esa-sceva/satcom-chunk-collection huggingface dataset:
# Plasmonics in the Ultraviolet with Aluminum, Gallium, Magnesium and Rhodium
## 1 Introduction
All of them, except Rh, form native oxides shells that wrap the NP and whose thickness highly depends on the exposure conditions: temperature and pressure. The thickness of this oxide shell can go from 1 nm, as in the case of Ga, to the complete destruction of the metal content of the NP, as in the case of Mg [21]. So, knowing how this oxide shell affects the plasmonic behavior of this type of NPs is a key point for researchers in UV plasmonics [22; 23; 24]. It will be addressed in the next two sections for the four most significant metals able to sustain UV LSPRs. It is worth mentioning that materials with UV plasmonic response can also be achieved by alloying elemental materials [25]. The review is organized as follows. Firstly, we will review the properties and related work on four metals with good plasmonic performance in the UV range. These metals have been divided in two different groups depending on their high/low oxidation tendency. Secondly, we will analyze the plasmonic response of NPs made of these materials with different shapes while considering their respective oxide shells. Finally, we present some examples of local-field cooperative effects in dimers. Figure 1: Faraday number (\(Fa\)) for different metals. The blue dot represents the resonant energy (Fröhlich frequency, frequency at which \(Re(\epsilon)=-2\)[15]) and the shadowed region the exciting photon energies above 3 eV. The blue solid line represents the range of energies studied in each case (this depends on the optical constants available in the literature [17; 18; 19; 20]).
## 4 Analytical Analysis of Two-Port Interconnections
### Sum of Integer-Order Integrator and Fractional-Order Differentiator Responses  
This topology has behavior indicating a significant global minimum of the ideal transfer magnitude because the decreasing and increasing magnitude in frequency response is given by the interconnection of the integrator and differentiator (Figure 7). The response has this form:  
\[K_{I\_D1}(s)\ =\ \frac{A_{1}}{\tau_{1}s}+A_{2}\tau_{2}s^{\alpha} \tag{18}\]  
that can be easily modified to the complex expression:  
\[K_{I\_D1}(\omega)\ =\ \frac{1}{\omega\tau_{1}}\Big{(}\omega^{1+\alpha}\tau_{1} \tau_{2}A_{2}\sin\!\left((1+\alpha)\frac{\pi}{2}\right)-\!\!\int\!\!\left[ \omega^{1+\alpha}\tau_{1}\tau_{2}A_{2}\cos\!\left((1+\alpha)\frac{\pi}{2} \right)+A_{1}\right]\!\Big{)} \tag{19}\]  
and the resulting magnitude and phase responses of the two-port are:  
\[\left|K_{I\_D1}(\omega)\right|\ =\ \frac{1}{\omega\tau_{1}}\sqrt{\left[\omega^{1+ \alpha}\tau_{1}\tau_{2}A_{2}\sin\!\left((1+\alpha)\frac{\pi}{2}\right)\right] ^{2}+\left[\omega^{1+\alpha}\tau_{1}\tau_{2}A_{2}\cos\!\left((1+\alpha)\frac{ \pi}{2}\right)+A_{1}\right]^{2}} \tag{20}\]  
\[\varphi_{I\_D1}(\omega)\ =\ \tan^{-1}\!\!\left[\frac{\omega^{1+\alpha}\tau_{1} \tau_{2}A_{2}\cos\!\left((1+\alpha)\frac{\pi}{2}\right)+A_{1}}{\omega^{1+ \alpha}\tau_{1}\tau_{2}A_{2}\sin\!\left((1+\alpha)\frac{\pi}{2}\right)}\right] \tag{21}\]  
Figure 6: 3D plot of the studied ideal transfer response (14) in dependence on the value of the \(\alpha\) variable: (**a**) magnitude vs order vs frequency dependence, (**b**) phase vs order vs frequency dependence.  
Figure 7: Practical solution of the summing response of fractional-order differentiator and integer order integrator.  
