esa-sceva/satcom-chunk-collection huggingface dataset:
# Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing
## 2 Portfolio Optimization  
Modern portfolio theory is about the optimal selection of assets for a given level of risk to achieve the maximal return [12]. This theory was first presented in 1952 by the pioneer in this field, [PERSON] [12]. His theory shows that investors should diversify their portfolios to achieve a maximum return under a given level of risk. The investors decide for themselves how to calculate the expected return and what return indicators are important. Suppose there are \(l\) assets. The expected return of a portfolio is given by  
\[E=\sum\limits_{i=1}^{l}X_{i}\mu_{i} \tag{1}\]  
Variables \(X_{i}\) with \(0\leq X_{i}\leq 1\) denote the portion of the asset \(i\) in the whole portfolio and \(\mu_{i}\) is the expected return of asset \(i\). Of course \(\sum\limits_{i=1}^{l}X_{i}=1\) holds. The expected risk of a portfolio is given by  
\[V=\sum\sum\sigma_{ij}X_{i}X_{j} \tag{2}\]  
Here, \(\sigma_{ij}\) denotes the covariance between assets \(i\) and \(j\).  
The investor has the possibility to select different combinations of \((E,V)\). It is assumed that all combinations are distributed according to Figure 1. The \(x\)-axis represents the expected return (the more right the better) and the \(y\)-axis represents the expected risk (the lower the better):  
The dashed/solid line in Figure 1 denotes the convex hull around a set of portfolios. That is, all envisioned portfolios lie inside that area. Every dot represents a single portfolio. The blue dot marks the portfolio where the expected return is the highest, while the portfolio marked with a green dot has got the lowest risk. As a trade-off for the lower risk, the portfolio represented by the green dot also yields lower expected returns than the portfolio represented by the blue dot. The solid part of the line in Figure 1 denotes the area where optimal portfolios may lie, which is also called the _efficient frontier_. Portfolios within the efficient frontier are considered optimal because they exhibit the best balance between maximizing the return \(E\) while also minimizing the risk \(V\). The red dot, for example, is not optimal, because the green dot has a lower expected risk with the same expected return. Similarly, the yellow dot has a lower expected return compared to the green dot with the same expected risk.  
Figure 1: Distribution of portfolios with \(E\) representing the expected return and \(V\) the expected risk. Figure inspired by [12].  
To find the best investment portfolios, the problem needs to be formulated as an optimization problem:  
\[min\sum\sum X_{i}X_{j}\sigma_{ij}-\sum X_{i}\mu_{i}\qquad s.t.\sum X_{i}=1 \tag{3}\]  
This is the constrained quadratic form of the portfolio theory of [PERSON]. Out of \(l\) assets, the ones with the lowest covariance \(\sigma_{ij}\) and highest return \(\mu_{i}\) shall be selected. The weighted percent values \(X_{i}\) describe how the different assets build up the solution portfolio [5].
# Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System
## 2 Literature Review
### Research Gaps  
The large-scale installation of small networks for the improvement of overall network features, including coverage, capacity and mobility management, is the main cause for con cern for 6G networks since it will result in a major downlink traffic overload. Because of the reduced access time and traffic offloading provided by proactive caching, these restrictions will be circumvented, thereby improving user experience. Additionally, comprehensive research is needed to clarify the combined optimization of different aspects, together with proactive caching, interference management, smartly coded strategies and scheduling methods that are crucial for the 6G networks, to allow for the widespread implementation of the 6G networks. Because of its self-optimization and automation capabilities, 6G networks need improvement with the deployment of massive volumes of data, DL protocols and big-data analytics. Also, energy supply and communication, and data aggregation need to be considered in the near future [10].
## 4 Results
### Landslide Susceptibility Maps  
A total of 48 maps have been produced for the three provinces, the four types of FR (GFR, EFR, CFR, RFR), and the four classifications (NB, Q, ELSUS and GY). An example of these maps for the province of Valencia, prepared with regression FR (RFR) and the GY classification, is shown in Figure 5. Appendix A shows general maps for the GY and NB classifications.  
