esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The CRO-SL: A Low-Level Ensemble Evolutionary Algorithm
### Substrate Layers Defined in the CRO-SL
Firefly Optimization (Fa): The Fa is a kind of swarm intelligence algorithm, first introduced by [PERSON] in [55]. The Fa is based on the flashing patterns and behaviour of fireflies in nature. The pattern movement of a firefly \(i\) attracted to another (brighter) firefly \(j\) is calculated as follows: \[\mathbf{x}^{t+1}_{i}=\mathbf{x}^{t}_{i}+\beta_{0}e^{-\gamma\tau_{ij}^{2}}( \mathbf{x}^{t}_{j}-\mathbf{x}^{t}_{i})+\alpha\epsilon^{t}_{i}\] (12) where \(\beta_{0}\) stands for the attractiveness at distance \(r=0\). The specific Fa mutation implemented in the CRO-SL is a modified version of the algorithm known as Neighborhood Attraction Firefly Algorithm (NaFa) [56]. It has been implemented as follows. When a coral (solution) in the reef belongs to the NaFa substrate, it is updated by following Equation (12). All the parameters of the equation are tuned during the CRO-SL evolution. The corals in the NaFa substrate consider as swarm a neighborhood among all the other corals in the reef (not only the NaFa substrate). Thus, the corals in the NaFa substrate are updated taking into account some solutions from other substrates, as all the corals in the reef share the same objective function (brightness for the NaFa substrate). Note that the Fa algorithms has been applied to different structural problems in previous works [57; 58], showing a good behaviour. 7. Water Wave Optimization (WWo): The WWo [59] is a recently proposed meta-heuristic based on the phenomena of water waves, such as propagation, refraction, and breaking. Three different procedures are then defined in this algorithm: Wave propagation: at each generation of the algorithm, each wave \(\mathbf{x}\) in the population is propagated, to generate another wave \(\mathbf{x}^{\prime}\) in the following way: \[x^{\prime}(d)=x(d)+rand(-1,1)\cdot\lambda L(d)\] (13)where \(rand(-1,1)\) is a uniformly distributed random number within the range [\(-1\),\(1\)], and \(L(d)\) is the length of the dth dimension of the search space (see in [59] for reference). Then, Wave refraction is simulated as  
\[x^{\prime}(d)=N\bigg{(}\frac{x^{*}(d)+x(d)}{2},\frac{|x^{*}(d)-x(d)|}{2}\bigg{)} \tag{14}\]  
where \(x^{*}(d)\) is the best solution found so far, and \(N(\cdot)\) is a Gaussian random number with mean \(\mu\) and standard deviation \(\sigma\). Finally, a wave breaking process is also simulated as  
\[x^{\prime}(d)=x(d)+N(0,1)\cdot\beta_{w}L(d) \tag{15}\]  
where \(\beta_{w}\) is the breaking coefficient. The implementation of the algorithm as CRO-SL is straightforward, as any solution within the substrate is just applied the set of operators described above, with the algorithm's parameters described in [59].
# Potential Metal Contamination in Foods of Animal Origin--Food Safety Aspects
## 3 Current Regulatory Standards  
The Codex Alimentarius is a collection of internationally adopted food standards and related texts presented in a uniform manner. These food standards and related texts aim to protect consumers' health and ensure fair practices in the food trade. The publication of the Codex Alimentarius is intended to guide and promote the elaboration and establishment of definitions and requirements for foods to assist in their harmonization and, in doing so, to facilitate international trade. The Codex Alimentarius includes provisions in respect to food hygiene, food additives, residues of pesticides and veterinary drugs, contaminants, labeling and presentation, methods of analysis and sampling, and import and export inspection and certification [132]. The legal procedure in the European Union (EU) is based and started on standards of Codex Alimentarius. Related to contaminants, EFSA (European Food Safety Authority) prepares a risk assessment based on European consumer behavior and food consumption data. These assessments sometimes lead to different results, which makes the EU limit system slightly different from the Codex regulatory environment [132]. There are significantly fewer legal requirements regarding heavy metal contamination in foods of animal origin today than in the past. This is probably due to the fact that metal compounds were used more widely in the last century for industrial, agricultural, and other purposes, which meant a greater risk of food contamination than in present days. However, the potential hazards posed by heavy metals to the consumer have by no means disappeared. At present, the multiple, long-term, and prolonged intake of smaller quantities is more typical, which can primarily lead to the development of chronic diseases due to the cumulative property of heavy metals [8]. Accordingly, the heavy metal contamination of different foods must continue to be regulated with acceptable and tolerable maximum limits. However, it should be noted that, in the case of contaminants of environmental origin--given their irregular and uncontrolled application and generation--the risk probability is significantly higher than in the case of substances with officially controlled use. European Union regulation is binding for all Member States of the European Union. The European Union regulates the maximum levels of certain contaminants in food at the regulatory level. However, the previous regulation (Regulation (EC) No 1881/2006) only laid down the maximum levels for lead, cadmium, and mercury, and it did not contain any provisions for arsenic in raw milk and dairy products, meat and offal of bovine animals, sheep, pigs and poultry, horsemeat, and the flesh of different fish species, crustaceans, bivalve mollusks, and cephalopods (Table 2) [133].