The ideal analysis of (18) shown in Figure 8 indicates two-sides of the magnitude response with different slopes and significant local minimum. The phase plot confirms the impact of the order on the phase value in high-frequency corner (between 0\({}^{\circ}\) and 90\({}^{\circ}\)).
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 4 Analysis of Simulation Result
### Application Scenario of the Proposed Scheme  
In the application of telemedicine, there will be attacks to tamper with, steal, and forge patient private information or medical image content, and these attacks may lead to a wrong diagnosis by the receiver and produce serious medical accidents, which is firmly not allowed. Therefore, in view of these possible attacks, while the secret information is embedded, the scheme in this paper also carries out an identity authentication on the medical image, as shown in Figure 12, which can not only protect the personal information of patients but also ensure the integrity and security of the medical image.
## 4 Trajectory Planning Model
### NSGA-II Algorithm Based on the Distance of Reference Points  
[PERSON] (2018) improved the basic genetic algorithm and proposed the adaptive Non-dominated Sorting Genetic Algorithm II (NSGA-II). Although the NSGA-II algorithm has high computational efficiency, it is difficult to choose among many solutions in the Pareto front. Therefore, in order to find the Pareto optimal solution of interest to decision makers, a dominance principle based on the distance of reference points is proposed.  
The dominance principle is introduced into the sorting part of the NSGA-II algorithm. The main feature of the dominating sorting algorithm is to retain the solution closest to the reference point as the optimal solution to the next generation; that is, the solution dominates other solutions in Pareto, as shown in Figure 5.  
In order to determine the optimal solution near the reference point, it is necessary to use an achievement scalar function (ASF) to measure the near-far relationship between the Pareto solution and the reference point (30; 31). The weighted Euclidean distance is used in this paper.  
\[Dist(x,g)=\sqrt{\sum_{i=1}^{M}w_{i}\bigg{(}\frac{f_{i}(x)-f_{i}(g)}{f_{i}^{max }-f_{i}^{min}}\bigg{)}^{2}} \tag{33}\]  
where \(x\) is the solution, \(g\) is the reference point, \(f_{i}^{max_{i}^{min}}\) are the maximum and minimum values of the ith objective function, respectively, and \(w_{i}\) is the weight of the \(i\)-th target.  
Suppose solution \(x_{1}\) dominates solution \(x_{2}\), if and only if the following conditions are met:  
\[D(x_{1},x_{2},g)=\frac{Dist(x_{1},g)-Dist(x_{2},g)}{Distmit_{max}<-\delta} \tag{34}\]  
where \(\delta\) is the threshold of the dominant relationship, which is mainly used to control the distribution range of the solution, \(\delta\in[0,1]\).  
The NSGA-II algorithm process is analyzed as follows in Figure 6, and the parameters of the NSGA-II algorithm are shown in Table 2.  
Figure 5: Dominant graph of reference point.
## 2 Site Study and Database  
The relation between the reflected GNSS signal and surface roughness is trivial knowledge in electromagnetic scattering. Regardless of the complexity in electromagnetic scattering theory, considerable research has contributed to determining soil moisture from GNSS-R, as summarized in the previous section. This research is different from the past research, as it contributes to the computational process of soil moisture with the derived dielectric constant, rather than reflectivity, because the dielectric constant plays a crucial role in understanding the physical characteristics of soil moisture. Accordingly, only flat surfaces were considered in the analysis in order to make the computation process for dielectric constant efficient. The sites used in the analysis were selected for the purposes of calibrating and validating SMAP soil moisture data. Descriptions regarding the sites and instruments used in analysis are discussed in below.
