esa-sceva/satcom-chunk-collection huggingface dataset:
# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
## 3 Experiments
### Evaluation Parameters  
The subjective visual evaluation is to judge the fusion effect by observing the details of the image, such as sharpness, color, and contour. The subjective visual evaluation will vary from person to person and can only represent some people's viewpoints, which can be one-sided, so some objective indicators are also needed to judge the quality of the fused image. In order to evaluate the fused images more objectively, the correlation coefficient (CC) [39], spectral angle mapping (SAM) [40], root mean square error (RMSE) [41], and Errur Relative Globale Adimensionnelle Desynthese (ERAGS) [42] are chosen in this paper to evaluate the fused images objectively. These evaluation metrics have been widely used to evaluate fusion images, and they are described in detail below.  
The CC reflects the linear correlation between the fused image and the reference image, which is defined as shown in Equation (10).  
\[CC(X_{t},H_{Gt})=\frac{\sum_{i=1}^{m}\sum_{i=1}^{n}\left[X_{t}(i,j)-u_{i} \right][H_{GT}(i,j)-u_{Gt}]}{\sqrt{\sum_{i=1}^{m}\sum_{i=1}^  
CC is closer to 1, it represents a stronger linear correlation between fusion \(X_{t}\) and \(H_{Gt}\), corresponding to a better fusion effect.  
The RMSE reflects the degree of pixel difference between the fused image and the reference image, which is defined as shown in Equation (11).  
\[\text{RMSE}(X_{t},H_{Gt})=\sqrt{\frac{1}{mn}{\sum_{i=1}^{m}{\sum_{i=1}^{n}{[X_{ t}(i,j)-H_{Gt}(i,j)]^{2}}}}} \tag{11}\]  
where \(M\) and \(N\) represent the image sizes of \(x_{t}\) and \(G_{Gt}\). RMSE reflects the degree of difference between images, so the smaller the RMSE value, the smaller the difference between the fused image and the reference image.  
SAM reflects the similarity of the spectral data between the fused and reference images, which is defined as shown in Equation (12):  
\[\text{SAM}(X_{t},H_{Gt})=cos^{-1}\frac{\left\langle{X_{v},H_{v}}\right\rangle }{\left\|{X_{v}}\right\|_{2}\left\|{H_{v}}\right\|_{2}} \tag{12}\]  
where \(H_{v}\) represents the spectral vector of the reference image and \(x_{v}\) represents the spectral vector of the fused image. The smaller the SAM value, the smaller the spectral difference between the reference image and the fused image, indicating that the degree of spectral distortion during the fusion process is smaller.  
The ERGAS reflects the degree of spectral distortion of the fused image, which is defined as shown in Equation (13):  
\[\text{ERGAS}=100\frac{T_{x}}{T_{h}}\sqrt{\frac{1}{N}{\sum_{i=1}^{N}{\frac{RMSE ^{2}(i)}{u_{i}^{2}}}}} \tag{13}\]  
where \(N\) represents the number of bands of the image, \(T_{x}\) and \(T_{h}\) are the resolutions of the PAN image and the HS image, respectively, \(RMSE^{2}(i)\) is the root mean square error of the ith band of the fused image and the reference image, and \(u_{i}\) represents the value of the ith band of the reference image. The smaller the value of ERGAS, the smaller the degree of spectral distortion.
## 2 Related Works
### SR-Based Graph Construction Methods  
Unlike the traditional graph generation approaches, the SR-based methods can learn the local relationship from samples and compute the well-discriminated edge weights of the graph. By encoding a certain sample as a sparse linear combination of all the other samples, the sparse coefficients of the linear combination can be viewed as the edge weights from the certain sample to all the other samples [13; 14]. By doing so, the graph that LP algorithm demanded could be generated.  
In addition to the most SR based methods, [PERSON] et al. proposed the probabilistic class structure regularized sparse representation (PCSSR) approach. In their work, the authors manage to incorporate the SR model with a probabilistic class structure that reflects the probabilistic relationship between each sample and each class. Further, with the probabilistic class structure provided, the distance between each two samples can be acquired according to the difference between their probabilistic class labels. Finally, a class structure regularization is developed using the distance between each two samples. The authors claim that, with the class structure regularizer, PCSSR can learn a more discriminative graph from the data, and as shown in the experimental results, the PCSSR method outperforms state of the art on Hyperion and airborne visible infrared imaging spectrometer (AVIRIS) hyperspectral data. The class structure regularizer and the full model of PCSSR are shown in Equations (1) and (2), respectively, where \(W\) is the adjacency matrix we need to obtain for the LP algorithm, \(M\) is the distance matrix and each entry \(M_{ij}\) represents the distance between two samples based on the difference between their probabilistic class label, and \(X\) denotes all samples in training set and testing set.  