# A Novel Variable Step Size Incremental Conductance Method with an Adaptive Scaling Factor
## 2 Determination of the Optimal Scaling Factor Values  
### Solar Cell Characteristics  
A solar cell can be modeled as an equivalent circuit as shown in Figure 1. The equivalent circuit includes a current source \(I_{g}\), a diode D, a series equivalent resistance \(R_{s}\), and a parallel equivalent resistance \(R_{p}\). The relationship of the output voltage and output current of a solar cell can then be represented by Equation (1).  
\[I_{T}=I_{g}-I_{S}\Big{\{}e^{\frac{1}{2}(R_{J}T+V_{T})}{KMN}\big{|}-1\Big{\}}- \frac{R_{s}I_{T}+V_{T}}{R_{P}} \tag{1}\]  
where \(A\) is the quality factor, \(K\) is the Boltzmann's constant, \(N\) is the number of cells connected in series, \(q\) is the electron charge, \(T\) is the temperature in Kelvin and \(I_{g}\), \(I_{T}\), and \(I_{s}\) are the photogenerated current, panel current, and saturation currents, respectively. The \(I_{g}\) can be represented by  
\[I_{g}=\frac{S}{1000}\times I_{SC} \tag{2}\]  
where \(I_{SC}\) stands for the short-circuit current of the PV module, and \(S\) is the irradiance level. Typically, the parallel equivalent resistance is much larger than the series equivalent resistance, and the \(R_{S}\) is small. Then, a concise form can be derived by neglecting the \(R_{P}\) and \(R_{S}\) to simplify (1)  
\[I_{T}(V_{T},S,T)=\frac{S}{1000}\times I_{SC}-I_{S}\Big{(}e^{\frac{dV_{T}}{KMN} }-1\Big{)} \tag{3}\]
## 4 Discussion
The new probability model was developed by investigating the residual error distribution of atmospheric parameters that were identified to distribute the normal distribution. The non-concentrating super-multi-junction solar cell was found to be robust and can keep almost the same maximum potential efficiency (50%) under the realistic conditions represented by Miyazaki, Japan (N 31.83\({}^{\circ}\), E 131.42\({}^{\circ}\)). Super-multi-junction solar cells are also robust to bandgap engineering of each junction. Therefore, future multi-junction cells may not be needed to tune the bandgap for matching the standard solar spectrum, or for relying upon artificial technologies like epitaxial lift-off (ELO), wafer-bonding, mechanical-stacking, and reverse-growth, but merely to be used for upright and lattice-matching growth technologies. However, we have two challenging techniques: one is the optical cap layer, which may be the directional photon coupling layer in the application of the photonics technologies, and the other is the high-quality epitaxial growth, with almost 100% of radiative efficiency (Figure 14). In comparison to the current level of the ERE of various solar cells that were collected by several authors [8; 107; 108; 109], the requirement of the super-multi-junction solar cells is extremely high. The best-measured ERE, to the best knowledge of authors, is 35% [104]. This is far less than 100%. For the improvement of ERE, a typical and straightforward approach is to reduce threading dislocation density [110]. The target of the threading dislocation density is at least 10\({}^{3}\) cm\({}^{-2}\), but should be as small as possible [110]. The function of the optical cap as the second technological challenge is the confinement of the photon. Any technological improvement in photon confinement typically using thin-film solar cells will be useful. A perfect solution is the use of the directional coupling of photons, typically used for communication technologies [111; 112; 113; 114]. Although these optical devices are used in a narrow band of the wavelength, we expect that we may find useful hints from such different technological fields.
