esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Automatic Geometric Corrections
### Geometric Modelling  
After the GCPs extraction algorithm, the geometric correction is continued with the geometric modelling and finally orthorectification. Figure 6 presents the workflow. The list of GCPs that were obtained from the GCP extraction procedure is additionally filtered with gross error removal algorithms that produce the final set of GCPs to refine the rational function geometric model. The bias-corrected results are then used in the orthorectification to generate the orthoimage.  
Figure 5: Three GCP extraction examples. (**a**) ideal example; (**b**) example with outdated reference data and incomplete detection; and, (**c**) example with fragmentary detection. The first two columns show the input elements, and the last three columns show results of all three steps of the matching procedure.  
Figure 6: Geometric rational function model (RFM) and orthorectification workflow.
# Efficient Transmit Antenna Subset Selection for Multiuser Space-Time Line Code Systems
## 3 TAS Selection Algorithms  
In this section, we first present previous optimal and suboptimal greedy TAS selection algorithms for the MMSE-precoded MU-STLC systems. Then, the popular Woodbury formula is exploited to obtain a suboptimal incremental SNR-based TAS selection method with more reduced complexity. Furthermore, we propose a decremental TAS selection algorithm based on ZF and MMSE criteria. Finally, an efficient algorithm for decremental TAS selection is developed by using the Woodbury formula.
## 4 Discussion
The case studies shown demonstrate that energy solutions are addressed by different institutions. The Indian examples (capital city vs. rural area) show, despite both projects relying on external funding (Asian Development Bank, international NGO), their energy solutions strongly differ; thecommunity/NGO-led project favoured an innovative pathway, while the public utility rebuilt and modernised the conventional infrastructure. It should also be recognised that the cause of the power outage and the community's energy needs (city/rural) strongly differed. While our study confirms that initial resistance from utilities (public, private) is evident across all case studies, business models of public utilities are changing. As the decisions by the centralised utility model (PG&E/California) and rule changes in favour of DES by energy regulators (Californian public energy utility, Australian Energy Regulator) confirm. The microgrid on the Greek Island Tilos is governed and financed through a supranational body (EU project), and demonstrates the success of community consultation, engagement of local energy companies and piloting replicable island solutions for distributed energy. In summary, the main considerations for Emergency DES are:  
* Different suites of technologies are available, and Emergency DES can gradually be upgraded or modified to meet and adjust to community needs;
* The size of the disaster-affected community and the scale of damage determines the technical response (microgrids might not be the quickest and most suitable solution at the time of recovery);
* Multi-level governance-style leadership assists the energy transition (administatively, financially, legally), and building climate resilience;
* Community engagement and consultancy processes are key to ensuring social acceptance and persistence of a microgrid (i.e., the organisation of maintenance and operation, community needs/various levels of income).
# Effect of Dry-Wet Cycle Periods on Properties of Concrete under Sulfate Attack
## 4 Conclusions  
In this study, concrete exposed to a sulfate solution with different dry-wet cycle periods was tested. The flexural strength, dynamic elastic modulus, and mass of the specimens were measured, and SEM images of different corrosion processes were recorded. The following conclusions can be drawn:  
1. The changes in concrete performance with different dry-wet cycles were essentially consistent. Within a certain range, the erosion of concrete became more apparent upon prolonging the dry-wet cycles. The relative flexural strength and relative dynamic modulus of the T3, T7, T14, and T21 specimens peaked at 28, 12, 6, and 4 dry-wet cycles, respectively. After 252 days of sulfate dry-wet cycles, the flexural strength of the single-cycle specimens decreased by 1.05%, 2.7%, 4.2%, and 5.6% on average, respectively.
2. Regarding the microstructure, increasing the dry-wet cycle period gradually increased the depth of influence of the sulfate attack inside the concrete, along with the number and volumes of erosion products at a given depth. These results well validate the variation law of macroscopic performance. However, for a given dry-wet cycle period, the content of erosion products gradually decreased with increasing depth.
3. The excessive extension of the dry-wet cycle period has little effect on the deterioration of concrete's performance; thus, such extension is not advised. According to the test results, the strengths and RDEMs of T14 and T21 showed similar variations. The microstructural analysis illustrated that the number and volume of attack products  
Figure 11: The microstructures of the specimens after sulfate dry–wet cycles for 210 days. **(a)** T3; **(b)** T7.  
