esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Optimization Model
### Design Constraints
#### 4.2.2 Tower Design Constraints  
The tower is the main support component of the wind turbine to withstand the aerodynamic loads and the total mass of the other components. The structural properties of the tower have a significant impact on the total cost of the wind turbine. An efficient, safe and economic design of the tower that meets all the design criteria is needed to minimize the total wind turbine cost. The loads acting on the tower mainly come from the aerodynamic loads on the rotor, and the wind loads on tower itself are also considered. The thrust force on the rotor is transferred to the tower top and causes tower deformation. In order to ensure structural stability and avoid large deformation, the maximum deformation at the top of the tower should not exceed the allowable deformation:  
\[d_{\text{max}}\leq d_{allow} \tag{6}\]  
The \(d_{allow}\) is the allowable deformation and it can be determined according to the reference [18]. In order to ensure the tower has an adequate structural performance during optimization process, the allowable deformation is set as [8]:  
\[d_{allow}\leq\frac{H_{opt}}{H_{orig}}d_{orig}\leq 1.25\frac{H_{opt}}{100} \tag{7}\]  
where \(H_{opt}\) is the height of the optimal tower, \(H_{orig}\) is the height of the original tower, and \(d_{\text{orig}}\) is the top deformation of the original tower.  
In order to verify the structural stability of the tower, the structural design needs to withstand thrust from the wind turbine rotor. For a tapered tower, the location of the critical stress is at about the middle of the tower (the bending moment is largest at the base, but so is the moment of inertia) [17]. In this work, we assumed that the critical location was at the middle of the tower. Then, the stress at that location can be calculated as:  
\[\sigma_{cr}=\frac{(m_{RNA}+m_{tower\_mid\_up})g}{A_{mid}}+\frac{TH_{mid}}{2I _{mid}} \tag{8}\]  
where \(\sigma_{cr}\) is the critical stress, \(m_{RNA}\) is the mass of rotor and nacelle assembly, \(m_{tower\_mid\_up}\) is the tower mass above the middle of the tower, \(A_{mid}\) is the area of the middle of the tower, \(T\) is the rotor thrust, \(H\) is the tower height, and \(I_{mid}\) is the area moment of inertia at the middle of the tower.  
The stress \(\sigma_{cr}\) generated by the loads cannot exceed the allowable stress:  
\[\sigma_{cr}<\sigma_{allow} \tag{9}\]  
The \(\sigma_{allow}\) is the allowable stress and the value of it is given by:  
\[\sigma_{allow}=\sigma_{ys}/\gamma_{tm} \tag{10}\]  
where \(\sigma_{ys}\) is the yield strength and \(\gamma_{tm}\) is the material safety factor of the steel. The yield strength of the steel is 345 MPa for the wall thickness between 16 mm and 40 mm. The material safety factor is set as 1.1 according to IEC 61400-1.
# Metal-to-Insulator Transition in Ultrathin Manganite Heterostructures
## 2 Epitaxial Growth of LSMO  
The LSMO films have been generally grown by PLD. Another technique of oxide-MBE (O-MBE) has recently been used as well, for LSMO growth. In this review, we will only introduce the growth of LSMO by PLD systems. A PLD is normally made of a KrF excimer laser; a rotational multiple-target assembly; a gas flow nozzle; a substrate heater, and Reflection High-Energy Electron Diffraction (RHEED) (see Figure 2a). The dynamic growth process can be monitored in real time by RHEED. In order to fabricate the multilayer, a multi-target holder is usually installed. Through choosing substrates with the proper lattice mismatch, and using the optimized growth condition, a layer-by-layer growth can be obtained. Since layer-by-layer growth will generate an intensity oscillation in RHEED diffraction spots, the thickness of the film can be precisely controlled at the unit cell level. Figure 2b displays a sketch of an oxide thin film on a substrate, where a further layer only starts to form when the existing surface is fully covered.  