\[R(W)=\sum_{i,j}\left|W_{ij}\cdot M_{ij}\right| \tag{1}\]  
\[\underset{W}{min}\frac{1}{2}\|X-XW\|_{F}^{2}+\lambda_{1}\|W\|_{1}+\lambda_{2} R(W)\text{ s.t. }diag(W)=0,\ W\geq 0, \tag{2}\]  
However, the probabilistic class structure used in the PCSSR paper in obtained only through another SR process, which fails to take into account the abundant spatial information in the HSI dataset. Despite the highly discriminative capability to achieve high classification accuracy, PCSSR suffers from the limitation for neglecting the spatial information of HSI. Since sample pixels have the characteristics of spatial continuity, failing to consider spatial information would miss such important characteristics that are beneficial for enhancing classification capability. Therefore, we conclude that the classification results by using only spectral information would lack spatial continuality and smoothness. In order to address the above-mentioned limitation, our work aims to incorporate the spatial distance information into PCSSR to improve the discriminative capability of PCSSR, which will be introduced and tested in the following sections.
# The Influence of Processing on the Bioactive Compounds of Small Berries
## 3 Comparative Analysis of Extraction and Analytical Methods for Determining Antioxidant Activity
### Analytical Methods for Determining Antioxidant Activity  
Several key assays used to measure antioxidant activity include radical/ROS-scavenging methods, non-radical redox potential-based methods, ferric-reducing antioxidant power (FRAP), and total phenolic content [6].  
The most used radical/ROS-scavenging assays are the oxygen radical absorbance capacity (ORAC) assay, 2,2-diphenyl-1-picrylhydrazyl (DPPPH) radical-scavenging assay, and Trolox equivalent antioxidant capacity (TEAC) assay [6; 7], each with advantages and disadvantages. The DPPH radical-scavenging assay is a mixed method based on single-electron transfer (ET) as the main reaction mechanism and hydrogen atom transfer (HAT) as a marginal reaction [16]. Although the DPPH radical-scavenging assay is a simple and inexpensive method, it is rather sensitive to several factors, including solvent type and concentration, freshness of the DPPH reagent, and the amount of present H and metal ions, which can form complexes with the antioxidants [6; 7]. The TEAC assay is based on the antioxidant compound reaction with ABTS**+ (2,2'-azino-bis(3-ethylbenzothazoline-6-sulfonic acid)), mainly involving the ET but also the HAT mechanism. The TEAC assay is a simple method, but presents limitations associated with sensitivity in regard to the antioxidant/ABTS**+ ratio and the correlation of the incubation time with the end-point reaction [6]. The ORAC assay is based on the HAT mechanism, and consists of the reaction between peroxyl radicals, generated from azo compounds, and a fluorescent probe, which results in fluorescence loss [6]. Since the methods involve colorimetric or fluorescent change, the presence of natural pigments and fluorophores in the extract represents a major limitation of these methods, due to potential interference with the absorbance and fluorescence of the reaction mixtures [6].  
FRAP is a simple ET method, in which antioxidant compounds determine the reduction of ferric (Fe3+) to ferrous (Fe2+) ions, resulting in an intense blue color that increases absorbance. The reaction time between antioxidants and Fe3+ can be extended from minutes to several hours [6]. The main limitation of this method relates to the fact that some antioxidants cannot reduce Fe3+, although they are able to reduce pro-oxidants, while any electron-donating compound with no antioxidant activity, but with redox potential below the Fe3+/Fe2+ pair, may act as a Fe3+ reductant, therefore falsely contributing to the FRAP value [7; 56].  
The total phenolic content is assayed with the Folin-Ciocalteu method, which presents multiple advantages--simplicity, reproducibility, and robustness--but also an important disadvantage related to the participation of some non-phenolic compounds found in the extract, such as sugars or amino acids, in the reduction of the Folin-Ciocalteu reagent [6].