The result of the LSM classifications reveal different aspects depending on the type of FR used and above all on the classification selected. The distribu  
Figure 5: Example of a susceptibility map for the province of Valencia (RFR with GY classification).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**RFR—G. YQUDEN**} & \multicolumn{2}{c}{**RFR—ELSUS**} \\
**Provines** & **GFR** & **GFR** & **GFR** & **GFR** & **GFR** \\ \hline Castellon (CST) & 0.867 & 0.849 & 0.909 & 0.883 & 0.872 & 0.923 \\ Valencia (VLC) & 0.895 & 0.805 & 0.916 & 0.888 & 0.883 & 0.923 \\ Alicante (ALC) & 0.821 & 0.922 & 0.950 & 0.904 & 0.950 & 0.959 \\ Mean & 0.862 & 0.858 & 0.926 & 0.892 & 0.902 & 0.935 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Kappa values from the comparison of RFR maps with other maps.  
shown in Figure 6 for the 'high' and'very high' susceptibility classes. This figure shows the percentage of surface area for each of these classes and for the Valencia Region as a whole (CST, VLC, and ALC).  
Figure 6 clearly shows that the classification systems determine the extent of high susceptibility classes (H and VH). Natural breaks is an optimistic classification with an mean value of 15% of pixels in these classes, although it is higher for the GFR type (possibly because the division of the slope factor is better defined in the high section). On the contrary, quantiles is a pessimistic classification, with close to 40% of pixels in the high classes. The GY and ELSUS rankings seem to be more balanced in their distribution. When the different FR methods are compared, GFR shows the highest number of pixels in these classes.
# Sphingolipidoses and Retinal Involvement: A Comprehensive Review
## 4 Subtypes of Sphingolipidoses
### Fabry Disease
#### 4.7.2 Retinal Manifestations
The authors found a direct relationship between the number of HRF and serum lyso-Gb3 levels,  
Figure 3: The illustration depicts a yellow ring outlining the contour of the FAZ. In patients with FD, the FAZ extends from the inner yellow ring to the outer yellow ring in the area indicated by the arrows due to structural changes in retinal vessels. (Graphics courtesy of [PERSON]). with significantly more HRF observed in the foveal and parafoveal regions of FD patients compared to controls [141]. These findings suggest that the presence of HRF could be a novel biomarker of intraretinal inflammation. Similar observations were made in other systemic diseases, such as multiple sclerosis, reinforcing the potential of HRFs as indicators of inflammation [142]. A schematic representation of HRFs within the inner retinal layers is shown in Figure 4.  
Retinal vessel dimensions measured in FD patients were smaller and thinner than those of healthy controls [143]. Using AO [144], sphingolipid deposits in the paravascular zone were identified and graded in increasing severity from 0 to 4. Despite normal fundus examination findings, AO revealed punctiform opacities (5-10 \(\upmu\)m) or diffuse opacifications along both veins and arteries in 14 enrolled patients. These deposits increased in density and continuity with disease severity, highlighting their potential utility as diagnostic tools for staging and monitoring FD [45]. The use of ERG, in conjunction with OCTA, can serve as a tool to support the diagnosis, evaluate retinal function, and detect potential microangiopathies that might otherwise go unrecognized. Indeed, ERG performed in patients with FD exhibit a significantly lower mean amplitude compared to healthy controls [137]. Multimodal ophthalmological imaging offers valuable insights into potential biomarkers for FD, aiding in the analysis and study of this pathology. A combined use of these biomarkers can deepen our understanding of FD-related retinal imaging and improve diagnostic and monitoring strategies.
## 3 Results
### Evaluation and Design of EPB within the Experiment
#### 3.2.4 Energy Efficiency Dimension (Energy Consumption)  
Directive 2012/27/EU (Art. 3) allows each Member State to set an indicative national contribution to the EU target based on primary energy consumption or final energy consumption, primary energy savings or savings in final energy consumption, or energy intensity. At the same time, however, Member States are to respect the EU's energy efficiency target for 2020 and 2030, which is set at 20% and 32.5%, respectively. The implementation of this target should result in EU primary energy consumption of no more than 1 474 Mtoe or final energy consumption of no more than 1 078 Mtoe in 2020.  