Figure 12: The microstructures of the specimens after sulfate dry–wet cycles for 210 days. **(a)** T3; **(b)** T21.  
inside the specimen showed trends of decreasing with increasing dry-wet cycle periods. Therefore, based on the strength and depth of single-cycle erosion, it is advisable to set the cycle period to 7-14 days when testing sulfate dry-wet attack on concrete under natural dry conditions.  
Conceptualization, J.-J.G. and K.W.; methodology, [PERSON]-J.G., [PERSON]-Q.L. and K.W.; analysis, [PERSON]-J.G., P.-Q.L., C.-L.W. and K.W.; investigation, [PERSON]-J.G., P.-Q.L. and C.-L.W.; resources, K.W.; writing and editing, J.-J.G., K.W. and [PERSON]-Q.L.; visualization, P.-Q.L.; supervision, C.-L.W.; project administration, K.W.; funding acquisition, J.-J.G. and C.-L.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51879244 and 51679221, and the Program for Innovative Research Team (in Science and Technology) at the University of Henan Province, grant number 20 IRTSTHN009.  
Not applicable.  
The financial support from the Zhengzhou University Postgraduate Independent Innovation Project and Zhengzhou University is also acknowledged.  
The authors declare no conflict of interest.
# Function Optimization and Parameter Performance Analysis Based on Gravitation Search Algorithm
## 3 Simulation Experiments and Results Analysis
### Simulation Results and Corresponding Analysis
As the \(\alpha\) increases, the convergence effect showed a decreasing trend and the convergence curves of the other three functions appear to show gradient optimization conditions. When \(\alpha\) is 35, the functions \(f_{2}\), \(f_{3}\) have the fastest convergence speed and reach the local optimal value early, but not the global optimal value. Overall, for different functions, the smaller \(\alpha\), the better the convergence performance. Compared to the slow convergence velocity, the functions with low dimension converge at a steeper point on the curve.
## 5 Controller Design  
The model under consideration requires two parallel SISO controllers: one for the regulation of the input/output pair \(u_{1}/\frac{\partial I}{\partial u_{1}}\) and another for \(u_{2}/\frac{\partial I}{\partial u_{2}}\). To this end and to replace the classical ESC integral controller of Figure 1, a pole placement controller (PPC) introduced by the authors in [15; 16] is investigated.  
At time \(k\), the gradient of the performance index with respect to the input signal \(u_{i}\), \(i\in\{1,2\}\) is given by:  
\[\left(\frac{\partial I}{\partial u_{i}}\right)_{k+1}=2\,a_{ii}(k)\,u_{i}(k-1) +b_{i}(k) \tag{26}\]  
This expression can be cast into the form of a state-space model  
\[\begin{array}{rcl}x_{k+1}&=&A_{d}\,x_{k}+B_{d}\,u_{k}+w_{k}\\ y_{k}&=&C_{d}\,x_{k}\end{array} \tag{27}\]  
with the following correspondence: \(x_{k}=\left(\frac{\partial I}{\partial u_{i}}\right)_{k}\), \(A_{d}=0\), \(B_{d}=2\,a_{ii}\), \(C_{d}=1\). \(b_{i}(k)\) is considered as a perturbation \(w_{k}=b_{i}(k)\).  
To ensure zero steady-state error under step disturbances, an integral action is introduced and pole-placement allows imposing a first-order closed-loop dynamic behavior. The dominant pole imposes the settling time \(t_{s}\) and the non-dominant pole is selected to be much faster, so as to allow for a fast transient. Using the certainty equivalence principle [31; 36], \(a_{ii}\) is replaced by its estimate \(\widehat{a}_{ii}\) and the controller gain is updated at each innovation.  
Although the control algorithm depends on the estimation provided by the recursive algorithm, the _PPC_ design is based on only one parameter \(a_{ii}\), which allows one to maintain some robustness level. Further, placing the closed-loop poles allows taking the a priori neglected dynamics into account. There is indeed a three time-scale defined by the estimator (the fastest), the linear dynamics, and the controller (the slowest), which should be respected during the pole placement procedure. The following simulation study will highlight these elements.