## 3 Results and Discussion
### Study of Adaptive Properties of Fungi
#### 3.3.2 Emulsifying Compounds Production  
One of the strategies of adaptation of microorganisms to contamination by hydrophobic compounds is the production of emulsifying compounds (biosurfactants), which increase the availability of the pollutant for its utilization. Now, separate reports on the production of biosurfactants by ligninolytic fungi are available. We found that the process of degradation of hydrophobic compounds (PAHs, isononylphenol, and oil) by most of the studied fungi was accompanied by the formation of foam, which may serve as an indirect sign of the production of substances with emulsifying activity. For all the studied fungi, we determined the emulsifying activity of the culture liquid after 24 and 48 h of cultivation (Table 3). In the control treatments (without pollutants), only a trace formation of foam was found; the E\({}_{24}\) value does not exceed 1%, and E\({}_{48}\) was about 0%. It was found that all studied fungi produced biosurfactants as a response to the presence of hydrophobic pollutants in the culture medium. The emulsifying activity increased from 2 to 70 times. It is shown that the production of emulsifying compounds depends not only on the nature of the pollutant, but also on the strain and species used. For example, from the five studied strains, the highest emulsifying activity was detected in _P. ostreatus_ f. Florida, while the lowest was in _P. ostreatus_ MUT. Among the representatives of the genus _Tametes_, the most active surfactant producer was _T. hirsuta_, and the least active was _T. gibbosa_. Now, separate reports on the production of biosurfactants by ligninolytic fungi are available. For example, the production of a biosurfactant that increases the solubility of hydrophobic compounds has been found in _Coriolus versicolor_, _Pleurotus ostreatus_[22; 23; 79], _Phellinus_ sp., and _Polyporus sulphureus_[23], and this biosurfactant was a multicomponent substance consisting of a protein portion and a polysaccharide [22]. The formation of emulsifiers in response to the presence of pollutants in the medium was also found in ascomycetes, among which _L. aphanocaldii_ and _C. herbarum_ were the most active. Although the production of biosurfactants by ascomycetes is well-known [80], that
# Dynamic Group Management Scheme for Sustainable and Secure Information Sensing in IoT
## 1 Introduction
The reason why sustainability is important is because an individual's life and the company's business are more closely dependent on IoT. After the development of the smart phone, many and various lives and businesses were re-conducted through the smart phone. As such, once an IoT environment is completely constructed, lives and business would continue based on IoT. It means that in this case, if there were even one disconnection in the continuity of information, there would be grave damage. In the smart factory environment, if even one piece of information does not arrive on time, the manufacturing process may be stopped or destroyed. In the smart automobile environment, if the sensor fails to collect the information even once, it could cause a traffic accident. Therefore, it is not optional but mandatory for IoT to provide sustainability. The most adequate sensor network routing protocol for the continuous collection and transmission of data is the LEACH routing protocol [11]. The LEACH protocol has a better efficiency of data transmission energy since it allocates cluster headers and sensor groups dynamically based on energy efficiency analysis. However, it is not adequate to utilize the LEACH protocol for the application of the existing group management technique because it allocates groups dynamically. Therefore, in this paper, the goal is to develop the secure authentication protocol which has high energy efficiency and whose service is sustainable in the IoT environment. For this purpose, this paper designs and proposes a group sensor secure communication protocol that solves the security vulnerabilities to which the existing sensor nodes are threatened, by utilizing the four kinds of fundamental arithmetic operations such as ADD operator and logical operations such as AND operator. The proposed secure communication protocols are also secure to a relay attack by using the distance-bounding technique between sensor nodes during the proposed authentication process. Also, this paper proposes the scheme to authenticate simultaneously all the sensor nodes in the ever-changing sensor group and to allocate group keys dynamically.
# A Multi-Band LNA Covering 17-38 GHz in 45 nm CMOS SOI
## 2 Circuit Design  
The schematic of the proposed LNA is shown in Figure 2. To obtain a high gain, the LNA consists of three stages, of which their basic structures are semblable, referring to the differential cascode amplifier. Regarding a multi-stage LNA, the input/output and interstage impedance matching are of importance. For this work, baluns are utilized to realize favorable matching. The first stage in the LNA is of the essence, as it has a great impact on NF, bandwidth, and gain. Compared with the subsequent stages, the first stage has source degeneration inductors and cross-coupling capacitors. The input and output ports of the LNA are matched to 50 \(\Omega\).
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 4 Proposed Secure Certificateless Group Authentication Scheme for D2D Communication  
With the purpose of providing an enhanced authentication scheme to D2D communication in specific scenarios, the certificateless group authentication scheme is presented in this paper. For a better description, the proposed scheme is divided into certificateless authentication with group key distribution and the subsequent continuous authentication utilizing smartphone sensor behavioral processing. In this section the former part is illustrated, while the latter part is presented in the following Section.  