Solving EPB with DesignBuilder Simulation  
Prior to the proposed reconstruction of the building cluster, of the locality of Vitezne n, Prague 6 (experiment), a simulation of the buildings in the named location, was carried out using DesignBuilder software for later comparison of savings in energy consumption and reduction in CO2 production. The savings' demonstration value will be the difference of the energy consumption indicators in kWh/(m\({}^{2}\).y); see Tables 4 and 5.  
Evaluation of the simulation results of the building cluster reconstruction: According to Table 1, a value of supplied energy of 50 to 70 kWh/(m\({}^{2}\).y) was required. Tables 4 and 5 show that the energy consumption after building cluster reconstruction decreased by 12.6% on average. The pattern of consumption is shown in Figure 16.
## 3 Results
### Kinetic Behaviors  
The influence of stirring time on the adsorption efficiency of CV dye onto DSD adsorbent is presented in Figure 6. As expected, the uptake process occurred rapidly in the first few minutes, with approximately 74% dye removal attained during 20 min (Figure 6a). This is because of the availability of vacant adsorption sites at the beginning and the high affinity of DSD adsorbent towards CV molecules in solution. The adsorptive removal efficiency of CV dye reached plateau values of adsorbed amounts after nearly 120 min. Further tests were carried out at 240 min to ensure that the adsorption process had reached real equilibrium. Figure 5: Pore size distribution of DSD sample and possibility of the pore filling diffusion of CV dye. Figure 6: (**a**) Contact time effect, and nonlinear relationship of PFO, PSO, Avrami, and Elovich kinetic models, (**b**) linear relationship of intraparticle diffusion model. The kinetics play an important part in the adsorption process, allowing it to be scaled up to small reactor capacity while maintaining efficiency and economics. Data of time-dependence tests were analyzed using four selective kinetic models. These models have been widely used in the literature to model the adsorption of various pollutants (Section 2.4). Table 4 summarizes the computed parameters of such models by using the origin software. According to the modeling results, the PSO (R\({}^{2}\) = 0.997, \(\chi^{2}\) = 0.221) and the Avrami (R\({}^{2}\) = 0.996, \(\chi^{2}\) = 0.347) models fit the experimental data better than the other models. The adsorbed amount of dye calculated by PSO and Avrami model was 27.4 and 26.1 mg/g, respectively, which is very close to value of qe (26.6 mg/g) obtained from the experiment. The PSO model suggests that chemical adsorption is involved in the adsorption process, whereas the Avrami model assumes that different adsorption mechanisms exist and the process subject to the change in mechanisms. According to the Avrami model, the half-life of the CV dye adsorption process was 8.1 min. To check the existence of the intraparticle diffusion mechanism during adsorption and determine the controlling steps in the whole process, the model of [PERSON] was used to fit the data of time-dependence. The intraparticle diffusion model curve is given by plotting qt vs. t\({}^{0.5}\) (as shown in Figure 6b). It can be observed that the straight-line plot shows a multilinear type with difference in slope and intercept (Table 4). This shows that intraparticle diffusion is involved in the adsorption process, but that it is not the only rate-limiting phase for the entire reaction [26]. The CV dye adsorption onto DSD powder is controlled by three steps occurring at the same time. The first straight portion depicts the