The precise control of thickness is in detail illustrated by an example shown in Figure 3. Here, the LSMO films were grown on TiO\({}_{2}\)-terminated SrTiO\({}_{3}\) (STO) (001) substrates [39]. Figure 3a,b shows the surface morphology of an as-treated STO substrate and an as-grown LSMO film, by atomic force microscopy (AFM), indicating an atomically flat surface. The atomically flat surfaces are also indicated from the 2D feature of RHEED diffraction pattern for both the STO substrate and the LSMO thin film (see the inset of Figure 3a,b). The real-time tracking of diffraction spot intensity simultaneously during the growth is shown in Figure 3c. RHEED intensity oscillation was observed for different oxygen growth partial pressure. One oscillation corresponds to the growth of one unit cell of LSMO. The observed slower growth rate in higher oxygen pressure is due to the side scattering of the plume by background gas, which reduces the amount of arriving species on substrates. The thickness can be controlled by regulating the laser ablation after a certain amount of oscillation. The atomically sharp interface between LSMO/STO was characterized by scanning trans  
Figure 2: (**a**) Schematic drawing of a pulsed laser deposition setup. (**b**) Unit cell control of the synthesis of a specific ABO\({}_{3}\) perovskite film (top layer) on a A\({}^{\prime}\)B\({}^{\prime}\)O\({}_{3}\) perovskite substrate (bottom layer).  
(STEM), as shown in Figure 3d. There is no obvious structure defect according to the STEM cross-section image. To understand the nature of the emergent properties at interface and ultrathin films, including the nature of thickness-driven MIT, it is essential to have atomic precision in both controlling thin film growth, and characterizing lattice structure and chemical composition using a technique such as high-resolution STEM.
# A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica
## 3 Results  
### Image Nonuniformity Correction Results  
Figure 4 shows an example of image brightness nonuniformity correction in the Antarctic coastal zone. Figure 4b is the correction result obtained using a common MASK method. There are luminance correction anomalies at the land-sea boundary in this result. Figure 4c shows the land-sea reference mask, which is obtained by setting a threshold T to distinguish the light and dark objects in Figure 4a. Figure 4d shows the results after using the land-sea auxiliary mask, and the results show that the method can improve the brightness correction anomaly in the land-sea boundary area and obtain a better correction effect.  
Figure 5 shows two other HY-1/CZI images with obvious nonuniformy phenomena in an inland Antarctic region. It can be seen from the corrected results that the nonuniform  
Figure 4: Comparison of the brightness nonuniformity correction effect after the introduction of the land–sea reference mask: (**a**) original image, (**b**) common MASK method results, (**c**) land-sea auxiliary mask, and (**d**) results after the introduction of a reference mask.  
effect of brightness is obviously eliminated visually, and the brightness of the whole scene is basically evenly distributed. Table 3 shows the standard deviations of the above three images before and after correction. The statistical results show that the standard deviation after correction is basically less than that before correction, which further indicates that the uniformity of the corrected image is better than that of the original image.
# A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
## 5 Future Directions
### Completeness in Hardness of Approximation  
A final direction we would like to highlight is to obtain more completeness in inapproximability results. Most of the results so far for FPT hardness of approximation either (i) rely on gap hypothesis or (ii) yield a hardness in terms of the W-hierarchy but the exact version of the problem is known to be complete on an even higher level (e.g., Dominating Set is known to be W[1]-hard to approximate but its exact version is W[2]-complete). We have discussed (i) extensively in Section 3.2 and some examples of (ii) in Section 3.1. There are also some examples of (ii) that are not covered here; for instance, Marx [293] showed W[t]-hardness for certain monotone/anti-monotone circuit satisfiability problems and the exact versions of these problems are known to be complete for higher levels of the W-hierarchy. The situation here is unlike that in the theory of NP-hardness of approximation; there the PCP Theorem [21,22] implies NP-completeness of optimization problems [294].  
Thus, in the parameterized inapproximability arena, the main question here is whether we can prove completeness results for hardness of approximation for the aforementioned problems. The two important examples here are: is \(k\)-Clique W[1]-hard to approximate, and is \(k\)-Dominating Set W[2]-hard to approximate? As discussed in Section 3.2, the former is also closely related to resolving PIH.  
