYD08v06) at daily, eight-day and monthly time steps served as a less precise, but more widely available and operational AOD source. Lastly, the fixed value was used as an estimate when no other AOD source was available.  
The AERONET and MODIS AOD derived blue-sky albedos are compared in Figure 5a. A total of 32 valid data points were available for the analysis. The difference in blue-sky albedo derived from the in situ atmospheric datasets and MODIS AOD was minimal with RMSE and bias of 0.0539 and \(-\)0.007 from the AERONET derived AOD and 0.0546 and \(-\)0.008 from the MODIS derived AOD. This congruency in results between these methods suggests that the use of the coarser MODIS AOD dataset can serve as relevant and valuable proxy for AOD at a global and operational scale. A comparison with a fixed value AOD of 0.20 was also performed at 20 sites totaling 295 validation datapoints (Figure 5b). The use of a fixed AOD value indicated a slightly weaker fit with tower data with an RMSE of 0.060 and bias of \(-\)0.003 as compared to the MODIS AOD values which returned a RMSE of 0.052 and \(-\)0.006 bias (Figure 5b).
# Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models
## 3 Network Architecture and System Model
### Task Generation Model  
Since the main service object of the IoV mesh network is vehicle users, and the network performance will affect the service experience of users, it is significant to analyze quantitatively the quality of user service under different network conditions. For this reason, this subsection proposes a method for modeling and analyzing traffic with a focus on user experience. First, the traffic model is constructed, and the traffic distribution model based on actual switch port forwarding is investigated for analyzing the real-time distribution of data flow within the network, which forms the basis for the subsequent construction of the indicator system and performance analysis.  
Before modeling the traffic generation of vehicles or RSUs, this subsection constructs the port routing table of each node based on the forwarding strategy of the actual switch, which provides the basis for the generation of task routes. By traversing the network nodes and utilizing [PERSON]'s shortest path algorithm, a route originating from one node to another network node is created; this route is then converted into a port route to facilitate task forwarding by consulting the node's routing table. This is shown in Figure 4. In the topology on the left side of the picture, the values on the links denote the path distance so that the route for Task 1 can be obtained by the [PERSON] algorithm as 1--2--4--5--6. In the topology on the right side of Figure 3, '1 2' denotes the port utilized by node 1 to communicate with node 2. The number of ports usually is finite.  
Then, the traffic generation of vehicles or RSUs is modeled to obtain the task distribution. The procedure is outlined as follows: (1) Divide the IoV mesh network into multiple grid spaces and count the number of vehicles and RSUs initiating tasks within each grid. (2) Randomly select the destination of each initiating task node. (3) Construct a traffic model to generate task routes for each initiating task node at each moment, ensuring task QoS values meet the criterion of the average packet loss rate across the network of less than 5%. (4) The network exists the high, medium and low priority tasks.
## 4 Numerical Examples and Validation of the Software
### Comparison to Other Methods
#### 4.3.2 Description Classical Augmented Lagrangian Regime  
This section is devoted to the description of the use of the classical augmented Lagrangian algorithm to the problem (33).  
The classical augmented Lagrangian scheme is given through the following Algorithm 4, cf. [20; 22].
```
Input : Define a starting values \(x_{h}^{(0),s}\), \(\lambda^{(0)}\), \(\alpha_{0}\) and define \(j:=0\). Output : Approximate solution \(x_{h}\) to (33) and approximate Lagrangian function \(\lambda_{h}\). The classical augmented Lagrangian algorithm is given through the following steps. S1: Approximate a solution to the following optimization problem.  Find \(x_{h}^{(j+1)}:=\big{(}c_{S,h}^{(j)},p_{S,h}^{j}\big{)}_{S\in\{A,B,C\}}\in X_{h}\) such that the following equation holds true: \[x_{h}^{(j+1)}=\operatorname*{argmin}_{(v_{S,h},q_{S,h})_{S\in\{A,B,C\}}}LS_{ a_{j}}\big{(}\big{(}v_{S,h},q_{S,h}\big{)}_{S\in\{A,B,C\}},\lambda^{(j)}\big{)}\] (35) and use \(x_{h}^{(0),s}\) as initial S2: If \(\big{(}\lambda^{j},x_{h}^{(j)}\big{)}_{L^{2}(\Omega)}<\varepsilon\) break the algorithm. S3: Update \(\lambda^{(j)}\) via the update rule: \[\lambda^{(j+1)}:=\big{(}\lambda^{(j)}-\alpha_{j}[c_{A}^{(j+1)},c_{B}^{(j+1)},c_{C}^{(j+1)}]\big{)}_{+}.\] Furthermore update \(x_{h}^{(j+1),s}:=x_{h}^{(j+1)}\)\(\alpha_{j+1}\) such that \(\alpha_{j}\simeq\alpha_{j+1}\). S4: Set \(j:=j+1\) and go to S1.