# Semi-Supervised Manifold Alignment Using Parallel Deep Autoencoders
## 5 Experiments and Results
### Experiments on 2-, 3- and 4-Manifold Alignment
#### 5.2.3 Results of 2-Manifold Alignment  
In the case of the 2D-2D motion data, limb \(u_{l}\) rotated around joint \(J_{1}\) in a circle and limb \(u_{2}\) rotated in another circle around joint \(J_{2}\). In three-dimensional space, this motion can be represented as a torus \(S^{1}\times S^{1}\), that is a 2-manifold. In the first row of Figure 5, we can see that for zero noise, the visualisations of the results for MAPA-inst, MALG and PDAE resulted in objects that resembled the expected torus-shaped surfaces, while the other methods produced cylinder-like deformations of torus-shaped surfaces. With increasing levels of noise (only three representative levels are displayed),the instance-level alignments were less stable than the feature-level alignments. The addition of noise led all traditional methods to fail by collapsing the resulting manifolds or misaligning the two sets. Visually, the best outcomes were achieved by PDAE where for all levels of noise, an object resembling a torus-like surface with minor deformations was obtained.  
Figure 5: Manifold alignments of 2D motion data under the influence of noise: The graphs visualise the outcomes of aligning datasets \(X\) (red) and \(Y\) (blue) using the seven methods mentioned at the top. Each row shows the results obtained under the influence of a different level of actuator noise (Rows 2–4) and coordinate noise (Rows 5–7). The expected result is a torus \(\mathrm{S}^{1}\times\mathrm{S}^{1}\). However, the outcomes of MAPA, MALG and MAGG tend to collapse into a cylinder or for noise ranges \(\geq\)\(\pm\)2\({}^{\circ}\) misalign or otherwise disintegrate, particularly at the instance level. The only exception seems to be MALG-feat at the highest level of actuator noise. Otherwise, the graphs in the rightmost column demonstrate that of all methods tested, PDAE has the best ability to produce the expected torus-like manifold at all levels of noise.  
The alignment errors \(\Delta\) in Table 2 indicate that MALG-feat resulted in the closest alignment among the conventional methods. The \(\Delta\) of the autoencoder was lower than that of MALG-feat for higher levels of noise. Table 2 also shows that PDAE had the lowest standard deviation (in parentheses) at high levels of noise. This indicates that PDAE more smoothly aligned than the other methods. It should be noted that low values for \(\Delta\) or \(\sigma\) can also occur when a torus manifold cannot correctly be established and uniformly collapses or projects into a simpler form as, for example, a cylinder in some cases of MALG-feat. We trained and tested PDAE with five different sets of initial random weights, and the standard deviation of the mean of the resulting alignment errors was about 0.0005 at zero noise. This indicates that the autoencoder results do not depend in a notable way on the selection of the initial random weights.
# Development of a Novel De-NOX Technology for the Aftertreatment of Ship Exhaust Gases
## 3 Results and Discussion
### Operation of Selective Catalytic Reduction Unit  
After the successful operation of the previously mentioned units that achieved the removal of SO\({}_{2}\) and significant reduction of the concentration of PMs, the SCR de-NO\({}_{\rm x}\) unit was set in operation. The study of the novel H\({}_{2}\)-SCR unit was the main target of the present work, and its main purpose was to reduce nitrogen oxide species (NO\({}_{\rm x}\)), especially NO, to N\({}_{2}\) in the presence of excess air and using H\({}_{2}\) as a reducing agent. The results obtained before and after the operation of the complete pilot unit (i.e., SWA unit, oxidation catalyst and SCR de-NO\({}_{\rm x}\) unit) are comparatively presented in Figure 6.  