Finally, we note that, while completeness results are somewhat rare in FPT hardness of approximation, some are known. We give two such examples here. First is the \(k\)-Steiner Orientation problem, discussed in Section 3.1.3; it is W[1]-complete to approximate [44]. Second is the Monotone Circuit Satisfiability problem (without depth bound), which was proved to be W[P]-complete by Marx [293]. However, it does not seem clear to us whether these techniques can be applied elsewhere, e.g., for \(k\)-Clique.  
Writing--original draft and writing--review and editing, A.E.F., K.C.S., E.L. and P.M. All authors have read and agreed to the published version of the manuscript.  
Andreas Emil Feldmann is supported by the Czech Science Foundation GACR (grant #19-27871X), and by the Center for Foundations of Modern Computer Science (Charles Univ. project UNCE/SCI/004). Karthik C. S. is supported by ERC-CoG grant 772839, the Israel Science Foundation (grant number 552/16), and from the Len Blavatnik and the Blavatnik Family foundation. Euiwoong Lee is supported by the Simons Collaboration on Algorithms and Geometry.  
The authors declare no conflict of interest.
## 2 Methods
### PV End of Life Management
#### 2.2.1 Life Cycle Inventories
In assessing the recycling of CIGS PV modules, we modeled the \"innovative recycling\" method referred by [PERSON] and [PERSON] [22]. This recycling procedure mostly involves chemical methods. This method permits the recycling of glass, EVA, selenium, indium, and gallium materials [19]. [PERSON] and [PERSON] did not report the electricity consumption for this method and we assumed that the consumption would be similar to the electricity consumption in the CdTe thin-film recycling. Materials that cannot be recovered during the recycling of PV panels are sent to landfills at their EoL for disposal. These materials include contaminated glasses that have trace amounts of semiconducting materials, contaminated chemicals that are generated during the recycling process, the sludge that contains metallic residues, plastic materials used in the wiring, encapsulant, backsheet, and sealant of the PV systems. Materials or sludges contaminated by heavy metals need to be sent to special landfills for hazardous waste disposal while the inert wastes are sent to the municipal non-hazardous landfills. Table 1 also summarizes the classification of the materials disposed of after the recycling of c-Si, CdTe, and CIGS PV panels. In modeling the disposal inventories of thin film CdTe and CIGS, we assumed the material composition would be very similar [21] and used the same disposal inventories for both types of PV technologies, as it was referred in First Solar PV recycling facility data [27].
# Synthesis and Application in Cell Imaging of
## 4 Discussion
In addition to studies on optical properties and biocompatibility issues, MeAcd12C and MedAcd12P were further subjected to cellular accumulation and bioimaging analysis. It was observed that the MeAcd12C speckles made their significant appearances surrounding the H184B5F5/M10 nucleus compared to the localization in HeLa cells. Moreover, MeAcd12C was distributed in the cytoplasm of the HeLa cells, and the appearance of the unexpectedly strong speckles might be due to aggregation. However, the specific localization of MeAcd12P in HeLa cells were not very prominent even under the conditions of reduced concentrations and incubation times. We were particularly interested in the cellular imaging of MeAcd12P in H184B5F5/M10 and HeLa cells. Considering the cell viability after treatment with the two compounds, MeAcd12P was demonstrated to possess specific killing ability towards HeLa cells than over H184B5F5/M10 cells. Cellular staining tests showed the two compounds to be uptaken by the cells and remained in the cytoplasm. Understanding both the fluorescence mechanism and subcellular localization effects are topics worthy of future investigation. Although MeAcd12C and MedAcd12P were observed to enter cells and showed fluorescent speckles in the cytoplasm, the results did not coincide well with the optical properties, which presented very low quantum yield in H\({}_{2}\)O. Considering that the two compounds were designed as surfactant-like chromophores along with the presence of fluorescent speckles, we further investigated whether the fluorescent speckles were due to the aggregation-induced emission (AIE) effects [34; 35]. The AIE effects on MeAcd12C and MedAcd12P were examined under varying H\({}_{2}\)O/THF mixed solvent system ratios(Figure S5). Therefore, the fluorescence enhancement of the two compounds was dependent on the AIE phenomenon but their emission wavelengths were irrelevant to the formation of fluorogens.