## 2 Fast Frequency Domain Method
### Development of Fast Frequency Domain Method
Thus, if  
\[\big{|}D_{N/2}\big{|}<0.05\text{max}\big{|}D_{j}\big{|}, \tag{24}\]  
by ignoring the second term on the right of Equation (23), an approximate solution can be obtained with negligible error, which is  
\[u_{n}=\sum_{j=0}^{N/2-1}H_{j}D_{j}e^{i\frac{2\pi inj}{N}}, \tag{25}\]  
To express conveniently, Equation (23) is called the accurate FFDM and Equation (25) is called the approximate FFDM. The first term on the right of Equation (23) can be calculated by inverse fast Fourier transform to significantly improve the calculation efficiency. Therefore, the FFDM not only retains the efficiency of the CFDM, but also is as accurate as the DTDM. At the same time, there is no need to construct the dual load in the FFDM which improves the calculation efficiency. For multi-degree-of-freedom (MDOF) system with complex damping, the governing equation of motion can be expressed as:  
\[[m]\{\dot{u}\}+(1+i\eta)[k]\{u\}=\{s\}f(t), \tag{26}\]  
in which \([m]\) and \([k]\) are the mass matrix and stiffness matrix, respectively. \(f(t)\) is the time variation of applied force, and \(\{s\}\) is spatial distribution vector. Given the natural frequencies \(\omega_{n}\) and associated mode shapes \(\big{\{}\phi_{n}\big{\}}\) (\(n=1\), \(2\), \(\ldots\), \(N\)), the displacement of the structure can be expressed into a linear combination of \(N\) modal coordinates. That is,  
\[\{u\}=\sum_{n=1}^{N}\big{\{}\phi\big{\}}_{n}q_{n}(t), \tag{27}\]  
Based on the mode superposition method, Equation (26) can be transformed into a series of generalized SDOF whose vibration equation for the \(q_{n}(t)\) can be written as  
\[M_{n}\ddot{q}_{n}+(1+i\eta)K_{n}q_{n}=\gamma_{n}f(t), \tag{28}\]  
in which \(M_{n}=\big{\{}\phi\big{\}}_{n}^{T}[m]\big{\{}\phi\big{\}}_{n}\) is the modal mass, \(K_{n}=\big{\{}\phi\big{\}}_{n}^{T}[k]\big{\{}\phi\big{\}}_{n}\) is the modal stiffness, \(\gamma_{n}=-\big{\{}\phi\big{\}}_{n}^{T}\{s\}/M_{n}\) is the modal participation factor, \(q_{n}\) is the modal coordinate. The computational efficiency of MDOF system is depended on that of the SDOF system by the mode superposition method. Therefore, the following is to mainly investigate the efficiency and accuracy of SDOF system.
# Monitoring of Reinforced Concrete Corrosion: Active and Passive Bars Exposed to Climate
## 4 Discussion
This approach allows for the combination of information regarding durability parameters and the mechanical behaviour of structural components. In certain cases, such as with high-strength active steels, corrosion initiation is not permitted due to the high risk of stress corrosion cracking or hydrogen embrittlement. As demonstrated in the example with the tendon, the proposed method allows for the clear detection of the onset of corrosion. In the case of reinforcements, corrosion can lead to local failures. The monitoring system enables the detection of the onset of corrosion, allowing for the protection or repair of the structure before visible deterioration occurs. For instance, corrosion inhibitors can be used, which can be evaluated in real-time by the system itself to determine their effectiveness.