The certificateless authentication and group key management method is presented, which mainly emphasizes on the verification for participating UEs. Our authentication design can be briefly divided into three different phases including _offline registration phase_, _authentication phase_, and _group key distribution phase_. Initially, the UE registration, along with vital key initialization, is made in the offline registration phase. Consequently, significant authenticating procedures are provided in the authentication phase. Thereafter, the group key is distributed in an efficient and reliable way. Moreover, the strategy for efficient key updating is also introduced, offering dynamic membership management for UEs. Certificateless encryption technique is applied for mutual verification between SN and UEs, where the key escrow issue can be drastically addressed. The proposed certificateless authentication for D2D communication is suitable for practical D2D scenarios in complex environments.
# Targeted Universal Adversarial Examples for Remote Sensing
## 3 Methodology
### Targeted Universal Adversarial Examples
#### 3.1.2 Loss Design  
For classification problems, cross-entropy loss \(\mathcal{L}_{ce}\) is the most common loss used, which is defined as  
\[\mathcal{L}_{ce}(f,x,y)=-\sum_{k=1}^{n_{v}}y^{(k)}\log f(x)^{(k)} \tag{9}\]  
where \(n_{v}\) denotes the number of classes in this classification task, and \(y\) is the true class of the input samples \(x\) with one-hot encoding.  
As we know, the attacker's goal is to maximize the cross-entropy loss \(\mathcal{L}_{ce}(f,x,y)\) for untargeted attacks and minimize the cross-entropy loss \(\mathcal{L}_{ce}(f,x,t)\) with target class \(t\). For targeted universal attacks, thus, we are trying to find the perturbations minimizing the targeted loss function, which is  
\[\mathcal{L}_{tu}(f,x,t,\delta_{t})=\mathbb{E}_{(x,y)\sim\mathcal{D}}[\mathcal{ L}_{ce}(\theta,x+\delta_{t},t)]. \tag{10}\]  
Thus, our final problem to solve is  
\[\delta_{t}:=\operatorname*{argmin}_{\|\delta_{t}\|_{\infty}\leq\epsilon} \mathcal{L}_{tu}(f,x,t) \tag{11}\]  
The training details are summarized in Algorithm 1.  
```
0: Dataset \(\mathcal{D}\), Victim model \(f\), Targeted class \(t\), Loss function \(\mathcal{L}_{tu}\), Number of iterations \(I\), Perturbation magnitude \(\epsilon\)  initialize \(\delta_{t}\sim\mathcal{N}(0,1)\)\(\triangleright\) Random initialization while\(i<I\)do  sample \((X,Y)\in\mathcal{D}\)\(\triangleright\) Mini batch \(X_{adv}=X+\delta_{t}\)\(\triangleright\) Craft adversarial examples \(g_{\delta_{t}}=\
abla\mathcal{L}_{tu}(f,X,t,\delta_{t})\)\(\triangleright\) Calculate gradients \(\delta_{t}=\operatorname*{Optim}(g_{\delta_{t}})\)\(\triangleright\) Update \(\delta_{t}\)\(\delta_{t}=\frac{\delta_{t}}{\|\delta_{t}\|_{p}}e\)\(\triangleright\) Projection endwhile
