esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### The Proposed ECT Image Quality Assessment Methods
#### 3.2.3 Graph Structural Similarity Index  
The Structural Similarity Index Measure (SSIM) [26] is a perceptual metric that measures the similarity between two images. It is designed to more accurately predict perceived image quality compared to traditional metrics like PSNR and MSE.  
The SSIM incorporates three factors: (i) luminance, i.e., the difference in brightness between the two images; (ii) contrast, i.e., the difference in contrast between the two images; and (iii) structure, i.e., the similarity in the patterns and textures of the two images. It can be determined from the following formula:  
\[SSIM(x,y)=[l(x,y)]^{\alpha}*[c(x,y)]^{\beta}*[s(x,y)]^{\gamma} \tag{8}\]  
where \(l(x,y)\), \(c(x,y)\), and \(s(x,y)\) are luminance, contrast, and structural similarity comparison functions given by Equations (9)-(11), respectively:  
\[l(x,y)=(2*\mu_{x}*\mu_{y}+C_{1})/(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}) \tag{9}\]  
\[c(x,y)=(2*\sigma_{x}*\sigma_{y}+C_{2})/(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2}) \tag{10}\]  
\[s(x,y)=(\sigma_{xy}+C_{3})/(\sigma_{x}*\sigma_{y}+C_{3}) \tag{11}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the average luminance, \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the variances, and \(\sigma_{xy}\) is the covariance of images x and y, respectively. Typically, \(\alpha=\beta=\gamma=1\) and \(C_{1}\), \(C_{2}\), and \(C_{3}\) are small constants that stabilize the division with small denominators.  
The Graph Structural Similarity Index Measure (G-SSIM) adapts the original SSIM for graph-based images by leveraging node grouping to capture local node structures. The G-SSIM calculates local structural similarities for each node group in the reference and target images. Local node values refer to each group's average value and standard deviation (contrast).  
The overall G-SSIM score is the mean of all the local structural similarity results for all the node groups. The local structural similarity is calculated based on the simplified SSIM equation (Equation (12)), where luminance is the average node value (i.e., the electrical  
Figure 7: Node grouping schema for a graph.  
permittivity), and contrast is the standard deviation within a group. The combined contrast (\(c_{ro}\)) is calculated using Equation (13) for each node group:  
\[SSIM=[(2l_{r}l_{o}+C_{1})(2c_{ro}+C_{2})]/[(l_{r}^{2}+l_{o}^{2}+C_{1})(c_{r}^{2} +c_{o}^{2}+C_{2})] \tag{12}\]  
where \(l_{r}\) is local luminance of reference image, \(l_{o}\) is local luminance of other image, \(c_{r}\) is local contrast of reference image, \(c_{o}\) is local contrast of other image, \(c_{ro}\) is combined local contrasts of a reference and another image, and \(C_{1}\) and \(C_{1}\) are constant values:  
\[c_{ro}=\frac{1}{N-1}\sum_{i=1}^{N}(r_{i}-l_{r})(o_{i}-l_{o}) \tag{13}\]  
where \(l_{r}\) is luminance of reference image, \(l_{o}\) is luminance of other image, \(c_{ro}\) is combined contrast of reference and other image, \(N\) is number of nodes values in the node group, \(r_{i}\) is reference image node value, and \(o_{i}\) is other image node value.
## 1 Introduction  
The economic effects of support schemes for renewable technologies and respective deployment of technologies are of considerable importance worldwide [1; 2; 3]. This also holds for Germany and the Renewable Energy Act (_Erneuerbare-Energien-Gesetz_, \(EEG\)) as a prominent example for a financial support instrument. The EEG promotes the feed-in of electricity from renewable energy sources and comprises two basic principles: it guarantees producers a fixed feed-in tariff (FiT) and also gives preference to electricity from renewable energy sources being fed into the German power grid over electricity from conventional sources. It is financed by a surcharge levied on electricity consumers, namely the \(EEG\)-_Lmlage_. However, this is a controversial topic in the public debate on the transformation of the German energy sector (_Energiewende_) [4; 5; 6; 7; 8; 9]. The controversy focuses both on the general economic efficiency of the \(EEG\) and the regional divergence of economic impacts. Some regionsare concerned about socio-economic impacts in the course of the _Entergiewende_ and value added impacts of the EEG. This is the most significant aspect for North Rhine-Westphalia (NRW), the most populous federal state in Germany with a strong energy-industrial basis (domestic coal mining, fossil-based power plants) and home to important industries (car manufacturing, mechanical engineering, as well as energy-intensive industries such as metal production and chemistry), while suffering from comparably poor socio-economic trends (e.g., GDP growth, tax base).  