# Potential Role of Plant Extracts and Phytochemicals Against Foodborne Pathogens
## 5 Plant Extracts and Phytochemicals against Parasites Causing Foodborne Diseases
### Entamoeba histolytica
investigated various isolated compounds from _Salvia polystachya_ against _E. histolytica_ and _G. lamblia_ trophozoites. Among the tested compounds, Linearolactone was most active showing IC\({}_{50}\) value of 22.9 and 28.2 mM against _E. histolytica_ and _G. lamblia_, respectively. Whereas, polystachynes A, B and D, exhibited modest antiprotozoan potentials with IC\({}_{50}\) values ranging from 117.0-160.6 mM against _E. histolytica_ and 107.5-134.7 mM against _G. lamblia_[250]. In the search for new antiprotozoan drugs from natural products, [PERSON] et al. studied the inhibitory effects of twenty-six traditional Mexican drugs against _E. histolytica_ and _G. lamblia_. Among the tested samples, _Chiranthodendon pentadactylon_ Larreat., _Annona cherimola_ Mill., _Punica granatum_, _Dichondra argentea_ Humb. & [PERSON]. [PERSON], _Chenopodium ambrosioides_ L. and _Clrysacatin mexicana_ A.Gray were most active showing IC\({}_{50}\) values of 2.5, 14.8, 29.5, 38.3, 45.2, 45.3 \(\mu\)g/mL, respectively against _E. histolytica_. Whereas, _Dorstenia contrajerva_ L., _Senna villosa_ (Mill.) [PERSON]. Irwin & Barneby, _Ruta chaperesis_ L., _Cocos nucifera_ L. and _Chiranthodendon pentadactylon_ [PERSON]. exhibited IC\({}_{50}\) values of 23.3, 32.1, 37.8, 44.1, 44.2 \(\mu\)g/mL, respectively against _G. lamblia_[251]. In another study, the same group investigated the antiprotozoan efficiency of crude extracts and isolated compounds from the roots of _Geranium mexicanum_ against _E. histolytica_ and _G. lamblia_. Among the crude samples, dichloromethane-methanol, ethyl acetate, fraction 13 (from ethyl acetate) and aqueous fractions showed IC\({}_{50}\) values of 79.2, 66.7, 51.5 and 221.6 \(\mu\)g/mL, respectively against _E. histolytica_ and 100.4, 63.7, 59.7 and 215.9 \(\mu\)g/mL, respectively against _G. lamblia_. Among the isolated compounds, epicatechin, \(\beta\)-sitosterol 3-O-\(\beta\)-D-glucopyranoside and tyramine revealed IC\({}_{50}\) values of 1.9, 65.6, 82.2 and 54.2 \(\mu\)g/mL, respectively against _E. histolytica_ and 1.6, 33.9, 61.5 and 68.9 \(\mu\)g/mL, respectively against _G. lamblia_[252]. The antiprotozoan potential of another traditional plant, _Rubus corifolius_ and its isolated compounds were investigated by [PERSON] et al. against _E. histolytica_ and _G. lamblia_. In the crude fractions, dichloromethane-methanol was most active with IC\({}_{50}\) values of 11.6 and 55.6 \(\mu\)g/mL, respectively against _E. histolytica_ and _G. lamblia_. Whereas, the isolated compounds including epicatechin, catechin, nigachigoside, \(\beta\)-sitosterol-3-\(O\)-\(\beta\)-D-glucopyranoside, hyperin, gallic acid and ellagic acid exhibited IC\({}_{50}\) value of 1.9, 65.5, 111.9, 82.16, 143.6, 220 and 56.5 \(\mu\)g/mL, respectively against _E. histolytica_ and 1.6, 34.0, 123.6, 61.5, 49.2, 70.3 and 24.9 \(\mu\)g/mL, respectively against _G. lamblia_[253].
###### Abstract  
Chlorophyll-_a_ concentration (Chl-_a_) is commonly used as a proxy for phytoplankton abundance in surface waters of large lakes. Mapping spatial and temporal Chl-_a_ distributions derived from multispectral satellite data is therefore increasingly popular for monitoring trends in trophic state of these important ecosystems. We evaluated products of eleven atmospheric correction processors (LEDAPS, LaSRC, Sen2 Cor, ACOLITE, ATCOR, C2 RCC, DOS 1, FLAASH, iCOR, Polymer, and QUAC) and 27 reflectance indexes (including band-ratio, three-band, and four-band algorithms) recommended for Chl-_a_ concentration retrieval. These were applied to the western basin of Lake Ontario by pairing 236 satellite scenes from Landsat 5, 7, 8, and Sentinel-2 acquired between 2000 and 2022 to 600 near-synchronous and co-located in situ-measured Chl-_a_ concentrations. The in situ data were categorized based on location, seasonality, and Carlson's Trophic State Index (TSI). Linear regression Chl-_a_ models were calibrated for each processing scheme plus data category. The models were compared using a range of performance metrics. Categorization of data based on trophic state yielded improved outcomes. Furthermore, Sentinel-2 and Landsat 8 data provided the best results, while Landsat 5 and 7 underperformed. A total of 28 Chl-_a_ models were developed across the different data categorization schemes, with RMSEs ranging from 1.1 to 14.1 \(\upmu\)g/L. ACOLITE-corrected images paired with the blue-to-green band ratio emerged as the generally best performing scheme. However, model performance was dependent on the data filtration practices and varied between satellites.  