```  
**Algorithm 1** Targeted universal attack.
## 6 Conclusions
\begin{tabular}{l l} \(K\) & constant reflecting pressure gradient term (-) \\ \(L\) & characteristic length (m) \\ \(Le\) & Lewis number (-) \\ \(M\) & magnetic field parameter (-) \\ \(Nb\) & Brownian motion parameter (-) \\ \(Nr\) & buoyancy ratio parameter (-) \\ \(Nt\) & thermophoresis parameter (-) \\ \(Nu_{x}\) & local [PERSON] number (-) \\ Pr & Prandtl number (-) \\ \(q_{m}\) & wall mass flux (m/s) \\ \(q_{r}\) & radiative heat flux (J/m\({}^{2}\)-s) \\ \(q_{m}\) & wall heat flux (W/m\({}^{2}\)) \\ R & Radiation-conduction parameter (-) \\ Re & Reynolds number (-) \\ Re\({}_{x}\) & local Reynolds number (-) \\ \(Sh_{x}\) & local Sherwood number (-) \\ \(t\) & time (s) \\ \(T\) & temperature (K) \\ \(Tr\) & temperature ratio parameter (K) \\ \(T_{w}\) & sheet temperature (K) \\ \(T_{\infty}\) & ambient temperature (K) \\ \(u\) & velocity component along the \(x\)-axis (m/s) \\ \(v\) & velocity component along the \(y\)-axis (m/s) \\ \(u_{e}\) & external velocity (m/s) \\ \(x,y\) & Rectangular coordinates lengthwise and perpendicular to the plate (m) \\ Greek symbols \\ \(\alpha\) & thermal diffusivity of the fluid (m\({}^{2}\)/s) \\ \(\beta_{T}\) & thermal expansion coefficient (I/K) \\ \(\beta_{C}\) & coefficient of mass expansion (-) \\ \(\delta_{p}\) & velocity slip parameter (-) \\ \(\delta_{T}\) & thermal slip parameter (-) \\ \(\omega\) & frequency of oscillation (I/s) \\ \(\Omega\) & non-dimensional frequency (-) \\ \(\lambda\) & mixed convection parameter (-) \\ \(\eta\) & similarity independent variable (-) \\ \(\theta\) & non-dimensional temperature (-) \\ \(\phi\) & nanoparticle volume fraction (-) \\ \(\mu\) & dynamic viscosity of the fluid (Ns/m\({}^{2}\)) \\ \(v\) & kinematic viscosity of the fluid (m\({}^{2}\)/s) \\ \(\rho\) & density of the base fluid (kg/m\({}^{3}\)) \\ \((\rho c)_{f}\) & volumetric heat capacity of the fluid (J/m\({}^{3}\)-K) \\ \((\rho c)_{p}\) & volumetric heat capacity of the nanoparticle material (J/m\({}^{3}\)-K) \\ \(\sigma\) & electric conductivity (Siemens/m) \\ \(\sigma_{1}\) & Stefan-Boltzmann constant (W/m\({}^{2}\)-K\({}^{4}\)) \\ \(\tau_{1}\) & ratio of the effective heat capacity of the nanoparticle to the fluid heat capacity (-) \\ \(\psi\) & stream function (m\({}^{2}\)/s) \\ Subscripts \\ ()\({}^{\prime}\) & denotes ordinary differentiation with respect to \(\eta\) \\ \end{tabular}
## 6 Conclusions  
The present investigation aimed to evaluate the influence of the seismic retrofitting of an existing RC building using single-concave friction pendulum devices in probabilistic terms. First of all, an existing RC building has been characterized from a geometrical point of view and the related material properties have been assessed by means of the results from destructive and non-destructive tests. Then, non-linear numerical models of the structure have been defined using fiber cross-sections approach for the fixed base (FB) structure and the base isolated (BI) system including proper modelling of the response of the FP bearings under seismic actions. Non-linear dynamic analyses have been carried out accounting 21 recorded seismic events with the three components. The records have been scaled to the value of the pseudo-acceleration of the design elastic spectrum for nearly-collapse limit state associated to 50 years reference life in correspondence of the fundamental periods of both the FB and BI structures. The results of the non-linear dynamic analyses highlight the effectiveness of the retrofitting intervention in the reduction of the value of the base shear. In particular, as in existing RC buildings the amount of shear reinforcements is almost lower than minimum values required by current design codes, the latter results can avoid the occurrence of brittle shear failure in columns pertaining to stiffer RC frames. Next, the probabilistic interpretation of the results in terms of interstory drift index and displacement at the level of the isolation devices has been carried out adopting a lognormal probabilistic model. The probabilities of exceedance associated to different values of the mentioned above parameters have been evaluated highlighting a reduction of more than 1 order of magnitude between the FB structure and the structure retrofitted by means of single-concave FP devices. This aspect is fundamental for the safety of the structure.  
Figure 15: Exceeding monovariate probabilities (logarithmic scale): FB model (**a**); BI model (**b**); Isolation level for BI model (**c**).  
**Author Contributions:** Conceptualization, P.C.; Data curation, D.G., C.A., M.F. and G.B.; Formal analysis, D.G., C.A., M.F., G.B. and L.G.; Investigation, D.G., C.A., M.F. and P.C.; Methodology, D.G., P.C., G.B. and L.G.; Project administration, P.C.; Supervision, P.C. and L.G.; Validation, L.G.; Visualization, C.A., G.B. and L.G.; Writing--original draft, D.G. and P.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work is a part of the collaborative activity developed by the authors within the framework of the Line 6--WP 2--Task 2.1--ReLUIS 2017.  
**Conflicts of Interest:** The authors declare no conflict of interest.