This paper analyses the socio-economic effects of the EEG in NRW and Germany, focusing on value added generation. As the year 2011 documented a surge in renewable energy technology investments in Germany, particularly in photovoltaics, this year is taken as an example. The analysis will consider the direct influences of the operation and production of renewable energy facilities in NRW itself, as well as the indirect effects of production and operation in the other federal states. These indirect effects concern, for example, households and companies in NRW who have to pay an \(EEG\) levy or by the manufacturers of wind turbines obtaining intermediate goods (e.g., steel or gears) from NRW. The remainder of this paper is structured as follows: Section 2 focuses on methodological aspects, presents a literature survey and the approach chosen for the NRW study. Section 3 analyzes the mechanisms of the \(EEG\) and presents structural data for NRW. Section 4 describes briefly the model and data used, as well as the \(EEG\)-based investment and operation impact channels relevant for NRW and Germany. The results of the analysis are presented in Section 5, demonstrating valued added impacts for NRW and for Germany. In Section 6 central assumptions and their relevance for results are discussed, and conclusions are drawn.
## 7 Sorbents Used in the TFME Technique
### Carbon Nanomaterials
#### 7.5.1 Graphene Materials  
Graphene is an allotropic variety of carbon containing one or more layers of atoms in the sp\({}^{2}\) hybridized state connected to each other on a single plane by strong \(\sigma\) bonds in a manner resembling a honeycomb structure.  
The non-hybridized \(\pi\) atomic orbitals, perpendicular to the plane on which the carbon atoms make the graphene layer, overlap to form a delocalized, all-encompassing atomic plane \(\pi\) electron arrangement responsible for the specific optical, thermal, mechanical, and electrical properties of graphene [78]. The atomic structure of graphene can be considered basic to other allotropic carbon varieties such as graphite, carbon nanotubes, and fullerenes. Stacked graphene layers form the graphite structure. The rolled-up graphite layer constitutes a carbon nanotube, while curled up in a sphere constitutes fullerene [78].  
Graphene and its derivatives, such as reduced graphene oxide (rGO) and graphene oxide (GO) are classified as graphene-based materials (GBMs). Graphene nanolayers allow the adsorption of molecules on both sides of the plane formed by carbon atoms. In contrast, in the case of nanotubes or fullerenes, adsorption of analyte molecules is not possible on the inner side of nanostructures due to steric crowding. Consequently, the specific surface areas of GBMs are very large (theoretical value for graphene: 2630 m\({}^{2}\) g\({}^{-1}\)) and much larger than the specific surface areas of carbon nanotubes and fullerenes [79].  
In regard to their high specific surface area, which is greater than that of SWCNTs and MWCNTs, rGO, and GO are used in extraction and microextraction techniques. Graphene-based materials adsorb various types of analytes via van der Waals-type interactions, electrostatic interactions, hydrogen bonds, \(\pi\)-\(\pi\), dispersion forces, coordination bonds, and hydrophobic effect [80]. Due to \(\pi\) electron systems, rGO adsorbs aromatic rings of organic compounds via strong \(\pi\)-\(\pi\) interactions. The functional groups in graphene oxide play a key role in forming hydrogen bonds or electrostatic interactions with organic compounds containing functional groups with oxygen and nitrogen atoms in the molecules [81].  
Table 7 summarizes examples of the application of graphene materials in the TFSPME technique presented in scientific publications.