```
**Algorithm 4**Classical augmented Lagrangian method  
The experimental setup needs to be described before the numerical experiments will be discussed in detail.  
First, note that, for all simulations discussed with Algorithm 4, the parameter \(\alpha_{j}\) was for all \(j\in\mathbb{N}\) given as \(\alpha_{j}=1\). For the comparison of the algorithm on different meshes, the algorithm was coupled with a loop of uniform refinements. The global Algorithm 5 is given through the following:
```
Input :Initial Triangulation \(\mathcal{T}_{0}\). Define a starting values \(x_{h}^{(0),s}\), \(\lambda^{(0)}\), \(\alpha_{0}\) and define \(j:=0\). Output :Approximate solution \(x_{h}\) to (33) and approximate Lagrangian function \(\lambda_{h}\) and final triangulation \(\mathcal{T}\). Perform the following steps: S1: Compute an Approximation \(x_{h_{\ell}}\) of a solution and a Lagrangian multiplier \(\lambda_{\ell}\) to (33) on the current triangulation \(\mathcal{T}_{\ell}\) using the initial values \(x_{h_{\ell}}^{(0),s}\) and \(\lambda_{\ell}^{(0)}\) using the Algorithm 4. S2: If \(\mathcal{T}_{\ell}\) has more than \(N\) nodes then break the loop. S3: Define the mesh \(\mathcal{T}_{\ell+1}\) as uniform red refinement of the triangulation \(\mathcal{T}_{\ell}\). For a definition of the uniform red refinement see [27]. S4: Define \(x_{h_{\ell}}^{(0),s}:=x_{h_{\ell}}\) and \(\lambda_{\ell}^{(0)}:=\lambda_{\ell}\). S5: Set \(\ell:=\ell+1\) and go to S1.
```
**Algorithm 5** Refinement strategy with the classical augmented Lagrangian algorithm  
The statement of the algorithm above finishes the description of the used classical augmented Lagrangian scheme.
## 1 Introduction  
The International Roughness Index (IRI) is a standard index to quantify road surface roughness from measured longitudinal road profiles [1]. IRI is linearly proportional to roughness [2]; thus when the IRI value increases, the roughness of the pavement increases; and an IRI value of zero means that the pavement is smooth [3]. Some researchers reported that the pavement fails when IRI values reach 3 m/km, and an overlay is needed [3]. IRI is a standardized roughness measurement to represent the reaction of a single tire on a vehicle suspension to roughness in a pavement surface when a quarter car simulation traveling at 80 km/h [4]. In the literature, IRI can be determined from profiles obtained from high-speed inertial profiling systems [5]. Nonetheless, one problem associated with IRI is that determination of this index in laboratories is time-consuming and complicated, thus, the accurate prediction of the IRI is essential and useful.  
Nowadays, machine learning (ML) methods have been applied widely in prediction of the IRI. [PERSON], et al. [6] proposed the grey forecast, genetic programming, and multiple regression for prediction the deterioration of IRI. [PERSON], [PERSON] and [PERSON] [3] used an artificial neural network (ANN) to predict the IRI for jointed plain concrete pavement sections. [PERSON], et al. [7] studied the relationships between IRI and pavement distress using a back-propagation neural network. [PERSON], et al. [8] discussed road profile estimation using ANN. [PERSON], et al. [9] analyzed the relationship between the IRI and cracking and rutting over 317 observations. It has been inferred from these studies that good models and approaches should be developed in order to obtain higher prediction accuracy of IRI.  