ch chlorophyll-_a_; multispectral remote sensing; satellite imagery; atmospheric correction; semi-empirical retrieval; band ratios; Western Lake Ontario  
8 March 2024  
Revised: 22 April 2024  
Accepted: 26 April 2024  
Published: 30 April 2024
## 2 Design of the State Vector
### Construction of the State Vector  
Given the necessity for information fusion on the velocity layer, while containing first-order derivatives and integrals, the state of the ICEKF is defined as a column vector consisting of 30 elements, as follows:  
\[\mathbf{X}=\left\{\mathbf{p}_{w}^{ic^{T}}\,\mathbf{v}_{w}^{c^{T}}\,\mathbf{v}_{w}^{i^{T}}\,\mathbf{ \overline{q}}_{w}^{ic^{T}}\,\mathbf{\overline{q}}_{w}^{i^{T}}\,\mathbf{\overline{u}}_{ w}^{c^{T}}\,\mathbf{\omega}_{c}^{c^{T}}\,\mathbf{\omega}_{i}^{i^{T}}\mathbf{b}_{ai}^{T}\mathbf{b}_{ai}^{ T}\mathbf{\lambda}\right\}^{T}, \tag{1}\]  
where \(\mathbf{v}_{w}^{c}\) is the linear velocity of the visual measurement derived from \(\mathbf{p}_{w}^{c}\); \(\mathbf{\omega}_{c}^{c}\) is the equivalent body angular velocity of the camera measured in the IMU frame; \(\mathbf{\overline{q}}_{w}^{i}\) and \(\mathbf{\omega}_{i}^{i}\) are the rotation velocity and body angular velocity, respectively; \(\mathbf{b}_{ai}\) and \(\mathbf{b}_{ai}\) are the biases of \(\mathbf{a}_{mi}\) and \(\mathbf{\omega}_{mi}\), respectively; \(\lambda\) is the scale coefficient of the monocular visual translation. The remaining variables are described in the last section. Let \(\mathbf{a}_{w}^{i}\) be the linear acceleration of the IMU measured in the world frame, where \(\mathbf{a}_{w}^{i}=\mathbf{R}_{w}^{i}(\mathbf{a}_{mi}-\mathbf{b}_{ai}-\mathbf{n}_{\mathbf{a}_{mi}})\). Additionally, \(\mathbf{\omega}_{i}^{i}=\mathbf{\omega}_{mi}-\mathbf{b}_{ai}-\mathbf{n}_{\mathbf{\omega}_{mi}}\).  
The relationships among the variables in the ICEKF state are shown in Figure 2.  
When the visual measurement is treated as a black box, \(\mathbf{v}_{w}^{c}=\mathbf{\dot{p}}_{w}^{c}\). To obtain \(\mathbf{\omega}_{c}^{c}\), \(\mathbf{\overline{q}}_{w}^{c}\) should be transformed to \(\mathbf{\overline{q}}_{w}^{c}\), which is the visual rotation described in the IMU frame. Then, \(\mathbf{\omega}_{c}^{c}\) can be obtained from the derivatives of the Euler angles recovered from \(\mathbf{\overline{q}}_{w}^{c}\). The coupling coefficients of the linear and angular velocities, \(\mu_{w},\mu_{\omega}\in[0,1]\), are independent of the state update. This design guarantees that the coupling weights are always adjustable, which increases the accuracy of motion estimation without impacting observability.  
To focus on the filter construction in this paper, the following is assumed:  
1. The drift of the visual world frame in the visual black-box measurement is negligible due to its slow variation.  
2. The outcomes acquired through the first-order derivation of measurement results are modeled as random walk, including \(\mathbf{\omega}_{c}^{c}\) and \(\mathbf{v}_{w}^{c}\).  
Figure 2: Relationships among the variables in the ICEKF state vector.