## 2 Materials and Methods
### Data Acquisition and Pre-Processing
#### 2.2.2 Field Data  
A total of seventeen plots (20 m by 20 m) were chosen in the study sites. Four plots were surveyed in the complex forest community structure study area at an altitude of 1600 m, six plots were surveyed in the moderately complex forest community structure study area at an altitude of 1800 m, and seven plots were surveyed in the simple forest community structure study area at an altitude of 2000 m. The location, DBH, and height of each individual tree with a DBH > 2 cm were measured from 25 September to 9 October 2021. The tree location was measured by a sonar rangefinder (POSTEX LASER, Haglof Sweden AB, Solleftea, Sweden), the tree height was measured by an altimeter, and the DBH was measured with a diameter tape. Detailed information is provided in Table 2.  
Figure 1: Study area location.
# Critical Review of Techniques for Food Emulsion Characterization
## 3 Techniques for Emulsion Characterization
### Fourier-Transform Infrared Spectroscopy (FTIR) Analysis  
Fourier-transform infrared (FTIR) spectroscopy is a powerful technique used to investigate the molecular structure and composition of emulsions [171]. It allows researchers to study the vibrational modes of emulsion components, such as water, oil, and surfactants, in great detail, providing valuable insights into their structural organization at the molecular level. This sophisticated technique can be employed for analyzing functional groups and chemical bonds present in emulsions that provide information about their stability, interfacial properties, and potential interactions with other ingredients [171].  
FTIR analysis has substantial utility in studying the phase behavior of emulsions; it can elucidate phenomena such as the formation and disruption of droplets, while also casting light on how different processing conditions impact the overall structure and stability of an emulsion [171]. The spectra obtained _via_ FTIR are especially instructive regarding molecular interactions within an emulsion system by revealing details like hydrogen bonding patterns or significant intermolecular forces [172]. Furthermore, FTIR enables precise identification and quantification of specific components or contaminants within emulsions, such as oxidation products [173] or trace impurities.  
Attenuated total reflection FTIR spectroscopy (FTIR-ATR) has emerged as an indispensable tool for the precise identification of characteristic absorption bands within the diverse components of emulsions. This analytical technique has unveiled intriguing insights into emulsion behavior. For example, the phenomenon of inducing subtle alterations in the secondary structures of proteins in water-in-oil (W/O) emulsions was discerned through this method [174]. FTIR-ATR has further been employed to investigate the distinct states of water in emulsions [175], pinpoint signals emanating from bulk water and the water residing within the interfacial layer of reverse micelles [176], and scrutinize the water structure near the surface of nanoparticles in W/O emulsions [20]. Notably, FTIR-ATR studies have shed light on the interaction between emulsifiers and water molecules within the interfacial layer of W/O emulsions, disrupting the hydrogen bonding network and thus impeding the coalescence of water droplets [177]. Additionally, the impact of the concentration of emulsifiers on emulsion stability has been thoroughly examined, with the -OH stretching vibration band serving as a sensitive indicator of the molecular interactions critical for stabilizing W/O emulsions [178]. FTIR-ATR spectroscopy has also proven useful for direct measurements of chemical changes occurring during lipid oxidation in complex food matrices without the need for the extraction of the fat phase [179].
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion
### Forecasting Soil Temperature at 50 cm Depth in Bangladesh
#### 5.3.3 Five-Day Lead-Time Forecast  
Similar to Section 5.3.1, in order to avoid the repetition of similar tables, we only report the main accuracy results on the testing datasets. The DT model achieved the lowest average RMSE (2.046 \({}^{\circ}\)C) and the highest average R value (0.921) under MEAN-MAX-MIN input scenario and a five-day time window. The RF model demonstrated the best performance under the third and eighth input scenarios, resulting in the lowest average RMSE (1.962 \({}^{\circ}\)C) and the highest average R (0.935). The GBT model using the third and  
Figure 10: Comparison of observed and optimal forecasted ST50 at four stations.  
eighth input scenarios achieved the highest accuracy under various time windows; in particular, a five-day time window achieved the lowest average RMSE (1.971 \({}^{\circ}\)C) and the highest average R value (0.931). In the case of the hybrid DT-GBT, the third and eighth input scenarios produced the smallest average RMSE and the largest average R values for each time window. Similarly, the hybrid RF-GBT consistently relied on the third and eighth input scenarios for all time windows. For RF-GBT forecast under a five-day time window, the third input scenario yielded the lowest average RMSE value (1.970 \({}^{\circ}\)C) and the eighth input scenario yielded the highest average R value (0.933).  