## 2 Related Works  
The advancement of VLC systems has been significantly driven by the integration of IRSs and advanced receiver technologies to address signal degradation in indoor environments. In this sense, the authors of [19] explored phase optimization in IRS-assisted wireless systems, analyzing the BER performance under varying phase configurations. Their study demonstrated that optimizing IRS phase settings can substantially improve system reliability by mitigating signal losses. While their work primarily focuses on traditional RF systems, it also provides insight into the importance of IRS control mechanisms, which are equally applicable to VLC systems to improve signal quality.  
In the work presented in [8], the authors compared metasurfaces and mirror arrays for IRS-based VLC systems, highlighting the trade-offs between the two techniques in terms of complexity, cost, and signal reflection efficiency. Their findings indicate that mirror arrays offer a practical and cost-effective solution for VLC systems, particularly in environments with significant NLoS components. Extending this work, in [20], the authors conducted a detailed channel characterization for IRS-assisted VLC systems, providing analytical models for signal propagation and reflection behavior. Their study underscored the critical role of IRS placement and reflection properties in optimizing the quality of the received signal.  
In the context of VLC systems for next-generation communication, the work in [4] evaluated the performance of IRS-assisted indoor VLC systems, emphasizing their potential to improve spectral efficiency and the SNR. Their work demonstrated that optimal IRS configurations could significantly enhance system performance, particularly when integrated with advanced optimization techniques. Similarly, the authors in [6] investigated the effects of multiple paths in indoor VLC environments, analyzing the impact of reflections on channel performance and the BER. Their findings highlighted the challenges posed by multipath propagation, particularly in complex indoor scenarios, and stressed the need for efficient reflection-based solutions.  
Together, these works highlight the importance of IRSs and advanced optimization techniques in overcoming VLC challenges, such as shadowing and NLoS conditions. However, while significant progress has been made, limited studies have explored the integration of IRS optimization, ADRs, and machine learning techniques. This work addresses this gap by proposing an RNN-based framework to optimize IRS placement and reflection properties, maximizing the SNR at ADRs. Furthermore, this study evaluates the combined performance of pyramidal and hemispherical ADRs using MRC and SBC mechanisms, providing a comprehensive solution to enhance the reliability of the VLC system in complex indoor environments.
## 4 Experimental Results and Discussion
### Investigation of Classification Performance
As shown in Figures 10 and 11, the spatial-spectral methods generally produced smoother classification maps than traditional spectral-based methods, which demonstrates that the spatial information in HSI is effective for improving classification performance. Moreover, the classification maps of SSRHE possessed fewer misclassified pixels, especially in the areas _Grass/Pasture_, _Wheat_, and _Stone-steel towers_ for Indian Pines dataset and _Asphalt_, _Meadows_, and _Shadows_ for PaviaU dataset. In terms of running time, SSRHE cost more time than traditional spectral-based methods because it needs to build models in both spatial domain and spectral domain of HSI data. Compared with other spectral-spatial algorithms, the running time of SSRHE did not increase significantly and was far less than SSCE. Thus, SSRHE was more effective than other DR methods to weaken the influence of noisy points and develop the discrimination power of embedding features. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Class** & **Train** & **Test** & **RAW** & **PCA** & **LDA** & **LPP** & **MFA** & **RLDE** & **DHLP** & **SSC** & **LPNPE** & **SSRHE** \\ \hline
1 & 332 & 6299 & 85.62 & 85.62 & 87.68 & 87.82 & 82.76 & 90.19 & 63.89 & 89.73 & 90.20 & 91.19 \\
2 & 933 & 17,716 & 94.65 & 94.57 & 94.88 & 94.76 & 93.90 & 97.73 & 66.69 & 96.70 & 97.53 & 98.12 \\
3 & 105 & 1994 & 65.15 & 64.64 & 63.34 & 67.00 & 61.84 & 74.77 & 61.58 & 72.37 & 77.28 & 78.6 \\