# Computational Analysis of PDE-Based Shape Analysis Models by Exploring the Damped Wave Equation
## 2 Theoretical Background
### PDE-Based Models  
Since we consider a PDE for generating the feature descriptor, the choice of a good PDE to meet this purpose is a core question of the shape-matching framework.  
In this paper, we want to focus on the wave and damped wave equation, respectively. Other PDEs such as the heat equation [4] have already been discussed for our framework to some extent [7; 9; 10].  
The classical wave and damped wave equation make use of the Laplace operator \(\Delta\). When we operate on \(\mathcal{M}\subset\mathbb{R}^{2}\), we can restrain the Laplace operator to the local variables of  
Figure 2: The one-dimensional analytical solution of the damped wave equation. The spatial interval is chosen from \(-\pi\) to \(\pi\) and \([0,10]\) for the time interval. The feature descriptor \(f_{x=0}\) is the solid black line and is the function \(u(x,t)\) restricted to \(x=0\). For more details on the solution and how we obtain it, we refer the reader to Section 5.  
our manifold. The corresponding operator is called Laplace-Beltrami operator (LBO) \(\Delta_{\mathcal{M}}\), and it will respect the curvature of the manifold. Applied to a scalar-valued function \(u\), we can write it as:  
\[\Delta_{\mathcal{M}}u=\frac{1}{\sqrt{|g|}}\sum_{i,j=1}^{2}\partial_{i}\bigg{(} \sqrt{|g|}g^{ij}\partial_{j}u\bigg{)} \tag{5}\]  
where \(|g|\) is the determinant of the metric tensor \(g\in\mathbb{R}^{2\times 2}\) that describes locally the geometry, and \(g^{ij}\) are the entries of its inverse; see, e.g., [11] for more details on the differential geometric notions. The LBO itself is related to the mean curvature of a shape [12]. Shape representations making use of the Gaussian curvature are also possible [13; 14].
## 3 Results and Discussion
### Effect of pH on Adsorption and the Distribution Coefficient  
Complexation is a pH-dependent factor; thus, the pH level plays a significant role in the adsorption of metal ions from industrial effluent. It influences the degree to which the adsorbent dissociates during the reaction and the accumulation of ions on the functional groups. Accordingly, the metal ions' adsorption on the resin was investigated for a pH range of 3.0-9.0. It was observed that, with an increase in the pH, the distribution of metal ions on the H-TTAPA resin increased to a maximum value and then decreased (Figure 7c). It was previously reported that a very low or a very high pH is not suitable for the adsorption and complexation of metal ions on H-TTAPA resin [54]. This is because hydrogen ions compete for the same binding sites and because the pH of the solution affects the chemical specifications of the metal ions. After the optimal pH is reached, a decrease in sorption occurs because of the formation of insoluble metal hydroxyl complexes. At a higher pH, the precipitation of insoluble metal ions hinders adsorption. Therefore, identifying and maintaining the optimal pH is necessary to ensure the maximum adsorption of metal ions. The H-TTAPA resin exhibited maximum adsorption at pH 5 for Fe(II), 6 for Pb(II) and Cu(II), and 7 for Cd(II) and Zn(II).  
The results for the distribution coefficient (Kd) from the batch experiments at various pH levels are presented in Figure 7c. A large value for Kd indicates the retention of metal ions by the resin through adsorption, while a smaller value indicates that a greater fraction of metal ions remain in the solution. Kd increased with a decrease in the acidity, with the optimal results obtained at pH 5-7. When the pH rises to a range at which H\({}^{+}\) ions are exchanged with the metal ions, the maximum exchange capacity is reached when all of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Order** & **Element** & \
\begin{tabular}{c} **Theoretical** \\ **Value (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Experimental** \\ **Value (\%)** \\ \end{tabular} & **Error Function (\(\chi\))** \\ \hline
1. & C & 36.36 & 36.04 & 0.008801 \\ \hline
2. & H & 3.03 & 3.00 & 0.009901 \\ \hline
3. & N & 28.28 & 27.98 & 0.010608 \\ \hline
4. & O & 32.32 & 32.02 & 0.009282 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Elemental analysis results for the H-TTAPA resin.  
ion-exchange sites are involved, and stable complexes form between the functional groups and metal cations.