**Best combination for five-day lead-time forecast:** The above comparison highlighted that the RF, utilizing third and eighth input factors under a five-day time window, emerged as the optimal forecasting model with the lowest average RMSE value (1.962 \({}^{\circ}\)C) and the largest average R value (0.935). Figure 11 demonstrates that the RF under a five-day time window offered a close fit with the observed values and superior accuracy at four stations. The main oscillation patterns of soil temperature were well captured, except for some of the highest/lowest soil temperature values.  
Figure 11: Comparison of observed and optimal forecasted ST50 at four stations.
# A Compact Monopole Wideband Antenna Based on DGS
## 2 Antenna Design Methodology and Optimization Process
### Parametric Analysis  
The antenna's performance is influenced by key electrical and geometric parameters, including radiator dimensions, DGS features, feed gap, feed type, and substrate dielectric constant. A parametric analysis was conducted on the DGS dimensions to assess their impact on surface current distribution, which alters the feed line's inductance and capacitance. Introducing rectangular and circular defects enhances effective inductance and fringing electric fields, increasing parasitic capacitance and improving bandwidth through better coupling between the ground and feed. In this study, by using Figure 3b, one parameter was varied at a time while keeping others fixed, optimizing the antenna's ground to a DGS-based partial ground plane. The analysis focused on the effects of varying dimensions on essential performance metrics, specifically bandwidth and reflection coefficient (S\({}_{11}\)). Key findings included length of L-slot vertical (L\({}_{\text{VS}}\)), width of L-slot vertical (W\({}_{\text{VS}}\)), spacing of L-slot horizontal (S), thickness of L-slot horizontal (T), and radius of semi-circular slot (R\({}_{\text{S}}\)).
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 3 Receiver Design Considerations for LIS: Proposed Improvements in Channel Estimation
### Optimized Pilot Design and Enhancing the Quality of Pilot Data  
#### 3.1.1 Orthogonal Pilot Sequences  
Orthogonal pilot sequences are fundamental in wireless communication systems for accurate channel estimation. By utilizing orthogonal pilot sequences, we aim to reduce pilot contamination and interference, thereby improving the accuracy of the channel estimate. Hadamard sequences fulfill these requirements effectively. This article uses Hadamard sequences to ensure minimal cross-correlation. In mathematical terms, for the two pilot sequences \(S_{i}\) and \(S_{j}\), orthogonal means [25]:  
\[S_{i}^{H}S_{j}=0\quad\text{for}\quad i\
eq j \tag{10}\]  
where \(S_{i}^{H}\) is the Hermitian (conjugate transpose) of \(S_{i}\).  
Orthogonality ensures that when these sequences are transmitted simultaneously, their cross-interference is minimized, allowing for the accurate separation and estimation of multiple channels at the receiver.
# Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction
## 3 Experimental Design  
### Dataset Description  
Panels data and meteorological data of the full dataset were used in this study as detailed in the methodology. Panels log their power reading during the daytime, i.e., sunrise to sunset. From the sunset to sunrise, the panel does not provide any information about their output power; however, we still have data about their average temperature. Therefore, to maximize the information in our data, we filtered out the period between sunset to sunrise which varies between winter and summer. We added the meteorological data, which consisted of cloudiness, relative humidity, and sun time, to the panels' data. Meteorological data were recorded as one sample per day while panels' data were recorded every 5 min. Therefore, we created three new columns for every panel's data file and assigned to those columns the meteorological values for that day by repeating it n times where n is the number of rows/entries in that panel's data file. In this way, we have built a connection between solar data and meteorological data. We used the same epochs numbers and batch size for two models owing to the comparison.  