As seen in Figure 6, the operation of the integrated pilot unit resulted in the significant reduction of the concentration of all four major pollutants (NO\({}_{\rm x}\), CO, SO\({}_{2}\) and PM), that are present in the exhaust gas stream of the cruise ship studied. In particular, the concentration of NO, CO, SO\({}_{2}\) and PM at the exit stream of the complete pilot unit was found to be 124 ppm, 426 ppm, 0 ppm and 69 mg/m\({}^{3}\). The conversion percentage of all three gaseous pollutants from the suggested combined technology is presented in Figure 7. In particular, the achieved reduction of NO\({}_{\rm x}\) species and SO\({}_{2}\) in the exhaust gas stream was found to be  
Figure 5: Steady-state concentrations of NO, CO and PM before and after the operation of the SWA unit.  
up to 80% and more than 99%, respectively, while the concentration of CO and PM was reduced by 34% and 72%, respectively, after the operation of the pilot unit.  
The International Maritime Organization (IMO) engine NO\({}_{\chi}\) limits are based on engine rated speed, with the lowest limits applicable to medium and high-speed engines. The application date of Regulation's 13 NO\({}_{\chi}\) emission limits is tied to the ship construction date [23]. The restrictions introduced by Tier I were compulsory for new diesel engines constructed or installed on ships from 1 January 2000. Tier II is mandatory applicable for ships constructed after 1 July 2010 as Tier III compliance is required for ECA and 200 miles Exclusive Economic Zone of U.S. from 1 January 2016, as well as for Baltic and North Sea from 1 January 2021. Under consideration for further application area expansion are zones like Mediterranean Sea and Black Sea [24; 25].  
Based on the above results (Figure 7), it can be safely claimed that the novel H\({}_{2}\)-SCR de-NO\({}_{\chi}\) unit used in the present work is quite efficient for the removal (reduction) of Nitrogen Oxides emitted from ship's main engines, achieving even the strictest regulations of IMO (Tier III). Moreover, it should be noted that further improvement of the de-NO\({}_{\chi}\) unit can be achieved by optimizing its overall design, catalyst mass, catalytic reactor's design (e.g., use of a monolithic reactor), etc.
## 4 Numerical Simulation Analysis of Slope Instability
### Calculation Conditions and Boundary Conditions  
The life and death element functions are used to simulate bank slope excavation. During the simulation process, the excavated slope soil element is deleted layer by layer  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Rock Mass and Fault Zoning**} & \multicolumn{2}{c}{**Severe**} & \multicolumn{2}{c}{**Deformation Parameters**} & \multicolumn{2}{c}{**Strength Parameters**} \\ \cline{2-6}  & \(\gamma\)/[kN/m\({}^{3}\)] & \(E\)/GPa & **Poisson’s Ratio \(\mu\)** & \(f\)/\(c\) & \(c\)/MPa \\ \hline \multirow{2}{*}{Intensely weathered} & 26.5 & 0.5 & 0.32 & 24 & 0.15 \\  & 28.2 & 4.5 & 0.27 & 40 & 0.8 \\  & 28.4 & 6.5 & 0.26 & 45 & 0.9 \\  & 28.5 & 18.0 & 0.25 & 50 & 1.1 \\  & 27.0 & 0.5 & 0.32 & 15 & 0.24 \\  & 26.0 & 0.5 & 0.32 & 16 & 0.25 \\  & 26.0 & 0.5 & 0.28 & 16 & 0.3 \\  & 26.0 & 0.5 &according to the actual construction sequence to analyze the impact of bank slope excavation on its disturbance.  
In the reservoir impounding analysis, the influence of impounding on the left bank slope is analyzed from no water to 450 m (normal pool level).  
The initial in situ stress is only considered a self-weight stress field without considering tectonic action. The slope surface is free, the foundation bottom is fixed and restrained, and normal constraints surround the foundation. During rainfall and water storage analysis, the initial groundwater level is set to a level with the slope toe, and the pore water pressure (including suction) is linearly distributed with depth. As the slope angle changes little, the rainfall infiltration intensity on the slope is uniformly set at 0.02 m/s.