### Advanced PV Technologies with Distinct Efficiencies
In [10], the chemical composition of the PVK has been adjusted, and the design of the metallic nanoparticles has been customized, focusing specifically on bi-metallic multi-shell elongated nanoparticles with double plasmon resonance. Consequently, self-assembled monolayers (SAMs) have become a viable organic material for bridging PVK and metal oxides (MOs) to improve interface passivation, adjust energy levels, and stop chemical corrosion, all of which contribute to improved cell performance [11]. The deposition techniques, molecular structure, and potential of SAMs in narrow-band gap PVK solar cells set them apart and are thought to be essential for PVK solar cell improvements in the solar PV industry in the future. In order to improve the effectiveness of the cells by creating an extra electrical field that facilitates charge production, transport, and collection, ferroelectric PVK solar cells are also being evaluated as halide PVKs. PVK composites, including intrinsic ferroelectric, micro phase-separated, and layer-structured, had been three different categories into which they were divided according to the degree of phase segregation [12]. These configurations greatly increase the efficiency of solar cells while providing a variety of optoelectronic features. Moreover, PVK/Si tandem solar cells have demonstrated remarkable achievement in solar energy production, surpassing the theoretical maximum of single-junction cells by achieving a validated conversion rate of 33.9%, thanks to advances in the surface texturing of Si bottom cells [13]. Silver Bismuth Iodide (AgBi2I7) thin films were also showing promise as materials for heterojunction UV photodetectors because of their outstanding optoelectronic qualities and stability in the environment [14]. Featuring an entire width at half its maximum of around 30 nm, the films had been effectively constructed on wide-bandgap Gallium Nitride (GaN) to produce an AgBi2I7/GaN heterojunction that demonstrated robust UV-A ray detection, displaying high-performance UV photodetectors. Several other compositions were merged to the PVK solar cells to improve its efficiency, such as ammonium hexafluorophosphate (NH4 PF6) [15], Poly(3-hexylthiophene) (P3 HT) [16], and magnetron-sputtered molybdenum rear electrodes [17].
## 4 Discussion
### Heat Flux, Heat Transfer Coefficient, and Superheat
#### 4.1.2 Incomplete Coverage  
The change in the heat flux and superheat of the heat transfer coefficient at incomplete coverage is shown in Figure 7.  
At 15 K \(<\Delta T\)sat \(<\) 17 K, all experimental sets entered the CHF region, lagging behind the complete coverage (at 12 K \(<\Delta T\)sat \(<\) 16 K). At 8.5 K \(<\Delta T\)sat \(<\) 15 K, the heat transfer coefficient of all experimental sets reached the maximum. The heat flux and heat transfer coefficient at a high mass flow rate were greater than those at a low mass flow rate, and the superheat of the high mass flow rate at \(l_{\rm max}\) was lower than that of the low mass flow rate, indicating that increasing the spray mass flow rate could enhance heat transfer. When the spray height and heat sink surface diameter were (10.3, 18), (10.3, 24), and (15.5, 24), the heat flux and heat transfer coefficient were higher than those of the other seven experimental sets ((10.3, 30), (10.3, 40), (15.5 30), (15.5, 40), (20.7, 30), (20.7, 40), (34.5, 40)). The heat flux and heat transfer coefficient of the other seven experimental sets were similar, as shown in Figure 8. The heat flux and heat transfer coefficient of the seven experimental sets were lower than 35 W\(\cdot\)cm\({}^{-2}\) and 2.5 W\(\cdot\)cm\({}^{-2}\cdot\)K\({}^{-1}\).  
Figure 8: Heat flux and heat transfer coefficient versus superheat of seven experimental sets with low heat flux and heat transfer coefficient at incomplete coverage.  
Figure 7: Heat flux and heat transfer coefficient versus superheat at incomplete coverage.  
At incomplete coverage, the heat sink surface diameter determined the gradient of the heat flux and heat transfer coefficient. The heat flux and heat transfer coefficient could be divided into four levels on the basis of the heat sink surface diameter. The influence of the heat sink surface diameter on the heat transfer was greater than that of the spray height.