Deep ANN and LSTM were utilized in this study to predict the daily solar radiation. Inputs were amorphous silicon PV panel in kWh, mono silicon PV panel in kWh, poly silicon PV panel in kWh, average atmospheric temperature in \({}^{\circ}\)C, average panel temperature in \({}^{\circ}\)C, daily average cloudiness, daily average relative humidity (%), and daily sunshine time in hours, and the output was the predicted radiation amount (W/m\({}^{2}\)).  
Four years' worth of data were utilized in the proposed study. The data were split into 3 years for training and 1 year for testing. Results in terms of mean square error (MSE) were computed for each model. The training set was split further into a training and validation set for both models. Eventually, the two trained models were evaluated using the testing  
Figure 5: Structure of neural networks with activation function.  
set. The assumption was that the trained model which has been trained on the 3 years of data can then be used to perform the prediction throughout the 4 th year with the same range of error. For both models, we concatenated the daily meteorological data to the data acquired from the PV panels. In order to preserve the data acquired by the panel, each daily metrological value was repeated in the rows corresponding to that day. We trained both models using the aggregated data by averaging every 12 rows (=60 min) and predicting the following 48 row's solar radiation (=predicting the solar radiation after 48 h). The data were then normalized between 0 and 1, and the normalized data were used for the learning process.
## 4 Strength Properties
### Unconfined Compressive Strength of Lime-Cement Stabilized Clay Soil
These observations confirm that optimal stabilization transforms clay from a ductile soil matrix to a quasi-cementitious composite with failure mechanisms governed by pozzolanic reaction product quality and distribution. Nano-silica and nano-clays demonstrate the potential to partially or fully replace traditional stabilizers like lime and cement in clay soil stabilization, though their efficacy depends on application-specific conditions. Studies indicate that nano-silica, as a sole additive, improves unconfined compressive strength (UCS) and acts as a strength enhancer for cement, lime, or fiber [52; 53]. For instance, [PERSON] and [PERSON] [54] show nano-silica added to cemented clay increases UCS due to enhanced pozzolanic reactions and microstructural densification, with some studies suggesting it can partially replace cement while achieving \"multi-fold\" strength improvements [55]. However, the net initial cost of nano-silica remains higher than traditional stabilizers [55]. Nano-clays, while less extensively studied, have shown promise in improving geotechnical properties, including strength and plasticity reduction [56]. Their ability to modify soil fabric at the nanoscale may yield comparable or superior results to lime in certain scenarios, such as altering plasticity characteristics [57]. While nano-additives offer advantages in targeted applications (e.g., rapid strength gain, reduced environmental footprint), traditional stabilizers like lime and cement remain more extensively validated in large-scale engineering practices. Hybrid approaches combining nano-materials with conventional additives may optimize cost-effectiveness and performance [58]. Further research is needed to establish standardized benchmarks for UCS improvements using nano-clays and nano-silica relative to lime-cement systems.
## 5 Case Studies
### Robust EPR Settings  
Both selected databases have been randomly divided into training (70%) and testing (30%) sets in order to be representative of the complete database. The training set was only used to identify the capacity equations, while the testing set was used to verify if the model's fit for the training dataset also maintained an adequate accuracy.  
Figure 4: Relationships between CS and each considered explanatory variable.  
A robust regression model has been employed to estimate the model's parameters [13]. Based on the observations given in the previous section, the Cauchy weight function has been adopted in the robust regression since it performs better than the other available algorithms in the case of vertical extreme observations [34]. The robust EPR technique has been iteratively performed by selecting different models comprising a number of parameters ranging from one to four. This leads to exploring the experimental and analytical variance of each model with increasing complexity. The variability of the exponents' values has been set to \([-2,2]\), while a step variability of 0.01 has been assumed. The maximum number of iterations of 1000 has been fixed, while a termination condition has been set to avoid inefficient loops and limit the computational effort. Based on that, the iterative procedure ends when the accuracy variability does not considerably vary (\(\pm 5\%\)) for at least one-third of the maximum number of iterations.