In this study, the main aim is to propose a new approach of adaptive network based fuzzy inference system (ANFIS) with various meta-heuristic optimizations namely the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop different hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named ANN was also used to compare with those hybrid models. For this purpose, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the proposed and applied models in terms of various criteria like coefficient of determination (R) and root mean square error (RMSE).
## 7 Discussion
### Future Directions
Some notable examples include the following:  
* Partnership with Germany: GIZ funding for feasibility studies and human capital training. * Total energies and Enel GH2 Project: With support from development banks (IDB, KfW), combining European expertise and Asian demand. * Alliance with Port of Rotterdam: To create a logistics chain connecting Magallanes with Europe. However, progress must be made on long-term purchase agreements with importing countries, ensuring profitability for producers. Magallanes has the potential to lead GH2 exports in the Southern Cone, but its consolidation depends on the following: (i) Agile public policies that harmonize industrial development and environmental protection; (ii) Public-private incentives that reduce financing gaps; and (iii) International networks that guarantee demand and scalability. If these elements are effectively articulated, the region could not only decarbonize foreign markets but also generate quality jobs and diversify its local economic matrix, becoming a global model for a just energy transition. Validating the assumption that electrolysis for green hydrogen is powered 100% by renewables is crucial for accurate carbon accounting and compliance with international standards like the EU's RED III, which emphasizes additionally and temporal matching. The importance of hourly temporal resolution is presented as follows:  
1. Temporal matching: Using hourly data for grid electricity, as done in EU e-fuel studies, ensures that hydrogen production synchronizes with renewable generation. This approach prevents overestimating the share of renewables and avoids indirect emissions from fossil-based grid electricity. 2. RED III Additionally Criteria: RED III requires that the renewable electricity used for hydrogen production must be additional (new capacity) and closely matched in time and location to hydrogen production. Hourly resolution is critical to demonstrate compliance with these criteria and to ensure that hydrogen is genuinely \"green\".
# Dual Partial Reversible Data Hiding Using Enhanced Hamming Code
## 2 Preliminaries
### Definition of Partial Reversible Data Hiding  
In conventional reversible data hiding (RDH) schemes, the cover image is identical to the original image, making complete recovery of the original content possible after data extraction. In contrast, the proposed method deliberately modifies the original image to generate two cover images by applying HC(7,4) Hamming code transformations to selected pixel groups. This controlled transformation enables efficient data embedding while preserving image quality and reversibility with respect to the generated cover images.  
As a result, the recovery process yields the cover images rather than the original image itself. A cover image differs slightly from the original due to the deterministic bit-level modifications applied during the encoding phase. This characteristic underlies the terminology of partially reversible data hiding (PRDH); while the embedding and extraction processes are fully reversible with respect to the cover images, the transformation from the original to the cover images is not reversible without additional information, which by design is not retained.  
In this dual-image PRDH framework, both cover images are constructed with pixel-wise modifications that preserve high fidelity to the original (PSNR exceeding 48 dB in our experiments), and both can be precisely recovered from the corresponding stego images. In this way, the proposed scheme achieves strong perceptual quality and operational reversibility while intentionally sacrificing full recovery of the unmodified original image in favor of increased embedding capacity and structural simplicity.