4 & 154 & 2910 & 77.22 & 77.36 & 81.79 & 79.01 & 77.02 & 84.13 & 59.15 & 84.78 & 87.83 & 89.26 \\
5 & 68 & 1277 & 98.83 & 98.83 & 98.84 & 99.30 & 99.77 & 99.53 & 87.82 & 99.37 & 99.77 & 99.77 \\
6 & 252 & 4777 & 60.26 & 60.32 & 65.17 & 65.47 & 69.72 & 70.36 & 95.89 & 73.86 & 89.68 & 85.22 \\
7 & 67 & 1263 & 75.30 & 75.30 & 66.67 & 75.69 & 71.26 & 80.36 & 94.4 & 88.60 & 86.06 & 90.18 \\
8 & 185 & 3497 & 80.27 & 80.27 & 74.39 & 81.42 & 77.36 & 84.79 & 99.77 & 82.85 & 84.68 & 79.33 \\
9 & 48 & 899 & 100 & 100 & 99.44 & 100 & 99.67 & 100 & 90.00 & 99.78 & 99.89 & 100 \\ \hline  & OA (\%) & & 84.92 & 84.88 & 85.40 & 86.27 & 84.73 & 89.70 & 78.00 & 89.60 & 91.30 & 92.59 \\  & AA (\%) & & 81.92 & 81.88 & 81.47 & 83.38 & 81.48 & 86.87 & 77.72 & 87.56 & 89.53 & 90.55 \\  & _KC_ & & 0.797 & 0.796 & 0.804 & 0.815 & 0.796 & 0.861 & 0.736 & 0.861 & 0.883 & 0.902 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results of different classifiers on the PaviaU dataset. Figure 10: Classification maps of different DR methods with 1-NN on the Indian Pines dataset. Note that the time cost of corresponding DR algorithms is marked in bracket.
## Materials and Methods  
### Geographical and Geological Framework  
Alcala de Ebro is found in the NE of the Iberian Peninsula (Figure 1), about 35 km northwest of the Spanish city of Zaragoza, the fifth largest city of Spain. It is approximately halfway between Madrid and Barcelona (about 330 km from each one). Geologically, it is located in the sedimentary basin of the Ebro River and surrounded by three mountain systems: Pyrenees, Iberian Mountain Range and Coastal-Catalan Mountain Range. The stratigraphy of the area consists mainly of a Tertiary rock substratum of marls and gypsum, strongly altered in its superficial levels, on which Quaternary materials are deposited of variable thickness and compactness. The gypsum presents a rather monotonous aspect, being constituted by white gypsum with nodular structure. Gray-greenish marls and shales appear associated with the gypsum materials or alternating with them [28; 29]. Quaternary formations outcrop widely throughout the area, arranged in several terrace levels associated with the Ebro River, as well as different glacial deposits and colluviums [29].  
Alcala de Ebro is a small town on the banks of the Ebro River, the largest river of the Iberian Peninsula, and it collects a large part of the meltwater from the southern slopes of the Pyrenees. Thus, Alcala de Ebro is located in the Ebro Corridor, a 6-km-width corridor defined by a succession of low and medium terraces on the right bank of the river that, in a staggered manner, makes the relief descend from heights of about 300 m to 215 m at the current riverbed. The urban center is found between heights 221 m and 226 m, occupying the lowest terrace (considered the current flood plain). Here, the river adopts a meandering geometry, according to its low longitudinal slope (around 0.06%), and constitutes the regional base level [29].  
In 1957 the river naturally changed its course, flooding part of Alcala de Ebro. This led to the construction of different breakwaters and walls, creating in 1982 a protective flood embankment of more than 2 km long. Besides, urbanization and agricultural development over time led to the occupation of a ravine (Juan Gaston ravine) whose basin covers a length of 31.8 km and an area of about 232 km\({}^{2}\). Such a ravine, which opens a partially dissymmetrical valley in the Tertiary materials of the Ebro Basin, is one of the natural drainage to the Ebro River, and its elimination currently entails the existence of underground inflows through the terrain. Both this phenomenon and the proximity of the Ebro River may be the fundamental factors that condition the problem of sinkholes and karst subsidence that affect the area under study.
## 1 Introduction
Moreover, real-time object-detection mechanisms require heavy I/O operations and accurate processing [9], making them computationally expensive. The system needs to continuously capture image frames from the camera feed, detect the object, draw the predicted bounding box, and track the target object in every frame. This heavy and complex computation can slow down the system, making it undesirable even with high accuracy. Therefore, a wise trade-off between speed and accuracy also needs to be considered.