## 2 Materials and Methods  
### Study Area  
The study area is the Chenghe watershed, located in southeastern Shanxi Province, China (Figure 1a). The watershed is located between latitudes 35\({}^{\circ}\)30\({}^{\prime}\)14\({}^{\prime\prime}\) N and 35\({}^{\circ}\)38\({}^{\prime}\)04\({}^{\prime\prime}\) N and longitudes 112\({}^{\circ}\)27\({}^{\prime}\)39\({}^{\prime\prime}\) E and 112\({}^{\circ}\)46\({}^{\prime}\)13\({}^{\prime\prime}\) E, covering an area of approximately 113 km\({}^{2}\), with elevations ranging from 730 to 1185 m (Figure 1b). The region is bordered by mountains to the east and west, with a landscape that consists of 60.8% mountainous areas, 30.9% hills, and 8.3% plains. Elevations range from 730 m to 1185 m. The climate is a semi-humid continental monsoon, with an average annual temperature of 11 \({}^{\circ}\)C, annual rainfall of 533.3 mm, and 188 frost-free days.  
The Chang River flows through the area from northeast to southwest, dividing the study region into eastern and western sections. Coal mines are primarily concentrated in the western part, which has been heavily impacted by mining activities. Much of the land has been damaged, though some areas have been reclaimed.  
The land use of the study area, including the damaged region, is illustrated in Figure 1c. The dominant land use is dryland, covering 52.8% of the area. Irrigated lands (4.6%) are primarily located along both sides of the Chang River, while forested areas (7.9%) are mainly distributed at higher elevations along the eastern and western borders. The geological formations in this region are composed of 10 types. One example is the Lishi Formation (Figure 1d), which is characterized by loess deposit in the middle and upper sections, along with grayish-yellow to brownish-yellow sub-sand and sub-clay layers. These are interbedded with ancient soil layers that range from brownish red to light red. The Taiyuan Formation is defined by cyclical layers consisting of shale interbedded with sandstone, coal, and limestone, with lake deposits (iron-aluminum ore) found at the base. Additionally, the Mid-Subformation of the Majiagou Formation is another dominant geological type,  
Figure 1: **(a)** Location of Changhe watershed in the Chinese Loess Plateau; (**b**) distribution of soil sampling locations and digital elevation model (DEM) with 30 m resolution downloaded from the a public platform of [[http://www.gscloud.cn/sources](http://www.gscloud.cn/sources)]([http://www.gscloud.cn/sources](http://www.gscloud.cn/sources)) (accessed on 9 September 2024); (**c**) the land use map was created by field investigation and survey in 2018; and (**d**) geological map from Shanxi Provincial Geological Prospecting Bureau.  
represented by the 5 th and 6 th sections of the Majiaogou Formation. The dominant soil type is calcaric cambisols, and the major crops grown in both dryland and irrigated areas include maize, potatoes, and wheat.
## 4 Parameter Tuning Results
### Random Forest  
RF is a classification and prediction technique that employs many decision trees and two tuning parameters, ntree and mtry. The ntree and mtry parameters considerably affect the performance of the RF classifier, as detailed in Section 3.2. As summarised in [44], ntree is the number of generated randomised trees. Predicting with a large number of trees improves the stability and accuracy of the results and the ntree value of 500 is usually used when the model is deployed for real-world practice. In comparison, mtry is the size of the randomly selected feature set, where the small value of mtry results in diversity in the model. The range of the values of mtry used in this study is from 1 to 10, and the values of ntree which further evaluate the characteristics of the RF model towards  
Figure 6: KNN model accuracy versus the number of neighbours.  
this tuning parameter are 100, 200, 500 and 1000. The graph in Figure 8 shows that a constant 100% cross-validation accuracy was obtained when the model was fed with all mtry and ntree values. We anticipate that the model can make 100% correct predictions regardless of the predictors utilised because of the small size and simplicity of the training dataset. However, the accuracy decreases when a large, sophisticated training dataset is used because of the increase in the number of predictors used, as demonstrated in [44]. Therefore, further investigation into the number of predictors is required to determine the model's best performance.  
Figure 8: RF model accuracy versus number of randomly selected variables.  
Figure 7: CART model accuracy versus max tree depth.