## Nomenclature  
\(a,b,h\)  
Plate dimensions in \(x\)-, \(y\)-, and \(z\)-directions  
\(f,a,s\)  
Subscripts designating FGM, actuator, and sensor layers, respectively  
\(T\)  
Temperature distribution  
\(T_{ij}T_{o}\)  
Temperature at the bottom and top surfaces, respectively  
\(T_{f_{i}}\),\(T_{f_{e}}\)  
Temperature at the bottom and top surface of FGM layer, respectively  
\(\lambda_{f}\)  
Thermal conductivity coefficient for FGM layer  
\(a_{0}\)  
Thermal expansion coefficient  
\(\beta_{xx},\beta_{y},\beta_{z}\)  
Stress-temperature coefficients in \(x\)-, \(y\)-, and \(z\)-directions  
\(P_{3}\)  
Pyroelectric constant  
\(C_{ij}(i,j=1.2,\dots,6)\)  
Relaxation moduli coefficients  
\(D_{x},D_{y},D_{z}\)  
Electric displacement  
\(Q_{ij}(i,j=1.2,\dots,6)\)  
Elasticity constant  
\(E_{x},E_{y},E_{z}\)  
Electric field in \(x\)-, \(y\)-, and \(z\)-directions  
\(E\)  
Young's modulus  
\(e\)  
Piezoelectric coefficient  
\(\eta_{1},\ \eta_{2},\ \eta_{3}\)  
Dielectric constants  
\(d_{1}\)  
Piezoelectric modulus  
\(\lambda_{xp},\lambda_{yp},\lambda_{zp}\)  
Thermal conductivity coefficient for piezoelectric layer in the \(x\)-, \(y\)-, and \(z\)-directions  
\(h_{f},h_{p}\)  
Thicknesses of the \(FGM\) and piezoelectric layers  
\(n,m\)  
Half-wave numbers in the \(x\)- and \(y\)-directions  
\(u,v\), and \(w\)  
Displacement components in the \(x\)-, \(y\)- and \(z\)-directions  
\(\sigma_{xx},\sigma_{y},\sigma_{z}\)  
Normal stresses  
\(\tau_{xy},\tau_{zx},\tau_{zy}\)  
Shear stresses  
\(\varepsilon_{x},\varepsilon_{y},\varepsilon_{z}\)  
Normal strains  
\(\tau_{xy},\tau_{2x},\tau_{yy}\)  
Shear strains  
\(\tau\)  
Relaxation time constant  
\(\delta_{f},\delta_{p}\)  
State vectors of the \(FGM\) and piezoelectric layers  
\(\psi\)  
Electric voltage  
\(v\)  
Poisson's ratio
# Time Series Irradiance Synthesizer for Optical GEO Satellite Downlinks in 5G Networks
## 1 Introduction
In this paper, a time series generator for the reproduction of received irradiance long-term statistics for an optical GEO downlink is proposed. The temporal behavior of the scintillation is modeled with the employment of stochastic differential equations (SDEs). The synthesizer is validated in terms of first and second order statistics with experimental results from the ARTEMIS optical link campaign and afterwards derived numerical results are reported [13; 14]. In a previous paper [15], a preliminary version of the proposed generator has been presented and only the first order statistics have been validated. In this contribution, an extensive validation of the synthesizer using both first and second order statistics results is performed and its usage for system-level simulation and for fading techniques modeling is highlighted. The methodology is based on the assumptions detailed below:  
* Downlink Propagation
* GEO satellite-to-ground optical link
* [PERSON]'s theory is considered and [PERSON]'s spatial spectrum of refractive index is applied
* Optical links with elevation angle greater than 20 deg are assumed
* A single collimated Gaussian beam is transmitted
* Perfect link pointing and tracking  
The remainder of the paper is structured as follows: in Section 2 a brief summary of ARTEMIS optical satellite experimental campaign is presented; in Section 3 the required theoretical background is reported including important metrics regarding the downlink's turbulence effects and followed by the proposed methodology for the generation of received irradiance time series; in Section 4 numerical results using the synthesized data are validated with experimental results derived from ARTEMIScampaign and simulated received power statistics for various weather conditions are reported using the validated methodology; and finally Section 5 concludes this paper.
## 3 Results and Discussion  
Specific Kinetics and Selection of Box-Belmken Design of Supercritical Fluid Extraction from Isochrysis galbana  
Optimum extraction time was set by analyzing the kinetics of the extraction carried out by SFE. Figure 2 shows the evolution of the performance and the TC accumulated vs. extraction time of 150 min. The condition was marked as a central point of the experimental design chosen (30 MPa, 50 \({}^{\circ}\)C, 4% v/v of co-solvent ethanol). Each extract was recovered at the established time point as described in the material and methods section and the percentage of the extractable was calculated. The yield extraction curve reached a plateau at 60 min and total carotenoids between 100 and 120 min.  