## 3 Materials and Methods
### GRASS GIS Framework Workflow and Process Organization
The maps were visualized with legends using the 'd.mon' and 'd.legend' GRASS GIS modules. * Algeria--land cover classes: The following 10 land cover classes were generated based on the spectral reflectance features of objects observed on the images: (1) Bare areas (desert sands); (2) salt hardpans; (3) water bodies (Chotts Melrhir and Merouane); (4) non-consolidated soils (5) consolidated soils; (6) sparse grassland; (7) sparse shrubland; (8) sparse trees (9) sparse vegetation; (9) grassland; (10) broadleaved shrublands. * FAO classes--identification: The features on the images and associated land cover classes were identified, annotated, and compared for changes in the years 2014-2022. These included visible changes in the time period for each year and fluctuations in salt levels. The number of classes was chosen based on the Food and Agriculture Organization's (FAO) Land Cover Legend Registry (LCLR); it was then modified and adapted to effectively represent the land cover types on the images with 30 m resolution, fit easily into memory during computer processing, and was large enough to illustrate the landscape structure in northeastern Algeria. Moreover, the vegetation parameters were chosen to ensure that patterns were included in the maps.
## 3 Methods  
### Prommethee II  
The PROMETHEE method was developed for the first time in 1982 [39] and then further developed in 1985 [40; 41]. It is now considered a well-known, applicable MCDM tool [42]. In order to practice PROMETHEE considering different MCDM situations, different versions within the corresponding literature have been developed, such as PROMETHEE I and PROMETHEE II. The former aims at providing a partial ordering for the investigated/involved alternatives, while the latter provides a full ranking of the alternatives [32]. According to several research studies and applications [32; 42], the practical steps for PROMETHEE II can be listed as follows:  
Step 1: Normalization of the decision matrix \(R_{ij}\) as follows:  
\[R_{ij}=[(X_{ij})\ -\ min\ (X_{ij})]/[max\ (X_{ij})\ -\ min\ (X_{ij})]\]  
where \(X_{ij}\) is the performance measure of the \(i\)th alternative with respect to the \(j\)th criterion; \(i=1,2,\ \ldots,\ n;j=1,2,\ \ldots,\ m\). For non-beneficial criteria, the equation can be rewritten as follows:  
\[R_{ij}=[max\ (X_{ij})\ -\ (X_{ij})]/[max\ (X_{ij})\ -\ min\ (X_{ij})]\]Step 2: Pairwise measurement of the evaluative differences among alternatives.  
Step 3: Determination of the preference function, \(P_{j}\) (\(i\), \(i^{\prime}\)), using the following function:  
\[P_{j}\) (\(i\), \(i^{\prime}\)) = 0 _if_\(R_{ij}\leq R_{i^{\prime}j}\]  
\[P_{j}\) (\(i\), \(i^{\prime}\)) = (\(R_{ij}-R_{i^{\prime}j}\)) _if_\(R_{ij}>R_{i^{\prime}j}\]  
Step 4: Determination of the aggregated preference function \(\pi\) (\(i\), \(i^{\prime}\)) considering the weight of each criterion as follows:  
\[\pi\) (\(i\), \(i\)) = \(\sum_{j=1}^{m}[W_{j}*P_{j}(i,\ i))\) / \(\sum_{j=1}^{m}W_{j}\]  
where \(W_{j}\) is the weight that represents the relative importance of criterion \(j\).  
Step 5: Identification of the \"leaving (positive) flow\" and the \"entering (negative) flow\" as follows:  
Leaving flow for alternative  
\[i\), \(\varphi^{+}(i)=\frac{1}{n-1}\ \sum_{i=1}^{n}\pi(i,\ i^{\prime});\ (i\
eq i ^{\prime})\]  
Entering flow for alternative  
\[i\), \(\varphi^{-}(i)=\frac{1}{n-1}\ \sum_{i^{\prime}=1}^{n}\pi(i,\ i);\ (i\
eq i ^{\prime})\]  
where \(n\) represents the number of involved alternatives; the \"leaving flow\" and the \"entering flow\" represent the extent to which a certain alternative is \"dominating\" the remaining alternatives and is \"dominated\" by the remaining alternatives, respectively.  
Step 6: Determination of the net outranking flow for alternative \(i\) follows:  
\[\varphi\ (i)=\ \varphi^{+}(i)-\ \varphi^{-}(i)\]  
Step 7: Ranking of the involved alternatives according to the values of \(\varphi\ (i)\); the alternative with the highest \(\varphi\ (i)\) represents the best alternative, and so.