## 2 A Novel FPSO-GAK Clustering Method
### Synthetic Data Test  
To validate the effectiveness of the proposed algorithm, a synthetic dataset was employed for testing. The synthetic dataset (Figure 2) contains 1000 data points divided into 120 clusters with random distribution of centroids, and the data variance within the clusters is 15. There are various enhanced versions of the K-Means algorithm now available. The proposed algorithm was compared with the state-of-the-art Random Swap clustering algorithm (RS) [50], a lightweight and efficient clustering algorithm with linear time complexity depending on the size of the data. The proposed algorithm contains meta-heuristic type methods whose time complexity will be higher than the time complexity of K-means arithmetic. According to the analysis in Section 3.5, it knows that the proposed algorithm's time complexity shows a basically linear relationship with the number of data. It is estimated that the time consuming of the proposed algorithm is about 20 to 50 times that of the random-swap clustering algorithm. At the cost of time complexity, the non-homogeneous search capability of the proposed algorithm can achieve better clustering results on medium and large data sets.  
Thirty clustering operations were performed on the synthetic data using the proposed algorithm and random swap clustering algorithms according to the accuracy convergence condition and a fixed number of iterations, respectively (the number of clusters for each clustering operation was selected randomly between 70 and 90). The average SC, SP, SSE and ACI (reference to Section 3.6) indexes were used to evaluate the results of clustering, as shown in Table 2.  
Table 2 demonstrates that the state-of-the-art Random Swap clustering algorithm is an effective clustering algorithm that achieves excellent results with respect to all clustering indexes. The ACI index of FPSO-GAK and Random Swap Clustering algorithms is greater than 0.15, indicating a substantial difference in their clustering-level similarity. The proposed algorithm slightly outperforms the Random Swap Clustering algorithm in many indexes except the time-consuming index. The time complexity of both algorithms shows a linear relationship with the amount of data, but the proposed algorithm will take an order of magnitude more time-consuming than the Random Swap Clustering algorithm. Considering the rapid increase of computing power nowadays, it is acceptable in applications to sacrifice a certain time loss to obtain the improvement of the clustering effect. In the subsequent test and analysis of the real dataset, we will further conduct a comparative analysis of the proposed algorithm with similar types of heuristics algorithms.
# Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing
## 4 Case Study
### Result  
A summary of the network coverage data for each of the conditions as obtained for the Atoll tool, using the recommended configurations from the ICT optimization tool, is shown in Table 5. The table shows the network signal strength in terms of weak, fade and no signal coverage across the entire area. For the case in which network coverage should be maintained above 77.5%, a single colocation could be allowed and one mast effectively switched off, as shown in Figure 9 and illustrated in Table 5. For the case in which network coverage should be maintained above 75%, a further colocation could be allowed and two masts effectively switched off, as shown in Figure 10 and also indicated in Table 5. Turning now to network and infrastructure costs, an analysis of the original costs with three independent sites (with coverage 81.42%) and one independent site (with coverage 75.24%) is shown in Table 6. As can be seen from the costs analysis presented, the level of cost can be reduced approximately by 60% if collocation is adhered to, moving from the three BTSs case to the one BTS case as shown in the table above, 237,065,800 NGN will be saved. In terms of equivalent CO\({}_{2}\) emissions, when a liter of diesel is burnt, roughly 2.68 kg of CO\({}_{2}\) is produced/emitted. Based upon the yearly fuel requirements (18,250 L) of a single BTS site, the yearly CO\({}_{2}\) emission by three BTSs is therefore 146,730 kg and for one BTS is 48,910 kg. In this instance, 97,820 kg of CO\({}_{2}\) emission can also be prevented through colocation.  
Figure 10: Area coverage with one BTS on.