Figure 2: Kinetics of improving extraction yield (Y, %) and total carotenoids content (TCC) from _I. galbana_ during 150 min. The working conditions were at the central point: 50 \({}^{\circ}\)C, 30 MPa, 4% co-solvent (ethanol, v/v), and a CO\({}_{2}\) flow rate of 7.2 g/min.  
For consolidating the maximum in all the variables studied, an optimal extraction time of 120 min was set for each SFE condition as after this time there was no increase in the amount of extracted material. In particular, the maximum value of total carotenoids recollected was approximately 99.80% (\(\sim\) 6.53 mg/g) and the extraction yield was 99.8% (\(\sim\) 4.67%, w/w) at 120 min.  
On the other hand, Box-Behnken statistical design was selected for this study because previous works demonstrated that models 2 K did not represent adequately the bioactive compounds extraction process (results not shown). Respect to the percentage of co-solvent, we also tested with less range (0%-4% v/v) and all variables content was improved under 4% ethanol. For this reason, we expand to 8% of the volume of ethanol in order to minimize the cost process under the Box-Behnken design. Table 1 shows the results of the ANOVA and Regression coefficients for each variable. Here, R-Square statistic indicated that the quadratic model was well adjusted in against lineal or second order. It means the variability in all studied variables. In the case of the adjusted R-square statistic were slightly smaller than R-squared and in the same way lack-of-fit determined that the selected model was adequate to describe the observed data. This test was performed by comparing the variability of the current model residuals to the variability between observations at replicate values of the independent variable. Since the \(p\)-value for lack-of-fit in the ANOVA table was greater or equal to 0.05, the model appears to be adequate for the observed data (except FAME data that was close to 0.05).
## 2 Metamaterial Sensor for eNDE and Theory
### Metallic Strip Gratings (MSGs)  
Metallic films on flexible substrates (polyimide or plastic) currently have superior mechanical properties compared to ones deposited on glass, in many aspects. Although the glass substrate is hard, the polyimide or plastic substrate is lighter, less expensive, flexible, and more suitable for use in small devices [77]. Other applications include protective coatings, EM shielding, and electric current conductors for microelectronic applications.  
Two types of MSG were taken into consideration:  
Flexible printed circuit with transparent polyimide support, 80 \(\upmu\)m thickness, with silver conductive strip of 10 \(\upmu\)m thickness, the width of the strips being \(x_{c}\) = 0.6 mm and the width of the slits being \(x_{d}\) = 0.4 mm;  
Silver strips realized with polyimide support of 65 \(\upmu\)m thickness made from a silver strip having 14 \(\upmu\)m thickness, the width of the strips being \(x_{c}\) = 1.2 mm and the width of the slits being \(x_{d}\) = 0.8 mm.  
For strips having \(x_{c}\) = 1.2 mm, we have taken into account interruptions as well as non-alignments. Silver strips were realized by successive deposition of silver paste, using an adequate stencil by screen printed method. The adhesion of silver on polyimide has been done with a thin film of resin. The conductive silver paste made from microparticles with concentration \(>\)80%, density 10.49 g/cm\({}^{3}\), resistivity 1 + 3 \(\times\) 10\({}^{-5}\)\(\Omega\) cm was used [78]. The silver paste has good adhesion and fast drying speed at room temperature. At frequencies around the value of 500 MHz, the permittivity of silver [79] is \(\varepsilon_{m}=-48.8+j\cdot 3.16\).  
The polyimide as flexible substrate can be easily embedded in 3D structures, also ensuring retention of bioactive species in the developed structure. They satisfy just about all the requirements for electronics applications, being lightweight, flexible, and resistant to heat and chemicals [80]. The flexible MSG structures are presented in Figure 3a,b.  
For the MSG having the width of strips \(x_{c}\) = 0.6 mm, the study is focused on the appearance of abnormal and/or evanescent modes for the cases where various dielectric fluids fill the gaps between the strips, and also improvement of the EM images to obtain a better spatial resolution in order to exceed the limit imposed by diffraction.  
Figure 3: Studied samples: **(a)** MSGs from flexible printed circuit; **(b)** MSGs realized with polyimide support; **(c)** Plates from FRPC.