## 2 Architecture and Operational Analysis
### Periodic Offset Characteristics  
Due to the different cycle lengths of the upstream and downstream intersections, the offset will shift in a fixed direction after each cycle, given the upstream intersection as a reference. The offset exhibits cyclical periodicity, meaning that after a certain number of upstream cycles, the offset will return to its initial state. With each repetition of the control cycle, the offset will sequentially shift, generating values for m steps. There are two possible transition cycles: a longer to shorter (L-S) cycle and a shorter to longer (S-L) cycle. For the L-S transition cycle, the generated offset, \(T_{os,L}(k=1,\ 2,\ \cdots,\ \infty)\), can be derived as  
\[\begin{split} T_{os,k}&=\ \left(k-\left\lfloor\frac{k}{m} \right\rfloor m\right)\Delta T\\ m&=\left(\frac{C_{L_{L}}^{T}}{C_{L_{L}}^{T}-C_{L_{L} }^{T}}\right)\end{split} \tag{1}\]  
Figure 1: Schematic diagram of the intersection.  
Figure 2: Architecture for vehicle–road collaboration control scenarios.  
where \(k=\) cycle number at the upstream intersection, \(\left\lfloor\frac{k}{m}\right\rfloor=\) lower integer of \(\frac{k}{m}\), \(m=\) number of (short) cycles at the upstream intersection for which the starts of the cycles coincide, \(\Delta T=\) offset for an L-S cycle transition, and \(C_{l_{u}}^{T}C_{l_{d}}^{T}\) = cycle lengths at the upstream and downstream intersections, respectively.  
For the S-L transition cycle, the generated offset, \(T_{os,k}^{\prime}(k=1,\,2,\,\cdots,\,\infty)\), can be derived as  
\[\begin{split} T_{os,k}^{\prime}&=\left(k-\left\lfloor \frac{k}{m^{\prime}}\right\rfloor m^{\prime}\right)\Delta T\\ m^{\prime}&=1+\left(\frac{C_{l_{u}}^{T}}{C_{l_{d} }^{T}-C_{l_{u}}^{T}}\right)\end{split} \tag{2}\]  
where \(\left\lfloor\frac{k}{m^{\prime}}\right\rfloor=\) lower integer of \(\frac{k}{m^{\prime}}\), \(m^{\prime}=\) number (long) cycles at the upstream intersection for which the starts of the cycles coincide, and \(\Delta T^{\prime}=\) offset for an S-L cycle transition. To illustrate, given \(C_{l_{u}}^{T}=\) 30 s and \(C_{l_{d}}^{T}=\) 40 s, \(m^{\prime}=\) 4, and \(\Delta T^{\prime}=\) 10 s. Then, \(T_{os,1}^{\prime}=\) 10 s, \(T_{os,2}^{\prime}=\) 20 s, \(T_{os,3}^{\prime}=\) 30 s, and \(T_{os,A}^{\prime}=\) 0 s. After this cycle, the previous sequence is repeated, starting with \(T_{os,5}^{\prime}=\) 10 s.  
In Equations (1) and (2), there is a transition from \(T_{os,k-1}\to T_{os,k}\left(T_{os,k-1}^{\prime}\to T_{os,k}^{\prime}\right)\), where the offset increases or decreases by the duration of the cycle at the downstream intersection.
## 4 Monitoring Method Based on Influence Weight Optimization  
### Influence Coefficient Calculation of Data Set  
In this paper, the influence coefficient obtained from the static characteristic analysis is used to interpret the input variables of the data model from the perspective of the rolling process mechanism model so that the monitoring model can achieve higher fault detection accuracy.  
Let the sample data matrix of the rolling process be \(\mathbf{X}=(\mathbf{X}_{1},\mathbf{X}_{2},\dots,\mathbf{X}_{m^{\prime}})\in\mathbf{R}^{n\times m^{ \prime}}\). The number of process variables is \(m^{\prime}\), and \(n\) is the sample size. In this section, on the basis of a large number of historical samples, the partial differential coefficient is calculated by means of chord tangent within a small variation range of variables.  
Divide the historical data matrix \(\mathbf{X}\) into \(N\) equal parts according to capacity: \(\mathbf{X}^{(1)},\mathbf{X}^{(2)},\dots,\mathbf{X}^{(N)}\). Calculate the mean value of each variable in each equal part as follows:  
\[\overline{\mathbf{X}}^{(i)}=\left(\overline{\mathbf{X}}^{(i)}_{1},\overline{\mathbf{X}}^{ (i)}_{2},\dots,\overline{\mathbf{X}}^{(i)}_{m^{\prime}}\right) \tag{31}\]  
where \(\overline{\mathbf{X}}^{(i)}_{m^{\prime}}\) is the mean of the \(m^{\prime}\) variable in the \(i\) equal fraction, \(i=1,2,\dots,N\).  
The mean vector \(\overline{\mathbf{X}}^{(i)}\) was obtained. In order to illustrate the use of \(\partial P\) to refer to the change value of rolling force, the partial differential values of the corresponding variables of each adjacent two vectors were taken as follows:  
\[\frac{\partial\overline{X}^{(k)}_{j}}{\partial P^{(k)}}=\frac{\overline{X}^{ (k+1)}_{j}-\overline{X}^{(k)}_{j}}{\partial P^{(k)}} \tag{32}\]  
where \(k=1,2,\dots,N-1;j=1,2,\dots,m^{\prime}\).  
The mean value of \((N-1)\) partial differential values of each relevant variable is calculated, and the partial differential coefficients are obtained as follows:  
\[\frac{\partial\mathbf{X}_{j}}{\partial\mathbf{P}}=\frac{1}{k}\sum_{1}^{k}\frac{ \partial\overline{X}^{(k)}_{j}}{\partial P^{(k)}} \tag{33}\]  
Combined with the influence coefficient expression of the quality fluctuation of each variable strip in Section 3.2 static comprehensive analysis of steady rolling, the final influence coefficient is obtained as follows:  
\[\mathbf{C}=(C_{1},C_{2},\dots,C_{m})\in\mathbf{R}^{1\times m} \tag{34}\]  
where \(m\) is the number of process variables.
## 4 Discussion
### Environmental Impact Assessment
The Paris Agreement, a global pact to protect our planet from the worst impacts of climate change, has set ambitious temperature targets (to hold the global average temperature increase to well below 2 \({}^{\circ}\)C above pre-industrial levels and pursue efforts to limit warming to 1.5 \({}^{\circ}\)C) and called for rapid and deep cuts to greenhouse gas emissions to ensure a sustainable future for all [87]. Therefore, to explore potential reductions in the operational emissions for the building under study, two scenarios were evaluated, using the baseline scenario (S0) as a point of comparison:  
* Scenario 1 (S1): A 7% reduction in natural gas consumption, achieved through a decrease in the indoor temperature of 1 \({}^{\circ}\)C;
* Scenario 2 (S2): Using an average future energy scenario for Romania (2023-2072) for electricity for a global warming target of 1.5 \({}^{\circ}\)C for office buildings with a global warming potential of 0.44 kg CO\({}_{2}\)e/m\({}^{2}\) and a 7% reduction in the natural gas consumption. Figure 11 presents the GWP of the total operational energy by floor area for all scenarios. The findings of this research demonstrated a decline from 27.09 kg CO\({}_{2}\)e/m\({}^{2}\)/year in the baseline scenario to a minimum of 15.99 kg CO\({}_{2}\)e/m\({}^{2}\)/year in the second scenario, which was consistent with the mean value of 13 kg CO\({}_{2}\)e/m\({}^{2}\)/year obtained in the review by [PERSON] et al. for non-residential buildings in the EU [77]. As [PERSON] et al. [73] highlighted, the rapid growth of new renewable energy technologies and improvements in energy efficiency practices contribute to promising opportunities to reduce the operational emissions of buildings. [PERSON] et al. [88] and [PERSON] et al. [89] shared that the selection of the energy mix and the impact of climate change within the context of a life cycle assessment study typically exert a pronounced influence on the resultant findings. A comparative analysis of the reductions for all 12 LCA indicators, with reference to the baseline scenario (Figure 12), demonstrates that incorporating a reduction of 1 \({}^{\circ}\)C for heating with natural gas and a prospective scenario for electricity to reach the objective of achieving a temperature increase of 1.5 \({}^{\circ}\)C will result in the most significant decreases for all LCA indicators. The most substantial reductions were observed in the utilization of renewable primary energy and fresh water, followed by the eutrophication and acidification potentials. Figure 11: Operational carbon emissions per floor area for all scenarios [kg CO\({}_{2}\)e/m\({}^{2}\)]. The projections indicate a decrease in both the carbon intensity of the energy supply and the energy intensity of building operations in coming decades, leading to an anticipated decline in the operational emissions from buildings [90].
# Recent Progress in Hybrid Additive Manufacturing of Metallic Materials
## 3 Hybrid Additive Manufacturing as a Joining Technique and Its Challenges
### Alloy Compatibility  
Minor alterations to an alloy's prescribed composition or thermal history significantly alter its performance and properties, such as resilience, toughness, and resistance to corrosion. Homogeneity in the microstructure is crucial for joint strength for dissimilar metals, as shown in a study by [PERSON] et al. [34]. Due to chemical and microstructural inhomogeneity, a hybrid built from MS1 and a wrought H-13 maraging steel substrate fractured at the interface. Porosity also contributed to the failure, as reported in the study. A solute variation of alloying elements was observed at the interface transition. The chemical composition changed abruptly from Ni, Mo, and Co-based MS1 to H-13, which is Cr-based. (Figure 6). Chromium diffused through the interface into the MS1 section [34].  
In support of this, [PERSON] et al. [51] noted that elemental diffusion caused alloying elements' depletion and micro-segregation in interdendritic regions. This is consistent  
Figure 6: EDS analysis of elemental distribution across the interface of MS1–H13 HB. Reprinted with permission from [26]. Copyright 2019 Elsevier B.V.  
with the observations of [PERSON] et al. [46], who studied a similar MS1-H13 hybrid material system with some heat treatment modifications. Despite achieving a homogeneous microstructure, adequate precipitation did not reach the interface due to insufficient aging of the required precipitates [46]. Subsequently, sharp compositional interfaces acted as potential gradients, which facilitated the movement of the alloying elements and impurities; these produced various mechanical and physio-thermal properties, which exacerbated the failure.  
The joining of dissimilar metals can be achieved in three ways, namely direct joining, the gradient path, and the intermediate section, as shown in Figure 7[52]. The application of each technique is given in the following section with a focus on LPBF. A summary of these joining techniques and their outcomes is given in Table 2.
## 2 Materials and Methods
### Morphological Grading and Cell Viability Assay  
NIH-3T3 cells were seeded in a 96-well plate (1 \(\times\) 10\({}^{4}\) cells per well, Corning, New York, NY, USA) with culture medium and left to settle overnight. Cells were then treated with a culture medium containing 0 (negative control), 0.25, 0.5, 1, 2, 5, and 10 \(\upmu\)g/mL of R-PE extract. The wells containing medium supplemented with 10% DMSO (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) were also included in the trial as a positive control group. After 24-h incubation, morphological grading for each group was analyzed using the definition from [25], with grades 0, 1, 2, 3, and 4 representing none, slight,mild, moderate, and severe reactivity, respectively. The culture medium was replaced with 1 mg/mL MTT reagent (Sigma-Aldrich, St. Louis, MO, USA) 48-h post-treatment and incubated for 3 h. Isopropanol was added into wells after the removal of the MTT reagent to dissolve formazan, and plates were analyzed using a spectrophotometer (Jasco Spectrophotometer V-630) at a wavelength of 570 nm [26]. If the level of cell viability for the highest R-PE extract dose was less than 70% of the control group, then it was considered to be cytotoxic [25]. The percentage of cell viability was calculated using the following equation:  
\[\text{Cell viability (optical density, OD) = (OD of exposed cells/OD of control cells) \times 100} \tag{1}\]